CN117993696B - Unmanned aerial vehicle power inspection-based risk management system and method - Google Patents
Unmanned aerial vehicle power inspection-based risk management system and method Download PDFInfo
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Abstract
The invention relates to the technical field of power line inspection, and particularly discloses a risk management system and method based on unmanned aerial vehicle power inspection, wherein the system comprises the following steps: the invention evaluates the risk level of each power line section according to the environment information and the history maintenance information of each power line section, can preferentially allocate limited patrol resources to areas with higher risks through risk evaluation, realizes optimal utilization of resources, carries out more frequent patrol on facilities with higher risk levels, is beneficial to finding potential faults and hidden danger in advance, avoids or reduces sudden faults of the facilities, acquires the power facility operation data of each power line section through an unmanned plane, acquires the power facility abnormality information, carries out quantitative evaluation on the abnormality degree of a power line, can more accurately predict the potential risks and faults, and improves the stability of a power system.
Description
Technical Field
The invention relates to the technical field of power line inspection, in particular to a risk management system and method based on unmanned aerial vehicle power inspection.
Background
The electric power is the basis of modern society operation, the safe operation of guarantee electric power system is crucial to economic development and social stability, electric power facilities such as transmission line, transformer substation etc. need regularly patrol and examine in order to prevent trouble and accident, along with unmanned aerial vehicle technology's rapid development, unmanned aerial vehicle has all showing in the aspect of flight stability, duration, loadability etc. and has promoted for unmanned aerial vehicle has extensive application potential in the electric power field of patrolling and examining, consequently need provide a risk management system and method based on unmanned aerial vehicle electric power is patrolled and examined, improves the power supply reliability.
For example, bulletin numbers: the invention patent of CN109300118B discloses a high-voltage power line unmanned aerial vehicle inspection method based on RGB images, which comprises the steps of utilizing monocular vision measuring equipment carried by an unmanned aerial vehicle to acquire video images of overhead lines and surrounding environment information thereof; regarding a power line as a power line object, analyzing physical structural characteristics of the power line, and providing a line edge pairing principle; the traditional LSD algorithm is improved by utilizing the complementary characteristics of RGB three-channel information, line segments in an image are extracted, and the line edge line is paired by using a line edge pairing principle; considering the color characteristics of the power line in the RGB image, the power line object is verified using this characteristic and the rule according to Hough transform. The invention adopts unmanned aerial vehicle inspection, is not influenced by factors such as topography, environment, state and the like, can monitor the distribution condition and surrounding environment of the power line in real time, quantitatively analyzes the problems such as the distribution condition of the power line by utilizing the power line extraction technology, and has the characteristics of high inspection efficiency, strong universality and good instantaneity.
For example, bulletin numbers: the invention patent of CN108037133B discloses an intelligent identification method and system for defects of power equipment based on unmanned aerial vehicle inspection images, wherein the method comprises the following steps: acquiring image data of the power transmission lines in batches by adopting a data stream mode; extracting image features, carrying out integrated analysis on the image features, realizing automatic positioning and identification of typical components, carrying out corresponding defect analysis on the typical components according to a set defect identification rule, and realizing analysis and identification of common defects; the analysis and recognition results are summarized and analyzed, and classified according to the type of the component, the type of the defect and the grade of the defect, so that classified inquiry can be conveniently carried out according to the requirements, and the local amplification display of the specific component can be carried out. The method can effectively excavate huge value of the unstructured big data of the power grid inspection, and fine inspection operation flow, and has an important effect on guaranteeing stable operation of the power transmission line.
Based on the above scheme, the power line inspection method has some defects in the aspects of the present, and the defects are specifically shown in the following layers: (1) The current power line inspection lacks a method for evaluating the risk level of the power line, the targeted inspection cannot be performed, and inspection resources cannot be fully utilized.
(2) The current power line inspection mainly relies on image recognition, so that operation data of power facilities cannot be fully acquired, and the accuracy of risk assessment of the power facilities is easily reduced.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a risk management system and a risk management method based on unmanned aerial vehicle power inspection, which can effectively solve the problems related to the background art.
In order to achieve the above purpose, the invention is realized by the following technical scheme: the first aspect of the invention provides a risk management system based on unmanned aerial vehicle power inspection, comprising: and the inspection task scheduling module is used for acquiring the environmental information and the historical maintenance information of each power line section, evaluating the risk level of each power line section and performing inspection task scheduling according to the risk level of each power line section.
The data acquisition processing module is used for acquiring power facility operation data of each power line section through the unmanned aerial vehicle, analyzing and obtaining power facility abnormality evaluation indexes of each power line section, and the power facility abnormality evaluation indexes are used for quantitatively evaluating the abnormality degree of the power line.
And the risk assessment early warning module is used for comprehensively assessing the fault risk of each power line section according to the environmental information of each power line section and the power facility operation data, and screening high-risk power line sections for early warning.
And the power inspection database is used for storing the power inspection related data.
As a further method, the power inspection related data specifically includes: the power facility normal operation reference standard temperature, reference standard humidity and critical wind speed, risk levels corresponding to each potential risk assessment value interval, abnormal noise waveform diagrams, reference standard power facility surface temperature, and reference standard RGB image and fault risk assessment threshold value of each power line section power facility.
As a further method, the risk level of each power line section is evaluated, and the routing inspection task is scheduled according to the risk level of each power line section, and the specific analysis process is as follows: the method comprises the steps of dividing a line section of a power line to be inspected, marking the line section as each power line section, deploying a plurality of environment monitoring points on each power line section, collecting environment information of each power line section, including temperature, humidity and wind speed of each environment monitoring point, and simultaneously obtaining reference standard temperature, reference standard humidity and critical wind speed of normal operation of a power facility from a power inspection database, and comprehensively analyzing to obtain environment interference values of each power line section.
And extracting the historical fault times and the adjacent maintenance implementation time of each power line section from the historical maintenance information of each power line section, and comprehensively analyzing to obtain potential risk assessment values of each power line section.
And matching the potential risk evaluation value of each power line section with the risk grade corresponding to each potential risk evaluation value interval stored in the power inspection database to obtain the risk grade of each power line section, arranging the risk grade of each power line section according to the sequence from high to low to obtain the inspection arrangement sequence of the power line section, and performing inspection task scheduling according to the inspection arrangement sequence of the power line section.
As a further method, the collecting power facility operation data for each power line section, wherein the power facility operation data includes: noise data, infrared thermal imaging data, and RGB image data of the electrical utility.
As a further method, the power facility abnormality evaluation index of each power line section is specifically analyzed as follows: and extracting noise intensity and a noise waveform diagram from the noise data of the power facilities of each power line section, performing waveform superposition matching on the noise waveform diagram and an abnormal noise waveform diagram stored in the power inspection database to obtain the waveform length of the noise waveform diagram and the superposition length of the abnormal noise waveform, and processing to obtain the noise abnormal characteristic value of the power facilities of each power line section.
And extracting temperature information of the surface of the electric power facility from the infrared thermal imaging data of the electric power facilities of each power line section, carrying out data processing on the temperature information of the surface of the electric power facility according to the temperature of the surface of the electric power facility stored in the electric power inspection database and analyzing to obtain abnormal characteristic values of the temperature of the electric power facilities of each power line section.
And (3) performing noise reduction processing on the RGB image data of the power facilities of each power line section, comparing the RGB image of the power facilities subjected to the noise reduction processing with the reference standard RGB image of each power line power facility stored in the power inspection database, extracting to obtain deviation pixel values of each pixel point, and processing to obtain the abnormal characteristic values of the surface images of the power facilities of each power line section.
And comprehensively analyzing according to the abnormal characteristic value of the noise of the electric power facility, the abnormal characteristic value of the temperature of the electric power facility and the abnormal characteristic value of the surface image of the electric power facility of each power line section to obtain an abnormal evaluation index of the electric power facility of each power line section.
As a further method, the comprehensive evaluation is performed on the fault risk of each power line section, and the high-risk power line section is screened for early warning, and the specific analysis process is as follows: and comprehensively analyzing and processing according to the environmental interference value and the power facility abnormality evaluation index of each power line section to obtain fault risk evaluation values of each power line section.
And extracting a fault risk assessment threshold value from the power inspection database, comparing the fault risk assessment value of each power line section with the fault risk assessment threshold value, and if the fault risk assessment value of a certain power line section is higher than the fault risk assessment threshold value, marking the power line section as a high-risk power line section for early warning feedback.
As a further method, the environmental interference value of each power line section is specifically calculated as: in the above, the ratio of/> Represents the/>Environmental interference value of individual power line sections,/>Representing natural constant,/>、/>And/>Respectively represent the/>First/>, of the power line sectionsTemperature, humidity and wind speed of individual environmental monitoring points,/>、/>And/>Respectively refer to the reference standard temperature, the reference standard humidity and the critical wind speed,/>And/>Respectively represent the set allowable critical deviation temperature and allowable critical deviation humidity,/>、/>And/>Respectively representing the environmental interference weight factors corresponding to the set temperature, humidity and wind speed,/>The number representing each power line section of road,，/>Representing the total number of power line sections,/>Number representing each environmental monitoring point,/>，/>Representing the total number of environmental monitoring points.
As a further method, the power facility abnormality evaluation index of each power line section is specifically calculated as: in the above, the ratio of/> Represents the/>Power facility anomaly assessment index for individual power line sections,/>、/>And/>Respectively represent the/>Abnormal characteristic value of noise of electric power facilities, abnormal characteristic value of temperature of electric power facilities and abnormal characteristic value of surface image of electric power facilities of each power line section,/>、/>And/>The power facility noise anomaly characteristic value, the power facility temperature anomaly characteristic value and the power facility surface image anomaly characteristic value are set to correspond to the power facility anomaly influence factors.
As a further method, the fault risk assessment value of each power line section is specifically calculated as: in the above, the ratio of/> Represents the/>Fault risk assessment value for individual power line sections,/>And/>Respectively represent the/>Environmental disturbance value and power facility abnormality assessment index for individual power line sections,/>And/>And respectively representing the set environment interference value and the fault risk influence factor corresponding to the power facility abnormality evaluation index.
The second aspect of the invention provides a risk management method based on unmanned aerial vehicle power inspection, comprising the following steps: and evaluating the risk level of each power line section according to the environmental information and the historical maintenance information of each power line section, and carrying out routing inspection task scheduling according to the risk level of each power line section.
The power facility abnormal evaluation index of each power line section is obtained through analysis by collecting power facility operation data of each power line section through the unmanned aerial vehicle, and the power facility abnormal evaluation index is used for quantitatively evaluating the abnormal degree of the power line.
And comprehensively evaluating the fault risk of each power line section according to the environment information of each power line section and the power facility operation data, and screening high-risk power line sections for early warning.
Compared with the prior art, the embodiment of the invention has at least the following advantages or beneficial effects:
(1) According to the risk management system and the risk management method based on unmanned aerial vehicle power inspection, the risk grade of each power line section is evaluated according to the environmental information and the historical maintenance information of each power line section, limited inspection resources can be preferentially allocated to areas with higher risks through risk evaluation, optimal utilization of the resources is achieved, more frequent inspection is conducted on facilities with higher risk grades, meanwhile, the unmanned aerial vehicle is used for collecting power facility operation data of each power line section, information collection of multiple angles is conducted on the power facilities, potential risks and faults can be accurately predicted, and stability of a power system is improved.
(2) According to the invention, the risk level of each power line section is evaluated, the potential fault risk of each power line section is quantitatively analyzed according to the environmental information and the historical maintenance information of each power line section, and the risk level of each power line section is evaluated and classified, so that more frequent and careful inspection can be performed on high-risk areas in a targeted manner, the safety of the areas is ensured, the possibility of accident occurrence is reduced, and meanwhile, limited inspection resources can be preferentially allocated to areas with higher risks, thereby realizing optimal utilization of resources.
(3) According to the invention, the unmanned aerial vehicle is used for collecting the power facility operation data of each power line section, evaluating the abnormal risk of the power facility of each power line section, and carrying out information collection on the power facility at multiple angles, so that the potential risk and faults can be predicted more accurately, the stability of a power system is improved, meanwhile, more detailed and accurate information is provided for comprehensively evaluating the power line, and more effective maintenance measures can be formulated.
Drawings
The invention will be further described with reference to the accompanying drawings, in which embodiments do not constitute any limitation of the invention, and other drawings can be obtained by one of ordinary skill in the art without inventive effort from the following drawings.
Fig. 1 is a schematic diagram of a system module connection according to the present invention.
FIG. 2 is a schematic flow chart of the method of the present invention.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without making creative efforts based on the embodiments of the present invention are included in the protection scope of the present invention.
Referring to fig. 1, a first aspect of the present invention provides a risk management system based on unmanned aerial vehicle power inspection, including: the system comprises a patrol task scheduling module, a data acquisition processing module, a risk assessment early warning module and a power patrol database.
And the power inspection database is used for storing the power inspection related data.
Specifically, the power inspection related data specifically includes: the power facility normal operation reference standard temperature, reference standard humidity and critical wind speed, risk levels corresponding to each potential risk assessment value interval, abnormal noise waveform diagrams, reference standard power facility surface temperature, and reference standard RGB image and fault risk assessment threshold value of each power line section power facility.
And the inspection task scheduling module is used for acquiring the environmental information and the historical maintenance information of each power line section, evaluating the risk level of each power line section and performing inspection task scheduling according to the risk level of each power line section.
Specifically, the risk level of each power line section is evaluated, and the routing inspection task scheduling is performed according to the risk level of each power line section, wherein the specific analysis process is as follows: the method comprises the steps of dividing a line section of a power line to be inspected, marking the line section as each power line section, deploying a plurality of environment monitoring points on each power line section, collecting environment information of each power line section, including temperature, humidity and wind speed of each environment monitoring point, and simultaneously obtaining reference standard temperature, reference standard humidity and critical wind speed of normal operation of a power facility from a power inspection database, and comprehensively analyzing to obtain environment interference values of each power line section.
It should be understood that, in this embodiment, the temperature, humidity and wind speed of each environmental monitoring point of each power line section are collected through the temperature sensor, the humidity sensor and the anemometer, the power facility needs to work normally in a suitable temperature and humidity environment, the extreme temperature and humidity conditions can all influence the normal operation of the power facility, for example, the excessive environment temperature can cause the overheat of the power facility, influence the normal operation of the power facility and even cause fire, when the humidity is higher, the performance of the insulating material can be reduced, the risk of short circuit and electric leakage is increased, and meanwhile, the excessive wind speed can cause the swing of the line, influence the stability or cause mechanical damage. By monitoring the temperature, humidity and wind speed of the environment, the interference degree of the environment factors on the normal operation of the power line can be estimated, and potential risks can be found in time and measures can be taken.
And extracting the historical fault times and the adjacent maintenance implementation time of each power line section from the historical maintenance information of each power line section, and comprehensively analyzing to obtain potential risk assessment values of each power line section.
It should be understood that, in this embodiment, the historical failure times of each power line section and the adjacent maintenance implementation time period are extracted, where the historical failure times of the power line reflect the reliability level of the line, the more the failure times, the worse the stability or health condition of the line may be indicated, the risk level should be rated as higher, the adjacent maintenance implementation time period may reflect the maintenance and maintenance condition of the power line, and if the maintenance time period is longer, it may mean that the line defect is larger or there is a problem that is difficult to solve, which may result in the risk level being increased. By monitoring the historical times of faults and the adjacent maintenance implementation time of each power line section, the accuracy of risk grade assessment of each power line section can be improved.
In a specific embodiment, the risk potential evaluation value of each power line section may not only identify various factors that may cause the power line to fail by constructing a fault tree and calculate the probability of occurrence of various faults, but also evaluate the risk potential of the power line by using a risk matrix and arranging the probability and severity of an accident in a matrix, and may also be obtained by the following calculation method, where a specific calculation expression is: In which, in the process, Represents the/>Potential risk assessment value of individual power line sections,/>Represents the/>The environmental interference values of the individual power line sections,And/>Respectively represent the/>Historical times of failure and adjacent repair implementation duration for individual power line sections,/>And/>Respectively representing preset critical historical fault times and critical adjacent maintenance implementation time length,/>、/>And/>Respectively representing potential risk influence factors corresponding to the set environment interference value, the historical fault times and the adjacent maintenance implementation time length,/>Number representing each power line section-，/>Representing the total number of power line segments.
It should be understood that, in the present embodiment, the risk potential evaluation value of each power line segment is obtained by comprehensively analyzing the environmental interference information, the number of historical faults and the duration of implementation of adjacent maintenance, so as to obtain quantitative evaluation data, which is used for quantitatively evaluating the risk potential of each power line segment, and providing a basis for evaluating the risk level of each power line segment, where the greater the environmental interference information, the number of historical faults and the duration of implementation of adjacent maintenance, the greater the corresponding risk potential evaluation value is, and the greater the environmental interference value, the number of historical faults and the risk potential influence factor corresponding to the duration of implementation of adjacent maintenance are used for improving the accuracy of the calculation result.
And matching the potential risk evaluation value of each power line section with the risk grade corresponding to each potential risk evaluation value interval stored in the power inspection database to obtain the risk grade of each power line section, arranging the risk grade of each power line section according to the sequence from high to low to obtain the inspection arrangement sequence of the power line section, and performing inspection task scheduling according to the inspection arrangement sequence of the power line section.
In a specific embodiment, the routing inspection task scheduling is performed according to the routing inspection arrangement order of the power line sections, the risk level of the power line section positioned in front is obtained, and the routing inspection attribute corresponding to the risk level is obtained at the same time, including the type of the routing inspection unmanned aerial vehicle equipment, the routing inspection starting time, the routing inspection ending time and the routing inspection height, and the routing inspection route of the unmanned aerial vehicle is planned according to the working area of the power line section, and the unmanned aerial vehicle is arranged to inspect the power line section at the routing inspection starting time.
In a specific embodiment, by evaluating the risk level of each power line section and quantitatively analyzing the potential fault risk of each power line section according to the environmental information and the historical maintenance information of each power line section, the risk level of each power line section is evaluated and classified, so that more frequent and careful inspection can be performed on high-risk areas in a targeted manner, the safety of the areas is ensured, the possibility of accident occurrence is reduced, and meanwhile, limited inspection resources can be preferentially allocated to areas with higher risks, so that the optimal utilization of the resources is realized.
The data acquisition processing module is used for acquiring power facility operation data of each power line section through the unmanned aerial vehicle, analyzing and obtaining power facility abnormality evaluation indexes of each power line section, and the power facility abnormality evaluation indexes are used for quantitatively evaluating the abnormality degree of the power line.
Specifically, collecting power facility operation data of each power line section, wherein the power facility operation data comprises: noise data, infrared thermal imaging data, and RGB image data of the electrical utility.
Further, the power facility abnormality evaluation index of each power line section comprises the following specific analysis processes: and extracting noise intensity and a noise waveform diagram from the noise data of the power facilities of each power line section, performing waveform superposition matching on the noise waveform diagram and an abnormal noise waveform diagram stored in the power inspection database to obtain the waveform length of the noise waveform diagram and the superposition length of the abnormal noise waveform, and processing to obtain the noise abnormal characteristic value of the power facilities of each power line section.
It should be appreciated that in this embodiment, the noise data of the electrical facility is collected by the acoustic sensor, and the noise anomaly is often an early sign of a device failure, and by monitoring and analyzing the noise, potential problems of the device can be found, which helps to make and adjust a preventive maintenance plan.
In a specific embodiment, the abnormal characteristic value of the noise of the electric power facility of each power line section can be obtained by not only learning a large amount of noise data by using a machine learning algorithm and an artificial intelligence technology and training a model capable of identifying the abnormal characteristic of the noise, but also obtaining vibration data of equipment by a vibration sensor and an analysis instrument so as to analyze the abnormal characteristic of the noise, and can also be obtained by the following calculation method, wherein the specific calculation expression is as follows: in the above, the ratio of/> Represents the/>Abnormal characteristic value of power facility noise of each power line section,/>And/>Respectively represent the/>Noise intensity and abnormal noise waveform coincidence length of each power line section,/>Representing a preset critical noise intensity,/>Represents the/>Waveform length of noise waveform diagram of individual power line sections,/>And/>The noise abnormality influencing factors corresponding to the set noise intensities and the abnormal noise waveform overlapping lengths are respectively shown.
It should be understood that, in this embodiment, the noise anomaly characteristic value of the power facility in each power line section is used to analyze the noise anomaly degree of the power facility by quantitatively evaluating the noise intensity and the anomaly condition of the noise waveform of the power facility, where the greater the noise intensity of the power facility is, the higher the overlap ratio of the noise waveform and the anomaly noise waveform is, the greater the noise anomaly characteristic value of the corresponding power facility is, and simultaneously, the noise anomaly influence factor corresponding to the overlapping length of the noise intensity and the anomaly noise waveform is introduced to improve the accuracy of the calculation result.
And extracting temperature information of the surface of the electric power facility from the infrared thermal imaging data of the electric power facilities of each power line section, wherein the temperature information comprises the temperature of each temperature monitoring point, and carrying out data processing on the temperature information of the surface of the electric power facility according to the temperature of the surface of the electric power facility stored in the electric power inspection database and analyzing to obtain the abnormal characteristic value of the temperature of the electric power facility of each power line section.
It should be understood that, in this embodiment, the infrared thermal imaging data of the electric power facility is acquired by the infrared thermal imager, and the infrared thermal imaging is to acquire the temperature distribution of the surface of the electric power facility by detecting the infrared radiation emitted by the object, and the infrared thermal imaging technology can detect hot spots of the electric power facility, that is, areas with abnormal temperatures, which may be early signs of faults, such as poor electrical connection, insulation damage, overload of equipment, mechanical wear, and the like, and by timely finding and processing the problems, potential faults and accidents can be prevented.
In a specific embodiment, the abnormal characteristic value of the temperature of the electric power facility of each power line section can be obtained by installing an online temperature monitoring system, monitoring the temperature change of the electric power facility in real time, finding abnormality in time through data analysis, processing and analyzing data acquired by a thermal imager by using professional thermal image data analysis software, identifying the temperature abnormality, deploying a plurality of temperature monitoring points, acquiring the temperature of each temperature monitoring point on the surface of the electric power facility of each power line section, and comprehensively calculating the abnormal characteristic value of the temperature of the electric power facility of each power line section, wherein the specific calculation expression is as follows: in the above, the ratio of/> Represents the/>Abnormal characteristic value of temperature of electric power facilities of each power line section,/>Represents the/>First/>, of the power line sectionsTemperature of each temperature monitoring point,/>Representing reference standard electrical utility surface temperature,/>Representing the defined deviation temperature of the setting,/>Indicating the set power facility temperature abnormality correction factor,/>Number indicating each temperature monitoring point/>，/>Indicating the total number of temperature monitoring points.
It should be understood that, in this embodiment, by monitoring and analyzing the surface temperature of the electric power facility, the abnormal temperature characteristic value of the electric power facility is used for quantitatively evaluating the abnormal temperature condition of the electric power facility, the surface temperature of the electric power facility deviates from the reference standard value, and the abnormal temperature characteristic value of the corresponding electric power facility is greater, and meanwhile, by disposing a plurality of temperature monitoring points in this embodiment, the temperature measurement error can be effectively reduced, and the accuracy of the calculation result is improved.
And (3) performing noise reduction processing on the RGB image data of the power facilities of each power line section, comparing the RGB image of the power facilities subjected to the noise reduction processing with the reference standard RGB image of each power line power facility stored in the power inspection database, extracting to obtain deviation pixel values of each pixel point, and processing to obtain the abnormal characteristic values of the surface images of the power facilities of each power line section.
It should be understood that in this embodiment, the RGB image of the electric power facility is acquired by the unmanned aerial vehicle-mounted camera, where the RGB image is composed of a plurality of pixels, and each pixel contains information of three color channels of red (R), green (G) and blue (B), and these information together determine the final color of the pixel, and the luminance value of each color channel is typically represented by an 8-bit value, where 0 indicates that the color channel does not emit light at all, and 255 indicates that the color channel reaches the maximum luminance. By analyzing the red channel value, the green channel value and the blue channel value of each pixel point in the RGB image, the surface image abnormality degree of the electric power facility can be quantitatively evaluated.
In a specific embodiment, the abnormal characteristic value of the surface image of the electric power facility of each power line section can be obtained by not only learning a large amount of image data by using a machine learning algorithm and an artificial intelligence technology and training a model capable of identifying the abnormal characteristic of the surface image, but also processing and analyzing the acquired image by using professional image processing and analyzing software such as OpenCV, HALCON and the like, and the abnormal characteristic of the surface can be identified by the following calculation method: in the above, the ratio of/> Represent the firstAbnormal characteristic value of surface image of electric power facility of each power line section,/>、/>And/>Respectively represent the/>First/>, of the power line sectionsRed channel value, green channel value and blue channel value of each pixel point,/>、/>And/>Respectively represent the/>The power line section power facility references the/>, in standard RGB imagesReference standard red channel value, reference standard green channel value and reference standard blue channel value for each pixel point,/>、/>And/>Respectively represent the set defined deviation red channel value, defined deviation green channel value and defined deviation blue channel value,/>、/>And/>Respectively representing surface image abnormality influencing factors corresponding to the set red channel value, green channel value and blue channel value,/>Number representing each pixel point,/>，/>Representing the total number of pixels.
It should be understood that, in this embodiment, by analyzing the abnormal characteristic value of the surface image of the electric power facility in each pixel point of the surface image of the electric power facility, the abnormal condition of the surface image of the electric power facility is quantitatively evaluated, and the larger the deviation between the color of each pixel point of the surface image of the electric power facility and the reference color is, the larger the abnormal characteristic value of the corresponding surface image of the electric power facility is, and meanwhile, the surface image abnormal influence factors corresponding to the red channel value, the green channel value and the blue channel value are introduced for improving the accuracy of the calculation result.
And comprehensively analyzing according to the abnormal characteristic value of the noise of the electric power facility, the abnormal characteristic value of the temperature of the electric power facility and the abnormal characteristic value of the surface image of the electric power facility of each power line section to obtain an abnormal evaluation index of the electric power facility of each power line section.
In a specific embodiment, by collecting the power facility operation data of each power line section for the unmanned aerial vehicle, evaluating the abnormal risk of the power facility of each power line section, and collecting information of multiple angles for the power facility, the potential risk and faults can be predicted more accurately, the stability of the power system can be improved, and meanwhile, more detailed and accurate information is provided for comprehensive evaluation of the power line, so that more effective maintenance measures can be formulated.
And the risk assessment early warning module is used for comprehensively assessing the fault risk of each power line section according to the environmental information of each power line section and the power facility operation data, and screening high-risk power line sections for early warning.
Specifically, the fault risk of each power line section is comprehensively evaluated, the high-risk power line section is screened for early warning, and the specific analysis process is as follows: and comprehensively analyzing and processing according to the environmental interference value and the power facility abnormality evaluation index of each power line section to obtain fault risk evaluation values of each power line section.
And extracting a fault risk assessment threshold value from the power inspection database, comparing the fault risk assessment value of each power line section with the fault risk assessment threshold value, and if the fault risk assessment value of a certain power line section is higher than the fault risk assessment threshold value, marking the power line section as a high-risk power line section for early warning feedback.
Specifically, the environmental interference value of each power line section is specifically calculated as: in the above, the ratio of/> Represents the/>Environmental interference value of individual power line sections,/>Representing natural constant,/>、/>And/>Respectively represent the/>First/>, of the power line sectionsTemperature, humidity and wind speed of individual environmental monitoring points,/>、/>And/>Respectively refer to the reference standard temperature, the reference standard humidity and the critical wind speed,/>And/>Respectively represent the set allowable critical deviation temperature and allowable critical deviation humidity,/>、/>And/>Respectively representing the environmental interference weight factors corresponding to the set temperature, humidity and wind speed,/>The number representing each power line section of road,，/>Representing the total number of power line sections,/>Number representing each environmental monitoring point,/>，/>Representing the total number of environmental monitoring points.
In a specific embodiment, the environmental interference value of each power line section can be obtained not only through the above calculation mode, but also by utilizing an unmanned aerial vehicle equipped with electromagnetic sensors and acoustic sensors to carry out line inspection, so as to obtain interference data with high space-time resolution, the remote sensing technology (such as synthetic aperture radar interferometry) can also be used for large-scale and non-contact electromagnetic environment monitoring, and the possible interference value can also be predicted by calculating electromagnetic field distribution around the line and the change of the electromagnetic field distribution along with the distance by using professional electromagnetic field simulation software (such as CST, ANSYS and the like) based on information of electric parameters, topography, meteorological conditions and the like of the power line.
It should be understood that, in this embodiment, the environmental interference value of each power line section is quantized and estimated data obtained by comprehensively analyzing the environmental temperature, the environmental humidity and the environmental wind speed of each power line section, which are used to reflect the interference degree of environmental factors on each power line section, where the greater the environmental temperature and the environmental humidity deviate from the normal standard values, the greater the environmental wind speed, the greater the corresponding environmental interference value, and the environmental interference weight factor corresponding to the temperature, the humidity and the wind speed is used to improve the accuracy of the calculation result.
Specifically, the power facility abnormality evaluation index of each power line section has a specific calculation expression: in the above, the ratio of/> Represents the/>Power facility anomaly assessment index for individual power line sections,/>、/>And/>Respectively represent the/>Abnormal characteristic value of noise of electric power facilities, abnormal characteristic value of temperature of electric power facilities and abnormal characteristic value of surface image of electric power facilities of each power line section,/>、/>And/>The power facility noise anomaly characteristic value, the power facility temperature anomaly characteristic value and the power facility surface image anomaly characteristic value are set to correspond to the power facility anomaly influence factors.
It should be understood that, in this embodiment, the power facility abnormality evaluation index of each power line section is obtained by analyzing the operation data of the power facility collected by the unmanned aerial vehicle, where the larger the power facility noise abnormality characteristic value, the power facility temperature abnormality characteristic value and the power facility surface image abnormality characteristic value are, the larger the corresponding power facility abnormality evaluation index is, and the power facility noise abnormality characteristic value, the power facility temperature abnormality characteristic value and the power facility surface image abnormality influence factor corresponding to the power facility abnormality characteristic value are introduced for improving the accuracy of the calculation result.
In a specific embodiment, the power facility abnormality evaluation index of each power line section can be obtained through the calculation mode, a device health state prediction model can be built through integrating multisource monitoring data, algorithms such as machine learning and deep learning are utilized, device fault risk level or residual service life prediction is output, device abnormality evaluation index is formed, data such as defect records, maintenance history, defect types and severity degree can be analyzed through statistics, defect rules can be revealed through a data mining technology, probability and influence of defects possibly occurring in the future are predicted, and the power facility abnormality evaluation index is generated.
Further, the fault risk assessment value of each power line section is specifically calculated as: in the above, the ratio of/> Represents the/>Fault risk assessment value for individual power line sections,/>And/>Respectively represent the/>Environmental disturbance value and power facility abnormality assessment index for individual power line sections,/>And/>And respectively representing the set environment interference value and the fault risk influence factor corresponding to the power facility abnormality evaluation index.
It should be understood that, in this embodiment, by analyzing the operation data of the power facilities collected by the unmanned aerial vehicle, the fault risk of each power line section is comprehensively and quantitatively estimated according to the interference degree of the environmental factors, the greater the operation data of the power facilities is abnormal, the greater the interference degree of the environmental factors is, the greater the corresponding fault risk assessment value is, and the fault risk influence factors corresponding to the environment interference value and the power facility abnormality assessment index are used for improving the accuracy of the calculation result.
In a specific embodiment, the fault risk assessment value of each power line section can be obtained through the calculation mode, a probability statistical method such as Monte Carlo simulation can be utilized, a probability model reflecting the system state can be constructed according to factors such as the fault rate, load characteristics, running environment and the like of each element (such as a transformer, a circuit breaker, a power transmission line and the like) of the power line, a chain reaction possibly caused by line faults can be analyzed through building a power system accident chain model, phenomena such as cascade tripping, voltage stability damage and system disconnection are included, a complex network theory, dynamic simulation or fuzzy comprehensive assessment method is applied, the probability of occurrence of the chain faults and the influence of the probability on the stability of the whole power system are assessed, and finally the fault risk assessment value of each power line section is calculated.
Referring to fig. 2, a second aspect of the present invention provides a risk management method based on unmanned aerial vehicle power inspection, including: step one, evaluating the risk level of each power line section according to the environmental information and the historical maintenance information of each power line section, and carrying out routing inspection task scheduling according to the risk level of each power line section.
And secondly, collecting power facility operation data of each power line section through the unmanned aerial vehicle, and analyzing to obtain power facility abnormality evaluation indexes of each power line section, wherein the power facility abnormality evaluation indexes are used for quantitatively evaluating the abnormality degree of the power line.
And thirdly, comprehensively evaluating the fault risk of each power line section according to the environmental information of each power line section and the operation data of the power facilities, and screening high-risk power line sections for early warning.
In a specific embodiment, by providing a risk management system and a method based on unmanned aerial vehicle power inspection, the risk level of each power line section is evaluated according to the environmental information and the historical maintenance information of each power line section, limited inspection resources can be preferentially allocated to a region with higher risk through risk evaluation, optimal utilization of resources is achieved, more frequent inspection is performed on facilities with higher risk level, meanwhile, the unmanned aerial vehicle is used for collecting power facility operation data of each power line section, information collection of multiple angles is performed on the power facilities, potential risks and faults can be predicted more accurately, and stability of a power system is improved.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
Claims (6)
1. Risk management system based on unmanned aerial vehicle electric power inspection, its characterized in that includes:
The inspection task scheduling module is used for acquiring environment information and historical maintenance information of each power line section, evaluating the risk level of each power line section and performing inspection task scheduling according to the risk level of each power line section;
The data acquisition processing module is used for acquiring power facility operation data of each power line section through the unmanned aerial vehicle, analyzing and obtaining power facility abnormality evaluation indexes of each power line section, wherein the power facility abnormality evaluation indexes are used for quantitatively evaluating the abnormality degree of a power line;
The risk assessment early warning module is used for comprehensively assessing the fault risk of each power line section according to the environmental information of each power line section and the power facility operation data, and screening high-risk power line sections for early warning;
The power inspection database is used for storing power inspection related data;
The risk level of each power line section is evaluated, the routing inspection task scheduling is carried out according to the risk level of each power line section, and the specific analysis process is as follows:
dividing a power line section of a power line to be inspected, marking the power line section as each power line section, deploying a plurality of environment monitoring points on each power line section, collecting environment information of each power line section, including temperature, humidity and wind speed of each environment monitoring point, and simultaneously obtaining reference standard temperature, reference standard humidity and critical wind speed of normal operation of a power facility from a power inspection database, and comprehensively analyzing to obtain environment interference values of each power line section;
According to the historical maintenance information of each power line section, the historical fault times and the adjacent maintenance implementation time length of each power line section are extracted, and the potential risk assessment value of each power line section is obtained through comprehensive analysis of the environmental interference value, the historical fault times and the adjacent maintenance implementation time length;
Matching the potential risk evaluation value of each power line section with the risk grade corresponding to each potential risk evaluation value interval stored in the power inspection database to obtain the risk grade of each power line section, arranging the risk grade of each power line section according to the sequence from high to low to obtain the inspection arrangement sequence of the power line section, and performing inspection task dispatching according to the inspection arrangement sequence of the power line section;
The collecting power facility operation data of each power line section, wherein the power facility operation data comprises: noise data, infrared thermal imaging data, and RGB image data of the electrical utility;
the power facility abnormality evaluation index of each power line section comprises the following specific analysis processes:
Extracting noise intensity and a noise waveform diagram from the noise data of the power facilities of each power line section, performing waveform superposition matching on the noise waveform diagram and an abnormal noise waveform diagram stored in the power inspection database to obtain the waveform length of the noise waveform diagram and the superposition length of the abnormal noise waveform, and processing to obtain the noise abnormal characteristic value of the power facilities of each power line section;
Extracting temperature information of the surface of the electric power facility from the infrared thermal imaging data of the electric power facilities of each power line section, performing data processing on the temperature information of the surface of the electric power facility according to the temperature of the surface of the electric power facility stored in the electric power inspection database and analyzing to obtain abnormal characteristic values of the temperature of the electric power facility of each power line section;
Performing noise reduction processing on RGB image data of electric power facilities of each power line section, comparing the RGB image of the electric power facilities subjected to the noise reduction processing with reference standard RGB images of the electric power line electric power facilities stored in an electric power inspection database, extracting to obtain deviation pixel values of all pixel points, and processing to obtain abnormal characteristic values of the electric power facility surface images of each power line section;
Comprehensively analyzing according to the abnormal characteristic value of the noise of the electric power facility, the abnormal characteristic value of the temperature of the electric power facility and the abnormal characteristic value of the surface image of the electric power facility of each power line section to obtain an abnormal evaluation index of the electric power facility of each power line section;
the comprehensive evaluation is carried out on the fault risk of each power line section, the high-risk power line section is screened for early warning, and the specific analysis process is as follows:
comprehensively analyzing and processing according to the environmental interference value and the power facility abnormality evaluation index of each power line section to obtain a fault risk evaluation value of each power line section;
and extracting a fault risk assessment threshold value from the power inspection database, comparing the fault risk assessment value of each power line section with the fault risk assessment threshold value, and if the fault risk assessment value of a certain power line section is higher than the fault risk assessment threshold value, marking the power line section as a high-risk power line section for early warning feedback.
2. The unmanned aerial vehicle power patrol-based risk management system according to claim 1, wherein: the power inspection related data specifically comprises: the power facility normal operation reference standard temperature, reference standard humidity and critical wind speed, risk levels corresponding to each potential risk assessment value interval, abnormal noise waveform diagrams, reference standard power facility surface temperature, and reference standard RGB image and fault risk assessment threshold value of each power line section power facility.
3. The unmanned aerial vehicle power patrol-based risk management system according to claim 1, wherein: the specific calculation expression of the environmental interference value of each power line section is as follows:
，
In the method, in the process of the invention, Represents the/>Environmental interference value of individual power line sections,/>Representing natural constant,/>、/>And/>Respectively represent the/>First/>, of the power line sectionsTemperature, humidity and wind speed of individual environmental monitoring points,/>、/>And/>Respectively refer to the reference standard temperature, the reference standard humidity and the critical wind speed,/>And/>Respectively represent the set allowable critical deviation temperature and allowable critical deviation humidity,/>、/>And/>Respectively representing the environmental interference weight factors corresponding to the set temperature, humidity and wind speed,/>Number representing each power line section-，/>Representing the total number of power line sections,/>Number representing each environmental monitoring point,/>，/>Representing the total number of environmental monitoring points.
4. The unmanned aerial vehicle power patrol-based risk management system according to claim 1, wherein: the power facility abnormality evaluation index of each power line section has the following specific calculation expression:
，
In the method, in the process of the invention, Represents the/>Power facility anomaly assessment index for individual power line sections,/>、/>And/>Respectively represent the/>Abnormal characteristic value of noise of electric power facilities, abnormal characteristic value of temperature of electric power facilities and abnormal characteristic value of surface image of electric power facilities of each power line section,/>、/>And/>The power facility noise anomaly characteristic value, the power facility temperature anomaly characteristic value and the power facility surface image anomaly characteristic value are set to correspond to the power facility anomaly influence factors.
5. The unmanned aerial vehicle power patrol-based risk management system according to claim 1, wherein: the specific calculation expression of the fault risk assessment value of each power line section is as follows:
，
In the method, in the process of the invention, Represents the/>Fault risk assessment value for individual power line sections,/>And/>Respectively represent the/>Environmental disturbance value and power facility abnormality assessment index for individual power line sections,/>And/>And respectively representing the set environment interference value and the fault risk influence factor corresponding to the power facility abnormality evaluation index.
6. A management method adopted by a risk management system based on unmanned aerial vehicle power inspection according to any one of claims 1 to 5, wherein: comprising the following steps:
Evaluating the risk level of each power line section according to the environmental information and the historical maintenance information of each power line section, and carrying out routing inspection task scheduling according to the risk level of each power line section;
Collecting power facility operation data of each power line section through an unmanned aerial vehicle, and analyzing to obtain power facility abnormality evaluation indexes of each power line section, wherein the power facility abnormality evaluation indexes are used for quantitatively evaluating the abnormality degree of a power line;
And comprehensively evaluating the fault risk of each power line section according to the environment information of each power line section and the power facility operation data, and screening high-risk power line sections for early warning.
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