10kV power distribution network power distribution system using transformer substation single-end measuring point

Abstract

The invention, which relates to the power grid field, discloses a 10kV power distribution network power distribution system using a single-end measuring point of a transformer station, comprising a data acquisition module used for acquiring current and voltage signals and environmental parameters such as temperature and humidity through the single-end measuring point of the transformer station; the data preprocessing module is used for cleaning, filtering and normalizing the collected data; the intelligent analysis module is used for analyzing the preprocessed data and identifying the operation state and potential problems of the power grid; the electric power allocation module is used for automatically adjusting electric power allocation in the power grid according to the analysis result; the fault early warning module is used for monitoring the state of the power grid in real time and early warning possible faults; the user interface module is used for displaying a power grid operation state, a deployment scheme and early warning information; and the report generation module is used for automatically generating a power grid operation report which comprises the contents of an operation state, fault early warning, a deployment scheme and the like.

Description

Technical Field

[0001] The present invention relates to the field of power grids, and in particular to a 10kV power distribution network power dispatching system using a single-end measuring point of a transformer substation. Background Art

[0002] In the power industry, 10kV distribution network, as one of the most common power distribution systems in cities and industrial production, undertakes a large number of power transmission and distribution tasks. Its operating efficiency and stability are directly related to the safety and reliability of the entire power grid. With the rapid development of social economy and the growing demand for electricity, the traditional 10kV distribution network has gradually revealed its shortcomings in terms of safety, reliability and economy, and cannot meet the needs of modern society for high-quality and high-efficiency power supply.

[0003] Traditional 10kV distribution networks mainly rely on manual inspections and manual control, and have problems such as untimely data collection, low processing efficiency, and slow fault response. In addition, due to the complex grid structure and numerous devices, traditional monitoring methods are difficult to achieve comprehensive perception and real-time monitoring of the grid operation status, resulting in potential safety hazards in the grid operation process. It is difficult to discover and deal with them in a timely manner.

[0004] Distribution networks in different regions and voltage levels have great differences in structure, equipment and technical level. How to carry out intelligent transformation according to the characteristics of different distribution networks is an urgent problem to be solved. In addition, the R&D and application costs of intelligent equipment are high. How to balance input and output and achieve a win-win situation of economic and social benefits is also an important aspect of the development of intelligent distribution networks that needs to be paid attention to. Summary of the invention

[0005] The present application provides a 10kV distribution network power dispatching system using a single-end measurement point in a substation, which is used to solve the technical problem that potential safety hazards in the operation of the existing 10kV distribution network are difficult to discover and handle in a timely manner.

[0006] In view of the above problems, the present application provides a 10kV distribution network power dispatching system using a single-end measuring point of a substation, including: a data acquisition module, used to collect current and voltage signals, as well as environmental parameters such as temperature and humidity through a single-end measuring point of a substation; a data preprocessing module, used to clean, filter and normalize the collected data; an intelligent analysis module, used to analyze the preprocessed data and identify the operating status and potential problems of the power grid; a power dispatching module, used to automatically adjust the power distribution in the power grid according to the analysis results; a fault warning module, used to monitor the power grid status in real time and warn of possible faults; a user interface module, used to display the operating status of the power grid, dispatching plans and warning information; a report generation module, used to automatically generate a power grid operation report, including operating status, fault warning, dispatching plan and other contents; a system maintenance module, used to regularly check the system operating status, automatically update software and maintain hardware.

[0007] In summary, the present invention mainly has the following beneficial effects:

[0008] 1. The present invention improves the accuracy and real-time performance of data acquisition, adopts high-precision sensors and high-speed communication networks, ensures the real-time and accurate acquisition of current, voltage signals and environmental parameters, and avoids the data lag and inaccuracy problems that may exist in traditional manual inspection methods.

[0009] 2. By integrating advanced feature extraction algorithms such as wavelet packet decomposition, the present invention enables the system to more accurately identify abnormal modes and potential fault characteristics in the operation status of the power grid, providing a reliable basis for subsequent power allocation and fault warning.

[0010] 3. The present invention uses an optimization algorithm to calculate the optimal power allocation plan, which can effectively reduce operating costs and improve the utilization efficiency of power resources while ensuring the safe and stable operation of the power grid.

[0011] 4. The present invention monitors the power grid status in real time and performs fault warning. The system can send out warning signals in time before potential faults occur, providing sufficient response time for operation and maintenance personnel, and effectively reducing the impact of faults on power grid operation.

[0012] 5. The present invention can regularly check the status of the system's software and hardware through the built-in system maintenance module, automatically perform software updates and hardware maintenance, and ensure the long-term stable operation of the system. At the same time, the addition of the security protection module also further enhances the system's network security protection capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a system framework diagram of a 10kV distribution network power dispatching system using a single-end measuring point in a substation according to the present invention. DETAILED DESCRIPTION

[0014] The present application provides a 10kV distribution network power dispatching system using a single-end measuring point in a substation, which is used to solve the technical problem that potential safety hazards in the operation of the existing 10kV distribution network are difficult to detect and handle in a timely manner.

[0015] After introducing the basic principles of the present application, the technical solutions in the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described here. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0016] Embodiment 1

[0017] A 10kV distribution network power dispatching system using a substation single-end measuring point, comprising:

[0018] Data acquisition module, used to collect current and voltage signals, as well as environmental parameters such as temperature and humidity through single-end measurement points in substations;

[0019] The data acquisition module is one of the core components of the intelligent 10kV distribution network power dispatch and fault warning system. It is responsible for collecting key data in the power grid operation process in real time and accurately, providing a basis for subsequent intelligent analysis and decision-making.

[0020] The data acquisition module includes a high-precision sensor and a high-speed communication network, specifically:

[0021] High-precision sensor: It uses industrial-grade high-precision current transformers and voltage transformers to collect current and voltage signals in the 10kV distribution network in real time and accurately. It also integrates temperature and humidity sensors to monitor the operating environment parameters of the power grid. The sensor has strong anti-electromagnetic interference ability, wide measurement range, and high precision, ensuring the accuracy and reliability of data collection;

[0022] High-speed communication network: Use optical fiber communication or 5G wireless communication technology to build a high-speed and stable data transmission network to transmit the collected current, voltage signals and environmental parameters to the data center in real time. The communication network has the advantages of low latency, high bandwidth, and strong anti-interference, ensuring the real-time and integrity of data transmission.

[0023] Data collection can be done in the following ways:

[0024] 1. Single-end measurement point acquisition in substation

[0025] Location selection: Data acquisition modules are usually installed at key locations in substations, using single-ended measurement point technology to collect current and voltage signals in the power grid. As an important node in the power grid, the operating status of the substation directly affects the safety and stability of the entire power grid.

[0026] Equipment configuration: In the substation, high-precision sensors are deployed on key lines. These sensors can sense and collect electrical parameter signals such as current and voltage in real time. These sensors usually have strong anti-electromagnetic interference capabilities, wide measurement range, and high precision to ensure the accuracy and reliability of data collection.

[0027] 2. Environmental parameter collection

[0028] Parameter type: In addition to current and voltage signals, the data acquisition module is also responsible for collecting key parameters in the power grid operating environment, such as temperature, humidity, etc. These environmental parameters have an important impact on the operating status and life of power grid equipment, and are therefore also an important part of data acquisition.

[0029] Equipment configuration: In order to accurately collect environmental parameters, the data acquisition module usually integrates environmental monitoring equipment such as temperature and humidity sensors. These devices can monitor the temperature and humidity changes in the power grid operating environment in real time and transmit the collected data to the data center for processing and analysis.

[0030] Data preprocessing module, used to clean, filter and normalize the collected data;

[0031] The main function of this module is to perform a series of processing on the original collected data to improve data quality and provide a more accurate and reliable data basis for subsequent intelligent analysis and decision-making.

[0032] The main purpose of data cleaning is to remove noise, outliers and missing values ​​from the original data to ensure the integrity and accuracy of the data. Through algorithms such as smoothing and denoising, random errors and interference signals in the data can be reduced and the signal-to-noise ratio of the data can be improved.

[0033] Outlier detection and removal uses statistical methods (such as box plots, Z scores, etc.) or rules based on domain knowledge to identify and remove outliers in the data to prevent them from having a negative impact on subsequent analysis.

[0034] For missing data points, interpolation methods (such as linear interpolation, Lagrange interpolation, etc.), mean filling, mode filling or model-based prediction filling can be used to supplement them to ensure the integrity of the data.

[0035] It is worth mentioning that the main purpose of filtering is to remove high-frequency noise and interference signals in the data and retain low-frequency components that are useful for subsequent analysis.

[0036] Optionally, a digital filter (such as a low-pass filter, a high-pass filter, a band-pass filter, etc.) is used to filter the raw data. The digital filter implements the signal filtering function through a mathematical algorithm, and has the advantages of flexible design and easy implementation.

[0037] Alternatively, for non-stationary signals, time-frequency analysis methods such as wavelet transform can be used for filtering. Wavelet transform can provide information about the signal in both the time domain and the frequency domain, and has unique advantages for analyzing non-stationary signals.

[0038] Among them, the main purpose of normalization is to convert data of different dimensions and orders of magnitude to the same scale for subsequent data analysis and processing.

[0039] Linear normalization: linearly scale the original data to the interval [0,1]. The specific formula is:

[0040]

[0041] Among them, X is the original data, X min and X max are the minimum and maximum values ​​in the data set, respectively.

[0042] Z-score standardization: convert the original data into a distribution with a mean of 0 and a standard deviation of 1. The specific formula is:

[0043]

[0044] Among them, X is the original data, μ and σ are the mean and standard deviation of the data set respectively.

[0045] Intelligent analysis module, used to analyze pre-processed data and identify grid operation status and potential problems;

[0046] In the intelligent 10kV distribution network power dispatch and fault warning system, it is responsible for in-depth analysis of pre-processed data to accurately identify the operating status and potential problems of the power grid. The intelligent analysis module receives data from the data pre-processing module, which has been cleaned, filtered and normalized to ensure the quality and consistency of the data.

[0047] It is worth mentioning that by extracting features from the data, the intelligent analysis module can identify the operating status of the power grid in real time, including normal status, overload status, and fault precursor status. After identifying potential problems in the operation of the power grid, the intelligent analysis module will further analyze the cause and nature of the problem.

[0048] The feature extraction algorithm in the intelligent analysis module adopts the wavelet packet decomposition method, and the specific process includes:

[0049] Perform multi-layer wavelet packet decomposition on the pre-processed current and voltage signals to extract signal features in different frequency bands;

[0050] According to the operation characteristics of the power grid, select the appropriate wavelet basis function and decomposition layer number to optimize the accuracy and efficiency of feature extraction;

[0051] The extracted features are statistically analyzed to identify abnormal patterns or potential fault characteristics in the power grid operation status.

[0052] At the same time, based on the current status and future trend forecasts, the intelligent analysis module can issue early warning signals to notify operation and maintenance personnel to pay attention to potential problems and take corresponding preventive measures. This prediction and early warning capability is of great significance to improving the reliability and stability of the power grid.

[0053] Optionally, as the scale and complexity of the power grid continue to increase, the intelligent analysis module supports the access of new types of data, the addition of new analysis functions, and the optimization of system performance.

[0054] The power allocation module is used to automatically adjust the power distribution in the power grid according to the analysis results. It is responsible for bringing the processing results of the data analysis module into the optimization algorithm for calculation, deriving the optimal power allocation plan, and automatically adjusting the power distribution in the power grid to optimize the allocation of power resources and improve the efficiency and stability of the power grid operation.

[0055] The power allocation module uses an optimization algorithm to calculate the optimal power allocation solution, and the specific steps include:

[0056] Based on historical data and real-time load forecasting results, a power grid load model is constructed, expressed as L(t), where t is time. The model takes into account factors such as daily load curves and seasonal changes;

[0057] Setting the objective function of power dispatch, such as minimizing the operating cost C, can be expressed as

[0058]

[0059] Among them, x i is the power allocation ratio of node i, p i is the unit cost of node i, y j is the gear adjustment of transformer j, q j is the unit adjustment cost of transformer j;

[0060] Considering the constraints on safe and stable operation of the power grid, such as node voltage amplitude constraints and branch power flow constraints, a set of constraints is constructed;

[0061] Using optimization algorithms such as genetic algorithm and particle swarm algorithm, the optimal power dispatching scheme is solved under the premise of ensuring safe and stable operation of the power grid, that is, minC is solved so that L(t) is satisfied and the power grid operates stably.

[0062] Output the optimal allocation plan, including the power distribution ratio of each node, transformer gear adjustment and other specific operation instructions.

[0063] Fault warning module, used to monitor the power grid status in real time and warn of possible faults;

[0064] The fault warning module collects real-time grid operation data by connecting to various sensors and monitoring equipment in the grid, including key parameters such as current, voltage, power factor, temperature, humidity, etc. These data reflect the real-time operation status of the grid and are the basis for fault warning.

[0065] The specific process of the fault warning module performing fault warning by real-time monitoring of the power grid status includes:

[0066] Real-time monitoring of current, voltage signals and environmental parameters in the power grid, and construction of a real-time state vector S(t), where t is time;

[0067] Set the warning threshold T. For key parameters such as current and voltage, the warning threshold T is determined based on the grid operation standards and historical data;

[0068] Calculate the deviation Δ between the real-time state vector S(t) and the warning threshold T = |S(t)-T|;

[0069] Once the deviation Δ is found to exceed the preset tolerance δ, that is, Δ>δ, the early warning mechanism is immediately triggered;

[0070] According to the severity and type of deviation, the fault level is evaluated and detailed warning information is generated;

[0071] Warning information is automatically sent to relevant personnel through the user interface or text messages, emails, etc., so that timely measures can be taken to deal with the fault.

[0072] The collected data is first cleaned, filtered and normalized by the preprocessing module to improve the quality and consistency of the data. Then, the fault warning module uses data analysis techniques such as statistical analysis, pattern recognition, and machine learning to conduct in-depth analysis of the processed data to identify abnormal conditions and potential fault risks in the power grid.

[0073] Based on the results of data analysis, the fault warning module can predict the type, location and time window of possible faults in the power grid. Once a potential fault risk is identified, the module will immediately issue a warning signal to notify the operation and maintenance personnel to pay attention and take corresponding measures. The warning signal can be issued in a variety of ways, such as sound and light alarms, SMS notifications, email reminders, etc., to ensure that the operation and maintenance personnel can receive the warning information in time.

[0074] After each warning, the deviation Δ is optimized using the following formula according to the feedback results of the warning information and the actual grid operation status:

[0075] Δ opt =α*Δ prev +(1-α)*Δ actual

[0076] Among them, Δ opt Represents the optimized deviation, Δ prev Indicates the deviation amount at the last warning, Δ actual It represents the actual deviation during this warning, α is the optimization coefficient, and its value range is 0≤α<1, which is used to control the weight of historical deviation and actual deviation in the optimization process;

[0077] Collect historical warning data, including the deviation Δ, warning threshold T, deviation tolerance δ, and accuracy of warning results at each warning;

[0078] Construct an optimization model, taking the accuracy of the warning results as the optimization target, and the warning threshold T and the deviation tolerance δ as the optimization variables;

[0079] Solve the optimization model to obtain the optimal combination of warning threshold T and deviation tolerance δ;

[0080] The optimized combination of T and δ is applied to the fault warning module to improve the accuracy and reliability of the warning;

[0081] The steps of solving the optimization model are as follows:

[0082] Initialize the particle swarm, where each particle represents a combination of a warning threshold T and a deviation tolerance δ, and set the initial velocity and position of the particle;

[0083] Calculate the fitness value F of each particle, that is, evaluate its performance on historical warning data based on the combination of the current warning threshold T and the deviation tolerance δ;

[0084] Update the individual optimal solution and the global optimal solution in the particle swarm. The individual optimal solution is the best historical position of the particle itself, and the global optimal solution is the best historical position of the entire particle swarm.

[0085] According to the individual optimal solution and the global optimal solution, the speed and position of the particle are updated to make the particle move towards a better solution;

[0086] Repeat the steps until the preset number of iterations is reached or the fitness value converges.

[0087] After each warning, the system will record the warning event and its related data, including the real-time state vector S(t), warning threshold T, deviation Δ, and triggered warning level, etc. These data will be used for subsequent analysis and optimization.

[0088] Based on the results of statistical analysis, the system tries different combinations of warning thresholds T and deviation tolerances δ to find the optimal parameter settings.

[0089] This step can be carried out through simulation experiments or historical data replay, that is, using different parameter combinations to simulate early warnings on historical data, and observing and recording the accuracy and timeliness of the early warning results.

[0090] By comparing the warning effects under different parameter combinations, the system selects the optimal combination of warning threshold T and deviation tolerance δ to improve the accuracy of warning and reduce the false alarm rate.

[0091] In order to further improve the efficiency and accuracy of optimization, the present invention adopts particle swarm optimization (PSO) to optimize the combination of warning threshold T and deviation tolerance δ.

[0092] In the particle swarm algorithm, each particle represents a possible solution, which is a combination of the warning threshold T and the deviation tolerance δ. The position of the particle represents the specific value of the solution, and the speed of the particle represents the direction and rate of change of the solution.

[0093] When the algorithm is initialized, a certain number of particles are randomly generated and given random positions and velocities. Then, the algorithm iteratively updates the positions and velocities of the particles until the optimal solution is found or the preset number of iterations is reached.

[0094] In each iteration, the particle adjusts its speed and position according to its own historical optimal position and the global optimal position of the entire group. Specifically, each particle calculates its own fitness, which is an evaluation indicator of the warning effect, such as accuracy, timeliness, etc., and updates its personal optimal position. Then, the particle calculates a new speed and position based on the global optimal position of the best particle in the group and its own position.

[0095] Through multiple iterations, the particles gradually converge to the optimal solution or the area close to the optimal solution. Finally, the algorithm outputs the optimal combination of warning threshold T and deviation tolerance δ as the new warning parameter setting of the system. The optimized combination of warning threshold T and deviation tolerance δ is applied to the fault warning module to replace the original fixed parameter setting.

[0096] In the subsequent power grid operation monitoring, the fault warning module will use the new warning parameters for real-time monitoring and warning to improve the accuracy of the warning and reduce the false alarm rate.

[0097] User interface module, used to display the grid operation status, deployment plan and early warning information;

[0098] Report generation module, used to automatically generate power grid operation reports, including operation status, fault warning, deployment plan, etc.;

[0099] The report generation module can automatically generate a power grid operation report, the specific contents of which include:

[0100] Overview of power grid operation status: summarize the overall operation status and key indicators of the current power grid;

[0101] Fault warning analysis: detailed list of warning information, fault level, occurrence time and possible causes;

[0102] Power allocation plan: Display the specific content and implementation effect of the optimal power allocation plan;

[0103] Environmental parameter monitoring: record the temperature, humidity and other parameters of the power grid operating environment and their changing trends;

[0104] Statistical chart display: intuitively display key data and change trends of power grid operation in the form of charts;

[0105] Report export and printing: Supports exporting reports to PDF, Word and other formats, and provides printing function.

[0106] The system maintenance module is used to regularly check the system operation status and automatically perform software updates and hardware maintenance.

[0107] The system maintenance module has the following functions:

[0108] Regular inspection: Set up a regular inspection plan to automatically detect the status of system hardware and software, including sensor accuracy, communication network stability, server load, etc.

[0109] Software update: Keep an eye on the latest version and patch information of system software in real time, automatically download and install update packages to ensure the perfection and security of system functions;

[0110] Hardware maintenance: Develop maintenance plans based on the service life and performance status of hardware equipment, including cleaning, calibration, replacement of damaged parts, etc.;

[0111] Log management: record all operation logs and fault logs during system operation to facilitate problem tracking and performance analysis;

[0112] Backup and recovery: Regularly back up system data and configuration files to ensure rapid recovery in the event of system failure or data loss;

[0113] Remote support: Provide remote technical support services to assist users in solving problems encountered during system use through remote desktop or online meetings.

[0114] It also includes a security protection module to strengthen system network security protection, ensure the security of data transmission and storage, and prevent data leakage and illegal access.

[0115] Those skilled in the art will understand that the various digital numbers such as the first and second involved in this application are only for the convenience of description, and are not used to limit the scope of this application, nor do they indicate a sequence of precedence. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated with each other are in an "or" relationship. "At least one" refers to one or more. At least two refers to two or more. "At least one", "any one" or similar expressions refer to any combination of these items, including any combination of single items (individuals) or plural items (individuals). For example, at least one item (individual, kind) of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0116] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in this application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0117] The steps of the method or algorithm described in this application can be directly embedded in hardware, software units executed by a processor, or a combination of the two. The software unit can be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, register, hard disk, removable disk, CD-ROM or any other form of storage medium in the art. Exemplarily, the storage medium can be connected to the processor so that the processor can read information from the storage medium and write information to the storage medium. Optionally, the storage medium can also be integrated into the processor. The processor and the storage medium can be arranged in an ASIC, and the ASIC can be arranged in a terminal. Optionally, the processor and the storage medium can also be arranged in different components in the terminal. These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are performed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0118] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the accompanying drawings are merely exemplary illustrations of the present application and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A 10kV distribution network power dispatching system using a single-ended measurement point in a substation, characterized in that include: Data acquisition module, used to collect current and voltage signals, as well as environmental parameters such as temperature and humidity through single-end measurement points in substations; Data preprocessing module, used to clean, filter and normalize the collected data; Intelligent analysis module, used to analyze pre-processed data and identify grid operation status and potential problems; A power allocation module, used to automatically adjust power distribution in the power grid according to the analysis results; Fault warning module, used to monitor the power grid status in real time and warn of possible faults; User interface module, used to display the grid operation status, deployment plan and early warning information; Report generation module, used to automatically generate power grid operation reports, including operation status, fault warning, deployment plan, etc.; The system maintenance module is used to regularly check the system operation status and automatically perform software updates and hardware maintenance.

2. The system according to claim 1, characterized in that The data acquisition module includes a high-precision sensor and a high-speed communication network, specifically: High-precision sensor: It uses industrial-grade high-precision current transformers and voltage transformers to collect current and voltage signals in the 10kV distribution network in real time and accurately. It also integrates temperature and humidity sensors to monitor the operating environment parameters of the power grid. The sensor has strong anti-electromagnetic interference ability, wide measurement range, and high precision, ensuring the accuracy and reliability of data collection; High-speed communication network: Use optical fiber communication or 5G wireless communication technology to build a high-speed and stable data transmission network to transmit the collected current, voltage signals and environmental parameters to the data center in real time. The communication network has the advantages of low latency, high bandwidth, and strong anti-interference, ensuring the real-time and integrity of data transmission.

3. The system according to claim 1, characterized in that The intelligent analysis module includes a feature extraction algorithm using a wavelet packet decomposition method, and the specific process includes: Perform multi-layer wavelet packet decomposition on the pre-processed current and voltage signals to extract signal features in different frequency bands; According to the operation characteristics of the power grid, select the appropriate wavelet basis function and decomposition layer number to optimize the accuracy and efficiency of feature extraction; The extracted features are statistically analyzed to identify abnormal patterns or potential fault characteristics in the power grid operation status.

4. The system according to claim 1, characterized in that The power allocation module uses an optimization algorithm to calculate the optimal power allocation solution, and the specific steps include: Based on historical data and real-time load forecasting results, a power grid load model is constructed, expressed as L(t), where t is time. The model takes into account factors such as daily load curves and seasonal changes; Setting the objective function of power dispatch, such as minimizing the operating cost C, can be expressed as Among them, x i is the power allocation ratio of node i, p i is the unit cost of node i, y j is the gear adjustment of transformer j, q j is the unit adjustment cost of transformer j; Considering the constraints on safe and stable operation of the power grid, such as node voltage amplitude constraints and branch power flow constraints, a set of constraints is constructed; Using optimization algorithms such as genetic algorithm and particle swarm algorithm, the optimal power dispatching scheme is solved under the premise of ensuring safe and stable operation of the power grid, that is, minC is solved so that L(t) is satisfied and the power grid operates stably. Output the optimal allocation plan, including the power distribution ratio of each node, transformer gear adjustment and other specific operation instructions.

5. The system according to claim 1, characterized in that The specific process of the fault warning module performing fault warning by real-time monitoring of the power grid status includes: Real-time monitoring of current, voltage signals and environmental parameters in the power grid, and construction of a real-time state vector S(t), where t is time; Set the warning threshold T. For key parameters such as current and voltage, the warning threshold T is determined based on the grid operation standards and historical data; Calculate the deviation Δ between the real-time state vector S(t) and the warning threshold T = |S(t)-T|; Once the deviation Δ is found to exceed the preset tolerance δ, that is, Δ>δ, the early warning mechanism is immediately triggered; According to the severity and type of deviation, the fault level is evaluated and detailed warning information is generated; Warning information is automatically sent to relevant personnel through the user interface or text messages, emails, etc., so that timely measures can be taken to deal with the fault.

6. The system according to claim 5, characterized in that The fault warning module also includes the following steps: After each warning, the deviation Δ is optimized using the following formula according to the feedback results of the warning information and the actual grid operation status: D opt =a*D prev +(1-a)*D actual Among them, Δ opt Represents the optimized deviation, Δ prev Indicates the deviation amount at the last warning, Δ actual It represents the actual deviation during this warning, α is the optimization coefficient, and its value range is 0≤α<1, which is used to control the weight of historical deviation and actual deviation in the optimization process; Collect historical warning data, including the deviation Δ, warning threshold T, deviation tolerance δ, and accuracy of warning results at each warning; Construct an optimization model, taking the accuracy of the warning results as the optimization target, and the warning threshold T and the deviation tolerance δ as the optimization variables; Solve the optimization model to obtain the optimal combination of warning threshold T and deviation tolerance δ; The optimized combination of T and δ is applied to the fault warning module to improve the accuracy and reliability of the warning; The steps of solving the optimization model are as follows: Initialize the particle swarm, where each particle represents a combination of a warning threshold T and a deviation tolerance δ, and set the initial velocity and position of the particle; Calculate the fitness value F of each particle, that is, evaluate its performance on historical warning data based on the combination of the current warning threshold T and the deviation tolerance δ; Update the individual optimal solution and the global optimal solution in the particle swarm. The individual optimal solution is the best historical position of the particle itself, and the global optimal solution is the best historical position of the entire particle swarm. According to the individual optimal solution and the global optimal solution, the speed and position of the particle are updated to make the particle move towards a better solution; Repeat the steps until the preset number of iterations is reached or the fitness value converges.

7. The system according to claim 1, characterized in that The report generation module can automatically generate a power grid operation report, the specific contents of which include: Overview of power grid operation status: summarize the overall operation status and key indicators of the current power grid; Fault warning analysis: detailed list of warning information, fault level, occurrence time and possible causes; Power allocation plan: Display the specific content and implementation effect of the optimal power allocation plan; Environmental parameter monitoring: record the temperature, humidity and other parameters of the power grid operating environment and their changing trends; Statistical chart display: intuitively display key data and change trends of power grid operation in the form of charts; Report export and printing: Supports exporting reports to PDF, Word and other formats, and provides printing function.

8. The system according to any one of claims 1 to 7, characterized in that It also includes a security protection module to strengthen system network security protection, ensure the security of data transmission and storage, and prevent data leakage and illegal access.

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