Intelligent power monitoring method and system based on unattended operation

Abstract

The invention relates to the field of power monitoring, and discloses an intelligent power monitoring method and system based on unattended operation, and the method comprises the steps: carrying out the preprocessing and feature extraction of collected equipment operation state data, power system data and environment data; environmental parameters and environmental behaviors in the transformer substation are monitored and analyzed in real time based on environmental data to perform anomaly detection, and after transformer substation faults caused by environmental reasons are eliminated, abnormal conditions of the equipment operation state and the power transmission process are further detected and early warned, and when no abnormal condition is detected, the equipment operation state and the power transmission process are monitored. According to the method, safety risks in the transformer substation are continuously analyzed and evaluated, risk grade division is carried out based on an evaluation result, early warning feedback is carried out, environment abnormity can be found in time, early warning can be carried out, fault reasons can be quickly locked, equipment faults and safety accidents caused by the environment are prevented, and the safety of the transformer substation is improved. And measures can be taken as soon as possible for risks existing in the transformer substation.

Description

Technical Field

[0001] The present invention relates to the technical field of power monitoring, and more particularly to an intelligent power monitoring method and system based on unattended operation. Background Art

[0002] With the rapid development of information technology, Internet of Things technology, artificial intelligence, and big data analysis technology, the automation level of power systems has been continuously improved. As an important part of the power system, the monitoring methods of intelligent substations are also constantly innovating to meet the growing power demand and ensure the safe, stable, and efficient operation of the power grid. Traditional substation monitoring systems often rely on manual operation and monitoring, which is not only inefficient but also has potential safety hazards. Therefore, a method for intelligent monitoring of substation power using artificial intelligence technology has emerged. It mainly realizes the automatic monitoring and control of substation equipment by using advanced sensors, actuators, and communication technologies, and uses big data analysis and artificial intelligence algorithms to process and analyze the operation data of the substation in real time, realizing remote monitoring and fault diagnosis of the substation.

[0003] However, the above process still has the following disadvantages:

[0004] Firstly, the existing intelligent power monitoring methods mainly directly use the data collected by sensors for power monitoring analysis, which increases the amount of data calculation, resulting in slow data processing speed and thus increasing the operation and maintenance costs.

[0005] Secondly, the existing intelligent power monitoring methods lack the analysis of substation environmental data alone. Without excluding problems caused by environmental anomalies, it is impossible to further detect anomalies in the operation of substation equipment and power transmission, and it is impossible to quickly lock the cause of the fault, which may lead to errors in the judgment of power faults in the substation.

[0006] Thirdly, the existing intelligent power monitoring methods lack the evaluation of potential safety risks in the substation after detecting the operation of equipment and power transmission, resulting in the inability to take countermeasures against the risks existing in the substation early. Summary of the Invention

[0007] To overcome the above-mentioned defects of the prior art, the present invention provides an intelligent power monitoring method and system based on unattended operation to solve the problems existing in the above background art.

[0008] The present invention provides the following technical solution: An intelligent power monitoring method based on unattended operation, comprising:

[0009] S1: Used to collect device operation status data, power system data, and environmental data in real time, and preprocess the collected device operation status data, power system data, and environmental data;

[0010] S2: Used to monitor the environmental parameters and environmental behaviors in the substation, real-time monitor and analyze the environmental status in the substation through environmental data, judge whether the environmental changes in the substation are normal, and if not, immediately carry out early warning processing;

[0011] S3: Based on normal environmental conditions, analyze the combination of device operation status data and environmental data to judge whether there are abnormal situations in the operation status of the device under normal environmental conditions;

[0012] S4: Based on normal environmental conditions, analyze the combination of power transmission data of the power system and environmental data to judge whether there are abnormal behaviors in the power transmission of the power system under normal environmental conditions;

[0013] S5: Based on the analysis of the safety risks in the substation, calculate the safety risk assessment coefficient, used to evaluate the potential safety risks in the substation, and classify the safety risks according to the evaluation results;

[0014] S6: Used to display the analysis results on the manager's terminal in real time, and automatically generate a substation monitoring report according to the analysis process.

[0015] Preferably, S1 monitors and collects the operation status data of various devices in the substation through sensors and intelligent components built into the devices, including device temperature, device vibration, device current, and device voltage;

[0016] Collect the current, voltage, active power, reactive power, and frequency of the power system through power meters, and read the status information and fault records of the protection relays through the communication interface of the relay protection device;

[0017] Collect the environmental parameters and environmental behavior data in the substation in real time by using the sensors and high-definition cameras in the substation;

[0018] Carry out preliminary preprocessing on the collected data, including data cleaning, data conversion, data synchronization, and data integration, then classify the preprocessed data according to the data type, and store it in the database.

[0019] Preferably, S2 analyzes the temperature change value, humidity change value, gas concentration change value, and wind speed change value of the substation over a period of time based on the environmental parameters collected by sensors, comprehensively calculates the environmental status detection coefficient, sets a preset environmental status threshold, and compares the environmental status detection coefficient with the preset environmental status threshold to detect whether there are abnormal changes in the environmental parameters in the substation;

[0020] When the environmental status detection coefficient is less than or equal to the preset environmental status threshold, it is detected that there are no abnormal changes in the environmental parameters in the substation, and the environmental status is continuously monitored;

[0021] When the environmental status detection coefficient is greater than the preset environmental status threshold, a warning prompt of environmental abnormality is sent to the management personnel terminal, and the management personnel are prompted to conduct on-site detection of the equipment operation and power system power transmission in the substation;

[0022] By performing abnormal detection on the environmental behavior pattern, analyzing the collected video stream using image processing technology, learning the historical data using machine learning algorithms, establishing a normal environmental pattern, and comparing it with the real-time environmental behavior data, it is detected whether there are abnormal situations in the environmental behavior pattern in the substation. If the real-time environmental behavior data does not match the normal behavior pattern, it is determined as abnormal, and the detected abnormal behavior is immediately sent to the management personnel terminal for warning prompt;

[0023] Meanwhile, if no abnormal environmental changes are detected in the substation, the analysis processes of S3 and S4 are continued.

[0024] Preferably, S3 is used to extract key feature parameters affecting equipment operation from the equipment status operation data and environmental data, including power, maximum vibration value, temperature change rate, and rotational speed fluctuation. Based on the extracted key feature parameters, the equipment operation status under normal environmental conditions is analyzed, and the equipment operation detection index is calculated;

[0025] By comparing the equipment operation detection index with the preset equipment operation threshold, it is used to monitor and judge the operation of the equipment, and visually display it in the form of a graph. When the value of the equipment operation detection index is greater than the preset equipment operation threshold, it is considered that there is an abnormal situation in the operation status of the equipment under normal environmental conditions, and the detected abnormal situation is immediately sent to the management personnel terminal for warning prompt; if no abnormal situation is detected, S5 is continued to analyze the safety risk.

[0026] Preferably, in S4, key characteristic parameters affecting power transmission of the power system are extracted from power transmission data and environmental data respectively, and the extracted key characteristic parameters are subjected to data fusion to form a power transmission characteristic set. Based on the power transmission characteristic set, the power transmission process under normal environmental conditions is analyzed, and a power transmission detection index is calculated;

[0027] By comparing the power transmission detection index with a preset power transmission threshold, when the value of the power transmission detection index is greater than the preset power transmission threshold, it is considered that an abnormal transmission behavior occurs in the power transmission state of the power system under normal environmental conditions, and the abnormal detection result is immediately given an early warning feedback; if no abnormal transmission behavior is detected, S5 is continued to analyze the security risks.

[0028] Preferably, in S5, substation environmental data, equipment operation status data, power transmission data and historical safety accident records within a certain historical period are collected, the historical data is preprocessed, a risk assessment model is established using the historical data, the potential security risks in the substation are evaluated according to the risk assessment model, and the value of the security risk assessment coefficient is output. Then, a low-risk threshold and a high-risk threshold are set through historical experience to classify the evaluated potential security risks.

[0029] Preferably, in S6, the comprehensive power detection result of the substation and the evaluated risk level are fed back to the manager's terminal, and are displayed on the manager's terminal in the form of graphs and tables. A substation monitoring report is automatically generated according to the environmental analysis result, equipment operation status analysis result, power transmission analysis result and security risk assessment result, and is sent to the manager's terminal together with the detected abnormal results.

[0030] To achieve the above object, the present invention provides the following technical solution: An unattended intelligent power monitoring system, implementing the above-mentioned unattended intelligent power monitoring method, includes:

[0031] Data acquisition module: used to collect equipment operation status data, power system data and environmental data in real time, and preprocess the collected equipment operation status data, power system data and environmental data;

[0032] Environmental status monitoring module: used to monitor the environmental parameters and environmental behaviors in the substation, monitor and analyze the environmental status in the substation through environmental data in real time, judge whether the environmental changes in the substation are normal, and if not, immediately perform early warning processing;

[0033] Device operation detection module: Based on normal environmental conditions, analyze the combination of device operation status data and environmental data to determine whether there are any abnormalities in the device operation status under normal environmental conditions;

[0034] Power transmission detection module: Based on normal environmental conditions, analyze the combination of power transmission data and environmental data of the power system to determine whether there are any abnormal behaviors in the power transmission of the power system under normal environmental conditions;

[0035] Safety risk assessment module: Based on the analysis of safety risks in the substation, calculate the safety risk assessment coefficient, which is used to evaluate the potential safety risks in the substation, and classify the safety risks according to the assessment results;

[0036] Result feedback module: Used to display the analysis results on the manager's terminal in real time, and automatically generate a substation monitoring report according to the analysis process.

[0037] Technical effects and advantages of the present invention:

[0038] (1) The present invention provides preprocessing and feature parameter extraction based on the collected device operation status data, power system data, and environmental data, which is beneficial to reducing the calculation amount, improving the data processing speed, and further reducing the operation and maintenance costs.

[0039] (2) The present invention provides abnormal detection of the environmental status of the substation first. On the basis of excluding problems in the substation caused by environmental abnormalities, continue to analyze the abnormalities of the device operation and power transmission in the substation alone, which can timely detect environmental abnormalities and give early warnings, is beneficial to quickly locking the cause of the failure, preventing equipment failures and safety accidents caused by the environment, and solving the problem of errors in the judgment of power failures in the substation.

[0040] (3) The present invention provides an assessment of the potential safety risks in the substation after detecting the device operation and power transmission, and classifies the potential safety risks, providing a quantitative basis for risk management, which is beneficial to making countermeasures against the risks existing in the substation as early as possible. Brief Description of the Drawings

[0041] Figure 1 It is a method step diagram of the present invention.

[0042] Figure 2 It is a system structure block diagram of the present invention.

[0043] Figure 3 It is a method flow chart of this embodiment. Detailed Embodiments

[0044] The technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the present invention. In addition, the forms of the various structures described in the following embodiments are merely examples, and an unattended intelligent power monitoring method and system according to the present invention are not limited to the various structures described in the following embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0045] As Figure 1 shown, this embodiment provides an unattended intelligent power monitoring method, including:

[0046] S1: used to collect device operation status data, power system data, and environmental data in real time, and preprocess the collected device operation status data, power system data, and environmental data.

[0047] In this embodiment, the S1 monitors and collects the operation status data of various devices in the substation through sensors and intelligent components built into the devices, including device temperature, device vibration, device current, and device voltage;

[0048] Collects the current, voltage, active power, reactive power, and frequency of the power system through power meters, and reads the status information and fault records of the protection relays through the communication interface of the relay protection device;

[0049] Collects the environmental parameters and environmental behavior data in the substation in real time by using sensors and high-definition cameras in the substation;

[0050] Performs preliminary preprocessing on the collected data, including data cleaning, data conversion, data synchronization, and data integration, then classifies the preprocessed data according to the data type, and stores it in the database.

[0051] It should be specifically noted that the method for collecting device operation status data is as follows:

[0052] Monitors and collects the operation status data of various devices in the substation through sensors and intelligent components built into the devices, including device temperature, device vibration, device current, and device voltage, and inputs the data collected by the sensors and intelligent components into the data acquisition terminal through the wireless communication interface;

[0053] The method for collecting power system data is as follows:

[0054] By connecting the wireless communication interface of the power instrument to the data acquisition terminal, the current, voltage, active power, reactive power, and frequency of the power system are collected, and the status information and fault records of the protection relay are read through the communication interface of the relay protection device. The power instrument includes an intelligent electricity meter and a power meter;

[0055] The acquisition method for environmental data is as follows:

[0056] By using temperature and humidity sensors, smoke sensors, gas sensors, and wind speed sensors, the environmental parameters in the substation are collected in real time, and high-definition cameras are installed at key positions in the substation to collect environmental behavior data in the substation in real time. The sensor data is input into the data acquisition terminal through the wireless communication interface of the sensor, and the video data recorded by the camera is automatically uploaded to the data acquisition terminal through the wireless network, so as to collect the environmental data in the substation.

[0057] S2: It is used to monitor the environmental parameters and environmental behaviors in the substation. By monitoring and analyzing the environmental status in the substation in real time through environmental data, it is judged whether the environmental changes in the substation are normal. If not, warning processing is immediately carried out.

[0058] In this embodiment, S2 analyzes the temperature change value, humidity change value, gas concentration change value, and wind speed change value of the substation over a period of time through the environmental parameters collected by the sensors, and comprehensively calculates the environmental status detection coefficient, and sets a preset environmental status threshold. The environmental status detection coefficient is compared with the preset environmental status threshold to detect whether there are abnormal changes in the environmental parameters in the substation;

[0059] When the environmental status detection coefficient is less than or equal to the preset environmental status threshold, it is not detected that there are abnormal changes in the environmental parameters in the substation, and the environmental status is continuously monitored;

[0060] When the environmental status detection coefficient is greater than the preset environmental status threshold, a warning prompt of environmental abnormality is sent to the management personnel terminal, and the management personnel are prompted to conduct on-site detection of the equipment operation and power transmission of the power system in the substation;

[0061] By performing abnormal detection on the environmental behavior pattern, using image processing technology to analyze the collected video stream, using machine learning algorithms to learn historical data, establishing a normal environmental pattern, and comparing it with the real-time environmental behavior data, it is detected whether there are abnormal situations in the environmental behavior pattern in the substation. If the real-time environmental behavior data does not match the normal behavior pattern, it is determined as abnormal, and the detected abnormal behavior is immediately sent to the management personnel terminal for warning prompt;

[0062] Meanwhile, if no abnormal environmental changes are detected in the substation, the analysis processes of S3 and S4 are continued.

[0063] Specifically, the specific analysis method for the changes in environmental parameters in the substation is as follows:

[0064] Step S211: Synchronously record the temperature data, humidity data, CO 2 concentration data, and wind speed data in the substation over a period of time through temperature sensors, humidity sensors, gas sensors, and wind speed sensors in the substation;

[0065] Step S212: Calculate the temperature change value , humidity change value , CO 2 concentration change value , and wind speed change value over a period of time respectively. The specific calculation formulas are , , , , where represents the final recorded temperature value, represents the initial recorded temperature value, represents the final recorded humidity value, represents the initial recorded humidity value, represents the final CO 2 concentration value, represents the initial CO 2 concentration value, represents the final wind speed value recorded in the substation, represents the initial wind speed value recorded in the substation;

[0066] Step S213: Based on the temperature change value, humidity change value, CO 2 concentration change value, and wind speed change value, comprehensively calculate the environmental detection coefficient as , where respectively represent the weight coefficients, and the specific values are determined according to the importance of each parameter's impact on the environmental state;

[0067] Step S214: By comparing the calculated environmental state detection coefficient R with the preset environmental state threshold , it is used to detect whether there are abnormal changes in the environmental parameters in the substation. If , it means that no abnormal changes in the environmental parameters in the substation are detected, and the detection of the equipment operation state in S3 and the detection of the power change situation of the power system in S4 are continued. If , it is detected that the change in environmental parameters within the substation at this time exceeds the normal change range, and an early warning prompt of environmental abnormality is immediately sent to the manager's terminal. At the same time, the manager is prompted to conduct on-site inspections on the equipment operation and power transmission of the power system within the substation;

[0068] The specific analysis method for the change in the environmental behavior pattern within the substation is as follows:

[0069] Step S221: Collect real-time video data within the substation through a monitoring camera, perform preprocessing operations on the video images, use a human detection algorithm to identify the human contours in the video, track the detected humans, and analyze their movement trajectories;

[0070] Step S222: The process of establishing a normal environmental pattern through a machine learning algorithm is as follows: Label historical video data, mark normal behaviors and intrusion behaviors, extract features such as human positions, speeds, and directions from video frames, use a supervised learning algorithm to classify normal and intrusion behaviors to construct a normal behavior model, and ensure the accuracy and generalization ability of the model through cross-validation;

[0071] Step S223: Input the real-time video stream into the trained normal behavior model. The model will extract key features from the video stream, including human contours or shapes, movement directions and speeds, target sizes, appearance times, and appearance positions, and compare the extracted real-time data features with the normal environmental patterns learned during previous training. An intrusion rule is set. If the real-time data does not match the normal behavior pattern and the intrusion rule is triggered, the model will determine it as an abnormal behavior. Once the model detects an intrusion behavior, the system will immediately send an early warning prompt to the manager's terminal.

[0072] S3: Based on normal environmental conditions, analyze the combination of equipment operation status data and environmental data to determine whether there are any abnormalities in the operation status of the equipment under normal environmental conditions.

[0073] In this embodiment, S3 is used to extract features from the equipment status operation data and environmental data, and extract key feature parameters that affect equipment operation, including power, maximum vibration value, temperature change rate, and rotational speed fluctuation. Analyze the operation status of the equipment under normal environmental conditions based on the extracted key feature parameters, and calculate the equipment operation detection index;

[0074] By comparing the device operation detection index with a preset device operation threshold value, it is used to monitor and judge the operation condition of the device, and visually display it in a graphical way. When the value of the device operation detection index is greater than the preset device operation threshold value, it is considered that there is an abnormal situation in the operation state of the device under normal environmental conditions, and the detected abnormal situation is immediately sent to the manager's terminal for early warning; if no abnormal situation is detected, continue to execute S5 to analyze the security risk.

[0075] Specifically, the analysis process for the device operation state includes:

[0076] Step S311: Extract key feature parameters from the device state operation data and environmental data for data fusion, and fuse them into a feature parameter set as ;

[0077] Step S312: Calculate the device operation detection index based on the extracted feature parameter set, and its specific calculation formula is , where represents the value of the jth extracted feature parameter, represents the maximum value of the jth feature parameter, represents the minimum value of the jth feature parameter, represents the weight coefficient of the jth feature parameter, and M represents the number of extracted feature parameters;

[0078] Step S313: Set the device operation threshold according to historical data and expert knowledge, and compare the device operation detection index D with the preset device operation threshold to judge whether there is an abnormal situation in the operation state of the device under normal environmental conditions. If , it is considered that the operation state of the device under normal environmental conditions is normal, and continue to perform the assessment of the security risk in the substation. If , it is considered that there is an abnormal situation in the operation state of the device under normal environmental conditions, and an early warning message is immediately generated and sent to the manager's terminal.

[0079] S4: Based on normal environmental conditions, analyze the power transmission data and environmental data of the power system in combination to judge whether there is any abnormal behavior in the power transmission of the power system under normal environmental conditions.

[0080] In this embodiment, S4 extracts key feature parameters affecting the power transmission of the power system from the power transmission data and environmental data respectively, and fuses the extracted key feature parameters to form a power transmission feature set, analyzes the power transmission process under normal environmental conditions according to the power transmission feature set, and calculates the power transmission detection index;

[0081] By comparing the power transmission detection index with a preset power transmission threshold, when the value of the power transmission detection index is greater than the preset power transmission threshold, it is considered that there is an abnormal transmission behavior in the power transmission state of the power system under normal environmental conditions, and the abnormal detection result is immediately given an early warning feedback; if no abnormal transmission behavior is detected, continue to execute S5 to analyze the security risks.

[0082] Specifically, the power transmission detection index is calculated and analyzed through the extracted set of characteristic parameters, and its specific calculation formula is , where I represents the actual current value of power transmission, V represents the actual voltage value of power transmission, represents the power transmission data function, T represents the actual temperature, H represents the actual humidity, V represents the actual wind speed, C represents the pollutant concentration, represents the function of environmental data, is the weight coefficient.

[0083] S5: Based on the analysis of the security risks in the substation, calculate the security risk assessment coefficient to evaluate the potential security risks in the substation, and classify the security risks according to the evaluation results.

[0084] In this embodiment, S5 collects the substation environmental data, equipment operation status data, power transmission data, and historical security accident records within a certain historical period, preprocesses the historical data, uses the historical data to establish a risk assessment model, evaluates the potential security risks in the substation according to the risk assessment model, and outputs the value of the security risk assessment coefficient. Then, set the low-risk threshold and high-risk threshold through historical experience to classify the evaluated potential security risks.

[0085] Specifically, the specific implementation steps for security risk assessment are as follows:

[0086] Step S511: Perform data cleaning, data standardization, and data integration on the collected historical substation environmental data, equipment operation status data, power transmission data, and historical security accident records;

[0087] Step S512: According to the characteristics of the substation, select indicators in aspects such as environment, equipment, and power transmission, and select appropriate risk assessment methods, such as the Analytic Hierarchy Process (AHP), Fuzzy Comprehensive Evaluation Method, etc., to construct a risk assessment model.

[0088] Step S513: According to the risk assessment model, calculate the weights of each indicator, score each indicator to obtain a scoring matrix, and based on the weights of each indicator and the scoring matrix, calculate the security risk assessment coefficient as , where W represents the weight vector and Q represents the scoring matrix;

[0089] Exemplarily, taking the analytic hierarchy process as an example, the calculation formula for establishing the judgment matrix is as follows:

[0090]

[0091] where represents the comparison of the importance between index p and index q;

[0092] Calculate the weight vector W: , represents the eigenvalue of the judgment matrix Q, represents the corresponding eigenvector;

[0093] According to the safety risk assessment coefficient S and the low-risk threshold and the high-risk threshold , the safety risks are classified into the following levels: If , the potential safety risk is classified as a low risk. If , the potential safety risk is classified as a medium risk. If , the potential safety risk is classified as a high risk.

[0094] S6: It is used to display the analysis results on the manager's terminal in real time and automatically generate a substation monitoring report according to the analysis process.

[0095] In this embodiment, the S6 feeds back the comprehensive power detection results of the substation and the evaluated risk levels to the manager's terminal, displays them on the manager's terminal in the form of graphs and tables, automatically generates a substation monitoring report according to the environmental analysis results, equipment operation status analysis results, power transmission analysis results, and safety risk assessment results, and sends it to the manager's terminal together with the detected abnormal results.

[0096] As Figure 2 shown, this embodiment provides an implementation system corresponding to an unattended intelligent power monitoring method, including a data acquisition module, an environmental status monitoring module, an equipment operation detection module, a power transmission detection module, a safety risk assessment module, and a result feedback module. The data acquisition module is connected to the environmental status monitoring module, the environmental status monitoring module is connected to the equipment operation detection module, the environmental status monitoring module is connected to the power transmission detection module, the equipment operation detection module is connected to the safety risk assessment module, the power transmission detection module is connected to the safety risk assessment module, and the safety risk assessment module is connected to the result feedback module.

[0097] The data acquisition module is used to collect device operation status data, power system data, and environmental data in real time, and preprocess the collected device operation status data, power system data, and environmental data;

[0098] The environmental status monitoring module is used to monitor the environmental parameters and environmental behaviors in the substation, monitor and analyze the environmental status in the substation through environmental data in real time, judge whether the environmental changes in the substation are normal, and if not, immediately perform early warning processing;

[0099] The device operation detection module analyzes the combination of device operation status data and environmental data based on normal environmental conditions to judge whether there are any abnormalities in the operation status of the device under normal environmental conditions;

[0100] The power transmission detection module analyzes the combination of power transmission data of the power system and environmental data based on normal environmental conditions to judge whether there are any abnormal behaviors in the power transmission of the power system under normal environmental conditions;

[0101] The safety risk assessment module analyzes the safety risks in the substation, calculates the safety risk assessment coefficient, is used to evaluate the potential safety risks in the substation, and classifies the safety risks according to the assessment results;

[0102] The result feedback module is used to display the analysis results on the manager's terminal in real time, and automatically generate a substation monitoring report according to the analysis process.

[0103] As Figure 3 shown, the process corresponding to an intelligent power monitoring method based on unattended provided in this embodiment includes:

[0104] Collect device operation status data, power system data, and environmental data, and perform preprocessing and feature extraction;

[0105] Analyze the abnormal situations of environmental parameters and environmental behaviors in the substation, and judge whether the environmental changes in the substation are normal;

[0106] Based on normal environmental conditions, analyze the device operation status and judge whether there are any abnormalities in the operation status of the device under normal environmental conditions;

[0107] Based on normal environmental conditions, analyze the power transmission of the power system and judge whether there are any abnormal behaviors in the power transmission of the power system under normal environmental conditions;

[0108] Based on the assessment and risk level classification of the safety risks in the substation;

[0109] Display the analysis results on the manager's terminal in real time.

[0110] Finally, the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

[0111] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or replacements, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application shall be subject to the protection scope of the claims.

Claims

1. An unattended intelligent power monitoring method, characterized in that: include: S1: used to collect equipment operation status data, power system data and environmental data in real time, and pre-process the collected equipment operation status data, power system data and environmental data; S2: It is used to monitor the environmental parameters and environmental behaviors in the substation. It monitors and analyzes the environmental status in the substation in real time through environmental data to determine whether the environmental changes in the substation are normal. If not, it will immediately issue an early warning. S3: Based on normal environmental conditions, analyze the equipment operation status data and environmental data to determine whether there is any abnormality in the operation status of the equipment under normal environmental conditions; S4: Based on normal environmental conditions, analyze the power transmission data of the power system in combination with the environmental data to determine whether the power transmission of the power system under normal environmental conditions has abnormal behavior; S5: Based on the analysis of the safety risks in the substation, the safety risk assessment coefficient is calculated to assess the potential safety risks in the substation, and the safety risks are graded according to the assessment results; S6: It is used to display the analysis results in real time on the management personnel terminal and automatically generate a substation monitoring report based on the analysis process.

2. The unattended intelligent power monitoring method according to claim 1 is characterized in that: The S1 monitors and collects the operating status data of various equipment in the substation, including equipment temperature, equipment vibration, equipment current and equipment voltage, through sensors and built-in intelligent components of the equipment; The current, voltage, active power, reactive power and frequency of the power system are collected through the power meter, and the status information and fault records of the protection relay are read through the communication interface of the relay protection device; By using sensors and high-definition cameras in the substation, the environmental parameters and environmental behavior data in the substation are collected in real time; The collected data is pre-processed, including data cleaning, data conversion, data synchronization and data integration. The pre-processed data is then classified according to the data type and stored in the database.

3. The unattended intelligent power monitoring method according to claim 2 is characterized in that: The S2 analyzes the temperature change value, humidity change value, gas concentration change value and wind speed change value of the substation over a period of time through the environmental parameters collected by the sensor, and comprehensively calculates the environmental state detection coefficient, and sets a preset environmental state threshold value, and compares the environmental state detection coefficient with the preset environmental state threshold value to detect whether there is an abnormal situation in the environmental parameter change in the substation; When the environmental state detection coefficient is less than or equal to the preset environmental state threshold, no abnormal changes in environmental parameters in the substation are detected, and the environmental state continues to be monitored; When the environmental status detection coefficient is greater than the preset environmental status threshold, an early warning of environmental abnormality is issued to the management terminal, and the management personnel are prompted to conduct on-site inspections of the equipment operation in the substation and the power transmission of the power system; By detecting abnormalities in environmental behavior patterns, using image processing technology to analyze the collected video streams, using machine learning algorithms to learn historical data, establishing a normal environmental pattern, and comparing it with real-time environmental behavior data, it is detected whether there are abnormalities in the environmental behavior pattern in the substation. If the real-time environmental behavior data does not match the normal behavior pattern, it is determined to be abnormal, and the detected abnormal behavior is immediately sent to the management terminal for early warning. Meanwhile, if no abnormal environmental changes in the substation are detected, the analysis process of S3 and S4 is continued.

4. The unattended intelligent power monitoring method according to claim 3 is characterized in that: The S3 is used to extract features from the equipment status operation data and environmental data, and extract key characteristic parameters that affect the equipment operation, including power, maximum vibration value, temperature change rate, and speed fluctuation, analyze the equipment operation status under normal environmental conditions according to the extracted key characteristic parameters, and calculate the equipment operation detection index; By comparing the equipment operation detection index with the preset equipment operation threshold, it is used to monitor and judge the equipment operation status and visualize it in a graphical manner. When the value of the equipment operation detection index is greater than the preset equipment operation threshold, it is considered that there is an abnormality in the operation status of the equipment under normal environmental conditions, and the detected abnormality is immediately sent to the management terminal for early warning; if no abnormality is detected, S5 is continued to analyze the security risks.

5. The unattended intelligent power monitoring method according to claim 3 is characterized in that: The S4 extracts key characteristic parameters that affect power transmission of the power system from the power transmission data and the environmental data respectively, and fuses the extracted key characteristic parameters to form a power transmission feature set, analyzes the power transmission process under normal environmental conditions according to the power transmission feature set, and calculates the power transmission detection index; By comparing the power transmission detection index with the preset power transmission threshold, when the value of the power transmission detection index is greater than the preset power transmission threshold, it is considered that the power transmission state of the power system under normal environmental conditions has abnormal transmission behavior, and the abnormal detection result is immediately given as an early warning feedback; If no abnormal transmission behavior is detected, continue to execute S5 to analyze the security risks.

6. The unattended intelligent power monitoring method according to claim 1 is characterized in that: The S5 collects substation environmental data, equipment operating status data, power transmission data and historical safety accident records over a period of historical time, pre-processes the historical data, uses the historical data to establish a risk assessment model, assesses the potential safety risks in the substation based on the risk assessment model, and outputs the value of the safety risk assessment coefficient. Then, low-risk thresholds and high-risk thresholds are set based on historical experience, and the assessed potential safety risks are graded.

7. The unattended intelligent power monitoring method according to claim 1 is characterized in that: The S6 feeds back the comprehensive power detection results and assessed risk levels of the substation to the management terminal, and displays them in the form of graphics and tables on the management terminal. It automatically generates a substation monitoring report based on the environmental analysis results, equipment operation status analysis results, power transmission analysis results and safety risk assessment results, and sends it to the management terminal together with the detected abnormal results.

8. An unattended intelligent power monitoring system, implementing an unattended intelligent power monitoring method according to any one of claims 1 to 7, characterized in that: include: Data acquisition module: used to collect equipment operation status data, power system data and environmental data in real time, and pre-process the collected equipment operation status data, power system data and environmental data; Environmental status monitoring module: used to monitor environmental parameters and environmental behaviors in the substation, monitor and analyze the environmental status in the substation in real time through environmental data, and determine whether the environmental changes in the substation are normal. If not, an early warning will be issued immediately; Equipment operation detection module: Based on normal environmental conditions, the equipment operation status data is combined with the environmental data for analysis to determine whether there are any abnormalities in the equipment's operation status under normal environmental conditions; Power transmission detection module: Based on normal environmental conditions, the power transmission data of the power system is combined with the environmental data for analysis to determine whether there is any abnormal behavior in the power transmission of the power system under normal environmental conditions; Safety risk assessment module: Based on the analysis of safety risks in the substation, the safety risk assessment coefficient is calculated to assess the potential safety risks in the substation, and the safety risks are graded according to the assessment results; Result feedback module: used to display the analysis results in real time on the management personnel terminal, and automatically generate substation monitoring reports based on the analysis process.

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