Intelligent power failure early warning system for power grid

Abstract

The invention discloses an intelligent power failure early warning system for a power grid, and the system comprises a data monitoring module which monitors the power operation data of each node in the power grid; the state analysis module is used for analyzing the power operation data and the historical power normal operation data through a machine learning algorithm, identifying abnormal change information and obtaining fault data by analyzing the abnormal change information; the power failure risk prediction module is used for constructing a power failure risk prediction model according to the fault data, the power operation data and the historical power failure data and predicting whether a power failure risk exists or not; the emergency processing module is used for generating an emergency processing scheme according to the emergency processing plan library, the fault data and the predicted power failure data; and the early warning notification module is used for sending the fault data, the predicted power failure data and the emergency processing scheme to related maintenance personnel through short messages and e-mails. By monitoring the running state of the power grid in real time, potential faults and power failure risks are found and early warned, and therefore the stability and reliability of the power grid are improved.

Description

Technical Field

[0001] The present invention relates to the field of electric power systems, and in particular to an intelligent power outage early warning system for a power grid. Background Art

[0002] As society's demand for electricity continues to increase, the stable operation of the power grid system is crucial to production and life. However, during the operation of the power system, due to factors such as natural disasters, equipment failures, and human errors, uncertain failures often occur, leading to power outages. The existing power grid monitoring system mainly relies on post-fault detection and repair, and cannot provide early warning of power outages, resulting in economic losses and user inconvenience. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides an intelligent power outage early warning system for a power grid.

[0004] A power grid intelligent power outage early warning system, comprising:

[0005] Data monitoring module: used to monitor the power operation data of each node in the power grid, including voltage, current, power and temperature;

[0006] State analysis module: used to connect with the data monitoring module and obtain historical power normal operation data, analyze the power operation data and historical power normal operation data through machine learning algorithms, identify abnormal change information, and obtain fault data by analyzing the abnormal change information, including faulty equipment, faulty equipment location and fault type;

[0007] Power outage risk prediction module: used to connect with the data monitoring module and the status analysis module to obtain historical power outage data, and build a power outage risk prediction model based on fault data, power operation data and historical power outage data, combined with random forest and support vector machine. The power outage risk prediction model is used to predict whether there is a power outage risk. If there is a power outage risk, the predicted power outage data is output;

[0008] Emergency processing module: used to connect with the data monitoring module, status analysis module, power outage risk prediction module and early warning notification module, obtain predicted power outage data, and generate emergency processing plans based on the emergency processing plan library, fault data and predicted power outage data.

[0009] Early warning notification module: used to connect with the data monitoring module, status analysis module, power outage risk prediction module and emergency processing module, and send fault data, predicted power outage data and emergency processing plans to the power dispatching center and related maintenance personnel via SMS and email.

[0010] Preferably, the data monitoring module includes:

[0011] Monitoring unit: used to monitor the power operation data of each node, each node includes a voltage sensor, a current sensor, a temperature sensor and a power sensor;

[0012] Data processing unit: used to filter, smooth and normalize power operation data;

[0013] Communication unit: used to upload the processed power operation data to the cloud platform via a wireless network or a wired network.

[0014] Preferably, the status analysis module includes:

[0015] Historical power normal operation data acquisition unit: used to acquire historical power normal operation data;

[0016] Algorithm engine unit: used to adopt a variety of machine learning algorithms, including decision tree support vector machine;

[0017] Abnormal identification unit: used to identify abnormal change information in power operation data through machine learning algorithms;

[0018] Abnormality detection unit: used to detect fault data based on abnormal change information.

[0019] Preferably, the power outage risk prediction module includes:

[0020] Historical power outage data acquisition unit: used to acquire historical power outage data in the database;

[0021] Data feature extraction unit: used to extract key features from fault data, power operation data and historical power outage data, including voltage fluctuation trends and power overload trends;

[0022] Algorithm engine unit: used to adopt multiple machine learning algorithms, including random forest and support vector machine;

[0023] Power outage risk prediction model building unit: used to build a power outage risk prediction model based on key features through random forest and support vector machine;

[0024] Prediction unit: used to predict the power outage risk according to the power outage risk prediction model and output the predicted power outage data;

[0025] Power outage risk prediction model optimization unit: used to continuously collect fault data, power operation data and historical power outage data, and then optimize the power outage risk prediction model.

[0026] Preferably, the emergency processing module includes:

[0027] Data acquisition unit: used to acquire predicted power outage data and fault data;

[0028] Emergency response plan library management unit: used to store and manage emergency response plan libraries of various power grids;

[0029] Emergency treatment plan generation unit: Generates corresponding emergency treatment methods based on the acquired predicted power outage data and fault data and in combination with the emergency treatment plan library;

[0030] Visual display unit: Displays the location of faulty equipment in the power grid, faulty equipment, power outage scope and emergency handling progress through a visualization platform.

[0031] Preferably, the early warning notification module includes:

[0032] Notification content generation unit: generates notification content based on fault data, predicted power outage data and emergency response plan;

[0033] Notification channel management unit: used to manage different notification channels, including SMS and email;

[0034] Notification sending unit: Send fault data, predicted power outage data and emergency response plans to the power dispatching center and related maintenance personnel via SMS and email.

[0035] Logging unit: records all notification information sent.

[0036] Preferably, a power grid intelligent power outage warning system further includes:

[0037] Operation optimization module: used to connect with the data monitoring module, status analysis module, power outage risk prediction module and early warning notification module, continuously analyze power operation data, fault data and predicted power outage data, obtain weak links and equipment or areas that cause faults, and provide optimization suggestions.

[0038] Preferably, the operation optimization module includes:

[0039] Data analysis unit: used to analyze power operation data, fault data and predicted power outage data, identify weak links in power grid operation and equipment or areas that may cause failures.

[0040] Optimization suggestion generation unit: used to generate grid operation optimization suggestions based on data analysis results, including increasing redundancy, equipment update or load optimization.

[0041] The present invention discloses a power grid intelligent power outage early warning system, which has the following beneficial effects:

[0042] By real-time monitoring of the grid operation status, potential failures and power outage risks can be discovered and warned in time, thereby improving the stability and reliability of the grid; analyzing power operation data and historical data, predicting power demand, and helping grid operators to allocate power resources more effectively; predicting power outages in advance to reduce the occurrence of unexpected power outages, thereby reducing the impact on residents and businesses; when predicting the risk of power outages, the system can automatically generate emergency response plans and notify relevant personnel in a timely manner to improve the efficiency of responding to emergencies; through automated monitoring and early warning, the need for manual inspections is reduced, and human resource costs and operation and maintenance costs are reduced; the data analysis and optimization suggestions provided can help decision makers make more scientific and reasonable grid management and optimization decisions; quickly processing large amounts of data improves the work efficiency of power controllers; through real-time monitoring and risk assessment of grid operation, the safety management level of the grid is improved and the probability of accidents is reduced; it helps to use energy more efficiently, reduce energy waste, and support environmental protection and sustainable energy policies; by reducing power outages and improving service quality, the satisfaction and trust of power users are enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 A structural diagram of a power grid intelligent power outage warning system provided by the present invention;

[0045] Figure 2 A structural diagram of a data monitoring module provided by the present invention;

[0046] Figure 3 A structural diagram of the state analysis module provided by the present invention;

[0047] Figure 4 A structural diagram of a power outage risk prediction module provided by the present invention;

[0048] Figure 5 A structural diagram of the emergency treatment module provided by the present invention;

[0049] Figure 6 A structural diagram of the warning notification module provided by the present invention;

[0050] Figure 7 This is a structural diagram of the operation optimization module provided by the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0053] refer to Figure 1 The present invention provides a power grid intelligent power outage early warning system, comprising the following steps:

[0054] A power grid intelligent power outage early warning system, comprising:

[0055] Data monitoring module 11: used to monitor the power operation data of each node in the power grid, including voltage, current, power and temperature;

[0056] The state analysis module 12 is used to connect with the data monitoring module 11 and obtain the historical power normal operation data, analyze the power operation data and the historical power normal operation data through the machine learning algorithm, identify the abnormal change information, and detect the fault data through the abnormal change information, including the faulty equipment, the location of the faulty equipment and the fault type;

[0057] The power outage risk prediction module 13 is used to connect with the data monitoring module 11 and the state analysis module 12, obtain historical power outage data, and build a power outage risk prediction model based on the fault data, power operation data and historical power outage data in combination with random forest and support vector machine. The power outage risk prediction model is used to predict whether there is a power outage risk. If there is a power outage risk, the predicted power outage data is output;

[0058] Emergency processing module 14: used to connect with data monitoring module 11, status analysis module 12 and power outage risk prediction module 13, obtain predicted power outage data, and generate emergency processing plan according to emergency processing plan library, fault data and predicted power outage data;

[0059] Early warning notification module 15: used to connect with the data monitoring module 11, the status analysis module 12, the power outage risk prediction module 13 and the emergency processing module 14, and send the fault data, predicted power outage data and emergency processing plan to the power dispatching center and related maintenance personnel through SMS and email.

[0060] Among them, faulty equipment includes lines, and abnormal change information includes abnormal changes such as abnormal voltage, abnormal current, abnormal power, abnormal temperature and abnormal equipment status. Voltage abnormalities include voltage fluctuations that are too high or too low, current abnormalities include current exceeding the safety threshold, power abnormalities include sudden increase or decrease in power consumption, temperature abnormalities include transformer equipment temperature that is too high, and equipment status abnormalities include data loss from a certain sensor; fault types include short circuit, excessive load and excessive temperature; historical power outage data includes power outage time, power outage causes including equipment failure and load overload, and equipment maintenance and repair records.

[0061] An intelligent power outage warning system for a power grid provided by the present invention can detect fault data in a timely manner, including faulty equipment, location and type, and quickly respond to potential risks. By outputting predicted power outage data, the operation and maintenance team can be helped to prepare in advance, reduce the impact on users, and reduce power outage incidents. By timely fault handling and reducing power outage incidents, user satisfaction is improved, detailed operation and fault information is recorded, and data support is provided for subsequent analysis and auditing, which is helpful for responsibility tracking and the formulation of improvement measures.

[0062] refer to Figure 2 In a preferred embodiment, the data monitoring module 11 includes:

[0063] Monitoring unit 111: used to monitor the power operation data of each node, each node includes a voltage sensor, a current sensor, a temperature sensor and a power sensor;

[0064] Data processing unit 112: used for filtering, smoothing and normalizing the power operation data;

[0065] Communication unit 113: used to upload the processed power operation data to the cloud platform via a wireless network or a wired network.

[0066] refer to Figure 3 In a preferred embodiment, the state analysis module 12 includes:

[0067] Historical power normal operation data acquisition unit: used to acquire historical power normal operation data;

[0068] Algorithm engine unit 121: used to adopt multiple machine learning algorithms, including decision tree support vector machine;

[0069] Anomaly identification unit 122: used to identify abnormal change information in power operation data through machine learning algorithms;

[0070] The abnormality detection unit 123 is used to detect fault data according to abnormal change information.

[0071] Specifically, if the voltage fluctuation is too high and the current exceeds the safety threshold, the faulty device is analyzed to be a certain transmission line, and the faulty device is located to obtain the specific location of the faulty device, and the fault type is determined to be voltage instability.

[0072] refer to Figure 4 In a preferred embodiment, the power outage risk prediction module 13 includes:

[0073] Historical power outage data acquisition unit 131: used to acquire historical power outage data in a database;

[0074] Data feature extraction unit 132: used to extract key features from fault data, power operation data and historical power outage data, including voltage fluctuation trend and power overload trend;

[0075] Algorithm engine unit 133: used to adopt multiple machine learning algorithms, including random forest and support vector machine;

[0076] A power outage risk prediction model building unit 134 is used to build a power outage risk prediction model by combining random forest and support vector machine according to key features;

[0077] Prediction unit 135: used to predict power outage risk according to the power outage risk prediction model and output predicted power outage data.

[0078] The power outage risk prediction model optimization unit 136 is used to continuously collect fault data, power operation data and historical power outage data, and then optimize the power outage risk prediction model.

[0079] The predicted power outage data includes the predicted power outage time, predicted power outage probability and predicted power outage cause. Specifically, the output result shows that within the next hour, the transmission line has a power outage risk, among which the predicted power outage probability is 70%. By combining random forest and support vector machine to build a prediction model, potential power outage risks can be predicted and measures can be taken in advance to reduce the occurrence of power outages.

[0080] refer to Figure 5 In a preferred embodiment, the emergency processing module 14 includes:

[0081] Data acquisition unit 141: used to acquire predicted power outage data and fault data;

[0082] Emergency processing plan library management unit 142: used for storing and managing emergency processing plan libraries of various power grids;

[0083] The emergency processing scheme generating unit 143 generates a corresponding emergency processing method according to the acquired predicted power outage data and fault data in combination with the emergency processing plan library; and improves the efficiency of handling emergencies according to the generated emergency processing scheme.

[0084] Visual display unit 144: displays the location of faulty equipment in the power grid, faulty equipment, power outage scope and emergency handling progress through a visualization platform.

[0085] refer to Figure 6 In a preferred embodiment, the early warning notification module 15 includes:

[0086] Notification content generating unit 151: generates notification content according to fault data, predicted power outage data and emergency handling plan;

[0087] Notification channel management unit 152: used to manage different notification channels, including SMS and email;

[0088] Notification sending unit 153: Sends fault data, predicted power outage data and emergency treatment plans to the power dispatch center and relevant maintenance personnel via SMS and email. Sending key information to relevant personnel in a timely manner improves the timeliness and coverage of early warning.

[0089] Log recording unit 154: records all sent notification information.

[0090] Specifically, the dispatch center receives the following information: Alert! Transmission line X has a 70% risk of power outage in the next hour. It is recommended to transfer the load to the backup line and send maintenance personnel to inspect and repair the transmission line.

[0091] A power grid intelligent power outage early warning system, further comprising:

[0092] refer to Figure 1 and Figure 7 , Operation optimization module 16: used to connect with the data monitoring module 11, the status analysis module 12, the power outage risk prediction module 13 and the early warning notification module 15, continuously analyze the power operation data, fault data and predicted power outage data, obtain the weak links and the equipment or areas that cause the fault, and provide optimization suggestions.

[0093] refer to Figure 7 In a preferred embodiment, the operation optimization module 16 includes:

[0094] Data analysis subunit 161: used to analyze power operation data, fault data and predicted power outage data, and identify weak links in power grid operation and equipment or areas that cause faults.

[0095] Optimization suggestion generation subunit 162: used to generate power grid operation optimization suggestions based on data analysis results, including increasing redundancy, equipment update or load optimization.

[0096] Specifically, if data analysis identifies that there is a risk of overload in the transmission lines of certain areas, it is recommended to add redundant lines in the area and perform equipment maintenance to reduce the risk of failure. Weak links and causes of failures are identified to provide decision support for the continuous optimization of the power grid.

Claims

1. A power grid intelligent power outage warning system, characterized in that: These include: Data monitoring module: used to monitor the power operation data of each node in the power grid, including voltage, current, power and temperature; State analysis module: used to connect with the data monitoring module and obtain historical power normal operation data, analyze the power operation data and the historical power normal operation data through machine learning algorithm, identify abnormal change information, and obtain fault data by analyzing the abnormal change information, including faulty equipment, faulty equipment location and fault type; A power outage risk prediction module is used to connect with the data monitoring module and the status analysis module to obtain historical power outage data, and to build a power outage risk prediction model based on the fault data, the power operation data and the historical power outage data in combination with random forest and support vector machine. The power outage risk prediction model is used to predict whether there is a power outage risk. If there is a power outage risk, the predicted power outage data is output; Emergency processing module: used to connect with the data monitoring module, the status analysis module, the power outage risk prediction module and the early warning notification module, obtain the predicted power outage data, and generate an emergency processing plan based on the emergency processing plan library, the fault data and the predicted power outage data. Early warning notification module: used to connect with the data monitoring module, the status analysis module, the power outage risk prediction module and the emergency processing module, and send the fault data, the predicted power outage data and the emergency processing plan to the power dispatching center and related maintenance personnel via SMS and email.

2. The intelligent power outage warning system for power grid according to claim 1, characterized in that: The data monitoring module comprises: Monitoring unit: used to monitor the power operation data of each node, each node includes a voltage sensor, a current sensor, a temperature sensor and a power sensor; Data processing unit: used for filtering, smoothing and normalizing the power operation data; Communication unit: used to upload the processed power operation data to the cloud platform via a wireless network or a wired network.

3. The intelligent power outage warning system for power grid according to claim 1, characterized in that: The state analysis module comprises: Historical power normal operation data acquisition unit: used to acquire historical power normal operation data; Algorithm engine unit: used to adopt a variety of machine learning algorithms, including decision tree support vector machine; Abnormal identification unit: used to identify abnormal change information in power operation data through machine learning algorithms; Anomaly detection unit: used to detect fault data according to the abnormal change information.

4. The intelligent power outage warning system according to claim 3 is characterized in that: The power outage risk prediction module includes: Historical power outage data acquisition unit: used to acquire historical power outage data in the database; Data feature extraction unit: used to extract key features from the fault data, the power operation data and the historical power outage data, including voltage fluctuation trend and power overload trend; Algorithm engine unit: used to adopt multiple machine learning algorithms, including random forest and support vector machine; Power outage risk prediction model building unit: used to build a power outage risk prediction model based on key features through random forest and support vector machine; Prediction unit: used to predict the power outage risk according to the power outage risk prediction model and output predicted power outage data; Power outage risk prediction model optimization unit: used to continuously collect fault data, power operation data and historical power outage data, and then optimize the power outage risk prediction model.

5. The intelligent power outage warning system for power grid according to claim 1, characterized in that: The emergency processing module includes: Data acquisition unit: used to acquire the predicted power outage data and the fault data; Emergency response plan library management unit: used to store and manage emergency response plan libraries of various power grids; Emergency treatment plan generation unit: Generates corresponding emergency treatment methods based on the acquired predicted power outage data and fault data and in combination with the emergency treatment plan library; Visual display unit: Displays the location of faulty equipment in the power grid, faulty equipment, power outage scope and emergency handling progress through a visualization platform.

6. The intelligent power outage warning system for power grid according to claim 5, characterized in that: The early warning notification module includes: Notification content generating unit: generating notification content according to the fault data, the predicted power outage data and the emergency handling plan; Notification channel management unit: used to manage different notification channels, including SMS and email; Notification sending unit: sends the fault data, the predicted power outage data and the emergency treatment plan to the power dispatching center and relevant maintenance personnel via SMS and email. Logging unit: records all notification information sent.

7. The intelligent power outage warning system for a power grid according to claim 1, characterized in that: Also includes: Operation optimization module: used to connect with the data monitoring module, the status analysis module, the power outage risk prediction module and the early warning notification module, continuously analyze the power operation data, the fault data and the predicted power outage data, obtain the weak links and the equipment or areas that cause the faults, and provide optimization suggestions.

8. The intelligent power outage warning system for power grid according to claim 7, characterized in that: The operation optimization module includes: Data analysis unit: used to analyze the power operation data, the fault data and the predicted power outage data, and identify weak links in power grid operation and equipment or areas that may cause faults. Optimization suggestion generation unit: used to generate grid operation optimization suggestions based on data analysis results, including increasing redundancy, equipment update or load optimization.

Images