Electric power monitoring management method and system for cell reconstruction

Abstract

The invention relates to the technical field of power management, in particular to a power monitoring management method and system for cell transformation. The method comprises the following steps: acquiring infrastructure data of a community power system through a remote terminal unit and intelligent electronic equipment, wherein the infrastructure data comprises the model, layout, service life and maintenance record of existing power equipment; analyzing weak links and potential risk points of the power system on the infrastructure data to obtain power risk assessment data; performing power load simulation according to the infrastructure data and the power risk assessment data by using a power distribution automation system, and mapping power supply states and efficiencies under different conditions so as to obtain power system characteristic diagram data; and performing simulation analysis on the electric energy quality in different power supply states according to the electric power system characteristic diagram data so as to obtain electric energy quality simulation data. According to the invention, the power load is monitored and optimized in real time, so that the stability and reliability of power supply are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power management, and in particular to a power monitoring management method and system for community reconstruction. Background Art

[0002] Community renovation refers to a series of engineering and management measures to update, upgrade and optimize the built communities, including: updating and improving the infrastructure such as roads, drainage systems, power facilities, and communication facilities in the community to improve the overall function of the community and the quality of life of residents; including the maintenance or replacement of building components such as residential facades, roofs, waterproofing, heating, ventilation, gas pipelines, elevators, etc. to ensure the safety and comfort of the buildings; improving the environmental quality of the community and the living experience of residents by increasing green space, garden landscapes, fitness facilities, leisure and entertainment facilities, etc.; introducing intelligent management systems such as intelligent security, intelligent parking, and intelligent lighting to improve the management level and living convenience of the community. In the development of modern cities, community renovation has become an important means to improve the quality of life of residents and improve the efficiency of resource utilization. With the increase in electricity demand and the changes in complex power loads, traditional community power systems face many challenges, including aging power equipment, low power supply efficiency, frequent power quality problems, and increased frequency of power failures. These problems have seriously affected the daily life and power safety of community residents.

[0003] Traditional power monitoring and management methods usually rely on regular inspections and manual maintenance, which makes it difficult to timely discover and resolve weak links and potential risks in the power system. In addition, due to the lack of intelligent monitoring and control methods, traditional methods are insufficient in dealing with complex changes in the power system and optimizing resource allocation. This leads to problems such as difficulty in ensuring power supply quality, untimely fault response, and waste of power resources. Summary of the invention

[0004] Based on this, it is necessary for the present invention to provide a power monitoring management method and system for community reconstruction to solve at least one of the above technical problems.

[0005] To achieve the above purpose, a power monitoring and management method for community reconstruction includes the following steps:

[0006] Step S1: Obtain infrastructure data of the community power system through remote terminal units and intelligent electronic devices, including the model, layout, service life and maintenance records of existing power equipment; analyze the weak links and potential risk points of the power system based on the infrastructure data, so as to obtain power risk assessment data;

[0007] Step S2: using the distribution automation system to simulate the power load according to the infrastructure data and the power risk assessment data, and mapping the power supply status and efficiency under different conditions, so as to obtain the power system characteristic diagram data; simulating and analyzing the power quality under different power supply states according to the power system characteristic diagram data, so as to obtain the power quality simulation data; establishing the power system optimization model according to the process parameters corresponding to the different power supply states and the power quality simulation data;

[0008] Step S3: adjusting the control parameters in real time according to the preset power supply quality standard and the power system optimization model, and constructing a multi-level feedback control system according to the adjusted control parameters, thereby obtaining a power management model;

[0009] Step S4: acquiring power fault data under different operating conditions; performing fault attribution analysis on the power fault data and extracting mapping relationships to obtain fault attribution data; performing fault prediction based on the fault attribution data and the power management model, and minimizing fault risk acquisition through integrated optimization technology to obtain optimized operating window data;

[0010] Step S5: obtaining real-time data from smart meters and other monitoring devices in the community; constructing a power consumption behavior model based on the real-time data; analyzing the power consumption behavior model based on abnormal power consumption behavior and energy-saving conditions, thereby obtaining power consumption optimization data;

[0011] Step S6: Generate intelligent dispatching plan data based on the power consumption optimization data and the optimal operation window data to adjust the power supply parameters in real time to optimize load distribution.

[0012] The present invention obtains detailed power system infrastructure data through remote terminal units and intelligent devices to provide data support for comprehensive evaluation; analyzes the weak links and potential risk points of the power system, promptly discovers hidden dangers that may cause power system failures, and improves the safety of the system; provides data support to formulate preventive maintenance plans, reduce the occurrence of sudden failures, and improve the reliability and stability of the power system. Through load simulation, the power supply status and efficiency under different conditions are understood to provide a basis for optimizing power load distribution; simulates and analyzes power quality, identifies power quality problems in the power system, and helps take measures to improve power quality; the power system optimization model established based on power quality and power supply status helps to optimize the operation of the power system and improve system efficiency. According to the power supply quality standard and optimization model, the control parameters are adjusted in real time to ensure that the power system operates in the best state; a multi-level feedback control system is constructed to achieve refined management of the power system and improve response speed and control accuracy. Fault prediction is performed based on historical fault data and management models to prevent potential faults; integrated optimization technology is used to minimize fault risks, extend the service life of power equipment, and improve system stability. Using real-time data from smart meters and monitoring equipment, we can accurately monitor electricity usage in the community and detect abnormal electricity usage. We can analyze energy-saving potential based on electricity behavior models, provide energy-saving suggestions to residents and managers, and reduce electricity consumption and costs. We can generate intelligent scheduling plans, adjust power supply parameters in real time, optimize load distribution, and ensure efficient operation of the power system. Through intelligent scheduling and optimized load distribution, we can improve the energy efficiency of the power system, reduce electricity costs, and reduce energy consumption.

[0013] Preferably, step S3 comprises the following steps:

[0014] Step S31: constructing a multi-objective optimization function according to a preset power supply quality standard and a power system optimization model to obtain a power supply quality optimization target model;

[0015] Step S32: extract key control parameters of the power system optimization model, establish parameter-target mapping relationship, and obtain control parameter influence matrix; wherein the key control parameters include transformer voltage regulation parameters, reactive power compensation device parameters and switch device status;

[0016] Step S33: performing multi-objective optimization solution according to the power quality optimization target model and the control parameter influence matrix to obtain an optimal control parameter set; dynamically adjusting the optimal control parameter set according to the real-time operation data set to obtain a dynamic control parameter adjustment strategy;

[0017] Step S34: designing a hierarchical distributed control architecture according to the dynamic control parameter adjustment strategy to obtain multi-level control system structure data, wherein the multi-level control system structure data includes control layers at the system level, the regional level, and the device level; designing a control system communication protocol and a data exchange mechanism for each control layer in the multi-level control system structure data to obtain a control system communication solution;

[0018] Step S35: performing control logic analysis on each control layer in the multi-level control system structure data, and performing local optimization and global coordination, thereby obtaining a multi-level control algorithm library;

[0019] Step S36: Systematically integrate the multi-level control algorithm library, the control system communication scheme, and the multi-level control system structure data to construct a complete multi-level feedback control system, thereby obtaining a power management model.

[0020] The present invention can optimize multiple power supply quality indicators, such as voltage stability, power factor, harmonic distortion, etc., by constructing a multi-objective optimization function to ensure the overall performance of the power supply system; setting clear power supply quality optimization goals is helpful to guide the subsequent optimization process and improve the power quality and reliability of the system; by weighing the priorities of different goals, it is ensured that resources are reasonably allocated to maximize benefits. Extracting key control parameters such as transformer voltage regulation parameters, reactive power compensation device parameters, etc. helps to clarify which parameters have the greatest impact on power supply quality; establishing a parameter-target mapping relationship makes the optimization process more accurate and targeted, which helps to improve the optimization effect; through the control parameter influence matrix, the problem area in the power system can be quickly identified and located, and targeted adjustments and optimizations can be made. Through multi-objective optimization solution, the optimal control parameter set is obtained to provide the best operating state for the system; the dynamic adjustment strategy can adjust the control parameters in a timely manner according to the real-time operating data set to ensure the best performance of the system under different operating conditions; this dynamic adjustment capability enables the system to respond quickly to changes in the external environment and improve the flexibility and adaptability of the power system. The design of hierarchical distributed control architecture can realize multi-level management at system level, regional level and equipment level, and optimize the execution efficiency of control strategy; the designed control system communication scheme ensures unimpeded information flow between layers, which is conducive to the efficient transmission and processing of real-time data. The coordination and integration of control systems at all levels realize the overall optimization of the system and improve the intelligent level of power management. The multi-level control algorithm library combines local optimization and global coordination, so that the system can achieve optimal control in a variety of scenarios; it provides flexible control logic and algorithms, so that the system can adapt to various complex operating environments and load changes; through the application of multi-level control strategies, the robustness and stability of the system are improved to deal with emergencies and failures. Integrating the multi-level control algorithm library, control system communication scheme, etc. into a unified system simplifies management and maintenance and improves the overall efficiency of the system; the constructed power management model provides a comprehensive system view, thereby realizing all-round power management and optimization; through the construction of a multi-level feedback control system, the automation and intelligence level of the power system is improved, and the refinement of power management is realized. These steps combine advanced technologies such as multi-objective optimization, dynamic adjustment and hierarchical distributed control, which significantly improves the power supply quality, operation efficiency and reliability of the power system. Through system integration and comprehensive management, refined and intelligent control of the power system is achieved, providing solid technical support for community transformation and power system optimization.

[0021] Preferably, step S4 comprises the following steps:

[0022] Step S41: Acquire power fault data under different operating conditions;

[0023] Step S42: using the distribution automation system and the intelligent power equipment to collect the operating parameters of the power system when the fault occurs in real time according to the power fault data, thereby obtaining the fault operating parameter data;

[0024] Step S43: extracting fault features from the fault operation parameter data to obtain a fault feature vector; classifying the fault instance according to the fault feature vector to obtain power fault type data, wherein the power fault type data includes equipment fault data, line fault data and system fault data;

[0025] Step S44: constructing a fault causal network describing the correlation relationship and propagation path between faults according to the power fault data and preset expert knowledge, thereby generating a fault propagation model;

[0026] Step S45: performing fault root identification and influencing factor extraction according to the power fault type data and the fault propagation model, thereby obtaining fault attribution data;

[0027] Step S46: Fault prediction is performed based on the fault attribution data and the power management model, and the fault risk is minimized through integrated optimization technology to obtain the optimized operation window data.

[0028] The present invention obtains power fault data under different operating conditions, which can cover various possible fault conditions and provide rich data support for subsequent fault analysis and processing; through the accumulation of historical fault data, the weak links of the system can be discovered, preventive maintenance can be carried out in advance, and the probability of fault occurrence can be reduced; collecting and analyzing fault data in advance can help to quickly identify the type and scope of the fault and improve the speed of fault response and repair. Through the real-time data acquisition of the distribution automation system and intelligent power equipment, the operating status when the fault occurs can be monitored in real time to understand the specific characteristics and impact of the fault; the acquisition of real-time operating parameter data makes it possible to accurately locate the fault, which helps to take measures quickly and reduce the impact range of the fault; the data collected in real time is more timely and accurate, providing a reliable data basis for fault analysis. By extracting fault feature vectors, the fault analysis process can be standardized and the accuracy and consistency of fault classification can be improved; fault type classification helps to quickly identify equipment faults, line faults or system faults, which is convenient for taking targeted countermeasures. Detailed fault type data provides a basis for fault management and helps to formulate fault handling priorities and strategies. Constructing a fault causal network can analyze the correlation between different faults and reveal the propagation path and impact range of the fault. Through the fault propagation model, the potential impact of the fault can be predicted, countermeasures can be taken in advance, and the fault risk can be reduced. The fault causal network and propagation model provide decision makers with a panoramic view of fault propagation, which helps to formulate a more scientific fault management strategy. Through fault attribution analysis, the root cause of the fault can be accurately located, which is convenient for taking effective repair measures. By identifying the main factors affecting the fault, targeted improvements and optimizations can be made to reduce the probability of similar faults. Fault root cause identification helps to improve the overall reliability and stability of the system and reduce system downtime. Through fault prediction, possible faults can be predicted in advance, preventive measures can be taken, and the impact of faults on the system can be reduced. Through the determination of the optimal operation window, the operating parameters of the system can be optimized under the premise of minimizing the risk of faults, and the operating efficiency and stability of the system can be improved. Predicting and minimizing the risk of faults in advance can help reduce the downtime and repair costs caused by faults and improve operational efficiency. These steps form a complete power system fault management process through real-time data collection, fault feature extraction, causal analysis and fault prediction. Through systematic fault analysis and optimization strategies, the reliability and stability of the power system can be effectively improved, the frequency and impact of faults can be reduced, and operation and maintenance costs can be reduced, providing a solid guarantee for community transformation and the long-term healthy operation of the power system.

[0029] Preferably, step S46 includes the following steps:

[0030] Step S461: Map the power system topology, equipment status, and operating parameters into a graph structure according to the power management model using graph neural network technology, thereby building a dynamic fault prediction model;

[0031] Step S462: Perform rolling prediction based on the dynamic fault prediction model and fault attribution data to generate a fault probability distribution within a future period of time, thereby obtaining a fault risk prediction map;

[0032] Step S463: Establishing a fault risk assessment system based on the fault probability, impact range and severity according to the fault risk prediction graph by using a multi-criteria decision-making method, thereby obtaining a risk assessment matrix;

[0033] Step S464: optimizing the system operating parameters with the goal of minimizing the overall failure risk according to the risk assessment matrix and the current operating parameters of the power system, thereby obtaining a risk minimization strategy;

[0034] Step S465: Simulate and verify the risk minimization strategy based on the digital twin model of the power system, and evaluate the effectiveness and feasibility of the strategy to obtain the optimized operation window data.

[0035] The present invention maps the topological structure, equipment status and operating parameters of the power system into the graph structure through the graph neural network technology, which can effectively capture the complex relationship and dynamic characteristics of the system; the graph neural network can process dynamically changing data, is suitable for real-time updated fault prediction models, and improves the accuracy and timeliness of predictions; combined with the rich data of the power management model, the dynamic fault prediction model can identify potential fault trends and associations, and improve the prediction ability of the overall system. The fault risk prediction map generated by rolling prediction can warn of potential fault risks in advance, giving maintenance and operating personnel more response time; the prediction map provides the probability distribution of faults in the future, which helps to understand the fault risk level of different equipment or areas and carry out targeted management; by understanding the distribution of fault risks, maintenance resources can be allocated more reasonably, focusing on high-risk areas and optimizing resource utilization. The multi-criteria decision-making method takes into account factors such as the probability of fault occurrence, the scope of impact and the severity, and establishes a comprehensive fault risk assessment system; the risk assessment matrix can refine different fault risks to specific equipment and areas, helping decision makers to carry out refined risk management; the risk assessment system provides a scientific basis for formulating countermeasures, supporting decision-making and priority division. The optimization of operating parameters with the goal of minimizing the overall failure risk can help reduce the overall failure risk of the system and improve the system stability and reliability; the optimized system operating parameters can improve the system's operating efficiency, reduce energy loss and operating costs; according to the real-time operating parameters and risk assessment matrix, the system can be dynamically adjusted to improve its ability to respond to sudden failures. Through the simulation verification of the digital twin model of the power system, the effectiveness of the risk minimization strategy can be verified to ensure that the strategy is feasible in practical applications; simulation verification can also evaluate the feasibility of the strategy, including its impact on system operation, resource requirements, etc., to ensure that the implementation of the strategy will not cause new problems; according to the simulation results, the strategy is adjusted and optimized to make it more in line with actual needs and improve the system's fault prevention and response capabilities. These steps form a comprehensive fault prediction and management system through advanced technologies such as graph neural network technology, rolling prediction, and multi-criteria decision-making methods; the system can continuously optimize operating parameters in a dynamic environment to reduce the probability and impact of failures; through simulation verification and evaluation, the effectiveness and feasibility of the strategy are ensured, providing a reliable fault management and optimization means for the community power system, and improving the overall operation efficiency and stability of the system.

[0036] The present invention also provides a power monitoring and management system for cell reconstruction, which is used to execute the above-mentioned power monitoring and management method for cell reconstruction. The power monitoring and management system for cell reconstruction comprises:

[0037] The infrastructure assessment module is used to obtain the infrastructure data of the community power system through remote terminal units and intelligent electronic devices, including the model, layout, service life and maintenance records of existing power equipment; analyze the weak links and potential risk points of the power system based on the infrastructure data, so as to obtain power risk assessment data;

[0038] The load simulation optimization module is used to simulate the power load based on the infrastructure data and power risk assessment data using the distribution automation system, and map the power supply status and efficiency under different conditions to obtain the power system characteristic diagram data; simulate and analyze the power quality under different power supply states based on the power system characteristic diagram data to obtain power quality simulation data; establish a power system optimization model based on the process parameters corresponding to different power supply states and the power quality simulation data;

[0039] A dynamic control adjustment module is used to adjust the control parameters in real time according to the preset power supply quality standards and the power system optimization model, and to construct a multi-level feedback control system according to the adjusted control parameters to obtain a power management model;

[0040] The fault prediction and prevention module is used to obtain power fault data under different operating conditions; perform fault attribution analysis on the power fault data and extract mapping relationships to obtain fault attribution data; perform fault prediction based on the fault attribution data and the power management model, and minimize fault risk acquisition through integrated optimization technology to obtain optimized operating window data;

[0041] The power consumption behavior analysis module is used to obtain real-time data from smart meters and other monitoring equipment in the community; build a power consumption behavior model based on real-time data; analyze the power consumption behavior model based on abnormal power consumption behavior and energy-saving conditions to obtain power consumption optimization data;

[0042] The intelligent scheduling execution module is used to generate intelligent scheduling plan data based on power consumption optimization data and optimal operation window data, so as to adjust power supply parameters in real time to optimize load distribution.

[0043] The present invention can fully grasp the current status of the power system by collecting and analyzing equipment models, layouts, service life and maintenance records, which provides basic data for subsequent risk assessment and optimization; by analyzing infrastructure data, it can identify weak links and potential risk points in the system, help prevent possible problems in advance, and reduce the probability of failures; obtaining power risk assessment data is helpful to formulate effective maintenance plans and upgrade strategies, and optimize the safety and reliability of the power system. By simulating loads under different conditions, it is possible to understand the performance of the power system under various load conditions, optimize the power supply status and efficiency; simulate and analyze the power quality, help identify the impact of the power supply status on the power quality, and ensure that the power supply quality meets the standards; establish a power system optimization model based on simulation data and process parameters, provide a scientific basis for subsequent dynamic control and optimization, and improve the overall performance of the system. Real-time adjustment of control parameters can effectively respond to various changes in the power system and ensure system stability and reliability; through a multi-level feedback control system, the power system can be hierarchically managed, the system performance can be optimized, and the power supply quality can be improved; the constructed power management model helps to comprehensively manage the power system, dynamically adjust and optimize, and improve power supply efficiency and reliability. Real-time acquisition of power fault data can timely understand the system's operating status and fault occurrence; analyzing the root causes and influencing factors of the fault can help formulate effective fault prevention and repair strategies; through fault prediction and optimization technology, potential faults can be identified in advance, the impact of faults on the system can be reduced, and the reliability and stability of the system can be improved. Through real-time data analysis of users' power consumption behavior, it helps to understand power consumption patterns and changes; identifying abnormal power consumption behavior and energy-saving opportunities, helping to formulate energy-saving strategies and optimize power consumption plans; providing power consumption optimization data can help reduce energy waste, reduce electricity costs, and improve energy utilization efficiency. Generating a scheduling plan based on the optimized data can dynamically adjust power supply parameters and optimize load distribution; real-time adjustment of power supply parameters can cope with fluctuations in power demand, optimize the allocation of system resources, and improve the flexibility and response speed of the power system; improve power supply efficiency, ensure that the system can operate stably under various load conditions, and improve users' power consumption experience and the overall efficiency of the system. These modules work together to significantly improve the management and optimization level of the community power system through comprehensive data collection, accurate load simulation, dynamic control adjustment, effective fault prediction and prevention, in-depth power consumption behavior analysis, and intelligent scheduling execution. This not only improves the stability and reliability of the power system, but also optimizes energy utilization efficiency, reduces operating costs and improves user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0045] Figure 1A schematic diagram of the steps of the power monitoring and management method for community reconstruction according to the present invention;

[0046] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0047] Figure 3 for Figure 1 Detailed step flow chart of step S2 in FIG. DETAILED DESCRIPTION

[0048] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0049] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0050] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0051] To achieve this, please refer to Figures 1 to 3 The present invention provides a power monitoring and management method for cell reconstruction, the method comprising the following steps:

[0052] Step S1: Obtain infrastructure data of the community power system through remote terminal units and intelligent electronic devices, including the model, layout, service life and maintenance records of existing power equipment; analyze the weak links and potential risk points of the power system based on the infrastructure data, so as to obtain power risk assessment data;

[0053] Step S2: using the distribution automation system to simulate the power load according to the infrastructure data and the power risk assessment data, and mapping the power supply status and efficiency under different conditions, so as to obtain the power system characteristic diagram data; simulating and analyzing the power quality under different power supply states according to the power system characteristic diagram data, so as to obtain the power quality simulation data; establishing the power system optimization model according to the process parameters corresponding to the different power supply states and the power quality simulation data;

[0054] Step S3: adjusting the control parameters in real time according to the preset power supply quality standard and the power system optimization model, and constructing a multi-level feedback control system according to the adjusted control parameters, thereby obtaining a power management model;

[0055] Step S4: acquiring power fault data under different operating conditions; performing fault attribution analysis on the power fault data and extracting mapping relationships to obtain fault attribution data; performing fault prediction based on the fault attribution data and the power management model, and minimizing fault risk acquisition through integrated optimization technology to obtain optimized operating window data;

[0056] Step S5: obtaining real-time data from smart meters and other monitoring devices in the community; constructing a power consumption behavior model based on the real-time data; analyzing the power consumption behavior model based on abnormal power consumption behavior and energy-saving conditions, thereby obtaining power consumption optimization data;

[0057] Step S6: Generate intelligent dispatching plan data based on the power consumption optimization data and the optimal operation window data to adjust the power supply parameters in real time to optimize load distribution.

[0058] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a power monitoring management method for community reconstruction of the present invention. In this example, the power monitoring management method for community reconstruction includes the following steps:

[0059] Step S1: Obtain infrastructure data of the community power system through remote terminal units and intelligent electronic devices, including the model, layout, service life and maintenance records of existing power equipment; analyze the weak links and potential risk points of the power system based on the infrastructure data, so as to obtain power risk assessment data;

[0060] The embodiments of the present invention deploy remote terminal units (RTUs) and intelligent electronic devices, such as smart meters and sensors, to automatically collect the model, layout, age and maintenance records of power equipment; use data analysis software, such as power system analysis tools, to identify weak links and potential risk points in the power system; and formulate a power risk assessment report based on the collected data and analysis results, indicating possible risk areas and levels.

[0061] Step S2: using the distribution automation system to simulate the power load according to the infrastructure data and the power risk assessment data, and mapping the power supply status and efficiency under different conditions, so as to obtain the power system characteristic diagram data; simulating and analyzing the power quality under different power supply states according to the power system characteristic diagram data, so as to obtain the power quality simulation data; establishing the power system optimization model according to the process parameters corresponding to the different power supply states and the power quality simulation data;

[0062] The embodiment of the present invention uses a distribution automation system to perform load simulation, inputs infrastructure data and risk assessment data as simulation parameters; creates a mapping relationship between power supply status and efficiency, simulates power system characteristics under different conditions, and generates characteristic diagram data; uses the characteristic diagram data to simulate and analyze the power quality under different power supply states, such as voltage fluctuations, frequency deviations, etc.; and establishes a power system optimization model based on the simulation results and process parameters to guide system adjustments and improvements.

[0063] Step S3: adjusting the control parameters in real time according to the preset power supply quality standard and the power system optimization model, and constructing a multi-level feedback control system according to the adjusted control parameters, thereby obtaining a power management model;

[0064] The embodiment of the present invention uses control software to adjust the control parameters of the power system in real time according to the power supply quality standard and the optimization model; constructs a multi-level feedback control system to ensure that the adjustment of the control parameters can respond to system changes in a timely manner; forms a power management model, integrates the control parameters and the feedback mechanism, so as to achieve stable operation of the power system.

[0065] Step S4: acquiring power fault data under different operating conditions; performing fault attribution analysis on the power fault data and extracting mapping relationships to obtain fault attribution data; performing fault prediction based on the fault attribution data and the power management model, and minimizing fault risk acquisition through integrated optimization technology to obtain optimized operating window data;

[0066] The embodiment of the present invention collects power fault data under different operating conditions, and uses a fault analysis tool to perform attribution analysis; identifies fault causes, extracts mapping relationships, and forms fault attribution data; combines fault attribution data with a power management model, and uses a prediction algorithm to perform fault prediction; uses integrated optimization techniques, such as a genetic algorithm or a simulated annealing algorithm, to minimize fault risks and obtain optimized operating window data.

[0067] Step S5: obtaining real-time data from smart meters and other monitoring devices in the community; constructing a power consumption behavior model based on the real-time data; analyzing the power consumption behavior model based on abnormal power consumption behavior and energy-saving conditions, thereby obtaining power consumption optimization data;

[0068] The embodiment of the present invention collects real-time electricity consumption data through smart meters and monitoring equipment, and transmits it to a central data processing platform; uses statistical analysis and machine learning technology to build an electricity consumption behavior model to analyze the electricity consumption patterns of residential and commercial users; analyzes the electricity consumption behavior model, identifies abnormal electricity consumption behavior and energy-saving potential, and forms electricity consumption optimization data.

[0069] Step S6: Generate intelligent dispatching plan data based on the power consumption optimization data and the optimal operation window data to adjust the power supply parameters in real time to optimize load distribution.

[0070] The embodiment of the present invention combines power consumption optimization data and optimized operation window data, and uses an intelligent scheduling algorithm to generate a scheduling plan; according to the scheduling plan, the power supply parameters are adjusted in real time, the power load distribution is optimized, and the efficiency and stability of the power supply are ensured; the intelligent scheduling plan is implemented through an automated control system to achieve dynamic management and optimization of the power system.

[0071] The present invention obtains detailed power system infrastructure data through remote terminal units and intelligent devices to provide data support for comprehensive evaluation; analyzes the weak links and potential risk points of the power system, promptly discovers hidden dangers that may cause power system failures, and improves the safety of the system; provides data support to formulate preventive maintenance plans, reduce the occurrence of sudden failures, and improve the reliability and stability of the power system. Through load simulation, the power supply status and efficiency under different conditions are understood to provide a basis for optimizing power load distribution; simulates and analyzes power quality, identifies power quality problems in the power system, and helps take measures to improve power quality; the power system optimization model established based on power quality and power supply status helps to optimize the operation of the power system and improve system efficiency. According to the power supply quality standard and optimization model, the control parameters are adjusted in real time to ensure that the power system operates in the best state; a multi-level feedback control system is constructed to achieve refined management of the power system and improve response speed and control accuracy. Fault prediction is performed based on historical fault data and management models to prevent potential faults; integrated optimization technology is used to minimize fault risks, extend the service life of power equipment, and improve system stability. Using real-time data from smart meters and monitoring equipment, we can accurately monitor electricity usage in the community and detect abnormal electricity usage. We can analyze energy-saving potential based on electricity behavior models, provide energy-saving suggestions to residents and managers, and reduce electricity consumption and costs. We can generate intelligent scheduling plans, adjust power supply parameters in real time, optimize load distribution, and ensure efficient operation of the power system. Through intelligent scheduling and optimized load distribution, we can improve the energy efficiency of the power system, reduce electricity costs, and reduce energy consumption.

[0072] Preferably, step S1 comprises the following steps:

[0073] Step S11: configuring the acquisition parameters of the remote terminal unit and the intelligent electronic device, and establishing a data acquisition network, thereby obtaining a data acquisition system configuration plan;

[0074] Step S12: collecting real-time operation information of power equipment according to the data acquisition system configuration plan, and collecting power consumption information of smart meters, so as to obtain a real-time operation data set;

[0075] Step S13: Obtain the model, specification and rated capacity information of the power equipment, and enter the installation date, service life, installation location and spatial layout information of the equipment, so as to obtain a static information data set of the equipment;

[0076] Step S14: Obtain historical maintenance record data of the equipment; and perform format unification and standardization processing on the historical maintenance record data, the real-time operation data set, and the equipment static information data set, so as to obtain infrastructure data;

[0077] Step S15: Analyze the weak links and potential risk points of the power system based on the infrastructure data to obtain power risk assessment data.

[0078] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in the embodiment of the present invention, step S1 includes the following steps:

[0079] Step S11: configuring the acquisition parameters of the remote terminal unit and the intelligent electronic device, and establishing a data acquisition network, thereby obtaining a data acquisition system configuration plan;

[0080] The embodiment of the present invention selects suitable remote terminal units (RTUs) and intelligent electronic devices, such as smart meters and sensors. Determine the parameters to be collected, such as current, voltage, power, temperature, etc. Design and establish a data acquisition network, which may include wireless networks, Ethernet or other communication technologies to ensure that data can be transmitted in real time and stably. Develop a detailed data acquisition system configuration plan, including equipment selection, network architecture, data transmission protocol, security measures, etc.

[0081] Step S12: collecting real-time operation information of power equipment according to the data acquisition system configuration plan, and collecting power consumption information of smart meters, so as to obtain a real-time operation data set;

[0082] According to the configuration scheme, the embodiment of the present invention deploys data acquisition equipment and starts the data acquisition process to collect real-time operating information of power equipment, such as load, current, voltage, etc., and collects power consumption information of smart meters; the collected data is organized into a real-time operating data set to provide raw data for subsequent analysis.

[0083] Step S13: Obtain the model, specification and rated capacity information of the power equipment, and enter the installation date, service life, installation location and spatial layout information of the equipment, so as to obtain a static information data set of the equipment;

[0084] The embodiment of the present invention collects static information such as the model, specification, rated capacity, etc. of the power equipment; records detailed information such as the installation date, service life, installation location, and spatial layout of the equipment; and organizes the collected static information into a device static information data set to provide basic data for a comprehensive evaluation of the power system.

[0085] Step S14: Obtain historical maintenance record data of the equipment; and perform format unification and standardization processing on the historical maintenance record data, the real-time operation data set, and the equipment static information data set, so as to obtain infrastructure data;

[0086] The embodiment of the present invention collects historical maintenance records of equipment, which may include information such as maintenance date, maintenance content, and replaced parts; integrates historical maintenance record data with real-time operation data sets and equipment static information data sets, and unifies and standardizes the format of the integrated data to facilitate data analysis and processing.

[0087] Step S15: Analyze the weak links and potential risk points of the power system based on the infrastructure data to obtain power risk assessment data.

[0088] The embodiments of the present invention use data analysis tools or software to conduct in-depth analysis of infrastructure data to identify weak links and potential risk points in the power system; apply statistical analysis, machine learning and other methods to evaluate the risk levels of different equipment and system parts; based on the analysis results, form power risk assessment data to provide decision support for the optimization and maintenance of the power system.

[0089] The present invention establishes a unified and systematic data acquisition network by configuring the acquisition parameters of remote terminal units and intelligent electronic devices; through a unified configuration scheme, the integrity and consistency of data acquisition are ensured, providing a reliable data basis for subsequent analysis; the configured acquisition system can monitor the operating status of power equipment in real time, promptly discover and respond to abnormal situations, and improve the safety of the system. Collect the real-time operation information of power equipment and the power consumption information of smart meters to obtain accurate real-time operation data, covering the whole process monitoring from power equipment to end users, ensuring the comprehensiveness and coverage of data; provide detailed power consumption information, help identify abnormal power consumption behavior, and provide data support for energy efficiency optimization and energy-saving measures. Obtain and record static information such as equipment model, specification, rated capacity, etc., establish equipment files, and facilitate equipment management and maintenance; record the installation date, service life, installation location and spatial layout of the equipment, which is helpful for planning equipment updates and optimizing layouts. The static information data set provides a basis for historical tracking and life cycle management of equipment, and supports preventive maintenance and planning. The data from different sources are formatted and standardized to ensure data consistency and comparability, and real-time operation data, equipment static information and historical maintenance records are integrated into a unified data set to provide comprehensive background information for subsequent analysis; standardized data provides a reliable data basis for the analysis of weak links and potential risk points in the power system. By analyzing infrastructure data, weak links in the power system, such as aging equipment or overloaded areas, are identified; potential risk points, such as insufficient maintenance or aging equipment, are identified, and early warning information is provided to help take timely measures to reduce risks; risk assessment data helps optimize the operation of the power system, improve the safety and reliability of the system, and reduce the possibility of failures.

[0090] Preferably, step S15 comprises the following steps:

[0091] Step S151: evaluating the remaining service life of existing power equipment based on equipment static information and maintenance records according to infrastructure data, thereby obtaining equipment health status data;

[0092] The embodiment of the present invention collects static information of the equipment, such as model, specification, rated capacity, installation date, service life, and obtains the historical maintenance record of the equipment, including maintenance date, maintenance content, replacement parts, etc.; uses predictive maintenance algorithms or expert systems, combined with equipment type, service life, and maintenance records, to evaluate the remaining service life of the equipment. Based on the evaluation results, equipment health status data is generated to reflect the current status and expected life of the equipment.

[0093] Step S152: Perform real-time load distribution analysis in each area based on the real-time status of equipment operation in the infrastructure data, and identify load concentration areas and potential overload points, thereby obtaining a real-time load distribution heat map;

[0094] The embodiment of the present invention collects the operating status data of power equipment in real time through smart meters and sensors, and uses data visualization tools, such as GIS system or heat map software, to display the power load in different areas; analyzes the real-time load data of each area, identifies load concentration areas and potential overload points, generates a real-time load distribution heat map, and intuitively displays the distribution of power load.

[0095] Step S153: extracting equipment layout and connection relationships based on infrastructure data, annotating equipment nodes and power supply paths, and constructing a digital topology model of the community power system, thereby obtaining a power system topology model;

[0096] The embodiment of the present invention collects the layout information and connection relationship of the equipment, including the equipment installation location, spatial layout and interconnection mode, and uses CAD software or professional power system design tools to draw the physical layout diagram of the power system. The physical layout diagram is converted into a digital topology model, and the equipment nodes and power supply paths are marked; a digital topology model of the community power system is constructed to provide a basis for system analysis and optimization.

[0097] Step S154: Identify weak links in the power system topology model based on the equipment health status data and the real-time load distribution heat map, and evaluate the risk level of the weak links, thereby obtaining a list of weak links and their corresponding risk level data;

[0098] The embodiment of the present invention combines equipment health status data and real-time load distribution heat map to analyze the power system topology model; use risk assessment tools or algorithms to identify the weak links of the system. Based on the analysis results, a list of weak links is generated, listing the key equipment or paths that may affect the stability of the system; the risk level of the weak links is assessed to generate corresponding risk level data.

[0099] Step S155: simulating the fault propagation path of the power system topology model based on the weak link list, and evaluating the impact degree of each risk point according to the risk level data, so as to obtain potential risk point analysis data;

[0100] The embodiment of the present invention uses power system simulation software or network analysis tools to simulate the propagation path of faults in the power system; according to the weak link list, the fault propagation path under different fault conditions is simulated. According to the risk level data, the impact of each risk point is evaluated, and the key nodes that may cause system-level faults are identified.

[0101] Step S156: Perform an overall system risk index analysis based on the weak link list and potential risk point analysis data to generate power risk assessment data for each region and equipment type.

[0102] The embodiment of the present invention comprehensively considers the health status of equipment, load distribution, topology and fault propagation path to conduct overall risk assessment of the system; uses risk assessment models, such as hierarchical analysis method or fuzzy comprehensive evaluation method, to calculate the overall risk index of the power system. Generates power risk assessment data by region and equipment type to provide decision support for the optimization and maintenance of the power system; formulates corresponding preventive measures and emergency plans based on the risk assessment results to improve the system's risk resistance.

[0103] The present invention accurately evaluates the remaining service life of the equipment by analyzing the static information and maintenance records of the equipment, and provides a scientific basis for the equipment update and maintenance plan; provides the health status data of the equipment, which helps to timely discover and replace aging or failing equipment, and avoid the risks brought by equipment failure; the health status data of the equipment can help optimize the maintenance strategy, extend the life of the equipment and reduce the maintenance cost. Through the load distribution heat map, the real-time load distribution of each area is clearly displayed, which is convenient for monitoring and management; the load concentration area and potential overload points are identified, and early warning information is provided to help timely adjust the load distribution, avoid equipment overload and system instability, optimize resource allocation according to the load distribution data, and improve the operation efficiency of the power system. Through the digital topological model, the equipment layout and connection relationship of the community power system are fully displayed, and a clear system structure diagram is provided. The topological model helps to analyze the power transmission path and node connection, identify key nodes and possible bottlenecks; the topological model provides data support for the design optimization and expansion of the power system, and helps to improve the stability and efficiency of the system. Identifying the weak links in the power system and conducting risk level assessment on them helps to systematically manage and reduce risks; according to the risk level data, high-risk areas or equipment are given priority to ensure the safe operation of the system; targeted preventive measures are provided to reduce the possibility of sudden failures and improve the reliability of the system. Through fault propagation path simulation, we can understand the propagation path and impact range of potential faults, and provide reference for the formulation of emergency plans; evaluate the impact of each risk point, help prioritize high-impact risk points, optimize resource allocation and management, establish a risk early warning system, and identify and respond to potential major risk events in advance. Provide power risk assessment data by region and equipment type to fully understand the overall risk status of the system; risk assessment data provides scientific decision-making support for system maintenance, upgrades and emergency plans; through overall risk index analysis, formulate long-term system optimization and risk management strategies to improve system stability and reliability.

[0104] Preferably, step S2 comprises the following steps:

[0105] Step S21: constructing a digital twin model of the community power system based on the infrastructure data and the power risk assessment data, including equipment parameters, topology structure and operating status, to obtain a digital twin model of the power system;

[0106] Step S22: using the distribution automation system to perform statistical analysis on the pre-acquired historical load data, and identify the daily load curve and seasonal variation characteristics, thereby obtaining load characteristic data;

[0107] Step S23: constructing short-term and medium- and long-term load forecasting models according to the load characteristic data, thereby obtaining a load forecasting model;

[0108] Step S24: using the load forecasting model to simulate load changes in different scenarios according to the potential risk points in the power risk assessment data, thereby obtaining a multi-scenario load simulation data set;

[0109] Step S25: Perform power flow calculation and state estimation based on the power system digital twin model and the multi-scenario load simulation data set, and analyze the voltage distribution, power flow direction and equipment load rate under different load conditions to obtain system operation status data;

[0110] Step S26: performing power supply reliability evaluation on the system operation status data based on SAIDI and SAIFI index calculation, thereby obtaining reliability evaluation data;

[0111] Step S27: Analyze the loss of each power device and line according to the system operation status data, calculate the overall efficiency of the system, and identify the efficiency bottleneck, so as to obtain energy efficiency analysis data;

[0112] Step S28: integrating the system operation status data, reliability assessment data and energy efficiency analysis data, and generating characteristic diagrams reflecting load-voltage and load-loss relationships, thereby obtaining power system characteristic diagram data;

[0113] Step S29: simulate and analyze the power quality under different power supply states according to the power system characteristic diagram data to obtain power quality simulation data; establish a power system optimization model according to the process parameters corresponding to different power supply states and the power quality simulation data.

[0114] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S2 in the embodiment of the present invention, step S2 includes the following steps:

[0115] Step S21: constructing a digital twin model of the community power system based on the infrastructure data and the power risk assessment data, including equipment parameters, topology structure and operating status, to obtain a digital twin model of the power system;

[0116] The embodiment of the present invention uses CAD software and BIM technology to collect detailed parameters and spatial layout of power equipment; collects equipment operation status data, including current, voltage, temperature, etc., in real time through Internet of Things (IoT) devices; deploys a digital twin model on a cloud platform, and uses simulation software such as MATLAB / Simulink or DNV GL to create a virtual model; ensures synchronization between the digital twin model and the physical equipment to achieve real-time monitoring and simulation.

[0117] Step S22: using the distribution automation system to perform statistical analysis on the pre-acquired historical load data, and identify the daily load curve and seasonal variation characteristics, thereby obtaining load characteristic data;

[0118] The embodiment of the present invention exports historical load data from the distribution automation system and uses a database management system such as SQL to store it; applies statistical analysis software, such as Pandas and Statsmodels libraries in R or Python, to clean and analyze the data; and identifies patterns in daily load curves, such as peak and trough periods, and seasonal variation characteristics, such as increased load due to air conditioning use in summer.

[0119] Step S23: constructing short-term and medium- and long-term load forecasting models according to the load characteristic data, thereby obtaining a load forecasting model;

[0120] The embodiment of the present invention selects a suitable forecasting technology, such as ARIMA, neural network or random forest; uses historical load data to train the forecasting model, and evaluates the model performance through the cross-validation method; deploys the trained model to the real-time data processing platform to perform short-term and medium- to long-term load forecasting.

[0121] Step S24: using the load forecasting model to simulate load changes in different scenarios according to the potential risk points in the power risk assessment data, thereby obtaining a multi-scenario load simulation data set;

[0122] The embodiment of the present invention sets different simulation scenarios, such as normal operation, holidays, extreme weather, etc., according to the load forecasting model; uses simulation software to perform multi-scenario load simulation to evaluate the system response under different conditions; collects simulation data to form a multi-scenario load simulation data set to provide a basis for risk assessment and system optimization.

[0123] Step S25: Perform power flow calculation and state estimation based on the power system digital twin model and the multi-scenario load simulation data set, and analyze the voltage distribution, power flow direction and equipment load rate under different load conditions to obtain system operation status data;

[0124] The embodiments of the present invention utilize power system analysis software, such as PowerWorld or PSSE, to perform power flow calculation and state estimation; analyze power flow under different load conditions, evaluate voltage stability and equipment load conditions; identify potential overload and voltage instability areas, and provide data support for system adjustments and preventive measures.

[0125] Step S26: performing power supply reliability evaluation on the system operation status data based on SAIDI and SAIFI index calculation, thereby obtaining reliability evaluation data;

[0126] The embodiment of the present invention calculates the power outage duration and frequency index based on the system operation data; uses reliability assessment tools, such as CAISO's Reliability Standards, to assess power supply reliability; and identifies factors that affect reliability, such as equipment failure rate, load fluctuation, etc.

[0127] Step S27: Analyze the loss of each power device and line according to the system operation status data, calculate the overall efficiency of the system, and identify the efficiency bottleneck, so as to obtain energy efficiency analysis data;

[0128] The embodiment of the present invention collects loss data of power equipment, such as line loss, transformer loss, etc., and uses energy efficiency analysis software, such as EnergyPlus or eQUEST, to evaluate the overall energy efficiency of the system; identify energy efficiency bottlenecks, and propose improvement measures, such as replacing high-efficiency transformers, optimizing line layout, etc.

[0129] Step S28: integrating the system operation status data, reliability assessment data and energy efficiency analysis data, and generating characteristic diagrams reflecting load-voltage and load-loss relationships, thereby obtaining power system characteristic diagram data;

[0130] The embodiments of the present invention use data visualization tools, such as Tableau or Power BI, to integrate data from different sources; generate characteristic graphs, such as load-voltage distribution graphs and load-loss relationship graphs, to intuitively display system performance; and identify potential areas for system optimization through characteristic graph analysis.

[0131] Step S29: simulate and analyze the power quality under different power supply states according to the power system characteristic diagram data to obtain power quality simulation data; establish a power system optimization model according to the process parameters corresponding to different power supply states and the power quality simulation data.

[0132] The embodiments of the present invention utilize the power system characteristic diagram data to simulate the power quality under different power supply conditions, such as harmonic analysis and voltage sag; according to the simulation results, the operating parameters of the power system, such as reactive power compensation and load distribution, are adjusted; an optimization model is established, and an optimization algorithm, such as linear programming or genetic algorithm, is used to optimize the system.

[0133] The digital twin model of the present invention can accurately simulate the equipment parameters, topology and operating status of the actual power system, and provide a panoramic system view; through the digital twin model, the system status can be monitored in real time, and simulation tests of various scenarios can be performed to improve the management and optimization capabilities of the system. The model can be used to identify potential faults and risks in advance, and provide early warning and decision support. Through historical load data analysis, the daily load curve and seasonal variation characteristics can be identified to help understand the load pattern and changes in electricity demand; identifying load characteristic data helps to optimize the allocation and scheduling of power resources, improve system operation efficiency, provide reliable load characteristic data support, and improve the accuracy of subsequent load forecasting models. Constructing a load forecasting model can provide short-term and medium-term electricity demand forecasts, help formulate more accurate power supply plans and management strategies, and the forecasting model can identify potential load peaks, help formulate response strategies, reduce power supply risks, and optimize the scheduling of power generation and transmission and distribution resources through accurate load forecasting, thereby reducing operating costs. Through multi-scenario load simulation, the system performance under different load conditions can be evaluated, which helps to identify the vulnerability and potential risks of the system, and provides load change data under different scenarios, which is helpful for formulating emergency plans and optimizing emergency response capabilities. Based on the simulation data, optimization strategies can be formulated to improve the system's coping ability and flexibility. Power flow calculation and state estimation can provide information on voltage distribution, power flow and equipment load rate in the system, help identify system bottlenecks and optimize paths; analyze system stability under different load conditions, provide early warning information, prevent system instability or collapse, optimize power flow, and improve the overall efficiency of equipment and systems. Through the calculation of SAIDI (system average outage duration) and SAIFI (system average outage frequency index), the power supply reliability of the system is evaluated, reliability data support is provided, the quality of power supply services is improved, and the power outage time for users is reduced; fault management strategies are identified and improved to reduce the frequency and duration of power outages. Analyze the loss situation in the system operation state, identify the main energy efficiency bottlenecks, and provide energy-saving improvement suggestions; by reducing losses and optimizing energy efficiency, reduce operating costs, improve economic benefits, improve energy utilization efficiency, reduce energy waste, and contribute to environmental protection and sustainable development. The characteristic diagram clearly shows the relationship between load and voltage, load and loss, helping to understand the system characteristics and behavior patterns; providing intuitive data support for the optimization and dispatch of the power system, helping to formulate more effective strategies, providing a comprehensive data view, and supporting multi-dimensional analysis and decision-making. Through simulation and analysis of power quality under different power supply conditions, problems can be identified and improvement measures can be proposed to improve power quality; an optimization model can be established to help optimize the operating parameters of the power system under different conditions and improve overall efficiency and reliability; and optimization model data support can be provided to provide a scientific basis for management strategies.

[0134] Preferably, step S29 includes the following steps:

[0135] Step S291: using electromagnetic transient simulation software to perform simulation analysis of power quality indicators based on voltage deviation, harmonic distortion and three-phase imbalance under different power supply states according to the power system characteristic diagram data, thereby obtaining power quality simulation data;

[0136] The embodiment of the present invention selects electromagnetic transient simulation software, such as PSCAD, EMTDC or Simulink's PoweSys toolbox, and sets simulation parameters including system configuration, load level and power supply conditions according to the power system characteristic diagram data; runs the simulation software to simulate power quality indicators such as voltage deviation, harmonic distortion and three-phase imbalance under different power supply states, collects simulation results, and generates power quality simulation data to provide a basis for subsequent analysis.

[0137] Step S292: collecting the process parameters of transformer voltage regulation and reactive power compensation according to the power quality simulation data, and analyzing the correlation between the process parameters and the power supply status and power quality, so as to obtain a process parameter influence matrix;

[0138] The embodiment of the present invention collects the process parameters of transformer voltage regulation and reactive power compensation equipment, such as voltage regulation range, compensation capacity, etc.; analyzes the power quality simulation data to identify the key process parameters that affect the power quality. Use statistical analysis methods, such as regression analysis or principal component analysis, to study the correlation between process parameters and power supply status and power quality indicators; construct a process parameter influence matrix to quantify the degree of influence of each parameter on the power quality indicators.

[0139] Step S293: using the power system characteristic diagram data, the power quality simulation data and the process parameter influence matrix as inputs, training a preset agent control model based on a deep reinforcement learning algorithm, thereby obtaining a preliminary power system optimization model;

[0140] The embodiment of the present invention selects a deep reinforcement learning algorithm, such as a deep Q network (DQN), a policy gradient method or an actor-critic method; prepares a training environment, including a simulation platform and computing resources, such as GPU acceleration, and defines optimization objectives, such as minimizing power quality indicators, improving system efficiency or reducing losses; uses power system characteristic diagram data, power quality simulation data and process parameter influence matrix as input, trains an agent control model, and adjusts model parameters, such as learning rate, exploration rate, etc., to improve model performance.

[0141] Step S294: Use the Monte Carlo simulation method to conduct large-scale scenario testing on the preliminary power system optimization model, and perform model tuning to obtain the power system optimization model.

[0142] The embodiment of the present invention uses the Monte Carlo simulation method to conduct large-scale scenario testing on the preliminary power system optimization model; generates random disturbances to simulate various uncertain factors, such as load fluctuations, equipment failures, etc.; runs the optimization model to evaluate its performance and robustness in different scenarios; collects test results, analyzes the advantages and disadvantages of the model, and identifies improvement directions; adjusts the model structure or parameters, performs model tuning, and improves the accuracy and adaptability of the model; verifies the performance of the optimization model to ensure that it can operate effectively in the actual power system.

[0143] The present invention simulates and analyzes key power quality indicators such as voltage deviation, harmonic distortion and three-phase imbalance through electromagnetic transient simulation software, and provides a comprehensive power quality status; effectively identifies power quality problems in the system, such as voltage instability, harmonic pollution, etc., which helps to take targeted measures; provides data support and analysis results, helps to formulate strategies and measures to improve power quality, and improves power supply reliability and user satisfaction. Through the analysis of the correlation between process parameters and power supply status and power quality, a process parameter influence matrix is ​​established to reveal the specific impact of parameters such as transformer voltage regulation and reactive power compensation on power quality; the process parameter influence matrix provides a scientific basis for the optimization of the power system, and helps to formulate more accurate regulation and compensation strategies; by adjusting the process parameters, power quality problems can be effectively improved, such as reducing harmonic distortion, balancing three-phase current, etc., and improving the overall performance of the system. The agent control model based on the deep reinforcement learning algorithm is trained to enable it to autonomously learn and adapt to changes in different power supply states and process parameters, and realize intelligent optimization of the power system. The model can make adaptive adjustments based on real-time data and system changes to improve the stability and efficiency of the system; provide efficient decision support to help the system make the best response in various complex situations and improve the elasticity and robustness of the system. The preliminary power system optimization model is tested on a large scale through Monte Carlo simulation methods to evaluate the performance and stability of the model in different scenarios; the model is tuned according to the simulation results to improve the accuracy and robustness of the model and ensure its reliability and effectiveness in practical applications; the simulation and tuning process can identify potential risks and weaknesses, provide effective risk assessment and management strategies, and ensure the robustness of the system under abnormal and extreme conditions. These steps work together to achieve full-process management from power quality analysis to system optimization. Through the combination of simulation and deep learning, not only can various problems in the power system be accurately identified and analyzed, but also intelligent optimization solutions can be provided. Ultimately, these measures will greatly improve the operating efficiency, stability and power supply quality of the power system, while reducing operational risks and costs.

[0144] Preferably, step S3 comprises the following steps:

[0145] Step S31: constructing a multi-objective optimization function according to a preset power supply quality standard and a power system optimization model to obtain a power supply quality optimization target model;

[0146] The embodiment of the present invention collects historical power supply data, including voltage level, frequency stability, load fluctuation, etc.; sets thresholds of power supply quality indicators such as voltage deviation and frequency stability according to national or international standards; uses multi-objective optimization software, such as MATLAB's optimization toolbox, to define objective functions, such as minimizing the sum of squares of voltage deviations, frequency deviations, etc.; and assigns weights to each optimization target to reflect its importance in the overall power supply quality.

[0147] Step S32: extract key control parameters of the power system optimization model, establish parameter-target mapping relationship, and obtain control parameter influence matrix; wherein the key control parameters include transformer voltage regulation parameters, reactive power compensation device parameters and switch device status;

[0148] The embodiment of the present invention identifies key control parameters that affect power supply quality, such as transformer tap position and switching state of reactive compensation device; uses design of experiment (DOE) method, such as orthogonal experimental design, to analyze the influence of each parameter on power supply quality index; constructs the relationship matrix between control parameters and power supply quality index according to the experimental results, and uses statistical software such as SPSS or R to perform data analysis.

[0149] Step S33: performing multi-objective optimization solution according to the power quality optimization target model and the control parameter influence matrix to obtain an optimal control parameter set; dynamically adjusting the optimal control parameter set according to the real-time operation data set to obtain a dynamic control parameter adjustment strategy;

[0150] The embodiment of the present invention selects a suitable multi-objective optimization algorithm, such as NSGA-II, to solve the problem, runs the optimization algorithm on a high-performance computing platform, and obtains an optimal parameter set; develops a real-time monitoring system to collect real-time data such as load and voltage, and dynamically adjusts control parameters based on these data.

[0151] Step S34: designing a hierarchical distributed control architecture according to the dynamic control parameter adjustment strategy to obtain multi-level control system structure data, wherein the multi-level control system structure data includes control layers at the system level, the regional level, and the device level; designing a control system communication protocol and a data exchange mechanism for each control layer in the multi-level control system structure data to obtain a control system communication solution;

[0152] The embodiment of the present invention designs a hierarchical control architecture including system level, regional level and device level; formulates communication protocols between various control layers, such as Modbus and IEC 61850, and designs data exchange formats and processes to ensure accurate transmission of information.

[0153] Step S35: performing control logic analysis on each control layer in the multi-level control system structure data, and performing local optimization and global coordination, thereby obtaining a multi-level control algorithm library;

[0154] The embodiments of the present invention develop corresponding control algorithms for different control layers, such as a local voltage control algorithm at the device level; ensure that the local optimization algorithm is consistent with the global optimization goal to avoid conflicts during the optimization process; and integrate the developed algorithms into an algorithm library for easy calling and updating.

[0155] Step S36: Systematically integrate the multi-level control algorithm library, the control system communication scheme, and the multi-level control system structure data to construct a complete multi-level feedback control system, thereby obtaining a power management model.

[0156] The embodiment of the present invention integrates the control algorithm library, communication scheme and control architecture into a unified platform; develops a user interface to provide system status monitoring, parameter configuration and optimization result display; tests the performance of the control system in a simulated environment and actual scenarios to ensure stability and reliability; and deploys the tested control system to the actual power system to perform real-time power supply quality management.

[0157] The present invention can optimize multiple power supply quality indicators, such as voltage stability, power factor, harmonic distortion, etc., by constructing a multi-objective optimization function to ensure the overall performance of the power supply system; setting clear power supply quality optimization goals is helpful to guide the subsequent optimization process and improve the power quality and reliability of the system; by weighing the priorities of different goals, it is ensured that resources are reasonably allocated to maximize benefits. Extracting key control parameters such as transformer voltage regulation parameters, reactive power compensation device parameters, etc. helps to clarify which parameters have the greatest impact on power supply quality; establishing a parameter-target mapping relationship makes the optimization process more accurate and targeted, which helps to improve the optimization effect; through the control parameter influence matrix, the problem area in the power system can be quickly identified and located, and targeted adjustments and optimizations can be made. Through multi-objective optimization solution, the optimal control parameter set is obtained to provide the best operating state for the system; the dynamic adjustment strategy can adjust the control parameters in a timely manner according to the real-time operating data set to ensure the best performance of the system under different operating conditions; this dynamic adjustment capability enables the system to respond quickly to changes in the external environment and improve the flexibility and adaptability of the power system. The design of hierarchical distributed control architecture can realize multi-level management at system level, regional level and equipment level, and optimize the execution efficiency of control strategy; the designed control system communication scheme ensures unimpeded information flow between layers, which is conducive to the efficient transmission and processing of real-time data. The coordination and integration of control systems at all levels realize the overall optimization of the system and improve the intelligent level of power management. The multi-level control algorithm library combines local optimization and global coordination, so that the system can achieve optimal control in a variety of scenarios; it provides flexible control logic and algorithms, so that the system can adapt to various complex operating environments and load changes; through the application of multi-level control strategies, the robustness and stability of the system are improved to deal with emergencies and failures. Integrating the multi-level control algorithm library, control system communication scheme, etc. into a unified system simplifies management and maintenance and improves the overall efficiency of the system; the constructed power management model provides a comprehensive system view, thereby realizing all-round power management and optimization; through the construction of a multi-level feedback control system, the automation and intelligence level of the power system is improved, and the refinement of power management is realized. These steps combine advanced technologies such as multi-objective optimization, dynamic adjustment and hierarchical distributed control, which significantly improves the power supply quality, operation efficiency and reliability of the power system. Through system integration and comprehensive management, refined and intelligent control of the power system is achieved, providing solid technical support for community transformation and power system optimization.

[0158] Preferably, step S4 comprises the following steps:

[0159] Step S41: Acquire power fault data under different operating conditions;

[0160] The embodiment of the present invention designs a data collection protocol to specify the type and format of fault data that needs to be collected; deploys a data collection agent to the distribution automation system to automatically record the time, location, and impact range of the fault; and organizes a team to manually collect fault information from on-site staff, maintenance records, and customer reports on a regular basis.

[0161] Step S42: using the distribution automation system and the intelligent power equipment to collect the operating parameters of the power system when the fault occurs in real time according to the power fault data, thereby obtaining the fault operating parameter data;

[0162] The embodiment of the present invention sets a trigger in the distribution automation system. When a specific fault signal is detected, the current, voltage and other parameters at the time of the fault are automatically recorded; the smart meter and sensor are configured to upload data to the central database in real time, and the timestamp is used to ensure the synchronization of the data; scripts are written or ready-made data acquisition software, such as NIDAQmx, is used to realize automatic data collection.

[0163] Step S43: extracting fault features from the fault operation parameter data to obtain a fault feature vector; classifying the fault instance according to the fault feature vector to obtain power fault type data, wherein the power fault type data includes equipment fault data, line fault data and system fault data;

[0164] The embodiment of the present invention uses Python's Scikit-learn library to preprocess the fault data, such as denoising and normalization; applies fast Fourier transform (FFT) to convert the time domain signal to the frequency domain to extract frequency features; trains a classifier, such as a random forest or a neural network, to classify the fault feature vector, and performs cross-validation to evaluate the model performance.

[0165] Step S44: constructing a fault causal network describing the correlation relationship and propagation path between faults according to the power fault data and preset expert knowledge, thereby generating a fault propagation model;

[0166] The embodiment of the present invention convenes power system experts to collect knowledge on fault association and propagation through seminars or workshops; uses professional causal network analysis tools, such as Tetrad or Cytoscape, to establish a fault causal relationship diagram; and uses historical fault cases to calibrate the network model to ensure that it reflects the actual situation.

[0167] Step S45: performing fault root identification and influencing factor extraction according to the power fault type data and the fault propagation model, thereby obtaining fault attribution data;

[0168] The embodiment of the present invention utilizes a causal network analysis tool to execute a fault root cause identification algorithm, such as rule-based reasoning or Bayesian network; combined with attribution theory, the direct cause and root cause of the fault are analyzed, and a detailed fault attribution report is written, including fault statistical analysis, cause inference, and suggested improvement measures.

[0169] Step S46: Fault prediction is performed based on the fault attribution data and the power management model, and the fault risk is minimized through integrated optimization technology to obtain the optimized operation window data.

[0170] The embodiment of the present invention develops a time series-based fault prediction model, using ARIMA or seasonal decomposition time series prediction methods; combines power management models, such as digital twin models, to perform fault simulation and evaluate fault risks under different operating conditions; uses a multi-objective optimization library, such as Python's deap library, to search for a strategy to minimize the fault risk, taking into account factors such as cost, reliability, and maintenance time.

[0171] The present invention obtains power fault data under different operating conditions, which can cover various possible fault conditions and provide rich data support for subsequent fault analysis and processing; through the accumulation of historical fault data, the weak links of the system can be discovered, preventive maintenance can be carried out in advance, and the probability of fault occurrence can be reduced; collecting and analyzing fault data in advance can help to quickly identify the type and scope of the fault and improve the speed of fault response and repair. Through the real-time data acquisition of the distribution automation system and intelligent power equipment, the operating status when the fault occurs can be monitored in real time to understand the specific characteristics and impact of the fault; the acquisition of real-time operating parameter data makes it possible to accurately locate the fault, which helps to take measures quickly and reduce the impact range of the fault; the data collected in real time is more timely and accurate, providing a reliable data basis for fault analysis. By extracting fault feature vectors, the fault analysis process can be standardized and the accuracy and consistency of fault classification can be improved; fault type classification helps to quickly identify equipment faults, line faults or system faults, which is convenient for taking targeted countermeasures. Detailed fault type data provides a basis for fault management and helps to formulate fault handling priorities and strategies. Constructing a fault causal network can analyze the correlation between different faults and reveal the propagation path and impact range of the fault. Through the fault propagation model, the potential impact of the fault can be predicted, countermeasures can be taken in advance, and the fault risk can be reduced. The fault causal network and propagation model provide decision makers with a panoramic view of fault propagation, which helps to formulate a more scientific fault management strategy. Through fault attribution analysis, the root cause of the fault can be accurately located, which is convenient for taking effective repair measures. By identifying the main factors affecting the fault, targeted improvements and optimizations can be made to reduce the probability of similar faults. Fault root cause identification helps to improve the overall reliability and stability of the system and reduce system downtime. Through fault prediction, possible faults can be predicted in advance, preventive measures can be taken, and the impact of faults on the system can be reduced. Through the determination of the optimal operation window, the operating parameters of the system can be optimized under the premise of minimizing the risk of faults, and the operating efficiency and stability of the system can be improved. Predicting and minimizing the risk of faults in advance can help reduce the downtime and repair costs caused by faults and improve operational efficiency. These steps form a complete power system fault management process through real-time data collection, fault feature extraction, causal analysis and fault prediction. Through systematic fault analysis and optimization strategies, the reliability and stability of the power system can be effectively improved, the frequency and impact of faults can be reduced, and operation and maintenance costs can be reduced, providing a solid guarantee for community transformation and the long-term healthy operation of the power system.

[0172] Preferably, step S46 includes the following steps:

[0173] Step S461: Map the power system topology, equipment status, and operating parameters into a graph structure according to the power management model using graph neural network technology, thereby building a dynamic fault prediction model;

[0174] The embodiment of the present invention collects node and edge data of the power system, including substations, transmission lines, loads, etc., and the connection relationships between them, and uses Python's NetworkX library to build a graph structure representation of the power system; selects a graph neural network framework, such as DGL (Deep Graph Library), to build a GNN model; uses historical fault data and power system operation parameters to train the GNN model, uses PyTorch or TensorFlow as the backend, and implements an online update mechanism for the model, so as to update the model parameters when new data is obtained.

[0175] Step S462: Perform rolling prediction based on the dynamic fault prediction model and fault attribution data to generate a fault probability distribution within a future period of time, thereby obtaining a fault risk prediction map;

[0176] The embodiment of the present invention applies the trained GNN model to infer the current power system state and predict the fault probability of each node and edge; uses the NumPy and SciPy libraries to perform statistical analysis of the probability distribution and generate the fault probability distribution; uses the Matplotlib or Seaborn library to visualize the fault probability distribution in the form of a heat map to display the failure risk of different equipment.

[0177] Step S463: Establishing a fault risk assessment system based on the fault probability, impact range and severity according to the fault risk prediction graph by using a multi-criteria decision-making method, thereby obtaining a risk assessment matrix;

[0178] The embodiment of the present invention defines multiple criteria for fault risk assessment, such as fault probability, number of affected users, and equipment maintenance cost; uses Python's Pandas library to process multi-criteria data, performs weight allocation and standardization, applies the analytic hierarchy process (AHP) to determine the relative importance of each criterion, and constructs a risk assessment matrix; uses fuzzy comprehensive evaluation or comprehensive weighted scoring methods to evaluate the risk levels of different faults.

[0179] Step S464: optimizing the system operating parameters with the goal of minimizing the overall failure risk according to the risk assessment matrix and the current operating parameters of the power system, thereby obtaining a risk minimization strategy;

[0180] The embodiment of the present invention determines an optimization target, such as minimizing the number of failures, reducing power outage time or reducing maintenance costs; uses a linear programming model or an integer programming model to optimize parameters according to a risk assessment matrix; uses a CPLEX or Gurobi optimizer to solve the parameter optimization problem, and considers the actual operating constraints of the system; and applies the optimization results to real-time control of the power system, such as adjusting the transformer tap position or switching a reactive compensation device.

[0181] Step S465: Simulate and verify the risk minimization strategy based on the digital twin model of the power system, and evaluate the effectiveness and feasibility of the strategy to obtain the optimized operation window data.

[0182] The embodiment of the present invention deploys a risk minimization strategy in the digital twin model of the power system, and uses simulation software such as MATLAB Simulink for testing; sets the simulation time span and fault injection scenario, and conducts multiple rounds of simulation experiments; collects key indicator data during the simulation process, such as the number of faults, system recovery time, voltage stability, etc.; compares the system performance before and after the simulation, and evaluates the effectiveness of the strategy, such as whether the fault risk is reduced and whether the system recovery time is shortened.

[0183] The present invention maps the topological structure, equipment status and operating parameters of the power system into the graph structure through the graph neural network technology, which can effectively capture the complex relationship and dynamic characteristics of the system; the graph neural network can process dynamically changing data, is suitable for real-time updated fault prediction models, and improves the accuracy and timeliness of predictions; combined with the rich data of the power management model, the dynamic fault prediction model can identify potential fault trends and associations, and improve the prediction ability of the overall system. The fault risk prediction map generated by rolling prediction can warn of potential fault risks in advance, giving maintenance and operating personnel more response time; the prediction map provides the probability distribution of faults in the future, which helps to understand the fault risk level of different equipment or areas and carry out targeted management; by understanding the distribution of fault risks, maintenance resources can be allocated more reasonably, focusing on high-risk areas and optimizing resource utilization. The multi-criteria decision-making method takes into account factors such as the probability of fault occurrence, the scope of impact and the severity, and establishes a comprehensive fault risk assessment system; the risk assessment matrix can refine different fault risks to specific equipment and areas, helping decision makers to carry out refined risk management; the risk assessment system provides a scientific basis for formulating countermeasures, supporting decision-making and priority division. The optimization of operating parameters with the goal of minimizing the overall failure risk can help reduce the overall failure risk of the system and improve the system stability and reliability; the optimized system operating parameters can improve the system's operating efficiency, reduce energy loss and operating costs; according to the real-time operating parameters and risk assessment matrix, the system can be dynamically adjusted to improve its ability to respond to sudden failures. Through the simulation verification of the digital twin model of the power system, the effectiveness of the risk minimization strategy can be verified to ensure that the strategy is feasible in practical applications; simulation verification can also evaluate the feasibility of the strategy, including its impact on system operation, resource requirements, etc., to ensure that the implementation of the strategy will not cause new problems; according to the simulation results, the strategy is adjusted and optimized to make it more in line with actual needs and improve the system's fault prevention and response capabilities. These steps form a comprehensive fault prediction and management system through advanced technologies such as graph neural network technology, rolling prediction, and multi-criteria decision-making methods; the system can continuously optimize operating parameters in a dynamic environment to reduce the probability and impact of failures; through simulation verification and evaluation, the effectiveness and feasibility of the strategy are ensured, providing a reliable fault management and optimization means for the community power system, and improving the overall operation efficiency and stability of the system.

[0184] Preferably, step S5 comprises the following steps:

[0185] Step S51: Acquire original real-time data of smart meters and other monitoring devices in the community;

[0186] The embodiment of the present invention deploys smart meters in a community, and sends data to a data acquisition server in real time through RS-485 or Ethernet connection; uses the built-in API or communication protocol of the meter, such as IEC 62056, to regularly pull or push meter data; and uses a database management tool, such as MySQL Workbench, to design a database model and store raw data.

[0187] Step S52: cleaning and preprocessing the original real-time data, and normalizing the data to obtain real-time data;

[0188] The embodiment of the present invention writes a data cleaning script, uses Python's Pandas library to process missing values, remove outliers and deduplicate data; applies data preprocessing techniques, such as data standardization or normalization, to ensure that the data is within an appropriate range; and stores the cleaned and preprocessed data in a new database table for subsequent analysis.

[0189] Step S53: extracting time characteristics of the real-time data based on the power consumption cycle and peak and valley characteristics, thereby obtaining power consumption time characteristic data;

[0190] The embodiments of the present invention analyze electricity consumption data and identify periodic characteristics of electricity consumption, such as daily cycles, weekly cycles and seasonal changes; use time series analysis methods, such as Fourier transform or wavelet transform, to extract time characteristics; and use statistical methods, such as calculating electricity consumption during peak and valley electricity price periods, to obtain electricity consumption time characteristic data.

[0191] Step S54: identifying the power consumption patterns of different types of users according to the power consumption time characteristic data, and performing cluster analysis to obtain user classification result data;

[0192] The embodiment of the present invention utilizes a clustering algorithm, such as K-means or hierarchical clustering, to classify the power consumption patterns of users; uses power consumption time characteristic data, such as power consumption during peak and valley periods, as input features of the clustering algorithm; and evaluates the clustering results, using indicators such as the silhouette coefficient to determine the optimal number of user classifications.

[0193] Step S55: constructing a power consumption behavior prediction model based on a deep learning model for each type of user according to the user classification result data, thereby obtaining a power consumption behavior model;

[0194] The embodiment of the present invention selects a suitable deep learning model for each type of user, such as a long short-term memory network (LSTM) or a convolutional neural network (CNN); uses the TensorFlow or Keras framework to build a deep learning model, and the input feature is the user's electricity usage time feature data; trains the model, uses a cross-validation method to evaluate the model performance, and adjusts the model parameters.

[0195] Step S56: Analyze the power consumption behavior model based on abnormal power consumption behavior and energy-saving conditions according to the preset external condition data, so as to obtain power consumption optimization data.

[0196] The embodiments of the present invention apply a trained electricity consumption behavior prediction model to predict the user's short-term and long-term electricity consumption behavior; combine external condition data, such as electricity price changes, weather forecasts, etc., to perform abnormal detection and energy-saving analysis of electricity consumption behavior; and use optimization algorithms, such as linear programming or dynamic programming, to find the optimal electricity consumption strategy that meets energy conservation and cost-effectiveness.

[0197] The present invention can comprehensively collect the user's electricity consumption information, including power consumption, load fluctuation, etc., by acquiring the original real-time data of smart meters and other monitoring equipment; the original real-time data provides a basis for subsequent analysis and processing, ensuring that all analysis work is based on real and complete data. Data cleaning and preprocessing can remove errors and outliers, fill missing data, and improve data quality; normalization processing standardizes the data to the same scale, which is helpful for subsequent analysis and model training, and avoids the impact of scale differences of different features on the analysis results. Extracting electricity consumption cycles and peak and valley features helps to identify users' electricity consumption behavior patterns, such as peak hours, valley hours, etc.; time feature data provides detailed information about users' electricity consumption patterns, making further analysis and modeling more accurate and targeted. By clustering and analyzing the electricity consumption patterns of different users, similar user groups can be identified, which is convenient for targeted management and optimization; user classification result data helps to formulate personalized electricity management strategies between different user groups and improve energy utilization efficiency. The power consumption behavior prediction model based on deep learning can capture complex power consumption behavior characteristics and provide high-accuracy predictions; the power consumption behavior model can predict future power consumption trends, help formulate more effective energy management strategies, and respond to possible power demand fluctuations in advance. The analysis of abnormal power consumption behavior and energy-saving conditions in the power consumption behavior model can identify power consumption patterns that do not meet expectations and optimize them; power consumption optimization data provides energy-saving potential and optimization opportunities, which helps to formulate energy-saving measures, improve energy efficiency, and reduce energy costs. These steps achieve a comprehensive analysis and optimization of power consumption behavior in the community by acquiring and processing real-time data, extracting time features, classifying and clustering users, building prediction models, and generating power consumption optimization data. Specific effects include improving data quality, identifying user power consumption patterns, providing personalized management solutions, making accurate power consumption forecasts, and formulating effective energy-saving measures. These steps help improve the accuracy and efficiency of energy management and provide a scientific basis and practical tools for optimizing the energy system of the community.

[0198] The present invention also provides a power monitoring and management system for cell reconstruction, which is used to execute the above-mentioned power monitoring and management method for cell reconstruction. The power monitoring and management system for cell reconstruction comprises:

[0199] The infrastructure assessment module is used to obtain the infrastructure data of the community power system through remote terminal units and intelligent electronic devices, including the model, layout, service life and maintenance records of existing power equipment; analyze the weak links and potential risk points of the power system based on the infrastructure data, so as to obtain power risk assessment data;

[0200] The load simulation optimization module is used to simulate the power load based on the infrastructure data and power risk assessment data using the distribution automation system, and map the power supply status and efficiency under different conditions to obtain the power system characteristic diagram data; simulate and analyze the power quality under different power supply states based on the power system characteristic diagram data to obtain power quality simulation data; establish a power system optimization model based on the process parameters corresponding to different power supply states and the power quality simulation data;

[0201] A dynamic control adjustment module is used to adjust the control parameters in real time according to the preset power supply quality standards and the power system optimization model, and to construct a multi-level feedback control system according to the adjusted control parameters to obtain a power management model;

[0202] The fault prediction and prevention module is used to obtain power fault data under different operating conditions; perform fault attribution analysis on the power fault data and extract mapping relationships to obtain fault attribution data; perform fault prediction based on the fault attribution data and the power management model, and minimize fault risk acquisition through integrated optimization technology to obtain optimized operating window data;

[0203] The power consumption behavior analysis module is used to obtain real-time data from smart meters and other monitoring equipment in the community; build a power consumption behavior model based on real-time data; analyze the power consumption behavior model based on abnormal power consumption behavior and energy-saving conditions to obtain power consumption optimization data;

[0204] The intelligent scheduling execution module is used to generate intelligent scheduling plan data based on power consumption optimization data and optimal operation window data, so as to adjust power supply parameters in real time to optimize load distribution.

[0205] The present invention can fully grasp the current status of the power system by collecting and analyzing equipment models, layouts, service life and maintenance records, which provides basic data for subsequent risk assessment and optimization; by analyzing infrastructure data, it can identify weak links and potential risk points in the system, help prevent possible problems in advance, and reduce the probability of failures; obtaining power risk assessment data is helpful to formulate effective maintenance plans and upgrade strategies, and optimize the safety and reliability of the power system. By simulating loads under different conditions, it is possible to understand the performance of the power system under various load conditions, optimize the power supply status and efficiency; simulate and analyze the power quality, help identify the impact of the power supply status on the power quality, and ensure that the power supply quality meets the standards; establish a power system optimization model based on simulation data and process parameters, provide a scientific basis for subsequent dynamic control and optimization, and improve the overall performance of the system. Real-time adjustment of control parameters can effectively respond to various changes in the power system and ensure system stability and reliability; through a multi-level feedback control system, the power system can be hierarchically managed, the system performance can be optimized, and the power supply quality can be improved; the constructed power management model helps to comprehensively manage the power system, dynamically adjust and optimize, and improve power supply efficiency and reliability. Real-time acquisition of power fault data can timely understand the system's operating status and fault occurrence; analyzing the root causes and influencing factors of the fault can help formulate effective fault prevention and repair strategies; through fault prediction and optimization technology, potential faults can be identified in advance, the impact of faults on the system can be reduced, and the reliability and stability of the system can be improved. Through real-time data analysis of users' power consumption behavior, it helps to understand power consumption patterns and changes; identifying abnormal power consumption behavior and energy-saving opportunities, helping to formulate energy-saving strategies and optimize power consumption plans; providing power consumption optimization data can help reduce energy waste, reduce electricity costs, and improve energy utilization efficiency. Generating a scheduling plan based on the optimized data can dynamically adjust power supply parameters and optimize load distribution; real-time adjustment of power supply parameters can cope with fluctuations in power demand, optimize the allocation of system resources, and improve the flexibility and response speed of the power system; improve power supply efficiency, ensure that the system can operate stably under various load conditions, and improve users' power consumption experience and the overall efficiency of the system. These modules work together to significantly improve the management and optimization level of the community power system through comprehensive data collection, accurate load simulation, dynamic control adjustment, effective fault prediction and prevention, in-depth power consumption behavior analysis, and intelligent scheduling execution. This not only improves the stability and reliability of the power system, but also optimizes energy utilization efficiency, reduces operating costs and improves user experience.

[0206] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0207] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A power monitoring and management method for community reconstruction, characterized in that: The following steps are involved: Step S1: Obtaining infrastructure data of the community power system through remote terminal units and intelligent electronic devices, including the model, layout, service life and maintenance records of existing power equipment; Analyze the weak links and potential risk points of the power system based on infrastructure data to obtain power risk assessment data; Step S2: using the distribution automation system to simulate the power load according to the infrastructure data and the power risk assessment data, and mapping the power supply status and efficiency under different conditions, thereby obtaining power system characteristic diagram data; simulating and analyzing the power quality under different power supply states according to the power system characteristic diagram data, thereby obtaining power quality simulation data; Establish a power system optimization model based on process parameters corresponding to different power supply states and power quality simulation data; Step S3: adjusting the control parameters in real time according to the preset power supply quality standard and the power system optimization model, and constructing a multi-level feedback control system according to the adjusted control parameters, thereby obtaining a power management model; Step S4: Acquire power fault data under different operating conditions; Perform fault attribution analysis on power fault data and extract mapping relationships to obtain fault attribution data; Fault prediction is performed based on fault attribution data and power management models, and the fault risk is minimized through integrated optimization technology to obtain the optimal operation window data; Step S5: Acquire real-time data from smart meters and other monitoring devices in the community; Build a power consumption behavior model based on real-time data; analyze the power consumption behavior model based on abnormal power consumption behavior and energy-saving conditions to obtain power consumption optimization data; Step S6: Generate intelligent dispatching plan data based on the power consumption optimization data and the optimal operation window data to adjust the power supply parameters in real time to optimize load distribution.

2. The power monitoring and management method for community reconstruction according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: configuring the acquisition parameters of the remote terminal unit and the intelligent electronic device, and establishing a data acquisition network, thereby obtaining a data acquisition system configuration plan; Step S12: collecting real-time operation information of power equipment according to the data acquisition system configuration plan, and collecting power consumption information of smart meters, so as to obtain a real-time operation data set; Step S13: Obtain the model, specification and rated capacity information of the power equipment, and enter the installation date, service life, installation location and spatial layout information of the equipment, so as to obtain a static information data set of the equipment; Step S14: Obtain historical maintenance record data of the equipment; and perform format unification and standardization processing on the historical maintenance record data, the real-time operation data set, and the equipment static information data set, so as to obtain infrastructure data; Step S15: Analyze the weak links and potential risk points of the power system based on the infrastructure data to obtain power risk assessment data.

3. The power monitoring and management method for community reconstruction according to claim 2 is characterized in that: Step S15 includes the following steps: Step S151: evaluating the remaining service life of existing power equipment based on equipment static information and maintenance records according to infrastructure data, thereby obtaining equipment health status data; Step S152: Perform real-time load distribution analysis in each area based on the real-time status of equipment operation in the infrastructure data, and identify load concentration areas and potential overload points, thereby obtaining a real-time load distribution heat map; Step S153: extracting equipment layout and connection relationships based on infrastructure data, annotating equipment nodes and power supply paths, and constructing a digital topology model of the community power system, thereby obtaining a power system topology model; Step S154: Identify weak links in the power system topology model based on the equipment health status data and the real-time load distribution heat map, and evaluate the risk level of the weak links, thereby obtaining a list of weak links and their corresponding risk level data; Step S155: simulating the fault propagation path of the power system topology model based on the weak link list, and evaluating the impact degree of each risk point according to the risk level data, so as to obtain potential risk point analysis data; Step S156: Perform an overall system risk index analysis based on the weak link list and potential risk point analysis data to generate power risk assessment data for each region and equipment type.

4. The power monitoring and management method for community reconstruction according to claim 3 is characterized in that: Step S2 includes the following steps: Step S21: constructing a digital twin model of the community power system based on the infrastructure data and the power risk assessment data, including equipment parameters, topology structure and operating status, to obtain a digital twin model of the power system; Step S22: using the distribution automation system to perform statistical analysis on the pre-acquired historical load data, and identify the daily load curve and seasonal variation characteristics, thereby obtaining load characteristic data; Step S23: constructing short-term and medium- and long-term load forecasting models according to the load characteristic data, thereby obtaining a load forecasting model; Step S24: using the load forecasting model to simulate load changes in different scenarios according to the potential risk points in the power risk assessment data, thereby obtaining a multi-scenario load simulation data set; Step S25: Perform power flow calculation and state estimation based on the power system digital twin model and the multi-scenario load simulation data set, and analyze the voltage distribution, power flow and equipment load rate under different load conditions to obtain system operation status data; Step S26: performing power supply reliability evaluation on the system operation status data based on SAIDI and SAIFI index calculation, thereby obtaining reliability evaluation data; Step S27: Analyze the loss of each power device and line according to the system operation status data, calculate the overall system efficiency, and identify the efficiency bottleneck, so as to obtain energy efficiency analysis data; Step S28: integrating the system operation status data, reliability assessment data and energy efficiency analysis data, and generating a characteristic diagram reflecting the load-voltage and load-loss relationship, thereby obtaining power system characteristic diagram data; Step S29: simulate and analyze the power quality under different power supply states according to the power system characteristic diagram data to obtain power quality simulation data; establish a power system optimization model according to the process parameters corresponding to different power supply states and the power quality simulation data.

5. The power monitoring and management method for community reconstruction according to claim 4 is characterized in that: Step S29 includes the following steps: Step S291: using electromagnetic transient simulation software to perform simulation analysis of power quality indicators based on voltage deviation, harmonic distortion and three-phase imbalance under different power supply states according to the power system characteristic diagram data, thereby obtaining power quality simulation data; Step S292: collecting the process parameters of transformer voltage regulation and reactive power compensation according to the power quality simulation data, and analyzing the correlation between the process parameters and the power supply status and power quality, so as to obtain a process parameter influence matrix; Step S293: using the power system characteristic diagram data, the power quality simulation data and the process parameter influence matrix as inputs, training a preset agent control model based on a deep reinforcement learning algorithm, thereby obtaining a preliminary power system optimization model; Step S294: Use the Monte Carlo simulation method to conduct large-scale scenario testing on the preliminary power system optimization model, and perform model tuning to obtain the power system optimization model.

6. The power monitoring and management method for community reconstruction according to claim 5 is characterized in that: Step S3 includes the following steps: Step S31: constructing a multi-objective optimization function according to a preset power supply quality standard and a power system optimization model to obtain a power supply quality optimization target model; Step S32: extract key control parameters of the power system optimization model, establish parameter-target mapping relationship, and obtain control parameter influence matrix; wherein the key control parameters include transformer voltage regulation parameters, reactive power compensation device parameters and switch device status; Step S33: performing multi-objective optimization solution according to the power quality optimization target model and the control parameter influence matrix to obtain an optimal control parameter set; dynamically adjusting the optimal control parameter set according to the real-time operation data set to obtain a dynamic control parameter adjustment strategy; Step S34: designing a hierarchical distributed control architecture according to the dynamic control parameter adjustment strategy to obtain multi-level control system structure data, wherein the multi-level control system structure data includes control layers at the system level, the regional level, and the device level; designing a control system communication protocol and a data exchange mechanism for each control layer in the multi-level control system structure data to obtain a control system communication solution; Step S35: performing control logic analysis on each control layer in the multi-level control system structure data, and performing local optimization and global coordination, thereby obtaining a multi-level control algorithm library; Step S36: Systematically integrate the multi-level control algorithm library, the control system communication scheme, and the multi-level control system structure data to construct a complete multi-level feedback control system, thereby obtaining a power management model.

7. The power monitoring and management method for community reconstruction according to claim 6 is characterized in that: Step S4 includes the following steps: Step S41: Acquire power fault data under different operating conditions; Step S42: using the distribution automation system and the intelligent power equipment to collect the operating parameters of the power system when the fault occurs in real time according to the power fault data, thereby obtaining the fault operating parameter data; Step S43: extracting fault features from the fault operation parameter data to obtain a fault feature vector; classifying the fault instance according to the fault feature vector to obtain power fault type data, wherein the power fault type data includes equipment fault data, line fault data and system fault data; Step S44: constructing a fault causal network describing the correlation relationship and propagation path between faults according to the power fault data and preset expert knowledge, thereby generating a fault propagation model; Step S45: performing fault root identification and influencing factor extraction according to the power fault type data and the fault propagation model, thereby obtaining fault attribution data; Step S46: Fault prediction is performed based on the fault attribution data and the power management model, and the fault risk is minimized through integrated optimization technology to obtain the optimized operation window data.

8. The power monitoring and management method for residential area reconstruction according to claim 7 is characterized in that: Step S46 includes the following steps: Step S461: Map the power system topology, equipment status, and operating parameters into a graph structure according to the power management model using graph neural network technology, thereby building a dynamic fault prediction model; Step S462: Perform rolling prediction based on the dynamic fault prediction model and fault attribution data to generate a fault probability distribution within a future period of time, thereby obtaining a fault risk prediction map; Step S463: Establishing a fault risk assessment system based on the fault probability, impact range and severity according to the fault risk prediction graph by using a multi-criteria decision-making method, thereby obtaining a risk assessment matrix; Step S464: optimizing the system operating parameters with the goal of minimizing the overall failure risk according to the risk assessment matrix and the current operating parameters of the power system, thereby obtaining a risk minimization strategy; Step S465: Simulate and verify the risk minimization strategy based on the power system digital twin model, and evaluate the effectiveness and feasibility of the strategy to obtain the optimized operation window data.

9. The power monitoring and management method for community reconstruction according to claim 8, characterized in that: Step S5 includes the following steps: Step S51: Acquire original real-time data of smart meters and other monitoring devices in the community; Step S52: cleaning and preprocessing the original real-time data, and normalizing the data to obtain real-time data; Step S53: extracting time characteristics based on power consumption cycle and peak-valley characteristics from real-time data, thereby obtaining power consumption time characteristic data; Step S54: identifying the power consumption patterns of different types of users according to the power consumption time characteristic data, and performing cluster analysis to obtain user classification result data; Step S55: constructing a power consumption behavior prediction model based on a deep learning model for each type of user according to the user classification result data, thereby obtaining a power consumption behavior model; Step S56: Analyze the power consumption behavior model based on abnormal power consumption behavior and energy-saving conditions according to the preset external condition data, so as to obtain power consumption optimization data.

10. A power monitoring and management system for community reconstruction, characterized in that: Used to execute the power monitoring management method for cell reconstruction according to claim 1, the power monitoring management system for cell reconstruction comprises: The infrastructure assessment module is used to obtain the infrastructure data of the community power system through remote terminal units and intelligent electronic devices, including the model, layout, service life and maintenance records of existing power equipment; analyze the weak links and potential risk points of the power system based on the infrastructure data, so as to obtain power risk assessment data; The load simulation optimization module is used to simulate the power load based on the infrastructure data and power risk assessment data using the distribution automation system, and map the power supply status and efficiency under different conditions to obtain the power system characteristic diagram data; simulate and analyze the power quality under different power supply states based on the power system characteristic diagram data to obtain power quality simulation data; establish a power system optimization model based on the process parameters corresponding to different power supply states and the power quality simulation data; A dynamic control adjustment module is used to adjust the control parameters in real time according to the preset power supply quality standards and the power system optimization model, and to construct a multi-level feedback control system according to the adjusted control parameters to obtain a power management model; The fault prediction and prevention module is used to obtain power fault data under different operating conditions; perform fault attribution analysis on the power fault data and extract mapping relationships to obtain fault attribution data; perform fault prediction based on the fault attribution data and the power management model, and minimize fault risk acquisition through integrated optimization technology to obtain optimized operating window data; The power consumption behavior analysis module is used to obtain real-time data from smart meters and other monitoring equipment in the community; build a power consumption behavior model based on real-time data; analyze the power consumption behavior model based on abnormal power consumption behavior and energy-saving conditions to obtain power consumption optimization data; The intelligent scheduling execution module is used to generate intelligent scheduling plan data based on power consumption optimization data and optimal operation window data, so as to adjust power supply parameters in real time to optimize load distribution.

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