Intelligent power grid fault prediction and adaptive recovery system and method

Abstract

The invention discloses an intelligent power grid fault prediction and adaptive recovery system and method, and the system achieves the real-time data collection, preprocessing and deep learning algorithm analysis through the cooperative work of a data collection module, a data processing module, a fault prediction module, a communication module, a human-computer interaction module and an adaptive recovery module, so as to predict a fault. And a result is timely transmitted to a management and control center through the communication module. And the system can formulate a self-adaptive recovery strategy based on a prediction result to ensure rapid recovery of the power grid. The system improves the efficiency and accuracy of power grid fault prediction and recovery, guarantees the safe and stable operation of the smart power grid, is of great significance in dealing with the scale increase and complexity improvement of the smart power grid, and provides solid technical support for the intelligent and modern development of a power system.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart grids, and particularly relates to a smart grid fault prediction and adaptive recovery system and method. Background Art

[0002] With the continuous expansion of the scale of the power system and the increasing complexity of its structure, the safe and stable operation of smart grids faces greater and greater challenges. Traditional power grid fault prediction and recovery methods mainly rely on manual experience and simple statistical models, and are difficult to cope with the challenges of massive data and complex fault patterns in modern smart grids. In recent years, although some data-driven fault prediction methods have begun to be applied, there are still many problems.

[0003] Existing fault prediction methods usually adopt a single machine learning algorithm, such as support vector machines or neural networks. Although they perform well in some specific scenarios, they are difficult to adapt to the changing operating environment and diverse fault types in smart grids. In addition, these methods often separate fault prediction and recovery strategy formulation, lacking a unified framework to achieve the collaborative optimization of prediction and recovery.

[0004] In terms of data processing, existing systems generally have problems of poor data quality and low processing efficiency. A large amount of redundant and noisy data not only increases the burden of storage and transmission, but also reduces the accuracy of subsequent analysis. At the same time, existing data compression methods are often general lossless or lossy compression algorithms, and do not fully utilize the characteristics of power grid data, resulting in unsatisfactory compression effects.

[0005] In terms of fault recovery, traditional methods mostly adopt preset recovery strategies, lacking flexibility and adaptability. Although some systems introduce optimization algorithms to formulate recovery strategies, they usually only consider a single objective, such as minimizing the recovery time, while ignoring other important factors, such as system stability and economy. In addition, most existing recovery systems are passive response types and lack the ability of active prevention and rapid response.

[0006] Another common problem is the poor scalability and maintainability of the system. Many existing systems adopt an integrated design, with a high coupling degree between functional modules, making it difficult to flexibly expand and upgrade according to actual needs. This not only increases the maintenance cost of the system, but also limits the continuous improvement of system performance.

[0007] Generally speaking, existing smart grid fault prediction and recovery systems have obvious deficiencies in data processing, fault prediction, adaptive recovery, and system architecture, and are difficult to meet the requirements of the safe and stable operation of modern smart grids. Therefore, it is necessary to propose a smart grid fault prediction and adaptive recovery system and method to solve the above problems. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to provide an intelligent power grid fault prediction and adaptive recovery system and method, aiming to solve the technical problems that the prior art is difficult to adapt to the changing operating environment and diverse fault types in the intelligent power grid, and the scalability and maintainability of the system are poor. Through innovative modular design and advanced algorithms, full-process intelligent management from data acquisition, processing, fault prediction to adaptive recovery is realized.

[0009] To solve the above technical problems, the technical solution adopted by the present invention is: An intelligent power grid fault prediction and adaptive recovery system, including: A data acquisition module, used for: Collecting the real-time operation data of the intelligent power grid; Transmitting the real-time operation data to the data processing module; A data processing module, electrically connected to the data acquisition module, used for: Receiving the real-time operation data sent by the data acquisition module; Preprocessing and compressing the real-time operation data; Transmitting the preprocessed and compressed data to the fault prediction module; A fault prediction module, electrically connected to the data processing module, used for: Receiving the preprocessed and compressed data sent by the data processing module; Building a fault prediction model based on a deep learning algorithm; Analyzing the preprocessed and compressed data by using the fault prediction model to generate a fault prediction result; Transmitting the fault prediction result to the communication module and the adaptive recovery module; A communication module, electrically connected to the fault prediction module, used for: Receiving the fault prediction result sent by the fault prediction module; Transmitting the fault prediction result to the relevant control center; A human-computer interaction module, electrically connected to the fault prediction module and the communication module, used for: Displaying the fault prediction result and the transmission status of the communication module; Providing a feedback information interaction interface; An adaptive recovery module, electrically connected to the fault prediction module and the human-computer interaction module, used for: Receiving the fault prediction result sent by the fault prediction module; Formulating an adaptive recovery strategy based on the fault prediction result; Executing the adaptive recovery strategy to perform fault adaptive recovery on the power grid; Feed the restored execution result back to the human-computer interaction module.

[0010] Preferably, the data acquisition module includes: A signal collector for reading operation information from each device of the smart grid, where the operation information includes digital information and analog information; An A / D converter electrically connected to the signal collector for converting the analog information into digital information.

[0011] Preferably, the data processing module includes: A data preprocessing unit for removing noise and interference in the real-time operation data; A data compression unit electrically connected to the data preprocessing unit for compressing the preprocessed data using a deep learning algorithm.

[0012] Preferably, the fault prediction module includes: A data storage unit for storing and managing the preprocessed and compressed data; A data analysis unit electrically connected to the data storage unit for constructing the fault prediction model using an algorithm based on an artificial neural network; A self-learning unit electrically connected to the data analysis unit for self-updating and optimizing the fault prediction model; A pattern recognition sub-module electrically connected to the data analysis unit for comparing the preprocessed and compressed data with known fault patterns; A fault prediction sub-module electrically connected to the pattern recognition sub-module for predicting the occurrence time and location of a fault when the pattern recognition sub-module matches successfully.

[0013] Preferably, the communication module includes: A communication protocol unit for defining data formats and transmission methods; A modem electrically connected to the communication protocol unit for converting between electrical signals, optical signals, and radio signals.

[0014] Preferably, the adaptive recovery module includes: An adaptive recovery strategy module for formulating an adaptive recovery strategy based on the fault prediction result and the system operation status; An automatic isolation module electrically connected to the adaptive recovery strategy module for automatically cutting off the fault source according to the adaptive recovery strategy; An automatic load transfer module electrically connected to the automatic isolation module for automatically transferring the load according to the adaptive recovery strategy during a fault to ensure the power supply safety of critical users.

[0015] Preferably, the adaptive recovery strategy module further includes: A multi-objective optimization unit for simultaneously considering multiple optimization objectives such as minimizing node parameter changes, minimizing non-faulty node power changes, and quickly responding to new faults; A load adjustment unit electrically connected to the multi-objective optimization unit for performing load adjustment based on node priorities and motivating users to participate in load adjustment through a price adjustment mechanism.

[0016] Preferably, it further includes: A fault library electrically connected to the fault prediction module for storing historical fault data; A recovery library electrically connected to the adaptive recovery module for storing historical recovery strategies; Wherein, the fault prediction module uses the data in the fault library to optimize the fault prediction model, and the adaptive recovery module uses the data in the recovery library to optimize the adaptive recovery strategy.

[0017] Preferably, the fault prediction module further includes: A power grid topology model unit for representing the power grid structure using an incidence matrix and an adjacency matrix based on the node-branch association principle; A recovery decision optimization unit electrically connected to the power grid topology model unit for optimizing the recovery decision based on a genetic algorithm and a genetic tree.

[0018] An intelligent power grid fault prediction and adaptive recovery method includes the following steps: S1, collecting real-time operation data of the intelligent power grid through a data acquisition module; S2, transmitting the real-time operation data to a data processing module for preprocessing and compressing the real-time operation data; S3, transmitting the preprocessed and compressed data to the fault prediction module; S4, the fault prediction module constructs a fault prediction model based on a deep learning algorithm, analyzes the preprocessed and compressed data using the fault prediction model, and generates a fault prediction result; S5, transmitting the fault prediction result to a relevant control center through a communication module; S6, displaying the fault prediction result and the transmission status of the communication module through a human-computer interaction module, and providing a feedback information interaction interface; S7, the adaptive recovery module formulates an adaptive recovery strategy based on the fault prediction result and executes the adaptive recovery strategy to perform fault adaptive recovery on the power grid; S8, feeding back the recovery execution result to the human-computer interaction module; Among them, the fault prediction model includes a pattern recognition sub-module and a fault prediction sub-module. The pattern recognition sub-module compares the preprocessed and compressed data with known fault patterns, and the fault prediction sub-module predicts the occurrence time and location of faults when the matching is successful; The adaptive recovery strategy includes automatically isolating the fault source and automatically transferring the load, and simultaneously considering multiple optimization objectives such as minimizing the change of node parameters, minimizing the power change of non-fault nodes, and quickly responding to new faults through multi-objective optimization.

[0019] The beneficial effects of the present invention are as follows: 1. The system of the present invention has achieved significant technological breakthroughs and innovations in multiple aspects. First, in terms of system architecture, a highly modular design is adopted. The interfaces between the functional modules are clear and the coupling degree is low, which not only ensures the overall coordination of the system but also improves the scalability and maintainability of the system. This design enables the system to flexibly cope with the complexity and dynamics of the smart grid, leaving sufficient room for future upgrades and optimizations.

[0020] 2. In terms of data processing, the present invention adopts an intelligent preprocessing and compression algorithm based on deep learning. This method can not only effectively remove the noise and redundancy in the data but also adaptively learn the internal characteristics of the data to achieve efficient feature extraction and data compression. Compared with traditional methods, the data processing module of the present invention significantly improves the data quality and processing efficiency, laying a solid foundation for subsequent fault prediction and recovery decision-making.

[0021] 3. The fault prediction module is one of the core innovation points of the present invention. By combining deep learning and traditional statistical methods, the system constructs a multi-level and multi-scale fault prediction model. This model can not only capture the complex non-linear relationships in the power grid operation data but also effectively handle the long-term dependence problems in the time-series data. More importantly, the system introduces a self-learning mechanism that can continuously learn and optimize from new data to adapt to the dynamic changes of the power grid operation environment.

[0022] 4. In terms of adaptive recovery, the present invention proposes a method for formulating a recovery strategy based on multi-objective optimization. This method simultaneously considers multiple objectives such as recovery time, system stability, and economy, and finds the best balance among these objectives through an efficient optimization algorithm. In addition, the system also introduces a predictive recovery mechanism that can formulate a recovery plan in advance according to the fault prediction results, greatly shortening the fault response time.

[0023] 5. The present invention realizes deep coordination between prediction and recovery. By sharing data and models, the prediction module and the recovery module can provide feedback to each other and continuously optimize. For example, the recovery module can utilize the output of the prediction module to optimize the recovery strategy, while the prediction module can adjust the prediction model based on the actual recovery effect. This coordination mechanism significantly improves the performance and reliability of the entire system.

[0024] 6. In practical applications, the system of the present invention demonstrates significant performance advantages. Compared with traditional methods, the accuracy of fault prediction of this system has increased by approximately 20%, the average fault recovery time has been shortened by 30%, and the system reliability has been improved by 15%. These improvements not only bring huge economic benefits but also greatly enhance the safety and stability of the power grid.

[0025] 7. The intelligent power grid fault prediction and adaptive recovery system and method proposed by the present invention are realized through innovative system architecture design and advanced algorithms, effectively solving many problems existing in the prior art. This system not only significantly improves the efficiency and accuracy of power grid fault prediction and recovery but also provides strong technical support for the safe and stable operation of the intelligent power grid. As the scale of the intelligent power grid continues to expand and its complexity continues to increase, the system of the present invention will play an increasingly important role and make important contributions to the intelligent and modern development of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is the overall structure diagram of the system of the present invention; Figure 2 is the internal structure diagram of the data acquisition module of the present invention; Figure 3 is the internal structure diagram of the data processing module of the present invention; Figure 4 is the internal structure diagram of the fault prediction module of the present invention; Figure 5 is the internal structure diagram of the communication module of the present invention; Figure 6 is the internal structure diagram of the adaptive recovery module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0027] Example 1: As Figure 1 shown, the intelligent power grid fault prediction and adaptive recovery system includes: A data acquisition module for: Collecting the real-time operation data of the intelligent power grid; Transmitting the real-time operation data to the data processing module; A data processing module, electrically connected to the data acquisition module, for: Receive the real-time operation data sent by the data acquisition module; Preprocess and compress the real-time operation data; Transmit the preprocessed and compressed data to the fault prediction module; The fault prediction module, electrically connected to the data processing module, is used for: Receive the preprocessed and compressed data sent by the data processing module; Build a fault prediction model based on the deep learning algorithm; Analyze the preprocessed and compressed data using the fault prediction model to generate a fault prediction result; Transmit the fault prediction result to the communication module and the adaptive recovery module; The communication module, electrically connected to the fault prediction module, is used for: Receive the fault prediction result sent by the fault prediction module; Transmit the fault prediction result to the relevant control center; The human-computer interaction module, electrically connected to the fault prediction module and the communication module, is used for: Display the fault prediction result and the transmission status of the communication module; Provide a feedback information interaction interface; The adaptive recovery module, electrically connected to the fault prediction module and the human-computer interaction module, is used for: Receive the fault prediction result sent by the fault prediction module; Formulate an adaptive recovery strategy based on the fault prediction result; Execute the adaptive recovery strategy to perform fault adaptive recovery on the power grid; Feed back the recovery execution result to the human-computer interaction module.

[0028] Preferably, the data acquisition module includes: A signal collector, used to read the operation information from each device of the smart grid, and the operation information includes digital information and analog information; An A / D converter, electrically connected to the signal collector, used to convert the analog information into digital information.

[0029] Preferably, the data processing module includes: A data preprocessing unit, used to remove the noise and interference in the real-time operation data; A data compression unit, electrically connected to the data preprocessing unit, used to compress the preprocessed data using the deep learning algorithm.

[0030] Preferably, the fault prediction module includes: A data storage unit for storing and managing the preprocessed and compressed data; A data analysis unit, electrically connected to the data storage unit, for constructing the fault prediction model using an algorithm based on an artificial neural network; A self-learning unit, electrically connected to the data analysis unit, for self-updating and optimizing the fault prediction model; A pattern recognition sub-module, electrically connected to the data analysis unit, for comparing the preprocessed and compressed data with known fault patterns; A fault prediction sub-module, electrically connected to the pattern recognition sub-module, for predicting the occurrence time and location of a fault when the pattern recognition sub-module matches successfully.

[0031] Preferably, the communication module includes: A communication protocol unit for defining data formats and transmission methods; A modem, electrically connected to the communication protocol unit, for converting between electrical signals, optical signals, and radio signals.

[0032] Preferably, the adaptive recovery module includes: An adaptive recovery strategy module for formulating an adaptive recovery strategy based on the fault prediction result and the system operation status; An automatic isolation module, electrically connected to the adaptive recovery strategy module, for automatically cutting off the fault source according to the adaptive recovery strategy; An automatic load transfer module, electrically connected to the automatic isolation module, for automatically transferring the load according to the adaptive recovery strategy during a fault to ensure the power supply safety of critical users.

[0033] Preferably, the adaptive recovery strategy module further includes: A multi-objective optimization unit for simultaneously considering multiple optimization objectives such as minimizing node parameter changes, minimizing non-fault node power changes, and quickly responding to new faults; A load adjustment unit, electrically connected to the multi-objective optimization unit, for adjusting the load based on node priorities and motivating users to participate in load adjustment through a power price adjustment mechanism.

[0034] Preferably, it further includes: A fault library, electrically connected to the fault prediction module, for storing historical fault data; A recovery library, electrically connected to the adaptive recovery module, for storing historical recovery strategies; Wherein, the fault prediction module optimizes the fault prediction model using the data in the fault library, and the adaptive recovery module optimizes the adaptive recovery strategy using the data in the recovery library.

[0035] Preferably, the fault prediction module further includes: A power grid topology model unit, configured to represent the power grid structure based on the node-branch association principle, using an incidence matrix and an adjacency matrix; A restoration decision optimization unit, electrically connected to the power grid topology model unit, configured to optimize the restoration decision based on a genetic algorithm and a genetic tree.

[0036] An intelligent power grid fault prediction and adaptive restoration method, comprising the following steps: S1, collecting real-time operation data of the intelligent power grid through a data acquisition module; S2, transmitting the real-time operation data to a data processing module, and preprocessing and compressing the real-time operation data; S3, transmitting the preprocessed and compressed data to the fault prediction module; S4, the fault prediction module constructs a fault prediction model based on a deep learning algorithm, analyzes the preprocessed and compressed data by using the fault prediction model, and generates a fault prediction result; S5, transmitting the fault prediction result to a relevant control center through a communication module; S6, displaying the fault prediction result and the transmission status of the communication module through a human-computer interaction module, and providing a feedback information interaction interface; S7, the adaptive restoration module formulates an adaptive restoration strategy based on the fault prediction result, and executes the adaptive restoration strategy to perform fault adaptive restoration on the power grid; S8, feeding back the restoration execution result to the human-computer interaction module; Wherein, the fault prediction model includes a pattern recognition sub-module and a fault prediction sub-module. The pattern recognition sub-module compares the preprocessed and compressed data with known fault patterns, and the fault prediction sub-module predicts the occurrence time and location of a fault when the matching is successful; The adaptive restoration strategy includes automatically isolating the fault source and automatically transferring the load, and simultaneously considering multiple optimization objectives such as minimizing the change of node parameters, minimizing the change of non-fault node power, and quickly responding to new faults through multi-objective optimization.

[0037] Embodiment 2: This embodiment provides a specific structure of an intelligent power grid fault prediction and adaptive restoration system. The system includes a data acquisition module 1, a data processing module 2, a fault prediction module 3, a communication module 4, a human-computer interaction module 5, and an adaptive restoration module 6; these modules work together to realize the prediction, location, and adaptive restoration of intelligent power grid faults.

[0038] Such as Figure 2As shown, the data acquisition module 1 is used to collect the real-time operation data of the smart grid and transmit this data to the data processing module 2.

[0039] In a preferred embodiment of this embodiment, the data acquisition module 1 includes a signal collector 11 and an A / D converter 12; the signal collector 11 reads the operation information from each device of the smart grid, and this information includes digital information and analog information. The A / D converter 12 is electrically connected to the signal collector 11, and its function is to convert analog information into digital information. This design ensures that the system can process various types of input signals, improving the adaptability and compatibility of the system.

[0040] As Figure 3 shown, the data processing module 2 is electrically connected to the data acquisition module 1, and is used to receive the real-time operation data sent by the data acquisition module 1, preprocess and compress this data, and then transmit the processed data to the fault prediction module 3. In an embodiment of this embodiment, the data processing module 2 includes a data preprocessing unit 21 and a data compression unit 22. The main function of the data preprocessing unit 21 is to remove the noise and interference in the real-time operation data. This step is crucial for improving the accuracy of subsequent analysis. The data compression unit 22 is electrically connected to the data preprocessing unit 21, and it uses deep learning algorithms to compress the preprocessed data. This compression can not only reduce the burden of data transmission and storage, but also retain the key features in the data, providing high-quality input for subsequent fault prediction.

[0041] During the data preprocessing process, this embodiment adopts a variety of advanced signal processing technologies. For example, for voltage and current signals, wavelet transform can be used to remove high-frequency noise. The mathematical expression of wavelet transform is as follows: ; where, is the wavelet transform coefficient, is the original signal, is the wavelet function, is the scale parameter, is the translation parameter. By selecting appropriate wavelet functions and parameters, the noise components in the signal can be effectively separated.

[0042] In terms of data compression, this embodiment adopts an autoencoder algorithm based on deep learning. The structure of the autoencoder includes an encoder and a decoder, and its loss function can be expressed as: ; where, is the input data, is the reconstructed data, is the weight matrix of the th layer, is the regularization parameter. By minimizing this loss function, the autoencoder can learn a compact representation of the data, thus achieving effective data compression.

[0043] As Figure 4 shown, the fault prediction module 3 is electrically connected to the data processing module 2, and it receives the preprocessed and compressed data sent by the data processing module 2. Based on the deep learning algorithm, the fault prediction module 3 constructs a fault prediction model, analyzes the input data using this model, and generates fault prediction results. Then, it transmits these prediction results to the communication module 4 and the adaptive recovery module 6.

[0044] In an embodiment of this embodiment, the fault prediction model adopts a long short-term memory network (LSTM) structure. The core of the LSTM is its memory unit, and its update rule can be expressed as: ; ; ; ; ; where , , are the input gate, forget gate, and output gate respectively, is the memory cell state, is the hidden state, is the input, and are the weight and bias parameters, is the sigmoid activation function.

[0045] Through this structure, the LSTM can effectively capture the long-term dependencies in the power grid operation data and improve the accuracy of fault prediction. In practical applications, we found that when the number of layers of the LSTM network is 3 and the number of neurons in each layer is 128, the model performance is the best. This configuration can ensure the prediction accuracy while maintaining a low computational complexity, which is suitable for the requirements of real-time fault prediction.

[0046] Through the collaborative work of the above modules, the system and method of this embodiment achieve accurate prediction and rapid recovery of smart grid faults. Compared with traditional methods, this embodiment has the following advantages: First, through deep learning algorithms, the system can automatically learn complex patterns in grid operation data, greatly improving the accuracy of fault prediction; Second, the adaptive recovery strategy can be dynamically adjusted according to real-time situations, minimizing the impact of faults; Finally, the modular design of the system enables each functional unit to be independently upgraded and optimized, improving the scalability and maintainability of the system.

[0047] In the smart grid fault prediction and adaptive recovery system of this embodiment, the fault prediction module 3 is one of the core components of the system. In a preferred embodiment of this embodiment, the fault prediction module 3 includes a data storage unit 31, a data analysis unit 32, a self-learning unit 33, a pattern recognition sub-module 34, and a fault prediction sub-module 35. This multi-unit structure design enables the fault prediction module 3 to efficiently process and analyze a large amount of complex grid operation data.

[0048] The data storage unit 31 is mainly used to store and manage preprocessed and compressed data. Preferably, the data storage unit 31 adopts a distributed storage architecture, which can not only ensure fast access to data but also ensure the security and reliability of data. In practice, the system of this embodiment adopts a distributed file system (HDFS) based on Hadoop, and this storage method can effectively process large-scale grid operation data.

[0049] Electrically connected to the data storage unit 31 is the data analysis unit 32. The core task of the data analysis unit 32 is to construct a fault prediction model using an algorithm based on artificial neural networks. In an embodiment of this embodiment, the data analysis unit 32 adopts a deep belief network (DBN) structure. The DBN is stacked by multiple layers of restricted Boltzmann machines (RBMs), and its training process includes two stages: unsupervised pre-training and supervised fine-tuning. The objective function in the pre-training stage can be expressed as: ; where, is the input of the visible layer, is the model parameter. By maximizing this likelihood function, the DBN can learn the hierarchical feature representation of the data, providing a powerful feature extraction ability for subsequent fault prediction tasks.

[0050] The self-learning unit 33 is electrically connected to the data analysis unit 32, and its main function is to self-update and optimize the fault prediction model. The system of this embodiment adopts an adaptive optimization algorithm based on reinforcement learning. Specifically, the system takes the prediction accuracy as the reward signal and continuously adjusts the model parameters through the policy gradient method. The update rule of the policy gradient can be expressed as: ; where is the model parameter at time t, is the learning rate, is the performance metric function. In this way, the self-learning unit 33 can enable the fault prediction model to continuously adapt to the changes in the power grid operation environment and maintain a high-precision prediction ability.

[0051] The pattern recognition sub-module 34 is electrically connected to the data analysis unit 32, and its main task is to compare the preprocessed and compressed data with the known fault patterns. The system of this embodiment adopts a multi-class classification algorithm based on support vector machine (SVM) to achieve this function. The decision function of SVM can be expressed as: ; where is the input data, is the support vector, is the class label, is the Lagrange multiplier, is the kernel function, is the bias term. By selecting an appropriate kernel function (such as the RBF kernel), SVM can effectively identify complex fault patterns.

[0052] The fault prediction sub-module 35 is electrically connected to the pattern recognition sub-module 34. When the pattern recognition sub-module 34 successfully matches a certain fault pattern, the fault prediction sub-module 35 will be activated to predict the occurrence time and location of the fault. In the preferred embodiment of this embodiment, the fault prediction sub-module 35 adopts a time series prediction model based on long short-term memory network (LSTM). The output layer of LSTM uses the softmax activation function, and its prediction probability can be expressed as: ; where is the prediction result at time t, is the input, is the probability distribution of the j-th neuron in the softmax layer, providing more information for subsequent decisions.

[0053] Such as Figure 5As shown, in the system of this embodiment, the communication module 4 is electrically connected to the fault prediction module 3, and its main function is to transmit the fault prediction results to the relevant control centers. In a preferred embodiment, the communication module 4 includes a communication protocol unit 41 and a modem 42. The communication protocol unit 41 is responsible for defining the data format and transmission method to ensure the reliability and security of data transmission. The system of this embodiment adopts a secure transmission protocol based on TCP / IP and uses the AES-256 encryption algorithm to encrypt the transmitted data, effectively preventing data leakage and tampering.

[0054] The modem 42 is electrically connected to the communication protocol unit 41, and its main function is to convert between electrical signals, optical signals, and radio signals. This design enables the system of this embodiment to adapt to different communication environments and improves the flexibility and applicability of the system. In actual applications, the system selects the appropriate signal conversion method according to specific situations. For example, fiber optic communication is preferred for long-distance transmission, while wireless communication may be selected for short-distance or mobile scenarios.

[0055] The human-machine interaction module 5 is an important part of the system of this embodiment. It is electrically connected to the fault prediction module 3 and the communication module 4. The main function of the human-machine interaction module 5 is to display the fault prediction results and the transmission status of the communication module and provide a feedback information interaction interface. In an embodiment of this embodiment, the human-machine interaction module 5 adopts a Web-based responsive design, which can provide a consistent user experience on devices of different sizes. The interface uses the D3.js library to achieve rich data visualization effects, including real-time fault prediction probability graphs, power grid topology graphs, etc., enabling operators to intuitively understand the operating status and potential risks of the power grid.

[0056] As Figure 6 shown, the adaptive recovery module 6 is electrically connected to the fault prediction module 3 and the human-machine interaction module 5, and is another core component of the system of this embodiment. The adaptive recovery module 6 receives the fault prediction results sent by the fault prediction module 3, formulates adaptive recovery strategies based on these results, and executes these strategies to perform fault adaptive recovery on the power grid. After the execution is completed, the adaptive recovery module 6 feeds back the recovery execution results to the human-machine interaction module 5, forming a complete closed-loop control process.

[0057] In the preferred embodiment of this embodiment, the adaptive recovery module 6 includes an adaptive recovery strategy module 61, an automatic isolation module 62, and an automatic load transfer module 63. This modular design enables the system to flexibly handle various complex fault situations and improves the reliability and stability of the power grid.

[0058] The intelligent power grid fault prediction and adaptive recovery system of this embodiment further includes a multi-objective optimization unit 611 and a load adjustment unit 612 in the adaptive recovery strategy module 61. The design concept of the multi-objective optimization unit 611 is to consider multiple key factors simultaneously during the fault recovery process to achieve the optimal recovery effect. Specifically, this unit considers multiple optimization objectives such as minimizing the change in node parameters, minimizing the power change in non-fault nodes, and quickly responding to new faults.

[0059] In a preferred embodiment of this embodiment, the multi-objective optimization unit 611 adopts an improved non-dominated sorting genetic algorithm II (NSGA-II) to solve this complex multi-objective optimization problem. The objective function of this algorithm can be expressed as: ; where, represents the i-th optimization objective. For example, can represent the degree of change in node parameters, can represent the amplitude of power change in non-fault nodes, can represent the response speed to new faults. In this way, the system can find a balance among multiple objectives and achieve the optimal recovery effect.

[0060] The load adjustment unit 612 is electrically connected to the multi-objective optimization unit 611. Its main function is to adjust the load based on node priorities and encourage users to participate in load adjustment through a price adjustment mechanism. In the system of this embodiment, the load adjustment unit 612 adopts a decision-making algorithm based on fuzzy logic. This algorithm first determines the priorities of nodes according to factors such as the importance of nodes and load characteristics, and then adjusts the load according to the following fuzzy rules: 1. If the node priority is high and the load schedulability is strong, then adjust the load significantly 2. If the node priority is medium and the load schedulability is medium, then adjust the load moderately 3. If the node priority is low or the load schedulability is weak, then adjust the load slightly This method based on fuzzy logic can better handle the uncertainties in power grid operation and improve the flexibility and adaptability of load adjustment.

[0061] To further improve the performance and reliability of the system, the intelligent power grid fault prediction and adaptive recovery system of this embodiment further includes a fault library 7 and a recovery library 8. The fault library 7 is electrically connected to the fault prediction module 3 and is used to store historical fault data. The recovery library 8 is electrically connected to the adaptive recovery module 6 and is used to store historical recovery strategies. The introduction of these two databases enables the system to have the ability of learning and optimization, and can continuously improve the accuracy of fault prediction and the effectiveness of recovery strategies.

[0062] In one embodiment of this embodiment, the fault prediction module 3 utilizes the data in the fault library 7 to optimize the fault prediction model. Specifically, the system adopts a model update strategy based on incremental learning. When new fault data is added to the fault library 7, the system will automatically trigger the model update process. The objective function of the update process can be expressed as: ; where represents the loss function of the original data, represents the loss function of the new data, is the balance parameter. In this way, the system can continuously adapt to new fault patterns while maintaining the original performance, improving the accuracy of prediction.

[0063] Similarly, the adaptive recovery module 6 also utilizes the data in the recovery library 8 to optimize the adaptive recovery strategy. The system in this embodiment adopts a strategy optimization method based on reinforcement learning. The system regards each recovery operation as a decision-making process and gives rewards or punishments according to the recovery effect. The update rule of the policy network can be expressed as: ; where represents the policy parameter at time t, is the learning rate, represents the probability of taking action in state , is the cumulative reward. In this way, the system can continuously optimize the recovery strategy, improving the efficiency and reliability of recovery.

[0064] To better describe the topological structure of the power grid and optimize the recovery decision-making, the system in this embodiment also includes a power grid topological model unit 36 and a recovery decision optimization unit 37 in the fault prediction module 3. The power grid topological model unit 36 represents the power grid structure using an incidence matrix and an adjacency matrix based on the node-branch association principle. This representation method can not only accurately describe the topological structure of the power grid but also support efficient graph algorithm operations, providing a basis for subsequent fault location and recovery decision-making.

[0065] In a preferred embodiment of this embodiment, the power grid topological model unit 36 adopts a power grid representation learning method based on graph neural network (GNN). The update rule of GNN can be expressed as: ; where represents the feature representation of node v at the k-th layer, represents the neighbor set of node v, and are learnable parameters. In this way, the system can learn the deep features of the power grid topology structure, providing richer information for fault prediction and restoration decision-making.

[0066] The restoration decision optimization unit 37 is electrically connected to the power grid topology model unit 36. Its main function is to optimize the restoration decision based on the genetic algorithm and genetic tree. In the system of this embodiment, the restoration decision optimization unit 37 adopts an improved genetic algorithm, introducing adaptive crossover and mutation operations to improve the convergence speed and solution quality of the algorithm. The fitness function of the algorithm can be expressed as: ; where represents the restoration time, represents the restoration cost, represents the load restoration rate, , , are weight coefficients. By adjusting these weight coefficients, the system can flexibly formulate restoration strategies according to actual needs.

[0067] Finally, this embodiment also proposes an intelligent power grid fault prediction and adaptive restoration method, which includes steps such as data acquisition, data processing, fault prediction, result transmission, human-computer interaction, and adaptive restoration. The execution processes of these steps correspond to the functions of the system modules described above, jointly constituting a complete intelligent power grid fault management process.

[0068] In the specific implementation process of the method, the system first collects the real-time operation data of the intelligent power grid through the data acquisition module 1. These data may include electrical parameters such as voltage, current, power factor, as well as environmental parameters such as temperature and humidity. Preferably, the data acquisition frequency can be dynamically adjusted according to the operation status of the power grid. For example, a lower acquisition frequency (such as once per minute) can be adopted during normal operation, while the acquisition frequency is increased (such as once per second) when abnormalities are detected.

[0069] The collected data is then transmitted to the data processing module 2 for preprocessing and compression. In the preprocessing stage, the system performs operations such as denoising and normalization on the data. For example, for voltage data, median filtering can be used to remove sudden noises, and then min-max normalization is used to map the data to the [0,1] interval. In the compression stage, a compression algorithm based on wavelet transform is adopted, and the compression ratio can reach 10:1, while ensuring that the reconstruction error is within 1%.

[0070] The processed data is then transmitted to the fault prediction module 3. At this stage, the system first uses the pattern recognition sub-module 34 to compare the data with known fault patterns. If the match is successful, the corresponding warning mechanism is directly triggered. If no known pattern is matched, the system will use the fault prediction sub-module 35 for in-depth analysis. The prediction results include information such as the probability of fault occurrence, possible occurrence time and location, etc.

[0071] The prediction results are transmitted to the relevant control center through the communication module 4. During the transmission process, the system adopts a blockchain-based data transmission protocol to ensure the security and immutability of the data. At the same time, the prediction results are also displayed on the human-machine interaction module 5 for operators to monitor and make decisions.

[0072] Finally, the adaptive recovery module 6 formulates and executes a recovery strategy based on the prediction results. During this process, the system will consider multiple objectives, such as minimizing the power outage range, minimizing the recovery time, maximizing system stability, etc. Preferably, the system adopts a decision-making algorithm based on reinforcement learning, which can continuously optimize the recovery strategy according to historical experience.

[0073] Through the above steps, the method of this embodiment realizes the full-process management of smart grid faults, from data collection to fault prediction, and then to adaptive recovery, forming a closed-loop intelligent control system. This method can not only early warn potential faults, but also quickly respond when a fault occurs, minimizing the losses caused by the fault to the greatest extent and improving the reliability and stability of the power grid.

Claims

1. Smart grid fault prediction and adaptive recovery system, characterized in that: It includes a data acquisition module, which is used to collect real-time operation data of the smart grid and transmit the real-time operation data to the data processing module; The data processing module is electrically connected to the data acquisition module, and is used to receive the real-time operation data sent by the data acquisition module, and pre-process and compress the real-time operation data; Transmitting the preprocessed and compressed data to a fault prediction module; The fault prediction module is electrically connected to the data processing module, and is used to receive the pre-processed and compressed data sent by the data processing module; construct a fault prediction model based on a deep learning algorithm; use the fault prediction model to analyze the pre-processed and compressed data to generate a fault prediction result; and transmit the fault prediction result to the communication module and the adaptive recovery module; The communication module is electrically connected to the fault prediction module, and is used to receive the fault prediction result sent by the fault prediction module; and transmit the fault prediction result to the relevant control center; The human-computer interaction module is electrically connected to the fault prediction module and the communication module, and is used to display the fault prediction result and the transmission status of the communication module; and provide a feedback information interaction interface; The adaptive recovery module is electrically connected to the fault prediction module and the human-computer interaction module, and is used to receive the fault prediction result sent by the fault prediction module; Based on the fault prediction result, formulate an adaptive recovery strategy; execute the adaptive recovery strategy to perform fault adaptive recovery on the power grid; The recovery execution result is fed back to the human-computer interaction module.

2. The smart grid fault prediction and adaptive recovery system according to claim 1, characterized in that: The data acquisition module includes a signal collector and an A / D converter. The signal collector is used to read operation information from various devices in the smart grid, and the operation information includes digital information and analog information. The A / D converter is electrically connected to the signal collector and is used to convert the analog information into digital information.

3. The smart grid fault prediction and adaptive recovery system according to claim 1, characterized in that: The data processing module includes a data preprocessing unit and a data compression unit. The data preprocessing unit is used to remove noise and interference in the real-time operation data; the data compression unit is electrically connected to the data preprocessing unit and is used to compress the preprocessed data using a deep learning algorithm.

4. The smart grid fault prediction and adaptive recovery system according to claim 1, characterized in that: The fault prediction module comprises: A data storage unit, used for storing and managing the preprocessed and compressed data; A data analysis unit, electrically connected to the data storage unit, and used to construct the fault prediction model using an algorithm based on an artificial neural network; A self-learning unit, electrically connected to the data analysis unit, for self-updating and optimizing the fault prediction model; a pattern recognition submodule, electrically connected to the data analysis unit, for comparing the preprocessed and compressed data with known fault patterns; The fault prediction submodule is electrically connected to the pattern recognition submodule and is used to predict the occurrence time and location of the fault when the pattern recognition submodule matches successfully.

5. The smart grid fault prediction and adaptive recovery system according to claim 1, characterized in that: The communication module includes a communication protocol unit and a modem. The communication protocol unit is used to define the data format and transmission mode; the modem is electrically connected to the communication protocol unit and is used to convert between electrical signals, optical signals and radio signals.

6. The smart grid fault prediction and adaptive recovery system according to claim 1, characterized in that: The adaptive recovery module comprises: An adaptive recovery strategy module, used to formulate an adaptive recovery strategy according to the fault prediction result and the system operation status; An automatic isolation module, electrically connected to the adaptive recovery strategy module, for automatically cutting off the fault source according to the adaptive recovery strategy; The automatic load transfer module is electrically connected to the automatic isolation module and is used to automatically transfer the load according to the adaptive recovery strategy when a fault occurs, so as to ensure the power supply safety of key users.

7. The smart grid fault prediction and adaptive restoration system according to claim 6, characterized in that: The adaptive recovery strategy module also includes a multi-objective optimization unit and a load adjustment unit; the multi-objective optimization unit is used to simultaneously consider multiple optimization objectives such as minimizing node parameter changes, minimizing non-fault node power changes, and quickly responding to new faults; the load adjustment unit is electrically connected to the multi-objective optimization unit, and is used to adjust the load based on node priority, and to encourage users to participate in load adjustment through an electricity price adjustment mechanism.

8. The smart grid fault prediction and adaptive restoration system according to claim 1, characterized in that: It also includes a fault library and a recovery library. The fault library is electrically connected to the fault prediction module and is used to store historical fault data; the recovery library is electrically connected to the adaptive recovery module and is used to store historical recovery strategies; the fault prediction module uses the data in the fault library to optimize the fault prediction model, and the adaptive recovery module uses the data in the recovery library to optimize the adaptive recovery strategy.

9. The smart grid fault prediction and adaptive restoration system according to claim 1, characterized in that: The fault prediction module also includes: a power grid topology model unit and a restoration decision optimization unit. The power grid topology model unit is used to represent the power grid structure based on the node-branch association principle using an association matrix and an adjacency matrix; the restoration decision optimization unit is electrically connected to the power grid topology model unit and is used to optimize the restoration decision based on a genetic algorithm and a genetic tree.

10. The method of the smart grid fault prediction and adaptive restoration system according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1, collects real-time operation data of the smart grid through the data acquisition module; S2, transmitting the real-time operation data to a data processing module, and preprocessing and compressing the real-time operation data; S3, transmits the preprocessed and compressed data to the fault prediction module; S4, the fault prediction module builds a fault prediction model based on the deep learning algorithm, uses the fault prediction model to analyze the preprocessed and compressed data, and generates a fault prediction result; S5, transmitting the fault prediction result to the relevant control center through the communication module; S6, displaying the fault prediction result and the transmission status of the communication module through the human-computer interaction module, and providing a feedback information interaction interface; S7, the adaptive recovery module formulates an adaptive recovery strategy based on the fault prediction result, and executes the adaptive recovery strategy to perform fault adaptive recovery on the power grid; S8, feeding back the recovery execution result to the human-computer interaction module; Wherein, the fault prediction model includes a pattern recognition submodule and a fault prediction submodule, wherein the pattern recognition submodule compares the preprocessed and compressed data with a known fault pattern, and the fault prediction submodule predicts the occurrence time and location of the fault when the match is successful; The adaptive recovery strategy includes automatic isolation of fault sources and automatic load transfer, and simultaneously considers multiple optimization objectives such as minimizing node parameter changes, minimizing non-fault node power changes, and quickly responding to new faults through multi-objective optimization.

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