A remote-controlled distributed energy station intelligent operation and maintenance management system and method

Abstract

The present invention relates to the field of intelligent energy management technology, specifically to a remote-controlled distributed energy station intelligent operation and maintenance management system and method, the system includes a fault identification module, a maintenance prediction module, a scheduling optimization module, a load prediction module, an energy adjustment module, and a maintenance window optimization module. The present invention improves the accuracy and response speed of fault diagnosis through in-depth analysis of real-time and historical data, uses genetic algorithms and long short-term memory networks to predict potential faults of equipment, can warn before problems occur, and allows operators to adjust maintenance plans and resource allocations in a timely manner, thereby reducing the risk of unexpected downtime, which is significantly better than traditional periodic inspections, can adjust the maintenance frequency according to the actual operating conditions of the equipment, reduce unnecessary maintenance activities, save costs, and effectively respond to fluctuations in energy demand and optimize energy utilization through dynamic analysis of load changes and energy output adjustments, providing higher energy management flexibility and efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent energy management, and in particular to a remote-controlled distributed energy station intelligent operation and maintenance management system and method. Background Art

[0002] The main purpose of smart energy management is to improve energy efficiency and reduce waste while ensuring the stability and reliability of energy dispatch. It usually includes real-time equipment operation monitoring, demand response, load control and predictive maintenance to automatically adjust energy consumption in response to grid demand or price changes, thereby optimizing energy costs and consumption. With the increasing popularity of distributed energy resources, smart energy management has also expanded to the integration, operation and maintenance of distributed energy to improve the flexibility and efficiency of the entire power system.

[0003] Among them, the remote-controlled distributed energy station intelligent operation and maintenance management system is an integrated technical solution that aims to optimize the operation and maintenance of distributed energy stations through remote monitoring and control, so that the performance and status of each energy station can be monitored in real time in the control room, and potential problems can be quickly identified and resolved, thereby reducing downtime and improving energy efficiency. Its main uses include real-time data monitoring, fault diagnosis, automated control, energy consumption optimization and maintenance scheduling.

[0004] Traditional systems rely on static maintenance plans and response measures and lack the ability to conduct in-depth analysis of real-time data, resulting in slow response to failures, increased operational risks and maintenance costs. Traditional systems are inefficient in processing large-scale distributed data and cannot effectively integrate and analyze data from various energy stations, resulting in resource allocation and energy distribution decisions that cannot be updated in real time, affecting the rapid response to changes in energy demand, resulting in energy supply shortages during peak hours and energy waste during low-demand periods. This lack of flexibility and efficiency is particularly prominent in the growing number of distributed energy stations, limiting the effective use of energy and the stability of energy stations. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a remote-controlled distributed energy station intelligent operation and maintenance management system and method.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a remote-controlled distributed energy station intelligent operation and maintenance management system, the system comprising:

[0007] The fault identification module collects characteristic parameters of energy equipment during operation based on historical operation and maintenance data, analyzes the correlation between characteristic parameters and equipment failures, and identifies abnormal modes based on equipment operation records to generate a fault mode data set;

[0008] The maintenance prediction module uses a genetic algorithm to analyze feature changes in the time series data through the failure mode data set, screens key features that cause energy equipment failures, calculates the failure probability of the energy equipment, and generates failure probability assessment information;

[0009] The scheduling optimization module uses the fault probability assessment information and a long short-term memory network to analyze the criticality of energy equipment, select equipment for priority maintenance, and sort the maintenance operations by time to obtain a priority maintenance sequence table;

[0010] The load prediction module analyzes the time trend of energy consumption according to the priority maintenance order table and the historical load data of the energy station, calculates the load demand in different time periods, and predicts future load changes to generate load change prediction information;

[0011] The energy adjustment module monitors the energy output level of the current energy station based on the load change prediction information, analyzes the energy distribution status of the energy station, adjusts the output parameters of the energy station according to the analysis results, and generates an energy output distribution strategy;

[0012] The maintenance window optimization module analyzes the optimal time for maintenance operations based on the energy output allocation strategy, selects idle time periods of the energy station operation to schedule maintenance tasks, and generates a maintenance task schedule.

[0013] The present invention has improvements in that the fault mode data set includes abnormal frequency, impact degree, and fault response time; the fault probability assessment information includes equipment model, fault type probability, and predicted fault occurrence period; the priority maintenance sequence table includes equipment identification, planned maintenance date, and maintenance urgency level; the load change prediction information includes predicted load peak, trough time point, and potential load increase or decrease percentage; the energy output allocation strategy includes adjusted power generation, power generation time, and adjusted energy supply area; the maintenance task schedule includes maintenance operation sequence, scheduled operation time, and operator configuration information.

[0014] The present invention is improved in that the maintenance prediction module comprises:

[0015] The feature change analysis submodule applies decision tree and random forest algorithms based on the fault mode data set to analyze the parameter fluctuations of multiple time series, determine the fluctuation trend, and determine the potential fault occurrence period through the fluctuation trend to generate feature fluctuation analysis results;

[0016] The key feature screening submodule screens features associated with equipment failures from the feature fluctuation analysis results, including temperature rise rate or abnormal pressure drop, determines key parameters for fault prediction, and generates a fault-related feature set;

[0017] The probability calculation submodule uses the fault association feature set and a genetic algorithm to calculate the statistical probability of the occurrence of the fault type, and integrates it into multi-type probability data to generate fault probability assessment information.

[0018] The present invention is improved in that the genetic algorithm is according to the formula:

[0019]

[0020] Calculate the probability of occurrence of multiple fault types;

[0021] Among them, P(x) is the probability of the target fault type, f(x) is the fitness of the fault type determined by the evaluation function in the genetic algorithm, w(x) is the weight coefficient of the fault type, λ is the adjustment coefficient, g(x) is the weighted value of the historical influencing factors of the fault type, n is the total number of fault types, and xi represents the i-th fault type.

[0022] The present invention is improved in that the scheduling optimization module includes:

[0023] The fault assessment submodule performs a fault risk analysis on each energy device based on the fault probability assessment information, marks the device parameters as abnormal, and classifies the energy devices for fault risk assessment to generate a device fault rating;

[0024] The priority determination submodule utilizes the equipment failure rating to compare the operation criticality and failure risk of the equipment, evaluates the maintenance urgency of the equipment, determines the priority of equipment maintenance, and generates a maintenance urgency ranking;

[0025] The scheduling submodule uses a long short-term memory network to schedule and allocate resources for maintenance tasks based on the maintenance urgency ranking, optimize resource utilization and time efficiency, determine the key equipment that should be maintained first, and generate a priority maintenance sequence table.

[0026] The present invention is improved in that the load prediction module comprises:

[0027] The data integration submodule integrates the real-time and historical load data of the energy station based on the priority maintenance sequence table, marks data abnormal points during maintenance, and analyzes the impact of maintenance on the load to generate load impact data;

[0028] The trend analysis submodule extracts and analyzes seasonal and cyclical trends of energy consumption based on the load impact data, determines seasonal changes and abnormal fluctuations in consumption patterns, and generates energy consumption pattern recognition results;

[0029] The load calculation submodule calculates the load demand in the future difference time period according to the energy consumption pattern recognition result during the scheduled maintenance period, predicts the load peak and valley value of the time period, and generates load change prediction information.

[0030] The present invention is improved in that the energy adjustment module comprises:

[0031] The load monitoring submodule monitors the real-time data stream based on the load change prediction information, records the energy output level of the current energy station, and regularly updates the data points to capture the instant load changes and generate real-time energy monitoring data;

[0032] The energy analysis submodule analyzes the matching degree between energy output and load demand based on the real-time energy monitoring data, identifies the difference between supply and demand, and compares historical data patterns to identify unusual energy consumption changes, and generates energy matching evaluation results;

[0033] The parameter adjustment submodule adjusts the output settings of the transformer and the distribution unit according to the energy matching evaluation result, optimizes the energy flow and allocation strategy, matches the consumption demand and the forecast data, and generates the energy output allocation strategy.

[0034] A remote-controlled distributed energy station intelligent operation and maintenance management method, the remote-controlled distributed energy station intelligent operation and maintenance management method is executed based on the remote-controlled distributed energy station intelligent operation and maintenance management system, comprising the following steps:

[0035] S1: Based on historical operation and maintenance data, deploy remote monitoring equipment to record the operating status of energy equipment, integrate historical operation and maintenance data with real-time characteristic parameters, and evaluate the correlation between parameter anomalies and equipment failures to generate a failure mode data set;

[0036] S2: Based on the failure mode data set, a genetic algorithm is used to analyze feature changes in the time series data, screen key features that cause equipment failures, calculate the statistical probability of the failure type, and combine multiple types of failure data to generate failure probability assessment information;

[0037] S3: Using the failure probability assessment information, a long short-term memory network is used to perform a failure risk assessment on each energy device, and the equipment maintenance priority is determined according to the failure risk and operation criticality of the equipment. A priority maintenance sequence table is generated through resource allocation and time scheduling optimization;

[0038] S4: Based on the priority maintenance order table, integrate the historical load data and real-time load information of the energy station, analyze the time trend of energy consumption, predict future load demand changes, calculate the load demand in different time periods, and generate load change prediction information;

[0039] S5: Based on the load change prediction information, monitor the energy output level of the current energy station, analyze the energy distribution status, select the idle period of the energy station operation for maintenance, optimize the maintenance time window, and generate energy distribution and maintenance task time.

[0040] Compared with the prior art, the advantages and positive effects of the present invention are:

[0041] In the present invention, the accuracy and response speed of fault diagnosis are improved through in-depth analysis of real-time and historical data, and the potential failures of equipment are predicted by genetic algorithms and long short-term memory networks. Warnings can be given before problems occur, allowing operators to adjust maintenance plans and resource allocations in a timely manner, thereby reducing the risk of unexpected downtime. It is significantly better than traditional periodic inspections, and the maintenance frequency can be adjusted according to the actual operating conditions of the equipment, reducing unnecessary maintenance activities and saving costs. By dynamically analyzing load changes and energy output adjustments, it can effectively respond to fluctuations in energy demand and optimize energy utilization, providing higher energy management flexibility and efficiency, including in the management of distributed energy resources, it can ensure coordination between different energy sites and optimize the entire energy flow and consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 The present invention proposes a module diagram of a remotely controlled distributed energy station intelligent operation and maintenance management system;

[0043] Figure 2 The present invention proposes a framework diagram of a remotely controlled distributed energy station intelligent operation and maintenance management system;

[0044] Figure 3 The present invention proposes a schematic diagram of a fault identification module in a remotely controlled distributed energy station intelligent operation and maintenance management system;

[0045] Figure 4 A schematic diagram of a maintenance prediction module in a remotely controlled distributed energy station intelligent operation and maintenance management system is provided for the present invention;

[0046] Figure 5 The present invention proposes a schematic diagram of a scheduling optimization module in a remotely controlled distributed energy station intelligent operation and maintenance management system;

[0047] Figure 6 The present invention proposes a schematic diagram of a load prediction module in a remotely controlled distributed energy station intelligent operation and maintenance management system;

[0048] Figure 7 The present invention proposes a schematic diagram of an energy adjustment module in a remotely controlled distributed energy station intelligent operation and maintenance management system;

[0049] Figure 8 The present invention proposes a schematic diagram of a maintenance window optimization module in a remotely controlled distributed energy station intelligent operation and maintenance management system;

[0050] Fig. 9The present invention proposes a flow chart of a remote-controlled distributed energy station intelligent operation and maintenance management method. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0052] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0053] Example

[0054] See also Figure 1 The present invention provides a technical solution: a remote-controlled distributed energy station intelligent operation and maintenance management system comprising:

[0055] The fault identification module is based on historical operation and maintenance data and uses remote monitoring equipment to collect characteristic parameters of energy equipment during operation, analyze the correlation between characteristic parameters and equipment failures, and identify abnormal patterns based on equipment operation records to generate a fault pattern data set;

[0056] The maintenance prediction module uses a genetic algorithm to analyze the feature changes in the time series data through the failure mode data set, screens the key features that cause energy equipment failures, and calculates the failure probability of the energy equipment through the features to generate failure probability assessment information;

[0057] The scheduling optimization module uses the failure probability assessment information and the long short-term memory network to analyze the criticality of energy equipment, select the equipment for priority maintenance, and sort the maintenance operations by time, formulate a maintenance scheduling plan, and obtain a priority maintenance sequence table;

[0058] The load forecasting module analyzes the time trend of energy consumption based on the priority maintenance sequence table and the historical load data of the energy station, calculates the load demand in different time periods, and predicts future load changes to generate load change forecast information;

[0059] The energy adjustment module monitors the energy output level of the current energy station based on the load change prediction information, analyzes the energy distribution status of the energy station, adjusts the output parameters of the energy station according to the analysis results, and generates an energy output distribution strategy;

[0060] The maintenance window optimization module analyzes the optimal time for maintenance operations based on the energy output allocation strategy, selects the idle time period of the energy station to schedule maintenance tasks, optimizes the maintenance time window, and generates a maintenance task schedule.

[0061] The failure mode data set includes abnormal frequency, impact degree, and failure response time. The failure probability assessment information includes equipment model, failure type probability, and predicted failure occurrence period. The priority maintenance sequence table includes equipment identification, planned maintenance date, and maintenance urgency level. The load change prediction information includes predicted load peak and trough time points, and potential load increase and decrease percentages. The energy output allocation strategy includes adjusted power generation, power generation time, and adjusted energy supply area. The maintenance task schedule includes maintenance operation sequence, scheduled operation time, and operator configuration information.

[0062] See also Figure 2 and Figure 3 ,The fault identification module includes a data collection submodule, a data analysis submodule, and a pattern recognition submodule;

[0063] The data collection submodule uses remote monitoring equipment to monitor the operating status of energy equipment based on historical operation and maintenance data, and records characteristic parameters in time series to generate characteristic data sets;

[0064] The data collection submodule is based on historical operation and maintenance data and uses remote monitoring equipment to collect the characteristic parameters generated during the operation of the equipment in real time through sensors installed on energy equipment. The parameters include voltage, current, temperature, etc. Each sensor will record different characteristic data and store them in the database in chronological order to form a characteristic data set. In this process, a specific sampling frequency is used to ensure the continuity and accuracy of the data. When processing the data, it will be denoised to eliminate errors and ensure the validity and accuracy of the characteristic data.

[0065] The data analysis submodule compares the current data with the historical fault data based on the feature data set, evaluates the correlation between parameter anomalies and equipment failures, and generates correlation analysis results;

[0066] The data analysis submodule is based on the feature data set. It compares the current real-time data with the historical fault data to evaluate the abnormality of the feature parameters. The process requires setting the baseline parameters and tolerance range. During the comparative analysis, the deviation of each feature parameter will be calculated and the degree of correlation between it and the equipment failure will be evaluated. Finally, the correlation analysis results will be used to determine whether the equipment has a fault. If some parameters exceed the set threshold, the parameters will be marked as abnormal, the abnormal feature values ​​and occurrence time will be recorded, and the correlation analysis results will be generated.

[0067] The pattern recognition submodule uses the correlation analysis results and applies the isolation forest algorithm to distinguish normal and abnormal operation modes, determine the potential failure points of the equipment, and screen abnormal modes to generate a failure mode data set;

[0068] The pattern recognition submodule uses the correlation analysis results and applies the isolation forest algorithm to identify normal and abnormal operating modes. At this time, the module will classify the operating behavior of the energy equipment according to the abnormal data marked in the correlation analysis results, and analyze the fault type of the equipment. Each equipment operation mode will be assigned different weights according to the different types of faults. This weight is used to screen out potential points of equipment failure and finally generate a fault mode data set. This data set will record abnormal modes, fault types and correlation analysis results to provide a reference for subsequent equipment maintenance.

[0069] See also Figure 2 and Figure 4 ,The maintenance prediction module includes a feature change analysis submodule, a key feature screening submodule, and a probability calculation submodule;

[0070] The feature change analysis submodule uses decision tree and random forest algorithms based on the fault mode data set to analyze the parameter fluctuations of multiple time series, determine the fluctuation trend, and determine the potential fault occurrence period through the fluctuation trend to generate feature fluctuation analysis results;

[0071] The feature change analysis submodule is based on the fault mode data set, and uses decision trees and random forest algorithms to analyze the changes in the characteristic parameters of energy equipment in different time periods to detect the fluctuation trend that causes equipment failure. This process requires time series analysis of the parameters. Through multiple sampling, the change amplitude and speed of each parameter are calculated to determine whether there are abnormal fluctuations. At the same time, the feature change analysis submodule will evaluate the correlation between different parameters to find out the relationship between potential faults and characteristic parameters, including changes in key indicators such as temperature and pressure. After obtaining the data, the feature fluctuation analysis results are generated, including the fluctuation curves of each parameter and the comparison results with historical fault data to clarify the occurrence time and trend of potential faults.

[0072] The key feature screening submodule screens the features associated with equipment failures from the feature fluctuation analysis results, including the temperature rise rate or abnormal pressure drop, determines the key parameters for fault prediction, and generates a fault-related feature set;

[0073] The key feature screening submodule extracts features associated with equipment failure from the feature fluctuation analysis results. It is necessary to determine which feature changes are most closely related to equipment failure. The features are a sharp rise in temperature or a sudden drop in pressure. According to the changing trend of the features, thresholds are set to determine key parameters. Then, the key parameters for fault prediction are screened out to generate a fault-related feature set. The feature set will contain all key parameters associated with equipment failure and their corresponding fluctuation trends.

[0074] The probability calculation submodule uses the fault-related feature set and a genetic algorithm to calculate the statistical probability of the occurrence of the fault type and integrates it into multi-type probability data to generate fault probability assessment information;

[0075] The probability calculation submodule uses the fault-related feature set and a genetic algorithm to calculate the occurrence probability of different fault types. By analyzing the fluctuation frequency of each feature and the fault history data, the probability relationship between each feature and the fault type is found. The fluctuation frequency of each parameter in the fault-related feature set within a certain period of time is calculated as the input parameter of the genetic algorithm. After multiple calculations, the statistical probability of various fault types is obtained, and fault probability assessment information is generated.

[0076] Genetic algorithm, according to the formula:

[0077]

[0078] Calculate the probability of occurrence of multiple fault types;

[0079] Among them, P(x) is the probability of the target fault type, f(x) is the fitness of the fault type determined by the evaluation function in the genetic algorithm, w(x) is the weight coefficient of the fault type, λ is the adjustment coefficient, which enhances the flexibility and adaptability of the model, g(x) is the weighted value of the historical influencing factors of the fault type, n is the total number of fault types, and xi represents the i-th fault type.

[0080] Execution process:

[0081] First, the fitness f(x) of each fault type is calculated, and the weight w(x) of each fault is determined by analyzing external historical data. Then, the weighted value g(x) of the historical influencing factors of the fault type is calculated, where g(x) can be obtained by combining historical fault data and external environmental factors. Then, a suitable adjustment coefficient λ is set to optimize the weight and the influence of historical factors. The specific method includes using historical effect feedback to adjust λ. Finally, the above calculation results are integrated, and the probability P(x) of each fault type is calculated according to the formula to generate fault probability assessment information.

[0082] See also Figure 2 and Figure 5 ,The scheduling optimization module includes a fault assessment submodule, a priority determination submodule, and a scheduling plan submodule;

[0083] The fault assessment submodule performs fault risk analysis on each energy device based on the fault probability assessment information, marks the device parameters as abnormal, and classifies the energy devices for fault risk assessment to generate device fault ratings;

[0084] The fault assessment submodule checks the various parameters of each energy device based on the fault probability assessment information and analyzes its failure risk. The module first collects the operating data of the equipment, including key parameters such as temperature and pressure, and uses the data to compare with the fault probability assessment information to identify high-risk parameters. The parameters are marked as abnormal and the equipment is classified into risk levels according to the severity of the abnormality. In this way, the potential risks of each device can be identified, the equipment failure rating can be generated, and the safe operation of key equipment can be ensured.

[0085] The priority determination submodule uses the equipment failure rating to compare the equipment's operational criticality and failure risk, evaluates the equipment's maintenance urgency, determines the equipment maintenance priority, and generates a maintenance urgency ranking;

[0086] The priority determination submodule uses equipment failure ratings to evaluate the maintenance urgency of equipment, considering the operational criticality of each device, such as energy output, system stability impact and other factors, and comparing them with the failure risk. In this process, the equipment's operating data and failure rating data are used to calculate the maintenance priority and determine which equipment should be maintained first. This calculation takes into account the severity of the fault type and the importance of the equipment in the entire energy, and generates a maintenance urgency ranking to ensure that critical equipment can be maintained in a timely manner and avoid large-scale functional failures.

[0087] The scheduling submodule uses a long short-term memory network to schedule maintenance tasks and allocate resources based on the maintenance urgency, optimize resource utilization and time efficiency, determine the key equipment that will receive maintenance first, and generate a priority maintenance sequence table;

[0088] The scheduling submodule uses a long short-term memory network (LSTM) to optimize the time scheduling and resource allocation of maintenance tasks based on the maintenance urgency sorting. The module analyzes the maintenance history and current fault status of the equipment to predict the duration and required resources of the maintenance task, including the configuration of technicians, the preparation of required materials, and the maintenance window. By optimizing the allocation and scheduling of resources, it can improve resource utilization efficiency and time efficiency, and ultimately determine the key equipment that receives maintenance first, and generate a priority maintenance sequence table. This schedule ensures that maintenance work can be carried out at the most appropriate time, reducing equipment downtime.

[0089] See also Figure 2 and Figure 6 ,The load prediction module includes a data integration submodule, a trend analysis submodule, and a load calculation submodule;

[0090] The data integration submodule integrates the real-time and historical load data of the energy station based on the priority maintenance sequence table, marks the data anomalies during the maintenance period, and analyzes the impact of maintenance on the load to generate load impact data;

[0091] The data integration submodule integrates the real-time and historical load data of the energy station based on the priority maintenance sequence table. The module uses the maintenance information to adjust the data set and identify data anomalies during maintenance, such as a sudden increase or decrease in energy consumption caused by maintenance operations. In addition, the module analyzes the specific impact of maintenance activities on the energy load, such as the reduction in energy consumption during downtime maintenance and the load recovery mode after maintenance. In this way, it can effectively identify maintenance-related load changes and generate corresponding load impact data.

[0092] The trend analysis submodule extracts and analyzes the seasonal and cyclical trends of energy consumption based on load impact data, determines seasonal changes and abnormal fluctuations in consumption patterns, and generates energy consumption pattern recognition results;

[0093] The trend analysis submodule extracts seasonal and cyclical trends in energy consumption based on load impact data. The module uses statistical analysis methods, such as moving average or exponential smoothing techniques, to analyze long-term trends and cyclical changes in the data, identifying, for example, energy consumption peaks during high temperatures in summer or energy consumption valleys during low temperatures in winter. In addition, the module also detects abnormal fluctuations that do not match the expected pattern, caused by equipment failures or changes in the external environment. Finally, the module generates energy consumption pattern recognition results, which reveal seasonal changes in consumption patterns and key abnormal fluctuation points.

[0094] The load calculation submodule calculates the load demand in the future differential time period according to the energy consumption pattern recognition results during the scheduled maintenance period, and predicts the load peak and valley values ​​in the time period to generate load change prediction information;

[0095] Based on the energy consumption pattern recognition results, the load calculation submodule calculates the load demand for different time periods in the future during the scheduled maintenance period. The module uses linear regression analysis or time series prediction to calculate the load peaks and valleys within a specific period. Through calculation, the module can predict the load changes during the maintenance period and its subsequent time periods. The load change prediction information finally generated will detail the expected load highs and lows, as well as load fluctuations, to help energy stations optimize resource allocation and energy scheduling.

[0096] See also Figure 2 and Figure 7 ,The energy adjustment module includes a load monitoring submodule, an energy analysis submodule, and a parameter adjustment submodule;

[0097] The load monitoring submodule monitors the real-time data stream based on the load change prediction information, records the current energy output level of the energy station, and regularly updates the data points to capture the instant load changes and generate real-time energy monitoring data;

[0098] The load monitoring submodule monitors the real-time data stream based on the load change prediction information. During this process, the submodule uses sensors and data acquisition to record the energy output level of the energy station, such as electricity and heat energy, in real time, and regularly updates the data points through data analysis technology to capture instant load changes. In addition, the module will also record key indicators related to load changes, such as power peaks and electricity consumption, in order to generate real-time energy monitoring data, provide continuous observation of the operating status of the energy station, and ensure the real-time and accuracy of the data.

[0099] The energy analysis submodule analyzes the matching degree between energy output and load demand based on real-time energy monitoring data, identifies the difference between supply and demand, and compares historical data patterns to identify unusual energy consumption changes and generate energy matching evaluation results;

[0100] The energy analysis submodule performs a matching analysis between energy output and load demand based on real-time energy monitoring data. It first determines whether the current energy output meets the load demand, analyzes the difference between supply and demand, and identifies unusual energy consumption changes by comparing historical data patterns, such as unplanned energy consumption peaks or valleys, which are caused by external events or equipment efficiency issues. Finally, the module generates an energy matching evaluation result, which reflects the efficiency and adaptability of energy output and provides a basis for adjusting the strategy.

[0101] The parameter adjustment submodule adjusts the output settings of transformers and distribution units according to the energy matching evaluation results, optimizes the energy flow and allocation strategy, matches consumption demand and forecast data, and generates energy output allocation strategy;

[0102] The parameter adjustment submodule adjusts the parameters of the key components of the energy station according to the energy matching evaluation results, including the output settings of the transformer and the distribution unit. According to the current energy demand and forecast data, the output parameters of the equipment are adjusted to optimize the energy flow and distribution strategy. The adjustment process includes setting the output voltage, current limit and corresponding load distribution rules to ensure that the energy supply matches the consumption demand while considering stability and safety. Finally, the generated energy output distribution strategy will explain the adjustment settings of each device and the expected energy distribution effect.

[0103] See also Figure 2 and Figure 8 ,The maintenance window optimization module includes a time analysis submodule, an idle time period selection submodule, and a maintenance scheduling submodule;

[0104] The time analysis submodule predicts the impact of energy demand during maintenance based on the energy output allocation strategy, calculates the energy supply capacity of the differential time window, evaluates the impact of downtime on the energy grid, and develops a feasible maintenance time strategy to generate a maintenance time optimization plan;

[0105] The time analysis submodule predicts the impact of energy demand during maintenance based on the energy output allocation strategy. It calculates the energy supply capacity in different time windows by analyzing the relationship between the predicted load demand and the existing energy supply capacity. It evaluates the specific impact of the situation on the energy grid by simulating various downtime situations, such as supply shortages or surpluses. Based on the analysis, it develops the best maintenance time strategy, taking into account minimizing the impact on the operation of the energy grid while ensuring the efficiency and safety of the maintenance work. Finally, the module generates a maintenance time optimization plan.

[0106] The idle period selection submodule identifies the low-load period or non-peak period of the energy station operation based on the maintenance time optimization scheme, selects the optimal time point for performing maintenance, optimizes the impact on critical operating time, and generates the optimal maintenance period selection;

[0107] The idle period selection submodule identifies the low-load period or non-peak period of energy station operation based on the maintenance time optimization plan. This module analyzes the periodic and seasonal changes in energy consumption and forecast data to determine during which periods maintenance will minimize the impact on critical operating time. In addition, considering safety and operating efficiency, those periods with relatively low energy demand are selected for maintenance, which not only ensures the stable operation of the energy system, but also optimizes the cost-effectiveness of maintenance operations and generates the optimal maintenance period selection.

[0108] The maintenance scheduling submodule selects the optimal maintenance period, arranges the date and time of the maintenance task, coordinates the necessary resources and personnel, and generates a maintenance task schedule;

[0109] The maintenance scheduling submodule arranges the date and time of the maintenance task based on the optimal maintenance period. This module coordinates the required resources and personnel, including maintenance tools, spare parts and technicians, to ensure that all resources are available within the specified maintenance time. The module also takes into account the complexity and time required for the maintenance tasks, and reasonably arranges the sequence and duration of the maintenance tasks to optimize the flow and efficiency of the entire maintenance process. Finally, the generated maintenance task schedule lists in detail the specific arrangements for each maintenance task, including the start and end times.

[0110] See also Fig. 9 , a remote-controlled distributed energy station intelligent operation and maintenance management method, comprising:

[0111] S1: Based on historical operation and maintenance data, deploy remote monitoring equipment to record the operating status of energy equipment, integrate historical operation and maintenance data with real-time characteristic parameters, and evaluate the correlation between parameter anomalies and equipment failures to generate a failure mode data set;

[0112] S2: Based on the failure mode data set, a genetic algorithm is used to analyze the feature changes in the time series data, screen the key features that cause equipment failures, calculate the statistical probability of the failure type, and combine multiple types of failure data to generate failure probability assessment information;

[0113] S3: Using the failure probability assessment information and the long short-term memory network, the failure risk assessment of each energy device is carried out. According to the failure risk and operation criticality of the equipment, the equipment maintenance priority is determined. Through resource allocation and time scheduling optimization, a maintenance priority list is generated.

[0114] S4: Based on the priority maintenance sequence table, integrate the historical load data and real-time load information of the energy station, analyze the time trend of energy consumption, predict future load demand changes, calculate the load demand in different time periods, and generate load change prediction information;

[0115] S5: Based on the load change prediction information, monitor the energy output level of the current energy station, analyze the energy distribution status, select the idle period of the energy station operation for maintenance, optimize the maintenance time window, and generate energy distribution and maintenance task time.

[0116] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A remote-controlled distributed energy station intelligent operation and maintenance management system, characterized in that: The system comprises: The fault identification module collects characteristic parameters of energy equipment during operation based on historical operation and maintenance data, analyzes the correlation between characteristic parameters and equipment failures, and identifies abnormal modes based on equipment operation records to generate a fault mode data set; The maintenance prediction module uses a genetic algorithm to analyze feature changes in the time series data through the failure mode data set, screens key features that cause energy equipment failures, calculates the failure probability of the energy equipment, and generates failure probability assessment information; The scheduling optimization module uses the fault probability assessment information and a long short-term memory network to analyze the criticality of energy equipment, select equipment for priority maintenance, and sort the maintenance operations by time to obtain a priority maintenance sequence table; The load prediction module analyzes the time trend of energy consumption according to the priority maintenance sequence table and the historical load data of the energy station, calculates the load demand in different time periods, and predicts future load changes to generate load change prediction information; The energy adjustment module monitors the energy output level of the current energy station based on the load change prediction information, analyzes the energy distribution status of the energy station, adjusts the output parameters of the energy station according to the analysis results, and generates an energy output distribution strategy; The maintenance window optimization module analyzes the optimal time for maintenance operations based on the energy output allocation strategy, selects idle time periods of the energy station operation to schedule maintenance tasks, and generates a maintenance task schedule; The maintenance prediction module includes: The feature change analysis submodule applies decision tree and random forest algorithms based on the fault mode data set to analyze the parameter fluctuations of multiple time series, determine the fluctuation trend, and determine the potential fault occurrence period through the fluctuation trend to generate feature fluctuation analysis results; The key feature screening submodule screens features associated with equipment failure from the feature fluctuation analysis results, including temperature rise rate or abnormal pressure drop, determines key parameters for fault prediction, and generates a fault-related feature set; The probability calculation submodule uses the fault association feature set and a genetic algorithm to calculate the statistical probability of the occurrence of the fault type, and integrates it into multi-type probability data to generate fault probability assessment information; The genetic algorithm, according to the formula: Calculate the probability of occurrence of multiple fault types; Where P(x) is the probability of the target fault type, f(x) is the fault type fitness determined by the evaluation function in the genetic algorithm, w(x) is the weight coefficient of the fault type, λ is the adjustment coefficient, g(x) is the weighted value of the historical influencing factors of the fault type, n is the total number of fault types, and x i represents the i-th fault type; The scheduling optimization module includes: The fault assessment submodule performs a fault risk analysis on each energy device based on the fault probability assessment information, marks the device parameters as abnormal, and classifies the energy devices for fault risk assessment to generate a device fault rating; The priority determination submodule uses the equipment failure rating to compare the operation criticality and failure risk of the equipment, evaluates the maintenance urgency of the equipment, determines the priority of equipment maintenance, and generates a maintenance urgency ranking; The scheduling submodule uses a long short-term memory network to schedule and allocate resources for maintenance tasks based on the maintenance urgency ranking, optimize resource utilization and time efficiency, determine the key equipment that will receive maintenance first, and generate a priority maintenance sequence table; The load prediction module includes: The data integration submodule integrates the real-time and historical load data of the energy station based on the priority maintenance sequence table, marks data abnormal points during maintenance, and analyzes the impact of maintenance on the load to generate load impact data; The trend analysis submodule extracts and analyzes seasonal and cyclical trends of energy consumption based on the load impact data, determines seasonal changes and abnormal fluctuations in consumption patterns, and generates energy consumption pattern recognition results; The load calculation submodule calculates the load demand in the future difference time period according to the energy consumption pattern recognition result during the scheduled maintenance period, predicts the load peak and valley value of the time period, and generates load change prediction information.

2. The remote-controlled distributed energy station intelligent operation and maintenance management system according to claim 1, characterized in that: The fault mode data set includes abnormal frequency, impact degree, and fault response time. The fault probability assessment information includes equipment model, fault type probability, and predicted fault occurrence period. The priority maintenance sequence table includes equipment identification, planned maintenance date, and maintenance urgency level. The load change prediction information includes predicted load peak, trough time points, and potential load increase or decrease percentages. The energy output allocation strategy includes adjusted power generation, power generation time, and adjusted energy supply area. The maintenance task schedule includes maintenance operation sequence, scheduled operation time, and operator configuration information.

3. The remote-controlled distributed energy station intelligent operation and maintenance management system according to claim 1 is characterized in that: The energy adjustment module comprises: The load monitoring submodule monitors the real-time data stream based on the load change prediction information, records the energy output level of the current energy station, and regularly updates the data points to capture the instant load changes and generate real-time energy monitoring data; The energy analysis submodule analyzes the matching degree between energy output and load demand based on the real-time energy monitoring data, identifies the difference between supply and demand, and compares historical data patterns to identify unusual energy consumption changes, and generates energy matching evaluation results; The parameter adjustment submodule adjusts the output settings of the transformer and the distribution unit according to the energy matching evaluation result, optimizes the energy flow and allocation strategy, matches the consumption demand and the forecast data, and generates the energy output allocation strategy.

4. A remote-controlled distributed energy station intelligent operation and maintenance management method, characterized in that: The remote-controlled distributed energy station intelligent operation and maintenance management system according to any one of claims 1 to 3 comprises the following steps: Based on historical operation and maintenance data, remote monitoring equipment is deployed to record the operating status of energy equipment, integrate historical operation and maintenance data with real-time characteristic parameters, and evaluate the correlation between parameter anomalies and equipment failures to generate a failure mode data set; Based on the failure mode data set, a genetic algorithm is used to analyze feature changes in time series data, screen key features that cause equipment failures, calculate statistical probabilities of failure types, and combine multiple types of failure data to generate failure probability assessment information; Using the failure probability assessment information, a long short-term memory network is used to perform a failure risk assessment on each energy device, and the equipment maintenance priority is determined according to the failure risk and operation criticality of the equipment. Through resource allocation and time scheduling optimization, a priority maintenance sequence table is generated; Based on the priority maintenance sequence table, historical load data and real-time load information of the energy station are integrated to analyze the time trend of energy consumption, predict future load demand changes, calculate load demands in different time periods, and generate load change prediction information; Based on the load change prediction information, the energy output level of the current energy station is monitored, the energy distribution status is analyzed, and the idle period of the energy station operation is selected for maintenance, the maintenance time window is optimized, and the energy distribution and maintenance task time are generated.

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