CN117674422A - Automatic power dispatching monitoring system with alarm function - Google Patents
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- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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Abstract
The invention discloses an automatic power dispatching monitoring system with an alarm function, which comprises: and a data acquisition module: the power data are used for monitoring the power system in real time; and a data transmission module: the power data acquisition module is used for transmitting the acquired power data to a monitoring center; and the real-time data processing and compressing module is used for: data cleaning, compression and storage are carried out on the received real-time data; and the real-time state analysis and fault prediction module is used for: analyzing the real-time data of the power system, and predicting faults; threshold setting and real-time alarm module: setting a threshold value of a real-time monitoring system, and triggering an alarm when the system parameter exceeds a set normal range; automatic scheduling and response mechanism module: the system makes decisions and automatically adjusts the operating state of the power system when an alarm occurs. The real-time problem of the system is solved, the change of the power system can be responded more quickly and accurately, and the overall efficiency of the power dispatching automation monitoring system is improved.
Description
Technical Field
The invention relates to the technical field of power dispatching, in particular to a power dispatching automation monitoring system with an alarm function.
Background
The automatic monitoring of power dispatching refers to the process of monitoring, analyzing and dispatching the power system in real time through advanced information technology and an automatic system. The monitoring system aims at ensuring stable operation of the power system, improving reliability and efficiency of the power system and guaranteeing quality of power supply.
In the power dispatching automation monitoring process, some technical problems may be faced, and the nature and the solution of the problems may be different according to specific system architecture, technical implementation and application scenarios, for example, the power system needs to be monitored and dispatched in real time, so that the monitoring system has enough real-time performance, and timely response to system state change is a specific technical challenge.
Disclosure of Invention
In order to solve the problems, the invention provides an automatic power dispatching monitoring system with an alarm function.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
an automated power dispatching monitoring system with an alarm function, comprising:
and a data acquisition module: the power data are used for monitoring the power system in real time;
and a data transmission module: the power data acquisition module is used for transmitting the acquired power data to a monitoring center;
and the real-time data processing and compressing module is used for: data cleaning, compression and storage are carried out on the received real-time data;
and the real-time state analysis and fault prediction module is used for: analyzing the real-time data of the power system, and predicting faults;
threshold setting and real-time alarm module: setting a threshold value of a real-time monitoring system, and triggering an alarm when the system parameter exceeds a set normal range;
automatic scheduling and response mechanism module: the system makes decisions and automatically adjusts the operating state of the power system when an alarm occurs.
Further: the data acquisition module comprises:
a power sensor and a frequency telemetry device;
setting a real-time data transmission channel between the power sensor and the remote measuring device and the monitoring center;
data quality control measures are implemented at the sensor end so as to improve the precision and accuracy of the acquired data:
y n ＝α·x n +(1-α)·y n-1
wherein y is n Is the filtered data, x n Is the collected original data, y n-1 Is the filtering result of the previous moment and alpha is the filtering coefficient.
Further: the data transmission module comprises:
and implementing a redundancy mechanism, adopting a standby communication path to ensure that the standby path can be switched when the main communication path fails, ensuring the continuity of data transmission, and calculating the reliability R of the redundancy mechanism by the following formula:
wherein P is i Is the probability of failure for each communication path and n is the number of paths. .
Further: the real-time data processing and compressing module comprises:
the received real-time data is cleaned, and the cleaning formula is as follows:
wherein y is n Is the data after cleaning, x n Is the original data;
compressing real-time data: the compression formula is as follows:
difference = x n -x n-1
Wherein x is n Is the current data, x n-1 Is the data of the previous time.
Further: the real-time state analysis and fault prediction module comprises:
real-time estimating current state of power system by using real-time data, state vector x n The estimation formula of (2) is:
wherein F is a state transition matrix, B is an input matrix, u n Is the input vector, w n Is process noise;
detecting anomalies in the power system using the real-time state estimate:
model training is performed by using historical data, and then real-time data is input into a model for prediction:
y n ＝f(W·x n +b)
wherein W is a weight matrix, b is a bias vector, and f is an activation function;
the output is interpreted to identify possible failure modes, and a mapping of the predicted results to actual conditions is established to assist the operator in understanding the current condition of the system.
Further: the threshold setting and real-time alarm module comprises:
selecting a key system parameter from the parameters monitored in real time as a monitoring object for setting a threshold value;
statistical analysis is performed on the selected monitoring parameters based on historical data, and a mean value mu and a standard deviation sigma are calculated:
where N is the number of historical data, x i Is the i-th historical data point;
setting a threshold value by using the statistical result:
upper threshold=μ+k·σ
Lower line threshold = μ -k σ
Where k is a set multiple, determining the sensitivity of the threshold;
monitoring the monitoring parameters acquired in real time, and triggering a real-time alarm when the monitoring parameters exceed a set upper limit or lower limit threshold;
when the real-time alarm is triggered, corresponding alarm information is generated, including alarm level, trigger time and specific parameters.
Further: the automatic scheduling and response mechanism module comprises:
a control algorithm is used to implement automatic scheduling of the power system:
where e (t) is the current error, K p 、K i And K d Proportional, integral and differential coefficients, respectively;
based on the output and the current system state, a specific strategy of automatic scheduling is formulated;
according to the output and scheduling strategies, a scheduling scheme of the power system is rapidly generated;
and automatically applying the generated scheduling scheme to the power system, and realizing automatic adjustment of the power system through an automatic control device.
Compared with the prior art, the invention has the following technical progress:
the invention realizes the efficient collection and rapid transmission of real-time data by selecting the high-precision sensor, using the compatibility of the communication protocol (such as IEC61850 standard) and optimizing the network bandwidth and delay, thereby improving the real-time performance of the monitoring system.
The invention adopts the real-time database technology to support high-speed writing and inquiring, and simultaneously ensures the rapid and effective processing of the real-time data by the monitoring center through the real-time data cleaning and compression, thereby further improving the real-time performance of the system.
The invention introduces a machine learning algorithm to perform real-time state analysis and fault prediction, and can more flexibly adapt to the dynamic change of the system compared with the traditional rule and static threshold method, thereby improving the adaptability of the real-time monitoring system to complex situations and enhancing the intelligence and accuracy of the system.
The invention timely captures system abnormality and generates real-time alarm by setting reasonable threshold and real-time alarm mechanism. By introducing an automatic scheduling and response mechanism, when an abnormality occurs, the system can quickly make a decision and automatically adjust the running state of the power system, so that the real-time response speed of the system is improved.
The invention considers the problem of system integration, continuously adjusts the real-time decision through the real-time state monitoring and feedback mechanism, and ensures the real-time performance and the robustness of the system scheduling scheme.
The method has the advantages that the method is more excellent in solving the problem of system instantaneity, can respond to the change of the power system more quickly and accurately, and improves the overall efficiency of the power dispatching automation monitoring system.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention.
In the drawings:
fig. 1 is a system configuration diagram of the present invention.
Detailed Description
The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present invention will be described below with reference to the accompanying drawings.
As shown in fig. 1, the present invention discloses an automatic power dispatching monitoring system with an alarm function, comprising:
and a data acquisition module: a high-precision data acquisition system is established, key parameters of the power system, including voltage, current, frequency and the like, are monitored in real time through a telemetry device and a sensor, and the selection of equipment is based on the compatibility of a communication protocol, for example, IEC61850 standard is used.
And a data transmission module: the collected real-time data can be timely transmitted to the monitoring center. A high bandwidth, low latency communication network is employed and redundancy mechanisms are implemented to prevent communication failures.
And the real-time data processing and compressing module is used for: and the monitoring center is used for rapidly and effectively processing the received real-time data, including data cleaning, compression and storage. Real-time database techniques, such as InfluxDB or TimescaleDB, are employed to ensure high-speed writing and querying.
And the real-time state analysis and fault prediction module is used for: real-time data of the power system is analyzed by applying real-time state estimation and intelligent algorithms to detect possible anomalies and faults. The fault prediction is performed using a machine learning model, such as a time series model or a neural network.
Threshold setting and real-time alarm module: setting a threshold value of the real-time monitoring system, and triggering an alarm when the system parameter exceeds a set normal range.
Automatic scheduling and response mechanism module: an automatic scheduling and response mechanism is established, so that the monitoring system can quickly make decisions and automatically adjust the running state of the power system when an alarm occurs.
The invention solves the real-time challenge of the power system by high-efficiency real-time data acquisition, transmission, processing and intelligent analysis and reasonable threshold value and automatic response mechanism, and ensures that the system can timely and accurately respond when facing to changes.
Specifically, the data acquisition module includes:
1. selection sensor and telemetry device: the selection of high precision sensors and telemetry devices suitable for use in power systems, such as current sensors, voltage sensors and frequency telemetry devices, ensures that these devices are capable of providing accurate real-time data.
2. Communication protocol selection: and by adopting IEC61850 standard, a unified communication protocol is adopted between the sensor and the telemetry device, so that the compatibility of the system is improved, and the IEC61850 defines the communication protocol and a data model in the automation of the power system.
3. Mounting a sensor and a telemetry device: the selected sensors and telemetry devices are installed on critical nodes of the power system to ensure coverage of the entire power network, and the installation location is selected based on the topology and monitoring requirements of the power system.
4. Real-time data transmission: a real-time data transmission channel between the sensor and the telemetry device and the monitoring center is arranged. High bandwidth, low latency communication networks, such as fiber optic communications, are employed to ensure rapid transmission of real-time data.
5. And (3) data quality control: data quality control measures, such as data filtering and calibration, are implemented at the sensor end to improve the accuracy and precision of the acquired data, and the data filtering formula is as follows:
y n ＝α·x n +(1-α)·y n-1
wherein y is n Is the filtered data, x n Is the collected original data, y n-1 Is the filtering result of the previous moment and alpha is the filtering coefficient.
Through the steps, a high-precision real-time data acquisition system is established, the timely monitoring and accuracy of key parameters of the power system are ensured, and a reliable data base is provided for the subsequent system instantaneity.
Specifically, the data transmission module includes:
1. network bandwidth optimization: high bandwidth communication networks, such as fiber optic communications, are selected to ensure that large amounts of real-time data can be transmitted to the monitoring center in a short period of time.
2. Network delay optimization: network latency is reduced by reducing routing paths for data transmissions, optimizing network devices and protocols.
3. Redundancy mechanism: and implementing a redundancy mechanism, adopting a standby communication path to ensure that the standby path can be switched when the main communication path fails, ensuring the continuity of data transmission, wherein the reliability R of the redundancy mechanism can be calculated by the following formula:
wherein P is i Is the probability of failure for each communication path and n is the number of paths.
4. Communication protocol selection: and selecting an efficient communication protocol to reduce communication overhead and improve transmission efficiency, and adopting a binary protocol or a compression algorithm to reduce the size of a data packet so as to reduce transmission time.
5. Real-time monitoring and adjustment: the real-time performance of the network, including bandwidth utilization, delay, etc., is monitored in real time during the transmission process, and the communication network is adjusted in real time according to the monitoring result, so as to maintain the optimal real-time performance.
Through the steps, the collected real-time data can be transmitted on a high-bandwidth low-delay communication network, and the reliability of the system is improved through a redundancy mechanism. This provides a reliable transport basis for subsequent real-time data processing.
Specifically, the real-time data processing and compressing module includes:
the received real-time data is processed, cleaned, compressed and stored in the monitoring center, and a real-time database technology is adopted to ensure high-speed writing and inquiring.
1. And (3) cleaning real-time data: the method for cleaning the received real-time data to remove abnormal values, noise or error data can be realized by setting a data range, a threshold value and the like, and the cleaning formula is as follows:
wherein y is n Is the data after cleaning, x n Is the original data.
2. Real-time data compression: the real-time data compression algorithm is adopted to reduce the storage space and improve the data transmission efficiency, namely only the variable quantity of data is transmitted, and differential coding is used, wherein the differential coding formula is as follows:
difference = x n -x n-1
Wherein x is n Is the current data, x n-1 Is the data of the previous time.
3. Real-time database technology: suitable real-time database technologies, such as InfluxDB or TimescaleDB, are selected to support high-speed writing and querying, and these databases typically employ a structure of a time-sequential database, which is suitable for storing large amounts of time-sequential data.
4. High-speed writing: the database is configured to enable high-speed writing, ensure that real-time data can be stored quickly, optimize the writing performance of the database, for example, by reasonable indexing, partitioning and the like.
5. High-speed query: the query performance of the database is optimized according to the real-time data query requirement of the monitoring center, and the real-time data is quickly retrieved and analyzed by using proper query sentences, indexes and aggregation operation.
Through the steps, the cleaning, compression and efficient storage of the real-time data are realized. This ensures that real-time data of the power system can be efficiently processed and stored, providing a reliable data basis for subsequent real-time state analysis and fault prediction.
Specifically, the real-time state analysis and fault prediction module includes:
1. real-time state estimation: the current state of the power system is estimated in real time by using real-time data, and the current state can be realized by a Kalman filtering method or an extended Kalman filtering method, wherein an estimation formula of the state vector xn is as follows:
wherein F is a state transition matrix, B is an input matrix, u n Is the input vector, w n Is process noise.
2. Abnormality detection: detecting an abnormality in the power system by setting a threshold value or adopting a statistical method by using the real-time state estimation value, wherein an abnormality detection formula is as follows:
3. machine learning model application: selecting an appropriate machine learning model, such as a time series model or a neural network, for fault prediction, model training using historical data, and then inputting real-time data into the model for prediction, the neural network model formula is as follows:
y n ＝f(W·x n +b)
where W is the weight matrix, b is the bias vector, and f is the activation function.
4. Interpretation of the prediction results: the output of the machine learning model is interpreted to identify possible failure modes, and a mapping of the predicted results to actual states is established to assist the operator in understanding the current condition of the system.
Through the steps, the estimation, abnormality detection and fault prediction of the real-time state of the power system are realized, and a basis is provided for the generation of real-time alarms, so that the alarms can be timely sent to operators when the abnormalities are found or potential faults are predicted.
Specifically, the threshold setting and real-time alarm module includes:
1. selecting monitoring parameters: key system parameters such as voltage, current, frequency and the like are selected from parameters monitored in real time and serve as monitoring objects for setting threshold values.
2. Statistical history data: statistical analysis is carried out on the selected monitoring parameters based on historical data, and a mean value calculation mean value mu and a standard deviation sigma are calculated:
where N is the number of historical data, x i Is the i-th historical data point.
3. Setting a threshold value: the statistical result is used for setting a threshold value, and the setting formula of the threshold value is as follows:
upper threshold=μ+k·σ
Lower line threshold = μ -k σ
Where k is a set multiple, determining the sensitivity of the threshold.
4. Real-time monitoring and judging: and monitoring the monitoring parameters acquired in real time, and triggering a real-time alarm when the monitoring parameters exceed a set upper limit or lower limit threshold.
5. Alarm generation: when a real-time alarm is triggered, corresponding alarm information is generated, including alarm level, trigger time, specific parameters, etc., which can be used for timely response of operators.
Through the steps, a threshold setting mechanism based on historical statistics is established and is used for monitoring system parameters in real time and generating real-time alarms when the parameters exceed the set threshold. This ensures timely perception and response to power system anomalies.
Specifically, the automatic scheduling and response mechanism module includes:
step 6, automatic dispatch and response mechanism
In this step, it will be described in detail how an automatic scheduling and response mechanism is established, ensuring that the monitoring system can quickly make decisions and automatically adjust the operating state of the power system when an alarm occurs.
1. Machine learning control algorithm selection: appropriate machine learning control algorithms, such as reinforcement learning, PID controllers, etc., are selected to achieve automatic scheduling of the power system. Different control algorithms can be selected according to the specific requirements of the system, taking a PID controller as an example, the output u (t) of which is calculated by the following formula:
where e (t) is the current error, K P 、K i And K d The proportional, integral and derivative coefficients, respectively.
2. And (3) formulating a scheduling strategy: based on the output of the machine learning algorithm and the current system state, a specific strategy of automatic scheduling is formulated, including setting new working points, adjusting control parameters and the like.
3. Real-time decision: and a real-time decision module is implemented in the monitoring system, a scheduling scheme of the power system is rapidly generated according to the output of the algorithm and the scheduling strategy, and the response time of the system is directly influenced by the speed of the real-time decision.
4. Automatically executing a scheduling scheme: the generated scheduling scheme is automatically applied to the power system, and automatic adjustment of the power system is realized through an automatic control device, wherein the automatic adjustment comprises the steps of changing the output of a generator, adjusting the load distribution and the like.
5. And (3) monitoring a system state: continuous monitoring of the real-time status of the power system during dispatch execution may be accomplished by real-time data provided by telemetry and sensors.
6. Feedback and adjustment: according to feedback information of the system, the real-time adjustment of the scheduling scheme can be realized by continuously optimizing control algorithm parameters, updating a model and the like.
Through the steps, an automatic scheduling and response mechanism is realized, a scheduling scheme is generated in real time through a machine learning control algorithm, the scheduling scheme is automatically applied to a power system, and the stability and efficiency of the system are maintained through a real-time monitoring and feedback mechanism. This ensures that the system can respond quickly when an anomaly occurs, reducing the need for human intervention.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but may be modified or substituted for some of the technical features described in the foregoing embodiments by those skilled in the art, even though the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (7)
1. An automated power dispatching monitoring system with an alarm function, comprising:
and a data acquisition module: the power data are used for monitoring the power system in real time;
and a data transmission module: the power data acquisition module is used for transmitting the acquired power data to a monitoring center;
and the real-time data processing and compressing module is used for: data cleaning, compression and storage are carried out on the received real-time data;
and the real-time state analysis and fault prediction module is used for: analyzing the real-time data of the power system, and predicting faults;
threshold setting and real-time alarm module: setting a threshold value of a real-time monitoring system, and triggering an alarm when the system parameter exceeds a set normal range;
automatic scheduling and response mechanism module: the system makes decisions and automatically adjusts the operating state of the power system when an alarm occurs.
2. The automated power dispatching monitoring system with alarm function of claim 1, wherein the data acquisition module comprises:
a power sensor and a frequency telemetry device;
setting a real-time data transmission channel between the power sensor and the remote measuring device and the monitoring center;
data quality control measures are implemented at the sensor end so as to improve the precision and accuracy of the acquired data:
y n ＝α·x n +(1-α)·y n-1
wherein y is n Is the filtered data, x n Is the collected original data, y n-1 Is the filtering result of the previous moment and alpha is the filtering coefficient.
3. The power dispatching automation monitoring system with alarm function of claim 2, wherein the data transmission module comprises:
and implementing a redundancy mechanism, adopting a standby communication path to ensure that the standby path can be switched when the main communication path fails, ensuring the continuity of data transmission, and calculating the reliability R of the redundancy mechanism by the following formula:
wherein P is i Is the probability of failure for each communication path and n is the number of paths.
4. A power dispatching automation monitoring system with alarm function as claimed in claim 3, wherein the real-time data processing and compression module comprises:
the received real-time data is cleaned, and the cleaning formula is as follows:
wherein y is n Is the data after cleaning, x n Is the original data;
compressing real-time data: the compression formula is as follows:
difference = x n -x n-1
Wherein x is n Is the current data, x n-1 Is the data of the previous time.
5. The automated power dispatching monitoring system with alarm function of claim 4, wherein the real-time status analysis and fault prediction module comprises:
real-time estimating current state of power system by using real-time data, state vector x n The estimation formula of (2) is:
wherein F is a state transition matrix, B is an input matrix, u n Is the input vector, w n Is process noise;
detecting anomalies in the power system using the real-time state estimate:
model training is performed by using historical data, and then real-time data is input into a model for prediction:
y n ＝f(W·x n +b)
wherein W is a weight matrix, b is a bias vector, and f is an activation function;
the output is interpreted to identify possible failure modes, and a mapping of the predicted results to actual conditions is established to assist the operator in understanding the current condition of the system.
6. The automated power dispatching monitoring system with alarm function of claim 5, wherein the threshold setting and real-time alarm module comprises:
selecting a key system parameter from the parameters monitored in real time as a monitoring object for setting a threshold value;
statistical analysis is performed on the selected monitoring parameters based on historical data, and a mean value mu and a standard deviation sigma are calculated:
where N is the number of historical data, x i Is the i-th historical data point;
setting a threshold value by using the statistical result:
upper threshold=μ+k·σ
Lower line threshold = μ -k σ
Where k is a set multiple, determining the sensitivity of the threshold;
monitoring the monitoring parameters acquired in real time, and triggering a real-time alarm when the monitoring parameters exceed a set upper limit or lower limit threshold;
when the real-time alarm is triggered, corresponding alarm information is generated, including alarm level, trigger time and specific parameters.
7. The automated power dispatching monitoring system with alarm function of claim 6, wherein the automated dispatching and response mechanism module comprises:
a control algorithm is used to implement automatic scheduling of the power system:
where e (t) is the current error, K p 、K i And K d Proportional, integral and differential coefficients, respectively;
based on the output and the current system state, a specific strategy of automatic scheduling is formulated;
according to the output and scheduling strategies, a scheduling scheme of the power system is rapidly generated;
and automatically applying the generated scheduling scheme to the power system, and realizing automatic adjustment of the power system through an automatic control device.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN118330353A (en) * | 2024-04-11 | 2024-07-12 | 暨南大学 | Power transformation equipment state monitoring system based on ubiquitous power internet of things |
| CN118336916A (en) * | 2024-04-12 | 2024-07-12 | 威海凯瑞电气股份有限公司 | ABCLINK electric comprehensive monitoring system |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN118330353A (en) * | 2024-04-11 | 2024-07-12 | 暨南大学 | Power transformation equipment state monitoring system based on ubiquitous power internet of things |
| CN118336916A (en) * | 2024-04-12 | 2024-07-12 | 威海凯瑞电气股份有限公司 | ABCLINK electric comprehensive monitoring system |
| CN118336916B (en) * | 2024-04-12 | 2024-09-27 | 威海凯瑞电气股份有限公司 | ABCLINK electric comprehensive monitoring system |
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