A method, device and medium for operation and maintenance monitoring based on distributed energy storage equipment

Abstract

The present application discloses an operation and maintenance monitoring method, device and medium based on distributed energy storage equipment, the method comprising: performing energy storage equipment status correlation analysis on distributed energy storage real-time parameters to determine the distributed energy storage linear time series state; performing abnormal node analysis on the distributed energy storage linear time series state to obtain the distributed energy storage equipment state; determining the distributed energy storage equipment abnormality handling strategy through a distributed abnormality handling configuration algorithm according to the distributed energy storage equipment state; performing time series analysis on the distributed energy storage equipment time series state to obtain distributed energy storage equipment state prediction data; determining a distributed energy storage maintenance strategy according to the distributed energy storage equipment state prediction data; performing equipment resource distribution configuration on the distributed energy storage maintenance strategy to obtain a resource scheduling strategy. The present application solves the technical problem of insufficient accuracy and rationality in the maintenance and abnormality monitoring of distributed energy storage equipment in the prior art through the above method.

Description

Technical Field

[0001] The present application relates to the field of energy storage system operation and maintenance technology, and in particular to an operation and maintenance monitoring method, equipment and medium based on distributed energy storage equipment. Background Art

[0002] With the widespread application of renewable energy and the rapid development of smart grids, distributed energy storage equipment, as an important part of the power system, has become increasingly important. Distributed energy storage equipment can flexibly store and release electrical energy, effectively alleviate grid pressure, improve energy efficiency, and promote the consumption of renewable energy.

[0003] Since distributed energy storage equipment is usually deployed in multiple different locations and is highly decentralized, it is very difficult to monitor the operation and maintenance of distributed energy storage equipment. Existing monitoring methods often rely on manual inspections and centralized monitoring systems. Manual inspections are not only inefficient and costly, but also difficult to detect and handle potential equipment failures in a timely manner. Due to the rich variety of distributed energy storage equipment, the accuracy and rationality of the maintenance and abnormal monitoring of distributed energy storage equipment are still insufficient. Summary of the invention

[0004] The embodiments of the present application provide an operation and maintenance monitoring method, device and medium based on a distributed energy storage device, which solves the technical problem of insufficient accuracy and rationality in the maintenance and abnormal monitoring of distributed energy storage devices in the prior art.

[0005] In a first aspect, an embodiment of the present application provides an operation and maintenance monitoring method based on a distributed energy storage device, characterized in that the method includes: obtaining distributed energy storage real-time parameters based on preset distributed energy storage storage nodes, and performing energy storage device status correlation analysis on the distributed energy storage real-time parameters to determine the distributed energy storage linear time series state; performing abnormal node analysis on the distributed energy storage linear time series state to obtain the distributed energy storage device state; wherein the distributed energy storage device state includes: distributed energy storage device abnormal state, distributed energy storage device time series state; according to the distributed energy storage device state, determining the distributed energy storage device abnormality handling strategy through a distributed abnormality handling configuration algorithm; performing time series analysis on the distributed energy storage device time series state to obtain distributed energy storage device state prediction data; according to the distributed energy storage device state prediction data, determining the distributed energy storage maintenance strategy through a distributed energy storage maintenance algorithm; performing device resource distribution configuration on the distributed energy storage maintenance strategy to obtain a resource scheduling strategy.

[0006] In one implementation of the present application, a storage device state correlation analysis is performed on distributed energy storage real-time parameters to determine the distributed energy storage linear time series state, specifically including: based on the distributed energy storage real-time parameters, determining the distributed energy storage device state category, and according to the distributed energy storage device state category, clustering the distributed energy storage real-time parameters to determine the distributed device correlation analysis data; performing frequent pattern growth analysis on the correlation analysis data to obtain the distributed energy storage parameter correlation; wherein the distributed energy storage parameter correlation includes: device parameter impact correlation and distributed device impact correlation; based on the distributed energy storage parameter correlation, the device state correlation is corrected to determine the distributed energy storage linear time series state.

[0007] In one implementation of the present application, an abnormal node analysis is performed on the linear time series state of distributed energy storage to obtain the state of distributed energy storage equipment, specifically including: performing abnormal state determination on the linear time series state of distributed energy storage to obtain abnormal node data; wherein the abnormal node data includes: historical abnormal nodes and real-time abnormal nodes; dividing the historical abnormal nodes into historical processing cycles to obtain the historical abnormal state of the equipment; performing abnormal type determination on the real-time abnormal nodes to determine the real-time abnormal state of the equipment; and determining the state of the distributed energy storage equipment based on the historical abnormal state of the equipment and the real-time abnormal state of the equipment.

[0008] In one implementation of the present application, according to the state of the distributed energy storage device, the distributed energy storage device abnormality handling strategy is determined through a distributed abnormality handling configuration algorithm, specifically including: classifying the distributed energy storage device state into abnormal states, and configuring key fields of the distributed energy storage device state after the abnormal state classification to obtain device state category parameters; based on the device state category parameters, determining the device state impact parameters through distributed energy storage device state impact analysis; according to the device state category parameters and the device state impact parameters, determining the distributed energy storage device abnormality handling strategy through distributed abnormality handling matching.

[0009] In one implementation of the present application, a time series analysis is performed on the time series state of a distributed energy storage device to obtain distributed energy storage device state prediction data, specifically including: based on the time series state of the distributed energy storage device, through stage trend analysis, determining the positive change trend of the energy storage state; performing a periodic data pattern analysis on the current state of the distributed energy storage to obtain the periodic change trend of the energy storage state; and obtaining the distributed energy storage device state prediction data by using a hierarchical analysis method to analyze the positive change trend of the energy storage state and the periodic change trend of the energy storage state.

[0010] In one implementation of the present application, a distributed energy storage maintenance strategy is determined based on the distributed energy storage device status prediction data through a distributed energy storage maintenance algorithm, specifically including: based on the distributed energy storage status prediction data, determining the distributed energy storage device simulation operation parameters through digital twin modeling; extracting abnormal state features of the distributed energy storage device simulation operation parameters, and determining the distributed energy storage maintenance node through real-time parameter synchronization based on the distributed energy storage abnormality judgment threshold; performing historical inertia maintenance matching on the distributed energy storage maintenance nodes to determine the distributed energy storage maintenance strategy.

[0011] In one implementation of the present application, a distributed energy storage maintenance strategy is configured for equipment resource distribution to obtain a resource scheduling strategy, specifically including: based on the distributed energy storage maintenance strategy, determining the expected data for distributed resource scheduling through scheduling prediction analysis; performing resource optimization control on the expected data for distributed resource scheduling to obtain the scheduling parameters of the distributed energy storage equipment; according to the scheduling parameters of the distributed energy storage equipment, determining the distributed energy storage resource allocation parameters through resource scheduling simulation, and obtaining the resource scheduling strategy based on the distributed energy storage resource allocation parameters.

[0012] In one implementation of the present application, after performing distributed resource allocation on the distributed energy storage maintenance strategy to obtain a resource scheduling strategy, the method also includes: performing a stage control effect evaluation on the resource scheduling strategy to obtain distributed resource scheduling optimization parameters; iteratively updating the resource scheduling strategy according to the distributed resource scheduling optimization parameters, and uploading the iteratively updated resource scheduling strategy to a preset scheduling strategy log.

[0013] In a second aspect, an embodiment of the present application further provides an operation and maintenance monitoring device based on a distributed energy storage device, characterized in that the device comprises: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: obtain distributed energy storage real-time parameters based on preset distributed energy storage storage nodes, and perform energy storage device status-related analysis on the distributed energy storage real-time parameters to determine the distributed energy storage linear time series state; perform abnormal node analysis on the distributed energy storage linear time series state to obtain the distributed energy storage device state; wherein the distributed energy storage device state includes: abnormal state of the distributed energy storage device and time series state of the distributed energy storage device; determine the abnormal handling strategy of the distributed energy storage device according to the state of the distributed energy storage device through a distributed abnormal handling configuration algorithm; perform time series analysis on the time series state of the distributed energy storage device to obtain distributed energy storage device state prediction data; determine the distributed energy storage maintenance strategy through a distributed energy storage maintenance algorithm according to the distributed energy storage device state prediction data; perform device resource distribution configuration on the distributed energy storage maintenance strategy to obtain a resource scheduling strategy.

[0014] In a third aspect, an embodiment of the present application further provides a non-volatile computer storage medium for operation and maintenance monitoring based on distributed energy storage equipment, storing computer executable instructions, characterized in that the computer executable instructions are configured to: obtain distributed energy storage real-time parameters based on preset distributed energy storage storage nodes, and perform energy storage equipment status-related analysis on the distributed energy storage real-time parameters to determine the distributed energy storage linear time series state; perform abnormal node analysis on the distributed energy storage linear time series state to obtain the distributed energy storage equipment state; wherein the distributed energy storage equipment state includes: abnormal state of distributed energy storage equipment and time series state of distributed energy storage equipment; determine the abnormal handling strategy of distributed energy storage equipment according to the state of distributed energy storage equipment through a distributed abnormal handling configuration algorithm; perform time series analysis on the time series state of distributed energy storage equipment to obtain distributed energy storage equipment state prediction data; determine the distributed energy storage maintenance strategy through a distributed energy storage maintenance algorithm according to the distributed energy storage equipment state prediction data; perform equipment resource distribution configuration on the distributed energy storage maintenance strategy to obtain a resource scheduling strategy.

[0015] The embodiments of the present application provide an operation and maintenance monitoring method, device and medium based on distributed energy storage equipment. By analyzing and predicting the abnormal state, maintenance strategy and resource allocation of the distributed energy storage equipment, the technical problem of insufficient accuracy and rationality in the maintenance and abnormal monitoring of the distributed energy storage equipment in the prior art is solved, the intelligent formulation of the operation and maintenance strategy of the distributed energy storage equipment is realized, and the accuracy and rationality of the maintenance and abnormal monitoring of the distributed energy storage equipment are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 A flow chart of an operation and maintenance monitoring method based on a distributed energy storage device provided in an embodiment of the present application;

[0018] Figure 2 A schematic diagram of the internal structure of an operation and maintenance monitoring device based on a distributed energy storage device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0020] The embodiments of the present application provide an operation and maintenance monitoring method, device and medium based on distributed energy storage equipment. By analyzing and predicting the abnormal state, maintenance strategy and resource allocation of the distributed energy storage equipment, the technical problem of insufficient accuracy and rationality in the maintenance and abnormal monitoring of the distributed energy storage equipment in the prior art is solved, the intelligent formulation of the operation and maintenance strategy of the distributed energy storage equipment is realized, and the accuracy and rationality of the maintenance and abnormal monitoring of the distributed energy storage equipment are improved.

[0021] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0022] Figure 1 A flow chart of an operation and maintenance monitoring method based on distributed energy storage equipment provided in an embodiment of the present application. Figure 1 As shown, an operation and maintenance monitoring method based on a distributed energy storage device provided in an embodiment of the present application specifically includes the following steps:

[0023] Step 101: Based on the preset distributed energy storage storage nodes, real-time parameters of distributed energy storage are obtained, and energy storage device state correlation analysis is performed on the real-time parameters of distributed energy storage to determine the linear time series state of distributed energy storage.

[0024] Specifically, the method includes: determining the state category of distributed energy storage equipment based on the real-time parameters of distributed energy storage, and clustering the real-time parameters of distributed energy storage according to the state category of distributed energy storage equipment to determine the distributed equipment correlation analysis data; performing frequent pattern growth analysis on the correlation analysis data to obtain the distributed energy storage parameter correlation; wherein the distributed energy storage parameter correlation includes: equipment parameter impact correlation and distributed equipment impact correlation; based on the distributed energy storage parameter correlation, the equipment state correlation is corrected to determine the distributed energy storage linear time series state.

[0025] The present application obtains real-time parameters of distributed energy storage based on preset distributed energy storage nodes, performs energy storage device status correlation analysis on the real-time parameters of distributed energy storage, determines the linear timing state of distributed energy storage, realizes the timing analysis of the state of distributed energy storage equipment, and improves the accuracy of the state of distributed energy storage equipment.

[0026] In the examples of the present application, a detailed explanation is given by the following Example 1.

[0027] Example 1: Due to the diversity of distributed energy storage devices, data transmission nodes are required according to actual conditions. This node is used for real-time transmission

[0028] First, various parameters of energy storage equipment are collected in real time from the preset distributed energy storage nodes, including voltage, current, temperature, SOC (state of charge), etc. Various parameters are classified to determine the status category of distributed energy storage equipment.

[0029] The K-means algorithm is a clustering algorithm based on iterative optimization. Its purpose is to divide the data set into k clusters so that each data point belongs to the nearest cluster and the center of the cluster (also called the centroid) is the average value of all data points. The algorithm continuously optimizes the division of clusters and the location of cluster centers through iteration until the convergence condition is reached.

[0030] The real-time parameters of distributed energy storage are clustered and the correlation between the parameters is analyzed. The frequent pattern growth algorithm is applied to mine the potential correlation between the parameters and form correlation analysis data.

[0031] According to the correlation analysis data and the classification results of the distributed energy storage equipment status, the current status of the distributed energy storage is corrected and evaluated.

[0032] Step 102: Perform abnormal node analysis on the distributed energy storage linear time series state to obtain the distributed energy storage device state.

[0033] Among them, the status of distributed energy storage equipment includes: abnormal status of distributed energy storage equipment and timing status of distributed energy storage equipment.

[0034] Specifically, it includes: determining the abnormal state of the distributed energy storage linear time series state to obtain abnormal node data; wherein the abnormal node data includes: historical abnormal nodes and real-time abnormal nodes; dividing the historical abnormal nodes into historical processing cycles to obtain the historical abnormal state of the equipment; determining the abnormal type of the real-time abnormal nodes to determine the real-time abnormal state of the equipment; determining the state of the distributed energy storage equipment based on the historical abnormal state of the equipment and the real-time abnormal state of the equipment.

[0035] The present application performs abnormal node analysis on the distributed energy storage linear time series state to obtain the distributed energy storage device state, thereby realizing the analysis of the historical state and real-time state of the abnormal state of the distributed energy storage device and improving the analysis efficiency of the abnormal state of the distributed energy storage device.

[0036] In the examples of the present application, detailed explanation is given by the following Example 2.

[0037] Example 2: Continuously monitor the linear time series state of the distributed energy storage system, collect and process relevant data. By monitoring the abnormal threshold of the data time series (i.e., the abnormal state threshold of each device parameter), abnormal nodes are identified. Abnormal node data includes historical abnormal nodes and real-time abnormal nodes.

[0038] The identified historical abnormal nodes are divided according to the historical processing cycle, and the abnormal nodes in each cycle constitute a set. The abnormal node set in each cycle is analyzed, and the historical abnormal state of the equipment is determined in combination with the historical operation and maintenance records and equipment performance parameters, including the abnormal type, occurrence frequency, duration, etc. Since the historical abnormal state includes the abnormal generation and processing process, the node position of this part is defined as the time sequence node where the abnormality occurs.

[0039] Respond quickly to abnormal nodes detected in real time and determine the abnormal type. According to the abnormal type, combined with the real-time operating parameters and historical abnormal status of the equipment, determine the real-time abnormal status of the equipment and evaluate its impact on the overall operation of the system.

[0040] It should be noted that the monitoring of abnormal states and the identification of abnormal types are consistent between historical abnormal nodes and real-time abnormal nodes.

[0041] The comprehensive status of the distributed energy storage device is determined by combining the historical abnormal status and real-time abnormal status of the device. The device status includes the abnormal status of the distributed energy storage device and the time series status of the distributed energy storage device. The abnormal status refers to whether the device is currently in an abnormal state, as well as the specific type and degree of the abnormality; the time series status reflects the performance change trend of the device in the time dimension.

[0042] Step 103: Determine the distributed energy storage device exception handling strategy according to the state of the distributed energy storage device through a distributed exception handling configuration algorithm.

[0043] Specifically, it includes: classifying the abnormal status of distributed energy storage equipment, and configuring the key fields of the distributed energy storage equipment status after abnormal status classification to obtain equipment status category parameters; based on the equipment status category parameters, determining the equipment status impact parameters through distributed energy storage equipment status impact analysis; according to the equipment status category parameters and the equipment status impact parameters, determining the distributed energy storage equipment abnormality handling strategy through distributed abnormality handling matching.

[0044] This application determines the distributed energy storage device exception handling strategy according to the state of the distributed energy storage device through a distributed exception handling configuration algorithm, realizes the intelligent generation of the distributed energy storage device exception handling strategy, and improves the rationality and formulation efficiency of the distributed energy storage device exception handling strategy.

[0045] In the examples of the present application, this is explained in detail through the following Example 3.

[0046] Example 3: For each abnormal state, identify the key fields that affect the state evaluation, such as battery temperature for overheating abnormalities, SOC for overcharge / overdischarge abnormalities, etc. Set thresholds, weights and other parameters for each key field, which will be used for subsequent state impact analysis and formulation of abnormality handling strategies.

[0047] Then, the equipment status category parameters are generated to describe the current abnormal state of the equipment. The impact of different abnormal states on the overall operation of the system, such as safety, efficiency, cost, etc., is analyzed, and the impact of each abnormal state on the current system is calculated based on the equipment status category parameters and combined with the evaluation model to generate the equipment status impact parameters.

[0048] According to the equipment status category parameter and the equipment status impact parameter, the association form of the severity level and the abnormality category is set. After the abnormality occurs and is analyzed, an intersection can be determined according to the equipment status category parameter and the equipment status impact parameter. The intersection is the processing solution of the distributed energy storage device abnormality processing strategy.

[0049] It should be noted that the abnormal handling strategy of distributed energy storage equipment can be analyzed through data analysis of historical processing strategies, or it can be weighted averaged according to the frequency of use and the response effect to obtain a suitable processing strategy.

[0050] Step 104: Perform time series analysis on the time series state of the distributed energy storage device to obtain distributed energy storage device state prediction data.

[0051] Specifically, it includes: based on the time series status of distributed energy storage equipment, determining the positive change trend of the energy storage status through stage trend analysis; performing periodic data pattern analysis on the current status of distributed energy storage to obtain the periodic change trend of the energy storage status; and obtaining the distributed energy storage equipment status prediction data by combining the positive change trend of the energy storage status and the periodic change trend of the energy storage status through hierarchical analysis method.

[0052] The present application implements the prediction of the state of distributed energy storage devices by performing time series analysis on the time series state of distributed energy storage devices to obtain distributed energy storage device state prediction data, thereby improving the accuracy and rationality of the time series analysis of distributed energy storage devices.

[0053] In the examples of the present application, a detailed explanation is given by the following Example 4.

[0054] Example 4: First, divide the current state of distributed energy storage into different stages according to factors such as time and load changes. Perform trend analysis on the energy storage state data in each stage to identify the positive change trend of the energy storage state, such as the trend of power increase and efficiency change.

[0055] Then, the historical data of distributed energy storage is periodically analyzed to identify the periodic change pattern of the energy storage state, and the key characteristic parameters in each cycle, such as peak value, valley value, fluctuation amplitude, etc., are extracted to reflect the periodic change trend of the energy storage state.

[0056] Finally, the positive change trend and periodic change trend of energy storage status are used as input, and the analytic hierarchy process (AHP) is used for weight distribution and comprehensive evaluation. According to the relative importance and mutual influence of each factor, a judgment matrix is ​​constructed and the weight vector is calculated. The weight vector is multiplied by the corresponding trend data to obtain the weighted storage state. The prediction results are output in the form of charts, reports, etc. for user reference and decision-making. The prediction results are output in the form of charts, reports, etc. for user reference and decision-making.

[0057] Step 105: Determine a distributed energy storage maintenance strategy based on the distributed energy storage device status prediction data and a distributed energy storage maintenance algorithm.

[0058] Specifically, it includes: based on the distributed energy storage status prediction data, determining the simulated operating parameters of the distributed energy storage equipment through digital twin modeling; extracting the abnormal state features of the simulated operating parameters of the distributed energy storage equipment, and determining the distributed energy storage maintenance nodes through real-time parameter synchronization based on the distributed energy storage abnormality judgment threshold; performing historical inertia maintenance matching on the distributed energy storage maintenance nodes to determine the distributed energy storage maintenance strategy.

[0059] This application determines the distributed energy storage maintenance strategy based on the distributed energy storage status prediction data and through the distributed energy storage maintenance evaluation, thereby realizing the intelligent formulation and iterative update of the distributed energy storage equipment maintenance strategy, and improving the rationality, accuracy and formulation efficiency of the distributed energy storage maintenance strategy.

[0060] In the examples of the present application, this is explained in detail through the following Example 5.

[0061] Example 5: First, obtain the state prediction data of the distributed energy storage system, use digital twin technology to build a virtual model of the distributed energy storage device, and simulate the actual operating state of the device. Through the simulated operation, obtain the simulated operating parameters of the distributed energy storage device, including the simulated power consumption and simulated temperature fluctuation of the distributed energy storage device.

[0062] Then, the abnormal state features of the simulated operation parameters are extracted to identify abnormal mode features that may cause equipment failure or performance degradation. According to the abnormal mode features, the abnormal judgment threshold of distributed energy storage is set to monitor the equipment status in real time and trigger maintenance warnings.

[0063] The real-time parameters of the distributed energy storage equipment in actual operation are synchronized with the simulation parameters to ensure the consistency between the model and the actual situation. When the real-time parameters exceed the abnormality judgment threshold, the distributed energy storage maintenance node is determined, that is, the specific equipment or components that need to be maintained.

[0064] Perform historical inertia maintenance matching on distributed energy storage maintenance nodes, that is, find solutions to similar faults or maintenance cases in historical maintenance records. Based on the historical inertia matching results and combined with the current equipment status, formulate a distributed energy storage maintenance strategy, including maintenance time, maintenance content, required resources, etc.

[0065] Finally, the effectiveness of the implemented maintenance strategy is periodically evaluated, and the performance data of the equipment after maintenance is collected. Based on the evaluation results, the distributed energy storage maintenance optimization parameters are calculated to guide the iterative update of the maintenance strategy. The iteratively updated distributed energy storage maintenance strategy is uploaded to the preset maintenance strategy log for subsequent reference and continuous optimization.

[0066] Step 106: Perform equipment resource distribution configuration on the distributed energy storage maintenance strategy to obtain a resource scheduling strategy.

[0067] Specifically, it includes: based on the distributed energy storage maintenance strategy, determining the expected data of distributed resource scheduling through scheduling prediction analysis; performing resource optimization control on the expected data of distributed resource scheduling to obtain the scheduling parameters of distributed energy storage equipment; according to the scheduling parameters of distributed energy storage equipment, determining the distributed energy storage resource allocation parameters through resource scheduling simulation, and obtaining the resource scheduling strategy based on the distributed energy storage resource allocation parameters.

[0068] After performing distributed resource allocation on the distributed energy storage maintenance strategy to obtain a resource scheduling strategy, the method also includes: performing a stage control effect evaluation on the resource scheduling strategy to obtain distributed resource scheduling optimization parameters; iteratively updating the resource scheduling strategy according to the distributed resource scheduling optimization parameters, and uploading the iteratively updated resource scheduling strategy to a preset scheduling strategy log.

[0069] The present application performs distributed resource allocation on the distributed energy storage maintenance strategy to obtain a resource scheduling strategy, thereby realizing the intelligent formulation and iterative update of the resource scheduling strategy for distributed energy storage equipment, and improving the rationality, accuracy and formulation efficiency of the distributed energy storage resource scheduling strategy.

[0070] In the examples of the present application, this is explained in detail through the following Example 6.

[0071] Example 6: First, obtain the maintenance strategy of the distributed energy storage system, including the maintenance cycle and maintenance content of the equipment. It is necessary to consider factors such as load demand and energy supply to determine the expected data for distributed resource scheduling, including power demand and power balance.

[0072] The resource optimization control is carried out on the expected data of distributed resource scheduling, and the maintenance status and remaining capacity of the energy storage equipment are considered. The scheduling parameters of the distributed energy storage equipment, including charging and discharging power and time, are obtained through the genetic algorithm optimization algorithm.

[0073] Then, according to the dispatching parameters of distributed energy storage equipment, the resource allocation under different dispatching schemes is simulated through resource dispatching simulation. Based on the simulation results, the distributed energy storage resource allocation parameters are determined, including power distribution and power regulation among various energy storage devices.

[0074] Based on the distributed energy storage resource allocation parameters, combined with the scheduling objectives and constraints, a resource scheduling strategy is generated.

[0075] Finally, the resource scheduling strategy is evaluated for its stage control effect, and data from the actual scheduling process is collected to evaluate the execution effect of the scheduling strategy. Based on the evaluation results, the distributed resource scheduling optimization parameters are calculated to guide the iterative update of the resource scheduling strategy. The iteratively updated resource scheduling strategy is uploaded to the preset scheduling strategy log for subsequent reference and continuous optimization.

[0076] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, the embodiment of this application also provides an operation and maintenance monitoring device based on a distributed energy storage device, whose structure is as follows: Figure 2 shown.

[0077] Figure 2A schematic diagram of the internal structure of an operation and maintenance monitoring device based on a distributed energy storage device provided in an embodiment of the present application. Figure 2 As shown, the device includes:

[0078] at least one processor 201;

[0079] and, a memory 202 communicatively connected to the at least one processor;

[0080] The memory 202 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 201 to enable at least one processor 201 to:

[0081] Based on the preset distributed energy storage storage nodes, the real-time parameters of distributed energy storage are obtained, and the energy storage device status correlation analysis is performed on the real-time parameters of distributed energy storage to determine the linear time series state of distributed energy storage; the abnormal node analysis is performed on the linear time series state of distributed energy storage to obtain the state of distributed energy storage equipment; wherein, the state of distributed energy storage equipment includes: abnormal state of distributed energy storage equipment, time series state of distributed energy storage equipment; according to the state of distributed energy storage equipment, the abnormal handling strategy of distributed energy storage equipment is determined through the distributed abnormal handling configuration algorithm; the time series analysis of the time series state of distributed energy storage equipment is performed to obtain the state prediction data of distributed energy storage equipment; according to the state prediction data of distributed energy storage equipment, the distributed energy storage maintenance strategy is determined through the distributed energy storage maintenance algorithm; the equipment resource distribution configuration is performed on the distributed energy storage maintenance strategy to obtain the resource scheduling strategy.

[0082] Some embodiments of the present application provide corresponding Figure 1 A non-volatile computer storage medium based on operation and maintenance monitoring of distributed energy storage equipment stores computer executable instructions, and the computer executable instructions are set as follows:

[0083] Based on the preset distributed energy storage storage nodes, the real-time parameters of distributed energy storage are obtained, and the energy storage device status correlation analysis is performed on the real-time parameters of distributed energy storage to determine the linear time series state of distributed energy storage; the abnormal node analysis is performed on the linear time series state of distributed energy storage to obtain the state of distributed energy storage equipment; wherein, the state of distributed energy storage equipment includes: abnormal state of distributed energy storage equipment, time series state of distributed energy storage equipment; according to the state of distributed energy storage equipment, the abnormal handling strategy of distributed energy storage equipment is determined through the distributed abnormal handling configuration algorithm; the time series analysis of the time series state of distributed energy storage equipment is performed to obtain the state prediction data of distributed energy storage equipment; according to the state prediction data of distributed energy storage equipment, the distributed energy storage maintenance strategy is determined through the distributed energy storage maintenance algorithm; the equipment resource distribution configuration is performed on the distributed energy storage maintenance strategy to obtain the resource scheduling strategy.

[0084] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the IoT device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0085] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0086] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0087] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0088] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0089] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0090] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0091] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0092] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0093] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0094] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. An operation and maintenance monitoring method based on distributed energy storage equipment, characterized in that: The method comprises: Based on the preset distributed energy storage storage nodes, real-time parameters of distributed energy storage are obtained, and the distributed energy storage real-time parameters are analyzed in relation to the energy storage device state to determine the linear time series state of the distributed energy storage; Perform abnormal node analysis on the distributed energy storage linear time series state to obtain the distributed energy storage device state; wherein the distributed energy storage device state includes: the distributed energy storage device abnormal state and the distributed energy storage device time series state; According to the state of the distributed energy storage device, determining the distributed energy storage device exception handling strategy through a distributed exception handling configuration algorithm; Performing time series analysis on the time series state of the distributed energy storage device to obtain distributed energy storage device state prediction data; Determining a distributed energy storage maintenance strategy through a distributed energy storage maintenance algorithm according to the distributed energy storage device state prediction data; Performing equipment resource distribution configuration on the distributed energy storage maintenance strategy to obtain a resource scheduling strategy; The distributed energy storage real-time parameters are analyzed in relation to the energy storage device status to determine the distributed energy storage linear time series status, specifically including: Based on the distributed energy storage real-time parameters, determine the state category of the distributed energy storage device, and according to the distributed energy storage device state category, perform data clustering on the distributed energy storage real-time parameters to determine the distributed device correlation analysis data; Performing frequent pattern growth analysis on the correlation analysis data to obtain distributed energy storage parameter correlation; wherein the distributed energy storage parameter correlation includes: device parameter impact correlation and distributed device impact correlation; Based on the distributed energy storage parameter correlation, the device is corrected for state correlation to determine the distributed energy storage linear time series state; According to the state of the distributed energy storage device, a distributed energy storage device exception handling strategy is determined through a distributed exception handling configuration algorithm, specifically including: Classifying the distributed energy storage device state into abnormal states, and configuring key fields of the distributed energy storage device state after the abnormal state classification to obtain device state category parameters; Based on the device status category parameter, determine the device status impact parameter through distributed energy storage device status impact analysis; According to the device state category parameter and the device state impact parameter, determining the distributed energy storage device exception handling strategy through distributed exception handling matching; Performing time series analysis on the time series state of the distributed energy storage device to obtain distributed energy storage device state prediction data specifically includes: Based on the time sequence state of the distributed energy storage device, determining the positive change trend of the energy storage state through stage trend analysis; Performing periodic data mode analysis on the current state of the distributed energy storage to obtain a periodic change trend of the energy storage state; The positive change trend of the energy storage state and the periodic change trend of the energy storage state are used to obtain the state prediction data of the distributed energy storage device through the hierarchical analysis method; According to the distributed energy storage device state prediction data, a distributed energy storage maintenance strategy is determined by a distributed energy storage maintenance algorithm, specifically including: Based on the distributed energy storage device state prediction data, determining the distributed energy storage device simulation operation parameters through digital twin modeling; Extract abnormal state features of the simulated operating parameters of the distributed energy storage device, and determine the distributed energy storage maintenance node through real-time parameter synchronization according to the distributed energy storage abnormality determination threshold; Performing historical inertia maintenance matching on the distributed energy storage maintenance node to determine the distributed energy storage maintenance strategy; The distributed energy storage maintenance strategy is configured with equipment resource distribution to obtain a resource scheduling strategy, which specifically includes: Based on the distributed energy storage maintenance strategy, the expected data of distributed resource scheduling is determined through scheduling prediction analysis; Performing resource optimization control on the expected data of distributed resource scheduling to obtain a distributed energy storage device scheduling parameter; According to the distributed energy storage device scheduling parameters, distributed energy storage resource allocation parameters are determined through resource scheduling simulation, and based on the distributed energy storage resource allocation parameters, the resource scheduling strategy is obtained.

2. The operation and maintenance monitoring method based on distributed energy storage equipment according to claim 1 is characterized in that: An abnormal node analysis is performed on the distributed energy storage linear time series state to obtain the distributed energy storage device state, specifically including: Perform abnormal state determination on the distributed energy storage linear time series state to obtain abnormal node data; wherein the abnormal node data includes: historical abnormal nodes and real-time abnormal nodes; Divide the historical abnormal nodes into historical processing cycles to obtain historical abnormal states of the equipment; Determine the abnormality type of the real-time abnormal node to determine the real-time abnormal state of the device; The state of the distributed energy storage device is determined based on the historical abnormal state of the device and the real-time abnormal state of the device.

3. The operation and maintenance monitoring method based on distributed energy storage equipment according to claim 1 is characterized in that: After performing distributed resource allocation on the distributed energy storage maintenance strategy to obtain a resource scheduling strategy, the method further includes: Evaluate the stage control effect of the resource scheduling strategy to obtain distributed resource scheduling optimization parameters; The resource scheduling policy is iteratively updated according to the distributed resource scheduling optimization parameter, and the iteratively updated resource scheduling policy is uploaded to a preset scheduling policy log.

4. An operation and maintenance monitoring device based on distributed energy storage equipment, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; In which, the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute an operation and maintenance monitoring method based on a distributed energy storage device as described in any one of claims 1-3.

5. A non-volatile computer storage medium based on operation and maintenance monitoring of distributed energy storage equipment, storing computer executable instructions, characterized in that: When executing the executable instructions, the computer implements an operation and maintenance monitoring method based on a distributed energy storage device as described in any one of claims 1 to 3.

Images