A method and system for intelligent equipment management based on power automation system

Abstract

The present invention relates to the field of power equipment management, and in particular to an intelligent equipment management method and system based on an electric power automation system, comprising collecting data information of the electric power system and electric power equipment through sensors in the electric power automation system, establishing a real-time workload model of the electric power equipment according to the preprocessed data; calculating a correction coefficient of the utilization rate of the electric power equipment in combination with the output result of the workload model of the electric power equipment, as well as the failure and aging conditions of the electric power equipment; establishing a neural network model for energy efficiency analysis of electric power system equipment according to the preprocessed data information related to the electric power system and electric power equipment, and obtaining the energy efficiency analysis result of the electric power system equipment; generating a device management model for dynamically optimizing the electric power automation system according to the corrected utilization rate of the electric power equipment and the output result of the neural network model for energy efficiency analysis of the electric power system equipment, and performing intelligent management on the equipment of the electric power automation system.

Description

Technical Field

[0001] The present invention relates to the field of power equipment management, and in particular to an intelligent equipment management method and system based on a power automation system. Background Art

[0002] With the continuous development of power systems, the number and types of equipment in power automation systems are increasing. Traditional equipment management methods often rely on manual inspections and regular maintenance, which have problems such as low efficiency, untimely fault detection, and high maintenance costs. For example, manual inspections may fail to detect sudden equipment failures during the inspection interval, and manual recording of equipment status is prone to errors and omissions.

[0003] In modern power systems, due to the rapid development of renewable energy and smart grid technology, various new types of equipment such as smart meters, energy storage devices, and smart switches are constantly emerging, and traditional equipment such as transformers and generators are also constantly being updated. This diverse and complex combination of equipment requires a more intelligent and efficient equipment management system to monitor, control, and optimize the operation of the power system. However, the existing technology still has the following problems: the operation data of the equipment cannot be effectively integrated and analyzed, making it difficult to accurately evaluate the status of the power equipment and predictive maintenance; it is impossible to grasp the magnitude of the equipment's work tasks based on the relevant information obtained from the power system equipment, resulting in excessive or light burdens on the power equipment; and the utilization rate of the power equipment is inaccurate, resulting in uneven resource allocation and waste; there is no effective analysis based on the energy efficiency of the power equipment itself, resulting in reduced equipment efficiency during the operation of the power system; there is no good evaluation method for the intelligent management of power equipment, and the management strategy cannot be effectively adjusted.

[0004] Therefore, a method and system for intelligent device management in a power automation system is needed. Summary of the invention

[0005] The present invention provides an intelligent device management method and system based on an electric power automation system, which aims to achieve comprehensive management of various electric power equipment through data analysis, intelligent algorithms, etc., reduce operating costs, improve system efficiency, reliability and safety, and promote further intelligence, efficiency and sustainable development of the electric power system.

[0006] The technical solution of the present invention is specifically as follows:

[0007] A method for managing intelligent equipment based on an electric power automation system comprises the following steps:

[0008] Step S1. Collect data information of the power system and power equipment through sensors in the power automation system, and establish a real-time working level model of the power equipment based on the pre-processed data;

[0009] Step S2. Calculate the power equipment utilization correction coefficient based on the output of the power equipment workload model and the power equipment failure and aging conditions;

[0010] Step S3. Based on the pre-processed data information related to the power system and power equipment, a neural network model for energy efficiency analysis of power system equipment is established to obtain energy efficiency analysis results of power system equipment;

[0011] Step S4. Generate a dynamic optimization equipment management model for the power automation system based on the output results of the corrected power equipment utilization rate and the power system equipment energy efficiency analysis neural network model, and perform intelligent management on the equipment of the power automation system.

[0012] Further, step S1 specifically includes:

[0013] In the real-time workload model of power equipment, the impact between the workload of power system equipment and the equipment working capacity and work plan is predefined.

[0014] in, Indicates Power equipment in The work tasks at that time; Indicates the factor that affects the working capacity of the power equipment itself on the workload; Indicates the factors affecting the working plan of power equipment on the equipment workload; Indicates The working capacity of each electrical equipment; represents the time loss coefficient; Indicates the frequency coefficient of peak-valley and low-peak period changes during the operation of power equipment; Indicates The power consumption coefficient of each electrical equipment; Indicates The resources required for each planned amount of work on the electrical equipment; Indicates The average planned work volume of each power equipment in the current time period; represents the adjustment coefficient; Indicates the weight coefficient, which adjusts the weight of the working capacity of the power equipment; Represents the weight coefficient, which adjusts the weight of the power equipment work plan.

[0015] Furthermore, the power equipment utilization rate is defined as , the calculation of the power equipment utilization correction coefficient specifically includes:

[0016] in, Indicates the correction factor of power equipment utilization; Indicates the influence coefficient of the working level of power equipment; Indicates the failure rate of power equipment; represents the baseline failure rate; Indicates the influence coefficient of power equipment failure rate; represents the dependence coefficient; Indicates the aging coefficient of power equipment; represents the aging rate constant of power equipment, ; Indicates the age of the electrical equipment.

[0017] Further, step S2 specifically includes:

[0018] Pre-set the first-level power equipment utilization comparison parameters Comparison parameters with the utilization rate of the second-level power equipment , < ; Correction factor of power equipment utilization Respectively and Compare and correct the power equipment utilization rate according to the comparison results to obtain the corrected power equipment utilization rate .

[0019] Further, step S3 specifically includes:

[0020] The power system equipment energy efficiency analysis neural network model includes an input layer, an energy efficiency analysis layer, a regulation layer, and an output layer; the obtained power system equipment-related data information is input into the input layer of the power system equipment energy efficiency analysis neural network, and the input layer is fully connected with the energy efficiency analysis layer; the data information is transmitted to the energy efficiency analysis layer, and the energy efficiency of the power equipment is analyzed in the energy efficiency analysis layer; the energy efficiency analysis layer transmits the analysis results to the regulation layer, and the parameters are adjusted in the regulation layer according to the output results of the energy efficiency analysis; the output layer generates the final power equipment energy efficiency analysis results.

[0021] Furthermore, the energy efficiency of the power equipment is analyzed in the energy efficiency analysis layer. The specific process is as follows:

[0022] in, Represents the input of the energy efficiency analysis layer; Represents the connection weight between the input layer and the energy efficiency analysis layer; Indicates the input Power equipment characteristic information; Represents the bias of the energy efficiency analysis layer; Represents the output of the energy efficiency analysis layer; Indicates that the power equipment is The actual energy consumption when , Indicates the end time of the recording; Indicates the expected energy consumption of power equipment; represents the learning factor; Indicated in The temperature of electrical equipment at all times; Indicates that when The influence coefficient of different environmental conditions on the operation of power equipment; Represents the information entropy in the analysis; Indicates the standard rated power of electrical equipment; Indicates the standard actual output power of the power equipment; Represents the information fusion parameter in the analysis process.

[0023] Further, step S4 specifically includes:

[0024] In generating the equipment management model of the dynamic optimization power automation system, a cost optimization objective function is defined, and the optimal solution of the objective function is calculated to achieve resource utilization in the power system;

[0025] Further, step S4 specifically includes:

[0026] According to the optimal solution of the objective function, the power system equipment management strategy is obtained, and the power equipment management results are comprehensively evaluated. ;in, Indicates the comprehensive evaluation results of power equipment management achievements; represents the optimal solution of the objective function of operating cost optimization; Indicates the operating efficiency coefficient of power equipment; represents the cost weight coefficient; Indicates the probability that the operating status of the power equipment is normal; Indicates the parameters of the impact of different equipment states on the power automation system; Standard parameter indicating the effectiveness of power equipment management; Indicates a constant value parameter.

[0027] An intelligent equipment management system based on a power automation system includes the following contents:

[0028] Power system equipment information collection module, power system equipment information processing module, power equipment actual workload module, power equipment utilization module, utilization correction module, power system equipment energy efficiency analysis module, dynamic optimization power equipment management module, and management results comprehensive evaluation module;

[0029] The power system equipment information collection module is responsible for collecting real-time data from the power system and various equipment, providing a basis for subsequent data processing and analysis;

[0030] The power system equipment information processing module processes and integrates the collected equipment information, cleans, converts and stores the data;

[0031] The power equipment real-time workload module is used to establish a real-time workload model of the power equipment based on the processed power equipment information and monitor the actual working conditions of the power equipment;

[0032] The power equipment utilization module calculates the power equipment utilization correction coefficient based on the output of the power equipment workload model and the power equipment failure and aging conditions;

[0033] The utilization correction module pre-sets the comparison parameters of the utilization of the power equipment, compares the correction coefficients of the utilization of the power equipment with the comparison parameters, and corrects the utilization of the power equipment according to the comparison results;

[0034] The power system equipment energy efficiency analysis module establishes a power system equipment energy efficiency analysis neural network model based on the pre-processed power system and power equipment related data information to analyze the power system equipment energy efficiency;

[0035] Dynamic optimization of power equipment management module, based on the optimal solution of the objective function, generates a dynamically optimized equipment management strategy to achieve intelligent equipment management;

[0036] The comprehensive management results evaluation module conducts a comprehensive evaluation of the power equipment management results and provides data-driven decision support.

[0037] Beneficial effects: 1. The present invention is based on a real-time workload model to achieve data-driven decision-making. By deeply understanding the equipment operation status, the system can formulate more accurate management strategies and scheduling plans to achieve intelligent equipment management; at the same time, the system can perform maintenance and formulate reasonable maintenance plans to improve the reliability and stability of the equipment, and avoid downtime and production losses caused by equipment failure; it can also be reasonably coordinated according to the work task allocation of the power equipment, reduce the risk of accidents caused by excessive equipment work, and improve the safety and reliability of the power system.

[0038] 2. The present invention combines the output results of the workload model with the actual failure and aging conditions of the equipment, and the utilization correction coefficient calculated can more accurately reflect the actual utilization of the equipment, and can accurately evaluate the utilization of the equipment, avoiding ignoring the actual situation based on the utilization derived only from the theoretical model; according to the calculated utilization correction coefficient, managers can more accurately evaluate the aging and failure conditions of the equipment and optimize maintenance strategies. At the same time, by correcting the utilization of power equipment, more accurate data support can be provided to managers, improving the accuracy and effectiveness of management decisions. By accurately determining the utilization of power equipment, the operating status and scheduling plan of the equipment can be better optimized, and the overall efficiency and performance of the system can be improved. The corrected utilization coefficient helps to achieve more effective resource allocation and utilization, and improves the system operation efficiency.

[0039] 3. The present invention, by establishing a neural network model for energy efficiency analysis of power system equipment, can conduct a detailed and comprehensive evaluation of the energy efficiency of equipment in the power system, help determine the energy efficiency level of the equipment, discover equipment with low energy efficiency, identify potential energy efficiency improvement space, and provide accurate analysis results for decision-making reference. It can also warn of potential problems, so as to take corresponding measures to improve the energy efficiency and performance of the equipment. In addition, based on the energy efficiency analysis results, optimization strategies can be formulated for different equipment, the operating parameters and working modes of the equipment can be adjusted, the energy efficiency and operating efficiency of the equipment can be improved, and the optimal state of equipment operation can be achieved.

[0040] 4. The present invention generates a dynamic optimization model by combining equipment utilization and energy efficiency analysis results to monitor the equipment operation status. Based on the model output, the system can detect potential equipment failures and problems, implement predictive maintenance, reduce the failure rate of power equipment, and improve the reliability of the system. Through the dynamic optimization model, managers can achieve intelligent management of power automation system equipment. Based on real-time data and model output, the system can automatically optimize the operation strategy. Based on the evaluation of the effects and results of the management strategy, the system can identify room for improvement, continuously optimize the management strategy, and improve the overall performance and efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A flowchart of an intelligent device management method based on an electric power automation system of the present invention;

[0042] Figure 2 This is a module diagram of an intelligent device management system based on an electric power automation system of the present invention. DETAILED DESCRIPTION

[0043] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. It should also be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0044] See attached Figure 1 This embodiment provides a smart device management method based on a power automation system, comprising the following steps:

[0045] S1. Install sensors on each device in the power automation system. Sensor types include but are not limited to temperature sensors, voltage sensors, current sensors, vibration sensors, humidity sensors, pressure sensors, displacement sensors, power quality sensors, battery status sensors, etc. to form an integrated circuit located in the power equipment. These sensors can collect the operating parameters of the equipment in real time, such as: the operating temperature, voltage value, current value of the power equipment, the vibration frequency of the equipment, environmental humidity, pressure changes, the location of the power equipment and its components, harmonics, voltage fluctuations, etc., battery charging and discharging status, etc.

[0046] The sensor transmits the collected data to the data acquisition terminal through wired or wireless communication methods, such as ZigBee, Bluetooth, Ethernet, etc. The data acquisition terminal performs preliminary sorting and format conversion on the received data to ensure the consistency and availability of the data.

[0047] The data acquisition terminal transmits the sorted data to the data center of the equipment management system of the power automation system. At the same time, the data center integrates the received data in real time, associates the data collected by the same device at different times, and forms the operation data sequence of the device; it uses existing technologies to pre-process the equipment data information, which involves timestamp processing, data alignment, data cleaning and other operations to ensure the accuracy of the data and provide support for subsequent analysis processes.

[0048] Based on the pre-processed power system and power equipment related data information, a real-time workload model for power equipment is established. The workload of power system equipment usually includes power generation, transmission, distribution, control, monitoring, maintenance, etc. Quantitative analysis of the equipment workload in the power automation system can reasonably plan and optimize resource allocation, ensure the operating efficiency of power equipment and system stability, and better manage and optimize the overall power automation system equipment.

[0049] In the embodiment of the present invention, the degree of influence between the workload of the power system equipment and the equipment working capacity and the work plan amount is predefined; wherein the working capacity of the power equipment refers to the ability to effectively perform its function under specific working conditions, that is, the performance level and work efficiency that can be achieved; the specific process is as follows:

[0050] in, Indicates Power equipment in The work tasks at that time; Indicates the factor that affects the working capacity of the power equipment itself on the workload; Indicates the factors affecting the working plan of power equipment on the equipment workload; Indicates The working capacity of each electrical equipment; represents the time loss coefficient; Indicates the frequency coefficient of peak-valley and low-peak period changes during the operation of power equipment; Indicates The power consumption coefficient of each electrical equipment; Indicates The resources required for each planned amount of work on the electrical equipment; Indicates The average planned work volume of each power equipment in the current time period; represents the adjustment coefficient; Indicates the weight coefficient, which adjusts the weight of the working capacity of the power equipment; Represents the weight coefficient, which adjusts the weight of the power equipment work plan.

[0051] The present invention realizes data-driven decision-making based on a real-time workload model. By deeply understanding the operation of the equipment, the system can formulate more accurate management strategies and scheduling plans to achieve intelligent equipment management. At the same time, the system can perform maintenance and formulate reasonable maintenance plans to improve the reliability and stability of the equipment and avoid downtime and production losses caused by equipment failure. It can also make reasonable coordination according to the distribution of work tasks of the power equipment, reduce the risk of accidents caused by excessive work of the equipment, and improve the safety and reliability of the power system.

[0052] S2. Combine the output results of the power equipment workload model, as well as the power equipment failure and aging conditions, to calculate the power equipment utilization correction coefficient ; and pre-define the power equipment utilization rate as The specific process is as follows:

[0053] in, Indicates the influence coefficient of the working level of power equipment; Indicates the failure rate of power equipment; represents the baseline failure rate; Indicates the influence coefficient of power equipment failure rate; represents the dependence coefficient; Indicates the aging coefficient of power equipment; represents the aging rate constant of power equipment, ; Indicates the age of the electrical equipment.

[0054] In the embodiment of the present invention, the first level power equipment utilization rate comparison parameter is preset Comparison parameters with the utilization rate of the second-level power equipment , < ; Correction factor of power equipment utilization Respectively and Compare and correct the power equipment utilization rate according to the comparison results to obtain the corrected power equipment utilization rate ; The specific process is as follows:

[0055] if , then the corrected power equipment utilization is , ;

[0056] if , then the corrected power equipment utilization is , ;

[0057] if , then the corrected power equipment utilization is , ;in, Indicates the utilization rate of power equipment under ideal conditions.

[0058] The present invention combines the output results of the workload model with the actual failure and aging conditions of the equipment, and the calculated utilization correction coefficient can more accurately reflect the actual utilization of the equipment, accurately evaluate the utilization of the equipment, and avoid ignoring the actual situation based on the utilization derived only from the theoretical model; according to the calculated utilization correction coefficient, managers can more accurately evaluate the aging and failure conditions of the equipment, optimize maintenance strategies, extend the life of the equipment, reduce maintenance costs, and improve the reliability and stability of the equipment. At the same time, by correcting the utilization of power equipment, more accurate data support can be provided to managers, the accuracy and effectiveness of management decisions can be improved, and the decision-making risks caused by inaccurate utilization estimates can be reduced. By accurately determining the utilization of power equipment, the operating status and scheduling plan of the equipment can be better optimized, and the overall efficiency and performance of the system can be improved. The corrected utilization coefficient helps to achieve more effective resource allocation and utilization, and improve the system operation efficiency.

[0059] S3. Based on the pre-processed data information related to the power system and power equipment, a neural network model for energy efficiency analysis of power system equipment is established to obtain the energy efficiency analysis results of the power system equipment.

[0060] Use existing technology to extract features from data information related to power system equipment and obtain feature sets of data related to power system equipment , represents the total number of features; Any element in can be It indicates that, Indicates feature information, . With the help of deep learning for data analysis, a neural network model for energy efficiency analysis of power system equipment is established. The energy efficiency of power system equipment is analyzed through multiple trainings to output accurate analysis results. The neural network model for energy efficiency analysis of power system equipment includes an input layer, an energy efficiency analysis layer, a regulation layer, and an output layer.

[0061] The obtained data information related to the power system equipment is input into the input layer of the power system equipment energy efficiency analysis neural network, and the input layer is fully connected with the energy efficiency analysis layer; the data information is transmitted to the energy efficiency analysis layer, and the energy efficiency of the power equipment is analyzed in the energy efficiency analysis layer to infer the energy efficiency of the power system equipment. The specific process is as follows:

[0062] in, Represents the input of the energy efficiency analysis layer; Represents the connection weight between the input layer and the energy efficiency analysis layer; Indicates the input Power equipment characteristic information; Represents the bias of the energy efficiency analysis layer; Represents the output of the energy efficiency analysis layer; Indicates that the power equipment is The actual energy consumption when , Indicates the end time of the recording; Indicates the expected energy consumption of power equipment; represents the learning factor; Indicated in The temperature of electrical equipment at all times; Indicates that when The influence coefficient of different environmental conditions on the operation of power equipment; Represents the information entropy in the analysis; Indicates the standard rated power of electrical equipment; Indicates the standard actual output power of the power equipment; Represents the information fusion parameter in the analysis process.

[0063] The energy efficiency analysis layer passes the analysis results to the adjustment layer, where the model parameters are continuously adjusted according to the output results of the energy efficiency analysis to improve the accuracy and performance of the model. The specific process is as follows:

[0064] in, Represents the input of the adjustment layer; Represents the connection weight between the energy efficiency analysis layer and the adjustment layer; Represents the bias of the adjustment layer; Represents the output of the adjustment layer; represents the adjustment factor; Indicates the data synchronization rate during the analysis process; Indicates the pressure bearing capacity of the power system; Indicates the pressure demand of the power system; Represents the adaptive factor.

[0065] The output layer is the last layer of the neural network model for power system equipment energy efficiency analysis. The adjustment layer passes the adjusted results to the output layer and is responsible for generating the final power equipment energy efficiency analysis results. Represents the output results of the neural network model for energy efficiency analysis of power system equipment.

[0066] By establishing a neural network model for energy efficiency analysis of power system equipment, the present invention can conduct a detailed and comprehensive evaluation of the energy efficiency of equipment in the power system, help determine the energy efficiency level of the equipment, discover equipment with low energy efficiency, identify potential energy efficiency improvement space, and provide accurate analysis results for decision-making reference. It can also warn of potential problems, so as to take corresponding measures to improve the energy efficiency and performance of the equipment. In addition, based on the energy efficiency analysis results, optimization strategies can be formulated for different equipment, the operating parameters and working modes of the equipment can be adjusted, the energy efficiency and operating efficiency of the equipment can be improved, and the optimal state of equipment operation can be achieved.

[0067] S4. Generate a dynamic optimization power automation system equipment management model based on the output results of the corrected power equipment utilization rate and power system equipment energy efficiency analysis neural network model; in this embodiment of the present invention, define a cost optimization objective function , in order to minimize the ratio between input and output, achieve more efficient resource utilization of the power system, and optimize the comprehensive management of power equipment. Set constraints, including equipment operation restrictions, energy supply restrictions, safety regulations, etc., to ensure the feasibility and compliance of the optimization plan. The specific process is as follows:

[0068] in, Indicates the number of devices in the power automation system; Indicates power equipment The state variables, Indicates power equipment In normal operation, Indicates power equipment In a state of malfunction or repair, ; Indicates power equipment exist operating costs at the time of Indicates power equipment exist Maintenance costs at the time of represents the total number of constraints; represents the reliability constraint of the power system, The first constraint parameters, , ; Indicates the state transition constraints of the power equipment. The first constraint parameters, , For example, the probability of a device changing from a normal state to a fault state is limited, and maintenance takes a certain amount of time, that is, when During maintenance time Inside, It cannot become 1 immediately; Represents the output results of the neural network model for energy efficiency analysis of power system equipment; Indicates the total amount of work tasks of the power system; It represents the control coefficient of the work task solution rate; Indicates The weight of each task; Indicates completion of The expected cost of a task.

[0069] Generate new solutions through simulated annealing random perturbations and jump out of local optimal solutions, set the initial temperature The end temperature is , the initial solution is The specific application process is as follows:

[0070] Energy gap ;in, represents the initial objective function value; represents the objective function value of the new solution; if , then accept the new solution , otherwise with probability Accept new interpretations.

[0071] Finally, the power system equipment management strategy is obtained based on the optimal solution of the objective function, and the power equipment management results are comprehensively evaluated. The equipment operation status and performance data are captured in time, and adjustments and optimizations are made quickly to improve the response speed and flexibility of the power system. The specific process is as follows:

[0072] in, Indicates the comprehensive evaluation results of power equipment management achievements; represents the optimal solution of the objective function of operating cost optimization; Indicates the operating efficiency coefficient of power equipment; represents the cost weight coefficient; Indicates the probability that the operating status of the power equipment is normal; Indicates the parameters of the impact of different equipment states on the power automation system; Standard parameter indicating the effectiveness of power equipment management; Indicates a constant value parameter.

[0073] The present invention generates a dynamic optimization model by combining the equipment utilization and energy efficiency analysis results to realize the monitoring of equipment operation status. According to the model output, the system can find potential equipment failures and problems, implement predictive maintenance, reduce the failure rate of power equipment, and improve the reliability of the system. Through the dynamic optimization model, managers can realize intelligent management of power automation system equipment. According to real-time data and model output, the system can automatically optimize the operation strategy. According to the evaluation of the effect and results of the management strategy, the system can identify the room for improvement, continuously optimize the management strategy, and improve the overall performance and efficiency of the system.

[0074] Refer to the attached Figure 2 This embodiment provides an intelligent device management system based on a power automation system, including the following contents:

[0075] Power system equipment information collection module, power system equipment information processing module, power equipment actual workload module, power equipment utilization module, utilization correction module, power system equipment energy efficiency analysis module, dynamic optimization power equipment management module, and management results comprehensive evaluation module;

[0076] The power system equipment information collection module is responsible for collecting real-time data from the power system and various equipment, including equipment operating status, energy consumption data, performance parameters, etc., to provide a basis for subsequent data processing and analysis;

[0077] The power system equipment information processing module processes and integrates the collected equipment information, cleans, converts and stores the data for use by subsequent modules;

[0078] The power equipment real-time workload module is used to establish a real-time workload model of the power equipment based on the processed power equipment information and monitor the actual working conditions of the power equipment;

[0079] The power equipment utilization module calculates the power equipment utilization correction coefficient based on the output of the power equipment workload model and the power equipment failure and aging conditions;

[0080] The utilization correction module pre-sets the comparison parameters of the utilization of power equipment, compares the correction coefficients of the utilization of power equipment with the comparison parameters, and corrects the utilization of power equipment according to the comparison results to ensure the accuracy and reliability of the utilization data;

[0081] The power system equipment energy efficiency analysis module establishes a power system equipment energy efficiency analysis neural network model based on the pre-processed power system and power equipment related data information to analyze the power system equipment energy efficiency;

[0082] Dynamic optimization of power equipment management module, based on the optimal solution of the objective function, generates a dynamically optimized equipment management strategy to improve the overall efficiency and performance of the system and realize intelligent equipment management;

[0083] The comprehensive management results evaluation module conducts a comprehensive evaluation of the power equipment management results and provides data-driven decision support.

[0084] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 produce 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.

[0085] 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.

[0086] 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.

[0087] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0088] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A smart device management method based on a power automation system, characterized in that: The following steps are involved: Step S1. Collect data information of the power system and power equipment through sensors in the power automation system, and establish a real-time working level model of the power equipment based on the pre-processed data; In the real-time workload model of power equipment, the impact between the workload of power system equipment and the equipment working capacity and work plan is predefined. in, Indicates Power equipment in The work tasks at that time; Indicates the factor that affects the working capacity of the power equipment itself on the workload; Indicates the factors affecting the working plan of power equipment on the equipment workload; Indicates The working capacity of each electrical equipment; represents the time loss coefficient; Indicates the frequency coefficient of peak-valley and low-peak period changes during the operation of power equipment; Indicates The power consumption coefficient of each electrical equipment; Indicates The resources required for each planned amount of work on the electrical equipment; Indicates The average planned work volume of each power equipment in the current time period; represents the adjustment coefficient; Indicates the weight coefficient, which adjusts the weight of the working capacity of the power equipment; Indicates the weight coefficient, which adjusts the weight of the power equipment work plan; Step S2. Calculate the power equipment utilization correction coefficient based on the output of the power equipment workload model and the power equipment failure and aging conditions; The utilization rate of power equipment is defined as , the calculation of the power equipment utilization correction coefficient specifically includes: in, Indicates the correction factor of power equipment utilization; Indicates the influence coefficient of the working level of the power equipment; Indicates the failure rate of power equipment; represents the baseline failure rate; Indicates the influence coefficient of power equipment failure rate; represents the dependence coefficient; Indicates the aging coefficient of power equipment; represents the aging rate constant of power equipment, ; Indicates the age of the electrical equipment; Step S3. Based on the pre-processed data information related to the power system and power equipment, a neural network model for energy efficiency analysis of power system equipment is established to obtain energy efficiency analysis results of power system equipment; Step S4. Based on the output results of the corrected power equipment utilization rate and the power system equipment energy efficiency analysis neural network model, generate a dynamic optimization power automation system equipment management model, define a cost optimization objective function, calculate the optimal solution of the objective function, and intelligently manage the power automation system equipment.

2. According to claim 1, a smart device management method based on a power automation system is characterized in that: The step S2 specifically includes: Pre-set the first-level power equipment utilization comparison parameters Comparison parameters with the utilization rate of the second-level power equipment , < ; Correction factor of power equipment utilization Respectively and Compare and correct the power equipment utilization rate according to the comparison results to obtain the corrected power equipment utilization rate .

3. According to claim 1, a smart device management method based on a power automation system is characterized in that: The step S3 specifically includes: The power system equipment energy efficiency analysis neural network model includes an input layer, an energy efficiency analysis layer, a regulation layer, and an output layer; the obtained power system equipment-related data information is input into the input layer of the power system equipment energy efficiency analysis neural network, and the input layer is fully connected with the energy efficiency analysis layer; the data information is transmitted to the energy efficiency analysis layer, and the energy efficiency of the power equipment is analyzed in the energy efficiency analysis layer; the energy efficiency analysis layer transmits the analysis results to the regulation layer, and the parameters are adjusted in the regulation layer according to the output results of the energy efficiency analysis; the output layer generates the final power equipment energy efficiency analysis results.

4. The intelligent device management method based on the power automation system according to claim 3 is characterized in that: In the energy efficiency analysis layer, the energy efficiency of power equipment is analyzed. The specific process is as follows: , in, Represents the input of the energy efficiency analysis layer; Represents the connection weight between the input layer and the energy efficiency analysis layer; Indicates the input Power equipment characteristic information; Represents the bias of the energy efficiency analysis layer; Represents the output of the energy efficiency analysis layer; Indicates that the power equipment is The actual energy consumption when , Indicates the end time of the recording; Indicates the expected energy consumption of power equipment; represents the learning factor; Indicated in The temperature of electrical equipment at all times; Indicates that when The influence coefficient of different environmental conditions on the operation of power equipment; Represents the information entropy in the analysis; Indicates the standard rated power of electrical equipment; Indicates the standard actual output power of the power equipment; Represents the information fusion parameter in the analysis process.

5. The intelligent device management method based on the power automation system according to claim 1 is characterized in that: The step S4 specifically includes: Establish a cost optimization objective function to achieve resource utilization in the power system. The specific process is as follows: in, Indicates the number of devices in the power automation system; Indicates power equipment state variables; Indicates power equipment exist operating costs at the time of Indicates power equipment exist Maintenance costs at the time of represents the total number of constraints; represents the reliability constraint of the power system, The first constraint parameters, , ; represents the state transition constraint of the power equipment, represents the The first constraint parameters, , ; Represents the output results of the neural network model for energy efficiency analysis of power system equipment; Indicates the total amount of work tasks of the power system; It represents the control coefficient of the work task solution rate; Indicates The weight of each task; Indicates completion of The expected cost of a task.

6. The intelligent device management method based on the power automation system according to claim 5 is characterized in that: The step S4 specifically includes: According to the optimal solution of the objective function, the power system equipment management strategy is obtained, and the power equipment management results are comprehensively evaluated. ;in, Indicates the comprehensive evaluation results of power equipment management achievements; represents the optimal solution of the objective function of operating cost optimization; Indicates the operating efficiency coefficient of power equipment; represents the cost weight coefficient; Indicates the probability that the operating status of the power equipment is normal; Indicates the parameters of the impact of different equipment states on the power automation system; Standard parameter indicating the effectiveness of power equipment management; Indicates a constant value parameter.

7. An intelligent device management system based on a power automation system, applied to the intelligent device management method based on a power automation system according to claim 1, characterized in that: Includes the following: Power system equipment information collection module, power system equipment information processing module, power equipment actual workload module, power equipment utilization module, utilization correction module, power system equipment energy efficiency analysis module, dynamic optimization power equipment management module, and management results comprehensive evaluation module; The power system equipment information collection module is responsible for collecting real-time data from the power system and various equipment to provide a basis for subsequent data processing and analysis; The power system equipment information processing module processes and integrates the collected equipment information, and cleans, converts and stores the data; The power equipment real-time workload module is used to establish a real-time workload model of the power equipment according to the processed power equipment information, and monitor the actual working conditions of the power equipment; The power equipment utilization rate module calculates the power equipment utilization rate correction coefficient based on the output result of the power equipment workload model and the power equipment failure and aging conditions; The utilization correction module pre-sets the comparison parameters of the utilization of the power equipment, compares the correction coefficients of the utilization of the power equipment with the comparison parameters respectively, and corrects the utilization of the power equipment according to the comparison results; The power system equipment energy efficiency analysis module establishes a power system equipment energy efficiency analysis neural network model based on the preprocessed power system and power equipment related data information to analyze the power system equipment energy efficiency; The dynamic optimization power equipment management module generates a dynamic optimization equipment management strategy based on the optimal solution of the objective function to achieve intelligent equipment management; The management results comprehensive evaluation module comprehensively evaluates the power equipment management results and provides data-driven decision support.

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