Power grid fault diagnosis system
Through the power grid fault diagnosis system, data acquisition, analysis and remote control modules are used, combined with machine learning algorithms, the rapid identification and isolation of power grid faults is achieved, the problem of low grid fault detection efficiency is solved, and the operation reliability and stability of the power grid is improved.
Patent Information
- Application Number
- CN202510554295.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-29
- Publication Date
- 2025-08-12
AI Technical Summary
In the prior art, the power grid fault detection efficiency is low, the response is not timely, and the in-depth analysis of the causes of the fault cannot be achieved, making it difficult to ensure the safe and stable operation of the power grid.
It adopts a power grid fault diagnosis system, including data acquisition module, data analysis module, display and alarm module, remote control module and data storage module, and uses machine learning algorithms and rule algorithms for fault identification and isolation, supports Ethernet, wireless communication and fiber optic communication, and provides real-time alarm and remote control functions.
It improves the efficiency and response speed of grid fault detection, realizes rapid identification of fault types and locations, and improves the reliability and stability of the grid.
Smart Images
Figure CN120474178A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of smart grids, and in particular to a grid fault diagnosis system. Background Art
[0002] With the development of my country's economic construction, the scale of power grids has continued to expand and the structure has become increasingly complex. Due to the influence of various factors, such as natural disasters, equipment aging, and human errors, power grid failures are inevitable. Therefore, how to quickly and effectively detect and eliminate these failures has become one of the key issues to ensure the safe and stable operation of the power grid. Currently, manual inspections are commonly used in existing technologies for fault detection. However, this method suffers from low efficiency and untimely response, and cannot meet the needs of large-scale, high-speed development of modern power grids. Furthermore, traditional manual inspections are also unable to provide in-depth analysis of the causes of faults, making it difficult to fundamentally resolve potential fault hazards. Summary of the Invention
[0003] In order to overcome the deficiencies in the background technology and solve existing technical problems, the present invention discloses a power grid fault diagnosis system, which can improve the efficiency of power grid fault detection and the processing response speed.
[0004] To achieve the above object, the present invention adopts the following technical solutions: A power grid fault diagnosis system includes the following signal connection modules: a data acquisition module for real-time acquisition of electrical parameter data information in the power grid, and transmission of the acquired data information to a data analysis module via a communication network module; a data analysis module for receiving and processing data from the data acquisition module, analyzing the operating status of the power grid, and identifying and judging the type and location of faults in the power grid based on set power grid operating standards and fault diagnosis models; a display and alarm module for real-time display of the operating status of the power grid, issuing an alarm signal when a power grid fault occurs, and indicating the location and type of the fault on a display interface; a remote control module for isolating or restoring the power grid fault after diagnosing the fault type and determining the fault location; a data storage module for storing power grid operating data, fault history records, and fault diagnosis reports, and providing the data analysis module with the necessary data backtracking information for fault analysis.
[0005] Furthermore, the communication network module is used to realize data transmission between the data acquisition module and the data analysis module, and supports Ethernet, wireless communication or optical fiber communication.
[0006] Furthermore, the fault diagnosis model classifies power grid faults based on historical data of power grid operation through machine learning algorithms or rule algorithms, and provides preliminary judgment results on the causes of faults. Fault types include short circuit, open circuit, overload and ground fault.
[0007] Furthermore, the electrical parameters include voltage, current, power, frequency and temperature in the power grid.
[0008] Furthermore, the alarm signal includes sound, cursor change, text message or email.
[0009] Furthermore, the remote control module includes the functions of automatically cutting off a faulty circuit and automatically restoring a normal circuit, and supports manual intervention of remote control instructions.
[0010] Due to the adoption of the above-mentioned technical solution, the present invention has the following beneficial effects: The power grid fault diagnosis system disclosed by the present invention has a data acquisition module that obtains various electrical parameters of the power grid in real time through sensors installed at various key locations of the power grid, and transmits them to the data analysis module for processing through the communication network module; the data analysis module can quickly identify faults in the power grid by analyzing the power grid operation data, and judge the fault type according to a preset diagnostic model; the display and alarm module feeds back the fault information to the power dispatcher in real time, provides necessary alarm information, and performs fault isolation operations through the remote control module when necessary; the design of this system has a high level of automation and fault response speed, which helps to improve the reliability and stability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS
[0011] Figure 1 It is an implementation structure diagram of the present invention. DETAILED DESCRIPTION
[0012] The technical solution of the present invention will be described below with reference to the accompanying drawings in the embodiments of the present invention.
[0013] Combined with attachment Figure 1 The power grid fault diagnosis system includes the following parts: The data acquisition module is used to collect electrical parameters such as voltage, current, power, frequency, and temperature in the power grid in real time, and transmit the collected data to the data analysis module through the communication network; The communication network module is used to realize data transmission between the data acquisition module and the data analysis module, and supports multiple communication protocols to ensure the real-time and accuracy of data. The communication network module supports multiple communication methods such as Ethernet, wireless communication, and fiber-optic communication to ensure stable and real-time data transmission between different areas of the power grid. For example, Ethernet can be used to connect different nodes to establish a stable wired network environment; wireless communication technology can also be used to realize distributed data collection and transmission; for example, fiber-optic communication technology can be used to realize real-time and high-bandwidth data transmission, etc. The data analysis module is used to receive and process data from the data acquisition module, analyze the operating status of the power grid, and identify and determine the type and location of faults in the power grid based on the set power grid operating standards and fault diagnosis model. The data analysis module includes a machine learning-based fault diagnosis model that can be trained based on historical power grid fault data to automatically identify and classify fault modes. For example, a classifier can be trained using supervised learning methods and applied to new data sets for prediction, thereby helping to quickly identify the cause and type of power grid faults. The fault diagnosis model uses machine learning or rule-based algorithms to classify grid faults based on historical and real-time grid operation data, and provides a preliminary diagnosis of the fault cause. Fault types include, but are not limited to, short circuits, open circuits, overloads, and ground faults. Furthermore, machine learning methods such as decision trees, neural networks, and support vector machines are used for training to improve the accuracy and robustness of fault diagnosis. For example, a decision tree algorithm can be used to build a multi-level classification model, and its accuracy and generalization capabilities can be improved through continuous iterative optimization. Alternatively, a deep learning framework such as TensorFlow or PyTorch can be used to build a neural network architecture, which can be trained with a large amount of labeled sample data to produce an efficient classifier. For example, the support vector machine (SVM) algorithm can be applied to anomaly detection tasks, distinguishing the boundary between normal and abnormal situations by maximizing the separation distance. SVM is a common supervised learning algorithm that can find an optimal hyperplane in given training data to separate samples of different categories to the greatest extent possible. In fault diagnosis, SVM can determine whether a device has failed based on its operating status data, such as temperature and pressure. The display and alarm module is used to display the operating status of the power grid in real time, and to send out an alarm signal when a fault occurs in the power grid, and to indicate the location and type of the fault on the display interface. The alarm signal includes but is not limited to sound, cursor change, text message or email, etc. The display and alarm module can include a visual user interface that displays the real-time operating status of the power grid and indicates the area, type and possible cause of the fault. For example, a map-like graphic can be displayed on the screen to represent the distribution of the entire power grid. When a fault occurs in a certain area, a corresponding icon or mark will pop up at the corresponding location to alert staff. In addition, an alarm can be issued by voice broadcast or other means to remind relevant personnel to go to the scene to check the situation as soon as possible. The remote control module is used to automatically or manually isolate or restore the grid fault after the system diagnoses and locates the fault. This ensures that the power load in the faulty area is reduced, prevents the fault from spreading to other areas, and ensures stable grid operation. For example, if a short circuit is detected on a line, the power supply will be automatically cut off to prevent escalation. Once the repair is complete, the line can be reopened to restore normal power supply. Furthermore, the module supports remote control of switchgear and other hardware facilities via a mobile terminal application, allowing engineers to monitor grid operation anytime, anywhere and make timely adjustments. The data storage module is used to store power grid operation data, fault history records, fault diagnosis reports and other information to facilitate subsequent fault analysis and improvement, as well as data backtracking.
[0014] To implement the power grid fault diagnosis system of the present invention, a large amount of operating data must first be collected from the power grid as basic information. This data generally includes various electrical parameters such as voltage, current, power, frequency, and temperature, as well as their time-varying trends. The collected raw data is then cleaned and organized to remove meaningless information and noise interference to avoid adverse effects on subsequent analysis. The specific cleaning process can be completed by selecting appropriate methods based on actual conditions, such as deleting duplicates, filling missing values, and standardization transformation. A suitable fault diagnosis model is then constructed based on the processed data. This model can help automatically identify the occurrence and cause of power grid faults. Common machine learning algorithms or rule-based algorithms are generally used to complete this task. Specifically, common classifiers such as logistic regression, random forest, and K-nearest neighbor algorithms can be selected for training to obtain a classification model. The Bayesian formula can also be used to calculate the probability value of each category to determine the category to which the current observation point belongs. The prepared data set is input into the pre-built model for training. During this process, the model parameters need to be continuously adjusted until the desired effect is achieved. Specifically, cross-validation techniques can be used to evaluate model performance and perform hyperparameter tuning. If necessary, a comprehensive test of the trained model is required to check whether its actual performance meets the expected goals. Specifically, a part of the data set can be extracted from another independent data set as test samples and predictive analysis can be performed on them; then the degree of difference between the true label and the predicted result can be compared to draw the final conclusion; if the error is small, it indicates that the model is relatively robust and reliable; otherwise, it is necessary to continue to adjust and optimize until it is satisfactory; the last step is to deploy the trained and tested model to an online environment for actual application; at this time, it can be integrated into the power grid management system and combined with other functional modules to play a role together.
[0015] The parts of the present invention that are not described in detail are prior art. It is obvious to those skilled in the art that the present invention is not limited to the details of the above-mentioned exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, no matter from which point of view, the above-mentioned embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is limited by the appended claims rather than the above description. Therefore, it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present invention, and any figure marks in the claims should not be regarded as limiting the content of the claims involved.
Claims
1. A power grid fault diagnosis system, characterized by: Contains the following signal connection modules: The data acquisition module is used to collect electrical parameter data information in the power grid in real time and transmit the collected data information to the data analysis module through the communication network module; The data analysis module is used to receive and process data from the data acquisition module, analyze the operating status of the power grid, and identify and determine the fault type and location in the power grid based on the set power grid operation standards and fault diagnosis model; Display and alarm module, used to display the operating status of the power grid in real time, send out an alarm signal when a fault occurs in the power grid, and indicate the location and type of the fault on the display interface; A remote control module is used to isolate or restore power grid faults after diagnosing the fault type and determining the fault location; The data storage module is used to store the grid's operating data, fault history records, and fault diagnosis reports, providing the data analysis module with the necessary data backtracking information for fault analysis.
2. The power grid fault diagnosis system according to claim 1, wherein: The communication network module is used to realize data transmission between the data acquisition module and the data analysis module, and supports Ethernet, wireless communication or optical fiber communication.
3. The power grid fault diagnosis system according to claim 1, wherein: The fault diagnosis model classifies power grid faults based on historical data of power grid operation through machine learning algorithms or rule-based algorithms, and provides preliminary judgment results on the causes of faults. Fault types include short circuit, open circuit, overload and ground fault.
4. The power grid fault diagnosis system according to claim 1, wherein: The electrical parameters include voltage, current, power, frequency and temperature in the power grid.
5. The power grid fault diagnosis system according to claim 1, wherein: The alarm signal includes sound, cursor change, text message or email.
6. The power grid fault diagnosis system according to claim 1, characterized in that: The remote control module includes the functions of automatically cutting off the fault circuit and automatically restoring the normal circuit, and supports manual intervention of remote control instructions.