CN118013450A - Photovoltaic optimization system based on total solar radiation calculation - Google Patents
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Abstract
The invention discloses a photovoltaic optimization system based on solar total radiation calculation, which relates to the technical field of photovoltaic optimization and comprises a data acquisition frequency setting module, a data acquisition environment dividing module, a data acquisition anomaly analysis module, a photovoltaic optimization process intelligent analysis module and an error accumulation risk grade dividing module; and setting a normal data acquisition frequency range under a fixed duration window during total solar radiation data acquisition according to the performance requirement of the system and the change characteristic of solar radiation. According to the invention, through monitoring and analyzing the total solar radiation data acquisition frequency, the data acquisition environment is divided, the abnormal acquisition environment is timely identified and processed, the risk of error accumulation is effectively reduced, and under the normal acquisition environment, the intelligent analysis is carried out on the process of photovoltaic optimization of the acquisition photovoltaic panel by using the data analysis model, so that the system is facilitated to identify the error accumulation phenomenon in the photovoltaic optimization process, and the accuracy and performance of the photovoltaic optimization process are further improved.
Description
Technical Field
The invention relates to the technical field of photovoltaic optimization, in particular to a photovoltaic optimization system based on total solar radiation calculation.
Background
The photovoltaic optimization system based on total solar radiation calculation is a technology for optimizing the performance of a photovoltaic power generation system by utilizing solar radiation data. First, the system learns the intensity and distribution of the solar energy source by measuring, recording and analyzing solar total radiation data, including direct radiation, scattered radiation, reflected radiation, and the like. And secondly, based on the data, the system can accurately calculate the effective solar energy received by the photovoltaic module, so as to optimize the layout, the inclination angle and the direction of the photovoltaic power generation system, and further improve the photovoltaic power generation efficiency to the greatest extent. Secondly, the system can dynamically adjust and optimize the photovoltaic system by monitoring the change of the total solar radiation data in real time. For example, the system may adjust the angle and direction of the photovoltaic panels according to weather forecast and seasonal changes to ensure that the photovoltaic modules always receive solar radiation at an optimal angle, thereby achieving higher energy conversion efficiency.
Error accumulation in a photovoltaic optimization system based on total solar radiation calculation refers to gradual accumulation of errors introduced during system operation, which gradually accumulate and affect the accuracy and performance of the system over time. Particularly, in long-term operation, even if the initial error is small, the continuous accumulation of the error can eventually lead to a large deviation between the prediction and actual conditions of the system, so that the efficiency and stability of the photovoltaic power generation system are reduced, and further, mismatch between the output of the photovoltaic power generation system and the power grid demand can be caused, so that instability problems of the power grid, such as frequency fluctuation and voltage fluctuation, can be caused, and serious consequences such as power grid faults and power failure can be even caused.
Accordingly, there is a need for optimization improvements in photovoltaic optimization systems based on total solar radiation calculation to overcome the above-mentioned problems.
The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
The invention aims to provide a photovoltaic optimization system based on solar total radiation calculation, which establishes a real-time monitoring and early warning mechanism through monitoring and analyzing solar total radiation data acquisition frequency, and effectively reduces the risk of error accumulation through dividing a data acquisition environment and timely identifying and processing an abnormal acquisition environment.
In order to achieve the above object, the present invention provides the following technical solutions: a photovoltaic optimization system based on solar total radiation calculation comprises a data acquisition frequency setting module, a data acquisition environment dividing module, a data acquisition anomaly analysis module, a photovoltaic optimization process intelligent analysis module and an error accumulation risk level dividing module;
setting a normal data acquisition frequency range under a fixed duration window during total solar radiation data acquisition according to the performance requirement of the system and the change characteristic of solar radiation;
according to the time sequence, acquiring actual data acquisition frequencies during solar total radiation data acquisition under a plurality of fixed duration windows, establishing an analysis set, comprehensively analyzing the actual data acquisition frequencies in the analysis set, and dividing the data acquisition environment into a normal acquisition environment and an abnormal acquisition environment;
the data acquisition process in the abnormal acquisition environment is further analyzed, so that relevant management staff can know the data, and the data is maintained and managed in a targeted mode;
under a normal acquisition environment, performing intelligent analysis on the process of photovoltaic optimization of an acquisition photovoltaic panel by using a data analysis model, and identifying an error accumulation phenomenon in the photovoltaic optimization process;
when the error accumulation condition is monitored, carrying out risk classification on the error accumulation, classifying the error accumulation into high risk error accumulation, medium risk error accumulation and low risk error accumulation, and taking different maintenance management measures on the photovoltaic panel according to different risk classes.
Preferably, according to the time sequence, acquiring actual data acquisition frequency in the process of acquiring solar total radiation data under a plurality of fixed duration windows to establish an analysis set, and calibrating the analysis set as R, thenWherein/>Representing actual data acquisition frequency, v representing the number of actual data acquisition frequency;
Calculating a data acquisition frequency average value and a data acquisition frequency discrete value by analyzing actual data acquisition frequencies in a set, respectively comparing the data acquisition frequency average value and the data acquisition frequency discrete value with a preset data acquisition frequency reference threshold value and a preset discrete reference threshold value, and dividing a data acquisition environment into a normal acquisition environment and an abnormal acquisition environment, wherein the dividing result is as follows:
if the average value of the data acquisition frequency is more than or equal to the reference threshold value of the data acquisition frequency and the discrete value of the data acquisition frequency is less than the discrete reference threshold value, calibrating the data acquisition environment under the fixed duration window as a normal acquisition environment;
If the average value of the data acquisition frequency is not more than or equal to the reference threshold value of the data acquisition frequency and the discrete value of the data acquisition frequency is less than the discrete reference threshold value, the data acquisition environment under the fixed duration window is marked as an abnormal acquisition environment.
Preferably, for an abnormal acquisition environment, the data acquisition process is further analyzed as follows:
If the average value of the data acquisition frequency is smaller than the reference threshold value of the data acquisition frequency and the discrete value of the data acquisition frequency is smaller than the discrete reference threshold value, calibrating the data acquisition environment under the fixed duration window as continuous abnormality;
if the average value of the data acquisition frequency is greater than or equal to the reference threshold of the data acquisition frequency and the discrete value of the data acquisition frequency is greater than or equal to the reference threshold of the discrete reference, or the average value of the data acquisition frequency is smaller than the reference threshold of the data acquisition frequency and the discrete value of the data acquisition frequency is greater than or equal to the reference threshold of the discrete reference, the data acquisition environment under the window with fixed duration is marked as an unstable anomaly.
Preferably, performance index information and space power generation information when the photovoltaic panel performs photovoltaic optimization are obtained, wherein the performance index information comprises power generation deviation and direct current conversion alternating current negative gain, the space power generation information comprises power generation difference of different areas, and after abnormal analysis processing is performed on the power generation deviation, the direct current conversion alternating current negative gain and the power generation difference of different areas, a power generation deviation index, a direct current conversion alternating current negative gain index and a power generation difference index of different areas are respectively generated.
Preferably, the power generation amount deviation index obtaining logic is as follows:
Collecting historical photovoltaic panel generating capacity data, and sampling according to fixed time intervals to form time sequence data, wherein the photovoltaic generating capacity data comprises the generating capacity of the photovoltaic panel and a corresponding time stamp;
The autoregressive integral moving average model is selected as a time series model, which is expressed as: wherein/> Is the power generation of the t time point,/>Is the power generation amount at the past p time points,Is a residual term, f is a functional relation of a time sequence model, and w represents the total number of residual terms;
Within a fixed time window, predicting the future generated energy by using the established time sequence model, and recording the predicted generated energy value as ；
Will predict the valueAnd actual value/>Comparing, calculating the generated energy deviation/>, of each time pointPower generation amount deviation/>The calculated expression of (2) is: /(I)The power generation amount deviation represents the difference between the model prediction and the actual observation value;
Calculating a power generation amount deviation index, wherein the calculated expression is as follows: wherein/> Representing the power generation deviation index,/>The power generation amount deviation of the ith time point is represented, namely, the difference value between the actual power generation amount and the predicted power generation amount of the photovoltaic panel, and n represents the total time point number.
Preferably, the logic for dc-to-ac negative gain index acquisition is as follows:
collecting performance data of the photovoltaic panel in a fixed duration window, including a phase angle between the voltage and the current, and calibrating the phase angle between the voltage and the current as ；
In a fixed duration window, calculating a power factor PF of each time point, wherein the power factor PF is used as an index for describing the phase relation between voltage and current in an alternating current circuit and is represented by a cosine value, and the calculation formula is as follows:；
on the basis of power factor calculation, a negative gain index is defined to evaluate the efficiency of direct current to alternating current conversion, the negative gain index is obtained by calculating the change rate of the power factors, namely the difference value between the power factor of each time point and the power factor of the last time point, and then the power factor of the last time point is divided, and the calculation formula is as follows: wherein/> Is the negative gain index of the j-th time point,/>Is the power factor of the j-th time point,/>Is the power factor of the j-1 th time point;
Calculating a direct current conversion alternating current negative gain index through the negative gain index of the time point, wherein the calculated expression is as follows: in the above, the ratio of/> Represents the DC conversion AC negative gain index, and N represents the total number of time points.
Preferably, the logic for obtaining the power generation amount difference index of the different areas is as follows:
dividing the photovoltaic panel into a plurality of sub-areas, enabling the areas of the sub-areas to be equal, and collecting solar total radiation data and generating capacity data of the photovoltaic panel in each area;
it is to be noted that, using devices such as solar radiometers or weather stations, a solar total radiation measuring instrument is arranged in a subarea of each photovoltaic panel, solar total radiation data can be measured and recorded, and an electric energy meter or a power meter is arranged in the subarea of each photovoltaic panel, so that the generating capacity data of each area can be recorded and monitored in real time.
Selecting a K mean value clustering algorithm to determine an optimal clustering number K;
Calculating a center point of the sample in each cluster, representing typical solar total radiation and power generation of the area, and calculating a cluster center formula: wherein/> Is the center point of the kth cluster,/>Is the number of samples of the kth cluster,/>Is the first sample in the kth cluster;
comparing the generated energy of each sub-area with the central point of the cluster to which the generated energy of each sub-area belongs, and calculating the generated energy difference indexes of different sub-areas, wherein the calculation formula of the generated energy difference indexes of different sub-areas is as follows: wherein/> Is the power generation amount difference index of the c-th area,/>Is the (u) th feature of the (c) th region,/>Is the center point of the u-th cluster, m is the feature number;
establishing a data set of the generated energy difference indexes obtained from all the subareas, sequencing the generated energy difference indexes in the data set according to the sequence, and screening out the maximum generated energy difference index as a mark By the power generation amount difference indexObtaining the power generation amount difference indexes of different areas, wherein the obtained formula is as follows: /(I)Wherein/>And representing the power generation quantity difference indexes of different areas.
Preferably, the generated energy deviation index generated in a fixed time window when the photovoltaic panel is subjected to photovoltaic optimization is obtainedDirect current conversion alternating current negative gain index/>Different regional power generation difference index/>Thereafter, the power generation amount deviation index/>Direct current conversion alternating current negative gain index/>Different regional power generation difference index/>Comprehensive analysis is carried out to generate error accumulation evaluation coefficient/>Estimating coefficient by error accumulation/>Performing quantitative evaluation on error accumulation during photovoltaic optimization of the photovoltaic panel;
Comparing and analyzing the error accumulation evaluation coefficient generated in the fixed time window with a preset error accumulation evaluation coefficient reference threshold value when the photovoltaic panel is subjected to photovoltaic optimization, generating an error signal if the error accumulation evaluation coefficient is greater than or equal to the error accumulation evaluation coefficient reference threshold value, and generating a high-efficiency signal if the error accumulation evaluation coefficient is smaller than the error accumulation evaluation coefficient reference threshold value.
Preferably, when generating an error signal within a fixed time window when photovoltaic optimization is performed on the photovoltaic panel, establishing an analysis set for a plurality of error accumulation evaluation coefficients generated subsequently, and comparing the acquired plurality of error accumulation evaluation coefficients with a preset first error accumulation evaluation coefficient gradient reference threshold and a preset second error accumulation evaluation coefficient gradient reference threshold, wherein the first error accumulation evaluation coefficient gradient reference threshold is greater than the second error accumulation evaluation coefficient gradient reference threshold, the second error accumulation evaluation coefficient gradient reference threshold is greater than the error accumulation evaluation coefficient reference threshold, and marking the number of error accumulation evaluation coefficients greater than or equal to the first error accumulation evaluation coefficient gradient reference threshold asMarking the number of error accumulation evaluation coefficients which are greater than or equal to the second error accumulation evaluation coefficient gradient reference threshold value and less than the first error accumulation evaluation coefficient gradient reference threshold value as/>Marking the number of error accumulation evaluation coefficients that are equal to or greater than the error accumulation evaluation coefficient reference threshold and that are less than the second error accumulation evaluation coefficient gradient reference threshold as/>；
Will be、/>/>Performing formulated analysis to generate an error accumulation risk measurement value/>The formula according to is: /(I)In the above, the ratio of/>、/>、/>Respectively/>、/>、/>And/>、/>、/>Are all greater than 0.
Preferably, the generated error accumulation risk measurement value is compared with a first error accumulation risk reference threshold value and a second error accumulation risk reference threshold value which are preset, wherein the first error accumulation risk reference threshold value is smaller than the second error accumulation risk reference threshold value, and the comparison analysis result is as follows:
If the error accumulation risk measurement value is greater than or equal to the second error accumulation risk reference threshold value, dividing hidden danger into high risk error accumulation;
If the error accumulation risk measurement value is smaller than the second error accumulation risk reference threshold value and larger than or equal to the first error accumulation risk reference threshold value, dividing the hidden danger into risk error accumulation;
If the error accumulation risk measurement value is smaller than the first error accumulation risk reference threshold value, the hidden danger is divided into low risk error accumulation.
In the technical scheme, the invention has the technical effects and advantages that:
According to the invention, a real-time monitoring and early warning mechanism is established through monitoring and analyzing the total solar radiation data acquisition frequency, so that the abnormal data acquisition frequency can be found in time, the gradual accumulation of errors is prevented, and the abnormal acquisition environment is identified and processed in time through dividing the data acquisition environment, so that the risk of error accumulation is effectively reduced.
According to the invention, under a normal acquisition environment, the generated energy deviation index, the direct current conversion alternating current negative gain index and the generated energy difference index of different areas generated by abnormal analysis processing are comprehensively analyzed to generate the error accumulation evaluation coefficient, and the error accumulation during photovoltaic optimization of the photovoltaic panel is quantitatively evaluated through the error accumulation evaluation coefficient, so that the system is facilitated to identify the error accumulation phenomenon in the photovoltaic optimization process, and the accuracy and performance of the photovoltaic optimization process are further improved.
When the error accumulation condition is monitored, the system can realize targeted maintenance and risk prevention by classifying the error accumulation risk into three grades of high, medium and low and adopting corresponding maintenance management measures aiming at different risk grades, which is beneficial to optimizing the distribution and utilization of maintenance resources, improving the maintenance efficiency and reducing the system operation risk, thereby ensuring the long-term stable operation and the power generation efficiency of the photovoltaic system.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for those skilled in the art.
Fig. 1 is a schematic block diagram of a photovoltaic optimization system based on total solar radiation calculation according to the present invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
The invention provides a photovoltaic optimization system based on solar total radiation calculation as shown in fig. 1, which comprises a data acquisition frequency setting module, a data acquisition environment dividing module, a data acquisition anomaly analysis module, a photovoltaic optimization process intelligent analysis module and an error accumulation risk level dividing module;
The data acquisition frequency setting module is used for setting a normal data acquisition frequency range under a fixed duration window during total solar radiation data acquisition according to the performance requirement of the system and the change characteristic of solar radiation;
The fixed duration window refers to:
Dividing the solar total radiation data acquisition process into a plurality of windows, wherein the duration in each window is equal, namely each window is called a fixed duration window;
Setting the normal data acquisition frequency range means that a certain data acquisition frequency range is determined according to the system performance requirement and the change characteristic of solar radiation, so that the system can accurately capture the change of the solar radiation, and real-time monitoring and adjustment can be performed within an acceptable range.
The performance requirements of the photovoltaic optimization system comprise minimum requirements of the system on solar radiation data acquisition frequency, requirements on real-time performance and accuracy and the like, and analysis of solar radiation characteristics comprises periodic changes, daily change rules, seasonal change rules and the like of solar radiation. And determining the minimum change time interval to be monitored by the system according to the change characteristics of solar radiation so as to ensure the accuracy of data acquisition.
The setting of the normal data acquisition frequency range can be fed back and adjusted according to the results of the field test and the system performance requirements, and the final normal data acquisition frequency range is determined. This may require multiple trials and adjustments to ensure that the data collected by the system can meet the actual requirements and maintain stability. Therefore, the setting of the normal data acquisition frequency range is not particularly limited herein, and can be adjusted according to actual requirements.
The data acquisition environment dividing module is used for acquiring actual data acquisition frequencies during solar total radiation data acquisition under a plurality of fixed duration windows according to a time sequence, establishing an analysis set, comprehensively analyzing the actual data acquisition frequencies in the analysis set, and dividing the data acquisition environment into a normal acquisition environment and an abnormal acquisition environment;
According to the time sequence, acquiring actual data acquisition frequency in the process of solar total radiation data acquisition under a plurality of fixed duration windows to establish an analysis set, and calibrating the analysis set as R Wherein/>Representing actual data acquisition frequency, v representing the number of actual data acquisition frequency;
Calculating a data acquisition frequency average value and a data acquisition frequency discrete value by analyzing actual data acquisition frequencies in a collection, comparing the data acquisition frequency average value and the data acquisition frequency discrete value with a preset data acquisition frequency reference threshold (the data acquisition frequency reference threshold is the minimum value of a normal data acquisition frequency range, if the data acquisition frequency is too low and is lower than the minimum value of the normal data acquisition frequency range, namely acquired data points are sparse, the system possibly cannot capture the instantaneous change of solar radiation, and the system cannot respond to the change of solar radiation in a short time in this way, so that the real-time adjustment and optimization of a photovoltaic system are affected, and errors are gradually accumulated) and the discrete reference threshold are respectively compared and analyzed, and dividing the data acquisition environment into a normal acquisition environment and an abnormal acquisition environment, wherein the dividing result is as follows:
if the average value of the data acquisition frequency is more than or equal to the reference threshold value of the data acquisition frequency and the discrete value of the data acquisition frequency is less than the discrete reference threshold value, calibrating the data acquisition environment under the fixed duration window as a normal acquisition environment;
If the average value of the data acquisition frequency is not more than or equal to the reference threshold value of the data acquisition frequency and the discrete value of the data acquisition frequency is less than the discrete reference threshold value, the data acquisition environment under the fixed duration window is marked as an abnormal acquisition environment.
It should be noted that, the discrete value of the data acquisition frequency may be represented by a standard deviation calculated from the actual data acquisition frequency in the analysis set, and the discrete value of the data acquisition frequency is calibrated asThen: /(I)In the above, the ratio of/>For the average of the actual data acquisition frequencies within the analysis set, i.e. the average of the data acquisition frequencies, q represents the total number of actual data acquisition frequencies within the analysis set.
The data acquisition abnormality analysis module is used for further analyzing the data acquisition process in an abnormal acquisition environment, so that relevant management staff can know the data acquisition abnormality analysis module conveniently, and the data acquisition abnormality analysis module is used for targeted maintenance and management;
For an abnormal acquisition environment, the data acquisition process is further analyzed, and the analysis process is as follows:
If the average value of the data acquisition frequency is smaller than the reference threshold value of the data acquisition frequency and the discrete value of the data acquisition frequency is smaller than the discrete reference threshold value, calibrating the data acquisition environment under the fixed duration window as continuous abnormality;
if the average value of the data acquisition frequency is greater than or equal to the reference threshold of the data acquisition frequency and the discrete value of the data acquisition frequency is greater than or equal to the reference threshold of the discrete reference, or the average value of the data acquisition frequency is smaller than the reference threshold of the data acquisition frequency and the discrete value of the data acquisition frequency is greater than or equal to the reference threshold of the discrete reference, the data acquisition environment under the window with fixed duration is marked as an unstable anomaly.
The intelligent analysis module of the photovoltaic optimization process is used for intelligently analyzing the process of collecting the photovoltaic panel to perform photovoltaic optimization by using the data analysis model under the normal collection environment, and identifying the error accumulation phenomenon in the photovoltaic optimization process;
And acquiring performance index information and space power generation information when the photovoltaic panel performs photovoltaic optimization, wherein the performance index information comprises power generation deviation and direct current conversion alternating current negative gain, the space power generation information comprises power generation difference of different areas, and after abnormal analysis processing is performed on the power generation deviation, the direct current conversion alternating current negative gain and the power generation difference of different areas, a power generation deviation index, a direct current conversion alternating current negative gain index and a power generation difference index of different areas are respectively generated.
The power generation amount deviation refers to a larger difference between the photovoltaic power generation amount predicted by the system and the actual power generation amount, and when the photovoltaic power generation amount predicted by the photovoltaic optimization system calculated based on the total solar radiation has a larger difference with the actual power generation amount, the error accumulation exists in the photovoltaic optimization process. This is because the optimization decision of the system is made based on the predicted power generation amount, and if there is a large difference between the predicted value and the actual value, the optimization measure taken by the system may not achieve the expected effect, but rather may introduce an additional error. Over time, these errors may accumulate, affecting the stability and performance of the system.
The acquisition logic of the power generation amount deviation index is as follows:
Collecting historical photovoltaic panel generating capacity data, and sampling according to fixed time intervals to form time sequence data, wherein the photovoltaic generating capacity data comprises the generating capacity of the photovoltaic panel and a corresponding time stamp;
The autoregressive integral moving average model is selected as a time series model, which is expressed as: wherein/> Is the power generation of the t time point,/>Is the power generation amount at the past p time points,Is a residual term, f is a functional relation of a time sequence model, and w represents the total number of residual terms;
Within a fixed time window, predicting the future generated energy by using the established time sequence model, and recording the predicted generated energy value as ；
Will predict the valueAnd actual value/>Comparing, calculating the generated energy deviation/>, of each time pointPower generation amount deviation/>The calculated expression of (2) is: /(I)The power generation amount deviation represents the difference between the model prediction and the actual observation value;
Calculating a power generation amount deviation index, wherein the calculated expression is as follows: wherein/> Representing the power generation deviation index,/>The power generation amount deviation of the ith time point is represented, namely, the difference value between the actual power generation amount and the predicted power generation amount of the photovoltaic panel, and n represents the total time point number.
According to the calculation expression of the generated energy deviation index, the larger the expression value of the generated energy deviation index generated in a fixed time window when the photovoltaic panel performs photovoltaic optimization is, the larger the hidden danger of the error accumulation phenomenon in the photovoltaic optimization process is, and otherwise, the smaller the hidden danger of the error accumulation phenomenon in the photovoltaic optimization process is.
When the photovoltaic panel is subjected to photovoltaic optimization, the efficiency in the process of converting direct current into alternating current is low, and error accumulation can be caused in the process of photovoltaic optimization. This is because in the conversion of direct current to alternating current, an inverter is often required to complete the conversion. The efficiency of an inverter is typically affected by a number of factors, including device quality, design structure, operating temperature, and the like. The inverter in the photovoltaic system often faces problems of temperature rise, voltage fluctuation and the like, and the problems can affect the working efficiency of the inverter. Therefore, if the efficiency of the inverter is low, a part of energy is lost in the conversion process, so that a difference exists between the actual alternating current output power and the expected value, and further, errors are accumulated in the photovoltaic optimization.
The logic for obtaining the DC conversion AC negative gain index is as follows:
collecting performance data of the photovoltaic panel in a fixed duration window, including a phase angle between the voltage and the current, and calibrating the phase angle between the voltage and the current as ；
It should be noted that, the power instrument is generally capable of measuring the current and the voltage in the circuit in real time and calculating the phase angle. These include power factor meters, digital power meters, and the like. Real-time values of current and voltage are measured by built-in sensors or externally connected current and voltage transformers, and the phase angle is calculated by an internal algorithm.
In a fixed duration window, calculating a power factor PF of each time point, wherein the power factor PF is used as an index for describing the phase relation between voltage and current in an alternating current circuit and is represented by a cosine value, and the calculation formula is as follows:；
on the basis of power factor calculation, a negative gain index is defined to evaluate the efficiency of direct current to alternating current conversion, the negative gain index is obtained by calculating the change rate of the power factors, namely the difference value between the power factor of each time point and the power factor of the last time point, and then the power factor of the last time point is divided, and the calculation formula is as follows: wherein/> Is the negative gain index of the j-th time point,/>Is the power factor of the j-th time point,/>Is the power factor of the j-1 th time point;
Calculating a direct current conversion alternating current negative gain index through the negative gain index of the time point, wherein the calculated expression is as follows: in the above, the ratio of/> Represents the DC conversion AC negative gain index, and N represents the total number of time points.
The calculation expression of the direct current conversion alternating current negative gain index shows that the larger the expression value of the direct current conversion alternating current negative gain index generated in a fixed time window when the photovoltaic panel performs photovoltaic optimization is, the larger the hidden danger of error accumulation phenomenon in the photovoltaic optimization process is indicated, and the smaller the hidden danger of error accumulation phenomenon in the photovoltaic optimization process is indicated otherwise.
When the power generation capacity of the photovoltaic panel in different areas is greatly different, the error accumulation in the photovoltaic optimization can be caused. This is because solar conditions, weather conditions, geographical environments, etc. in different areas can directly affect the total amount of solar radiation and the efficiency of power generation received by the photovoltaic panel. For example, some areas may be sunny throughout the year, while other areas may be blocked by seasonal weather changes or terrain, resulting in significant differences in the power generation efficiency of the photovoltaic panels. If these differences are ignored during the photovoltaic optimization process, there may be a large deviation between the predictions of the system and the actual power generation conditions, causing error accumulation.
The logic for obtaining the power generation amount difference indexes of different areas is as follows:
dividing the photovoltaic panel into a plurality of sub-areas, enabling the areas of the sub-areas to be equal, and collecting solar total radiation data and generating capacity data of the photovoltaic panel in each area;
it is to be noted that, using devices such as solar radiometers or weather stations, a solar total radiation measuring instrument is arranged in a subarea of each photovoltaic panel, solar total radiation data can be measured and recorded, and an electric energy meter or a power meter is arranged in the subarea of each photovoltaic panel, so that the generating capacity data of each area can be recorded and monitored in real time.
Selecting a K mean value clustering algorithm to determine an optimal clustering number K;
common methods for determining the optimal cluster number K are, for example, elbow rule, contour coefficient, etc.
Calculating a center point of the sample in each cluster, representing typical solar total radiation and power generation of the area, and calculating a cluster center formula: wherein/> Is the center point of the kth cluster,/>Is the number of samples of the kth cluster,/>Is the first sample in the kth cluster;
comparing the generated energy of each sub-area with the central point of the cluster to which the generated energy of each sub-area belongs, and calculating the generated energy difference indexes of different sub-areas, wherein the calculation formula of the generated energy difference indexes of different sub-areas is as follows: wherein/> Is the power generation amount difference index of the c-th area,/>Is the (here power generation) th feature of the c-th region,/>Is the center point of the u-th cluster, m is the feature number;
establishing a data set of the generated energy difference indexes obtained from all the subareas, sequencing the generated energy difference indexes in the data set according to the sequence, and screening out the maximum generated energy difference index as a mark By the power generation amount difference indexObtaining the power generation amount difference indexes of different areas, wherein the obtained formula is as follows: /(I)Wherein/>And representing the power generation quantity difference indexes of different areas.
According to the calculation expression of the power generation amount difference indexes of different areas, the larger the expression value of the power generation amount difference indexes of different areas generated in a fixed time window when the photovoltaic panel performs photovoltaic optimization is, the larger the hidden danger of error accumulation phenomenon in the photovoltaic optimization process is indicated, and otherwise, the smaller the hidden danger of error accumulation phenomenon in the photovoltaic optimization process is indicated.
Acquiring an electricity generation amount deviation index generated in a fixed time window when the photovoltaic panel performs photovoltaic optimizationDirect current conversion alternating current negative gain index/>Different regional power generation difference index/>Thereafter, the power generation amount deviation index/>Direct current conversion alternating current negative gain index/>Different regional power generation difference index/>Comprehensive analysis is carried out to generate error accumulation evaluation coefficient/>Estimating coefficient by error accumulation/>And carrying out quantitative evaluation on error accumulation when the photovoltaic panel is subjected to photovoltaic optimization.
The specific implementation manner of the above comprehensive analysis is not particularly limited herein, and the power generation amount deviation index can be realizedDirect current conversion alternating current negative gain index/>Different regional power generation difference index/>The mode of comprehensive analysis can be realized, and in order to realize the technical scheme of the invention, the invention provides a specific implementation mode;
error accumulation evaluation coefficient The generated calculation formula is as follows: in the above, the ratio of/> 、/>、/>Respectively, generating capacity deviation index/>Direct current conversion alternating current negative gain index/>Index of difference in electric power generation of different regions/>And/>、/>、/>。
The calculation formula shows that the larger the expression value of the generated energy deviation index generated in the fixed time window is, the larger the expression value of the direct current conversion alternating current negative gain index is, and the larger the expression value of the generated energy difference index in different areas is, namely the error accumulation evaluation coefficient generated in the fixed time window is when the photovoltaic panel performs photovoltaic optimizationThe larger the expression value of the (C) is, the larger the hidden danger of the error accumulation phenomenon in the photovoltaic optimization process is, and the smaller the hidden danger of the error accumulation phenomenon in the photovoltaic optimization process is.
The error accumulation risk level classification module is used for classifying the risk level of the error accumulation when the error accumulation condition is monitored, classifying the error accumulation into high risk error accumulation, medium risk error accumulation and low risk error accumulation, and taking different maintenance management measures for the photovoltaic panel according to different risk levels;
Comparing and analyzing the error accumulation evaluation coefficient generated in the fixed time window with a preset error accumulation evaluation coefficient reference threshold value when the photovoltaic panel is subjected to photovoltaic optimization, generating an error signal if the error accumulation evaluation coefficient is greater than or equal to the error accumulation evaluation coefficient reference threshold value, and generating a high-efficiency signal if the error accumulation evaluation coefficient is smaller than the error accumulation evaluation coefficient reference threshold value.
When the photovoltaic panel performs photovoltaic optimization, an error signal is generated in a fixed duration window, the hidden danger of error accumulation phenomenon in the photovoltaic optimization process is larger, the error accumulation phenomenon possibly exists in the photovoltaic optimization process, and when the photovoltaic panel performs photovoltaic optimization, a high-efficiency signal is generated in the fixed duration window, the photovoltaic optimization process can be performed efficiently.
When an error signal is generated in a fixed time window when photovoltaic optimization is performed on the photovoltaic panel, an analysis set is established for a plurality of error accumulation evaluation coefficients generated later, and the acquired plurality of error accumulation evaluation coefficients are compared with a preset first error accumulation evaluation coefficient gradient reference threshold and a preset second error accumulation evaluation coefficient gradient reference threshold, wherein the first error accumulation evaluation coefficient gradient reference threshold is larger than the second error accumulation evaluation coefficient gradient reference threshold, the second error accumulation evaluation coefficient gradient reference threshold is larger than the error accumulation evaluation coefficient reference threshold, and the number of error accumulation evaluation coefficients larger than or equal to the first error accumulation evaluation coefficient gradient reference threshold is marked asMarking the number of error accumulation evaluation coefficients which are greater than or equal to the second error accumulation evaluation coefficient gradient reference threshold value and less than the first error accumulation evaluation coefficient gradient reference threshold value as/>Marking the number of error accumulation evaluation coefficients that are equal to or greater than the error accumulation evaluation coefficient reference threshold and that are less than the second error accumulation evaluation coefficient gradient reference threshold as/>；
Will be、/>/>Performing formulated analysis to generate an error accumulation risk measurement value/>The formula according to is: /(I)In the above, the ratio of/>、/>、/>Respectively/>、/>、/>And/>、/>、/>Are all greater than 0.
From the calculation expression of the error accumulation risk consideration value, the error accumulation risk consideration valueThe larger the expression value of the (c) is, the larger the risk of error accumulation is shown when the photovoltaic panel performs photovoltaic optimization, and otherwise, the larger the risk of error accumulation is shown when the photovoltaic panel performs photovoltaic optimization.
Comparing the generated error accumulation risk measurement value with a preset first error accumulation risk reference threshold value and a preset second error accumulation risk reference threshold value, wherein the first error accumulation risk reference threshold value is smaller than the second error accumulation risk reference threshold value, and comparing and analyzing results are as follows:
If the error accumulation risk measurement value is greater than or equal to the second error accumulation risk reference threshold value, dividing hidden danger into high risk error accumulation;
If the error accumulation risk measurement value is smaller than the second error accumulation risk reference threshold value and larger than or equal to the first error accumulation risk reference threshold value, dividing the hidden danger into risk error accumulation;
If the error accumulation risk measurement value is smaller than the first error accumulation risk reference threshold value, the hidden danger is divided into low risk error accumulation.
When the photovoltaic panel is monitored to be subjected to photovoltaic optimization, error accumulation exists, the risk classification of the error accumulation is crucial, and different maintenance management measures are conveniently taken for the photovoltaic panel according to different risk classes:
For high risk error accumulation, immediate emergency maintenance measures need to be taken to prevent further damage or performance degradation. This may include:
On-site inspections and diagnostics are performed to determine the root cause of the problem.
Repair or replacement of the affected photovoltaic panel or system component.
Enhancing the monitoring and alarming system, periodically inspecting, and closely monitoring the performance change of the system.
For risk error accumulation, appropriate maintenance measures need to be taken to prevent potential problems from further developing. This may include:
The monitoring frequency is enhanced, and the potential problems are found timely.
Periodic maintenance and checks are performed to ensure that the system is functioning properly.
System software is updated or parameters are adjusted to improve performance and stability.
For low risk error accumulation, preventive maintenance measures may be taken to ensure long term stable operation of the system. This may include:
the photovoltaic panel surface is cleaned periodically to ensure maximum solar energy absorption.
Periodic performance evaluations and calibrations are performed to ensure that the system is operating properly.
The skill of the operator is regularly trained and updated to improve maintenance efficiency and accuracy.
According to the invention, a real-time monitoring and early warning mechanism is established through monitoring and analyzing the total solar radiation data acquisition frequency, so that the abnormal data acquisition frequency can be found in time, the gradual accumulation of errors is prevented, and the abnormal acquisition environment is identified and processed in time through dividing the data acquisition environment, so that the risk of error accumulation is effectively reduced.
According to the invention, under a normal acquisition environment, the generated energy deviation index, the direct current conversion alternating current negative gain index and the generated energy difference index of different areas generated by abnormal analysis processing are comprehensively analyzed to generate the error accumulation evaluation coefficient, and the error accumulation during photovoltaic optimization of the photovoltaic panel is quantitatively evaluated through the error accumulation evaluation coefficient, so that the system is facilitated to identify the error accumulation phenomenon in the photovoltaic optimization process, and the accuracy and performance of the photovoltaic optimization process are further improved.
When the error accumulation condition is monitored, the system can realize targeted maintenance and risk prevention by classifying the error accumulation risk into three grades of high, medium and low and adopting corresponding maintenance management measures aiming at different risk grades, which is beneficial to optimizing the distribution and utilization of maintenance resources, improving the maintenance efficiency and reducing the system operation risk, thereby ensuring the long-term stable operation and the power generation efficiency of the photovoltaic system.
The above formulas are all formulas with dimensionality removed and numerical calculation, the formulas are formulas with the latest real situation obtained by software simulation through collecting a large amount of data, and preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. The photovoltaic optimization system based on the total solar radiation calculation is characterized by comprising a data acquisition frequency setting module, a data acquisition environment dividing module, a data acquisition anomaly analysis module, a photovoltaic optimization process intelligent analysis module and an error accumulation risk grade dividing module;
setting a normal data acquisition frequency range under a fixed duration window during total solar radiation data acquisition according to the performance requirement of the system and the change characteristic of solar radiation;
according to the time sequence, acquiring actual data acquisition frequencies during solar total radiation data acquisition under a plurality of fixed duration windows, establishing an analysis set, comprehensively analyzing the actual data acquisition frequencies in the analysis set, and dividing the data acquisition environment into a normal acquisition environment and an abnormal acquisition environment;
the data acquisition process in the abnormal acquisition environment is further analyzed, so that relevant management staff can know the data, and the data is maintained and managed in a targeted mode;
under a normal acquisition environment, performing intelligent analysis on the process of photovoltaic optimization of an acquisition photovoltaic panel by using a data analysis model, and identifying an error accumulation phenomenon in the photovoltaic optimization process;
when the error accumulation condition is monitored, carrying out risk classification on the error accumulation, classifying the error accumulation into high risk error accumulation, medium risk error accumulation and low risk error accumulation, and taking different maintenance management measures on the photovoltaic panel according to different risk classes.
2. The photovoltaic optimization system based on total solar radiation calculation according to claim 1, wherein according to time sequence, an analysis set is established by acquiring actual data acquisition frequency during total solar radiation data acquisition under a plurality of fixed duration windows, and the analysis set is calibrated as R, thenWherein/>Representing actual data acquisition frequency, v representing the number of actual data acquisition frequency;
Calculating a data acquisition frequency average value and a data acquisition frequency discrete value by analyzing actual data acquisition frequencies in a set, respectively comparing the data acquisition frequency average value and the data acquisition frequency discrete value with a preset data acquisition frequency reference threshold value and a preset discrete reference threshold value, and dividing a data acquisition environment into a normal acquisition environment and an abnormal acquisition environment, wherein the dividing result is as follows:
if the average value of the data acquisition frequency is more than or equal to the reference threshold value of the data acquisition frequency and the discrete value of the data acquisition frequency is less than the discrete reference threshold value, calibrating the data acquisition environment under the fixed duration window as a normal acquisition environment;
If the average value of the data acquisition frequency is not more than or equal to the reference threshold value of the data acquisition frequency and the discrete value of the data acquisition frequency is less than the discrete reference threshold value, the data acquisition environment under the fixed duration window is marked as an abnormal acquisition environment.
3. A photovoltaic optimisation system based on total solar radiation calculation according to claim 2, wherein for an abnormal collection environment the data collection process is further analysed as follows:
If the average value of the data acquisition frequency is smaller than the reference threshold value of the data acquisition frequency and the discrete value of the data acquisition frequency is smaller than the discrete reference threshold value, calibrating the data acquisition environment under the fixed duration window as continuous abnormality;
if the average value of the data acquisition frequency is greater than or equal to the reference threshold of the data acquisition frequency and the discrete value of the data acquisition frequency is greater than or equal to the reference threshold of the discrete reference, or the average value of the data acquisition frequency is smaller than the reference threshold of the data acquisition frequency and the discrete value of the data acquisition frequency is greater than or equal to the reference threshold of the discrete reference, the data acquisition environment under the window with fixed duration is marked as an unstable anomaly.
4. The photovoltaic optimization system based on solar total radiation calculation according to claim 1, wherein performance index information and space power generation information are obtained when photovoltaic optimization is performed on a photovoltaic panel, wherein the performance index information comprises power generation deviation and direct current conversion alternating current negative gain, the space power generation information comprises different region power generation differences, and a power generation deviation index, a direct current conversion alternating current negative gain index and different region power generation difference indexes are respectively generated after abnormal analysis processing is performed on the power generation deviation, the direct current conversion alternating current negative gain and the different region power generation differences.
5. The photovoltaic optimization system based on total solar radiation calculation of claim 4, wherein the power generation deviation index acquisition logic is as follows:
Collecting historical photovoltaic panel generating capacity data, and sampling according to fixed time intervals to form time sequence data, wherein the photovoltaic generating capacity data comprises the generating capacity of the photovoltaic panel and a corresponding time stamp;
The autoregressive integral moving average model is selected as a time series model, which is expressed as: wherein/> Is the power generation of the t time point,/>Is the power generation amount at the past p time points,Is a residual term, f is a functional relation of a time sequence model, and w represents the total number of residual terms;
Within a fixed time window, predicting the future generated energy by using the established time sequence model, and recording the predicted generated energy value as ；
Will predict the valueAnd actual value/>Comparing, calculating the generated energy deviation/>, of each time pointPower generation amount deviation/>The calculated expression of (2) is: /(I)The power generation amount deviation represents the difference between the model prediction and the actual observation value;
Calculating a power generation amount deviation index, wherein the calculated expression is as follows: wherein/> Representing the power generation deviation index,/>The power generation amount deviation of the ith time point is represented, namely, the difference value between the actual power generation amount and the predicted power generation amount of the photovoltaic panel, and n represents the total time point number.
6. The photovoltaic optimization system based on total solar radiation calculation of claim 5, wherein the logic for dc-to-ac negative gain index acquisition is as follows:
collecting performance data of the photovoltaic panel in a fixed duration window, including a phase angle between the voltage and the current, and calibrating the phase angle between the voltage and the current as ；
In a fixed duration window, calculating a power factor PF of each time point, wherein the power factor PF is used as an index for describing the phase relation between voltage and current in an alternating current circuit and is represented by a cosine value, and the calculation formula is as follows:；
on the basis of power factor calculation, a negative gain index is defined to evaluate the efficiency of direct current to alternating current conversion, the negative gain index is obtained by calculating the change rate of the power factors, namely the difference value between the power factor of each time point and the power factor of the last time point, and then the power factor of the last time point is divided, and the calculation formula is as follows: wherein/> Is the negative gain index of the j-th time point,/>Is the power factor of the j-th time point,/>Is the power factor of the j-1 th time point;
Calculating a direct current conversion alternating current negative gain index through the negative gain index of the time point, wherein the calculated expression is as follows: in the above, the ratio of/> Represents the DC conversion AC negative gain index, and N represents the total number of time points.
7. The photovoltaic optimization system based on total solar radiation calculation of claim 6, wherein the logic for obtaining the power generation difference index of different areas is as follows:
dividing the photovoltaic panel into a plurality of sub-areas, enabling the areas of the sub-areas to be equal, and collecting solar total radiation data and generating capacity data of the photovoltaic panel in each area;
selecting a K mean value clustering algorithm to determine an optimal clustering number K;
Calculating a center point of the sample in each cluster, representing typical solar total radiation and power generation of the area, and calculating a cluster center formula: wherein/> Is the center point of the kth cluster,/>Is the number of samples of the kth cluster,Is the first sample in the kth cluster;
comparing the generated energy of each sub-area with the central point of the cluster to which the generated energy of each sub-area belongs, and calculating the generated energy difference indexes of different sub-areas, wherein the calculation formula of the generated energy difference indexes of different sub-areas is as follows: wherein/> Is the power generation amount difference index of the c-th area,/>Is the (u) th feature of the (c) th region,/>Is the center point of the u-th cluster, m is the feature number;
establishing a data set of the generated energy difference indexes obtained from all the subareas, sequencing the generated energy difference indexes in the data set according to the sequence, and screening out the maximum generated energy difference index as a mark By the power generation amount difference index/>Obtaining the power generation amount difference indexes of different areas, wherein the obtained formula is as follows: /(I)Wherein/>And representing the power generation quantity difference indexes of different areas.
8. The photovoltaic optimization system based on total solar radiation calculation according to claim 7, wherein the generated energy deviation index generated in a fixed time window when photovoltaic optimization is performed on the photovoltaic panel is obtainedDirect current conversion alternating current negative gain index/>Different regional power generation difference index/>Thereafter, the power generation amount deviation index/>Direct current conversion alternating current negative gain index/>Different regional power generation difference index/>Comprehensive analysis is carried out to generate error accumulation evaluation coefficient/>Estimating coefficient by error accumulation/>Performing quantitative evaluation on error accumulation during photovoltaic optimization of the photovoltaic panel;
Comparing and analyzing the error accumulation evaluation coefficient generated in the fixed time window with a preset error accumulation evaluation coefficient reference threshold value when the photovoltaic panel is subjected to photovoltaic optimization, generating an error signal if the error accumulation evaluation coefficient is greater than or equal to the error accumulation evaluation coefficient reference threshold value, and generating a high-efficiency signal if the error accumulation evaluation coefficient is smaller than the error accumulation evaluation coefficient reference threshold value.
9. The photovoltaic optimization system based on total solar radiation calculation according to claim 8, wherein when generating an error signal within a fixed time window while photovoltaic panel is photovoltaic-optimized, an analysis set is established for a number of subsequently generated error accumulation assessment coefficients, and the number of acquired error accumulation assessment coefficients is compared with a preset first error accumulation assessment coefficient gradient reference threshold and a second error accumulation assessment coefficient gradient reference threshold, wherein the first error accumulation assessment coefficient gradient reference threshold is greater than the second error accumulation assessment coefficient gradient reference threshold, the second error accumulation assessment coefficient gradient reference threshold is greater than the error accumulation assessment coefficient reference threshold, and the number of error accumulation assessment coefficients greater than or equal to the first error accumulation assessment coefficient gradient reference threshold is marked asMarking the number of error accumulation evaluation coefficients which are greater than or equal to the second error accumulation evaluation coefficient gradient reference threshold value and less than the first error accumulation evaluation coefficient gradient reference threshold value as/>Marking the number of error accumulation evaluation coefficients that are equal to or greater than the error accumulation evaluation coefficient reference threshold and that are less than the second error accumulation evaluation coefficient gradient reference threshold as/>；
Will be、/>/>Performing formulated analysis to generate an error accumulation risk measurement value/>The formula according to is: in the above, the ratio of/> 、/>、/>Respectively/>、/>、/>And/>、/>、/>Are all greater than 0.
10. The photovoltaic optimization system based on total solar radiation calculation according to claim 9, wherein the generated error accumulated risk measurement value is compared with a first error accumulated risk reference threshold and a second error accumulated risk reference threshold, which are preset, and the comparison analysis results are as follows:
If the error accumulation risk measurement value is greater than or equal to the second error accumulation risk reference threshold value, dividing hidden danger into high risk error accumulation;
If the error accumulation risk measurement value is smaller than the second error accumulation risk reference threshold value and larger than or equal to the first error accumulation risk reference threshold value, dividing the hidden danger into risk error accumulation;
If the error accumulation risk measurement value is smaller than the first error accumulation risk reference threshold value, the hidden danger is divided into low risk error accumulation.
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