Method, device and equipment for optimizing operation and maintenance strategy of device and storage medium

Abstract

The application provides a method and device for optimizing operation and maintenance strategy of equipment, and a storage medium. The method comprises the following steps: constructing a failure rate prediction model of the power equipment based on an initial deterioration rate of the power equipment and real-time state operation parameters; constructing an operation and maintenance strategy optimization model for real-time monitoring of the power equipment based on the failure prediction model and failure downtime, failure loss cost, operation and maintenance time, operation and maintenance cost and load loss caused by operation and maintenance of the power equipment under multiple different operation and maintenance levels; solving the operation and maintenance strategy optimization model based on preset constraints to determine an optimal operation and maintenance scheme; determining a first deterioration rate of the power equipment under the optimal operation and maintenance scheme, and executing the optimal operation and maintenance scheme if the first deterioration rate is less than the initial deterioration rate; otherwise, resetting the initial deterioration rate of the power equipment and solving the operation and maintenance strategy optimization model again. The operation and maintenance strategy provided by the application has high accuracy.

Description

Technical Field

[0001] This invention relates to the field of power equipment maintenance technology, and in particular to a method, apparatus, equipment, and storage medium for optimizing equipment operation and maintenance strategies. Background Technology

[0002] In a power system, electrical equipment is a very important component. To ensure the normal operation of the power distribution system, it is necessary to do a good job in the operation and maintenance of electrical equipment.

[0003] Distribution network operation and maintenance (O&M) serves as a bridge between the transmission network and users, and is a crucial guarantee for improving the reliability of power system operation and the efficiency of equipment utilization. Currently, distribution O&M mainly includes three types: reactive O&M, scheduled O&M, and condition-based O&M. Reactive O&M involves repairing or replacing equipment after a failure. Scheduled O&M involves maintenance at regular intervals, primarily relying on equipment parameters and the maintenance personnel's experience. Condition-based O&M relies on condition monitoring and diagnostic technologies to develop maintenance plans based on the equipment's operating status.

[0004] With the development of smart grids, digital grids, and the energy internet, online monitoring technology for power equipment has been greatly advanced, and power equipment operation and maintenance is shifting from periodic maintenance to condition-based maintenance. However, existing operation and maintenance strategies have low accuracy and cannot meet the needs of smart grids and digital grids. Summary of the Invention

[0005] This invention provides a method, apparatus, device, and storage medium for optimizing device operation and maintenance strategies, in order to solve the problem of low accuracy in current operation and maintenance strategies.

[0006] In a first aspect, embodiments of the present invention provide a method for optimizing the operation and maintenance strategy of a device, including:

[0007] Based on the initial degradation rate and real-time operating parameters of power equipment, a failure rate prediction model for power equipment is constructed.

[0008] Based on the fault prediction module and the fault downtime, fault loss cost, operation and maintenance time, operation and maintenance cost, and load loss caused by operation and maintenance of power equipment under various operation and maintenance levels, an operation and maintenance strategy optimization model for real-time monitoring of power equipment is constructed.

[0009] Based on preset constraints, the operation and maintenance strategy optimization model is solved to determine the optimal operation and maintenance solution.

[0010] Under the optimal operation and maintenance scheme, determine the first degradation rate of the power equipment under this scheme. If the first degradation rate is less than the initial degradation rate, then execute the optimal operation and maintenance scheme; otherwise, reset the initial degradation rate of the power equipment and solve the operation and maintenance strategy optimization model again.

[0011] In one possible implementation, a failure rate prediction model for the power equipment is constructed based on the initial degradation rate and real-time operating parameters, including:

[0012] Construct a health assessment model for power equipment;

[0013] The health index of power equipment is determined based on the initial deterioration rate, real-time operating parameters, and health assessment model.

[0014] Based on the health index, the historical failure rate of power equipment, and the historical health assessment index, a failure rate prediction model for power equipment is determined.

[0015] In one possible implementation, the health index of the power equipment is determined based on its initial degradation rate, real-time operating parameters, and a health assessment model, including:

[0016] Input the target state operating parameters into the health assessment model to obtain the health index of the target state operating parameters;

[0017] The health index with the highest value among all the health indices of the target state operating parameters is determined as the real-time health index of the power equipment.

[0018] The ratio of the real-time health index to the initial degradation rate is determined as the health index of the power equipment.

[0019] In one possible implementation, the health assessment model H for electrical equipment is:

[0020]

[0021] Where H′=max{H1′,H2′,…,H n ′,…,H k ′},

[0022]

[0023] H represents the health index of electrical equipment; H n 'This represents the health index of the nth operating parameter of the power equipment; v i x represents the initial degradation rate of the power equipment, with an initial value of 1; i This represents the real-time monitoring value of the operating parameters for the nth state. These represent the upper and lower limits for the normal state, slight degradation, and severe degradation of power equipment, respectively.

[0024] In one possible implementation, based on the fault prediction module and the fault downtime, fault loss cost, maintenance time, maintenance cost, and load loss caused by maintenance of power equipment under various different maintenance levels, an operation and maintenance strategy optimization model for real-time monitoring of power equipment is constructed, including:

[0025] Based on the failure rate prediction model and the failure downtime and failure loss cost of power equipment under various operation and maintenance levels, a failure loss model for power equipment is constructed.

[0026] Based on the operation and maintenance time and cost of power equipment under various operation and maintenance levels, an operation and maintenance loss model for power equipment is constructed.

[0027] Based on the difference in loss costs before and after operation and maintenance of power equipment, the operation and maintenance benefits of power equipment are constructed;

[0028] Based on the objectives of minimizing power equipment failure losses, minimizing operation and maintenance losses, and maximizing operation and maintenance benefits, an optimization model for operation and maintenance strategies for real-time monitoring of power equipment is constructed.

[0029] In one possible implementation, the operation and maintenance strategy optimization model Z max for:

[0030] Z max =[ω1B-ω2(E)] F +E M )] max ;

[0031] Where B represents the operation and maintenance efficiency of the power equipment, and E... F For losses due to power equipment failures, E M The operation and maintenance losses of power equipment are represented by ω1 and ω2, which are the weights of the operation and maintenance benefits and losses of power equipment, respectively.

[0032] In one possible implementation, determining the first degradation rate of the power equipment under the optimal operation and maintenance scheme includes:

[0033] Obtain real-time status operating parameters of power equipment when it is running according to the optimal operation and maintenance plan;

[0034] The first degradation rate is determined based on a pre-built equipment degradation rate model, real-time operating parameters, and historical operating parameters of power equipment under a specified maintenance plan.

[0035] Secondly, embodiments of the present invention provide a device for optimizing the operation and maintenance strategy of an equipment, comprising:

[0036] The first model module is constructed to build a failure rate prediction model for power equipment based on the initial degradation rate and real-time operating parameters of the power equipment.

[0037] A second model module is constructed to build an operation and maintenance strategy optimization model for real-time monitoring of power equipment based on the fault prediction module and the fault downtime, fault loss cost, operation and maintenance time, operation and maintenance cost, and load loss caused by operation and maintenance under various operation and maintenance levels.

[0038] The operation and maintenance plan determination module is used to solve the operation and maintenance strategy optimization model based on preset constraints to determine the optimal operation and maintenance plan;

[0039] The execution module is used to determine the first degradation rate of the power equipment under the optimal operation and maintenance scheme. If the first degradation rate is less than the initial degradation rate, the optimal operation and maintenance scheme is executed; otherwise, the initial degradation rate of the power equipment is reset and the operation and maintenance strategy optimization model is solved again.

[0040] In one possible implementation, a first model module is constructed for building a health assessment model for power equipment;

[0041] The health index of power equipment is determined based on the initial deterioration rate, real-time operating parameters, and health assessment model.

[0042] Based on the health index of power equipment, the historical failure rate of power equipment, and the historical health assessment index of power equipment, a failure rate prediction model for power equipment is determined.

[0043] In one possible implementation, a first model module is constructed to input the target state operating parameters into the health assessment model to obtain the health index of the target state operating parameters;

[0044] The health index with the highest value among all the health indices of the target state operating parameters is determined as the real-time health index of the power equipment.

[0045] The ratio of the real-time health index to the initial degradation rate is determined as the health index of the power equipment.

[0046] In one possible implementation, the health assessment model H for electrical equipment is:

[0047]

[0048] Where H′=max{H1′,H2′,…,H n ′,…,H k ′},

[0049]

[0050] H represents the health index of electrical equipment; H n'This represents the health index of the nth operating parameter of the power equipment; v i x represents the initial degradation rate of the power equipment, with an initial value of 1; i This represents the real-time monitoring value of the operating parameters for the nth state. These represent the upper and lower limits for the normal state, slight degradation, and severe degradation of power equipment, respectively.

[0051] In one possible implementation, a second model module is constructed to build a failure loss model for power equipment based on the failure rate prediction model and the failure downtime and failure loss cost of power equipment under various different operation and maintenance levels.

[0052] Based on the operation and maintenance time and cost of power equipment under various operation and maintenance levels, an operation and maintenance loss model for power equipment is constructed.

[0053] Based on the difference in loss costs before and after operation and maintenance of power equipment, the operation and maintenance benefits of power equipment are constructed;

[0054] Based on the objectives of minimizing power equipment failure losses, minimizing operation and maintenance losses, and maximizing operation and maintenance benefits, an optimization model for operation and maintenance strategies for real-time monitoring of power equipment is constructed.

[0055] In one possible implementation, the operation and maintenance strategy optimization model Z max for:

[0056] Z max =[ω1B-ω2(E)] F +E M )] max ;

[0057] Where B represents the operation and maintenance efficiency of the power equipment, and E... F For losses due to power equipment failures, E M The operation and maintenance losses of power equipment are represented by ω1 and ω2, which are the weights of the operation and maintenance benefits and losses of power equipment, respectively.

[0058] In one possible implementation, the execution module is used to obtain real-time status operating parameters of the power equipment when it is running according to the optimal operation and maintenance plan;

[0059] The first degradation rate is determined based on a pre-built equipment degradation rate model, real-time operating parameters, and historical operating parameters of power equipment under a specified maintenance plan.

[0060] Thirdly, embodiments of the present invention provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method as described in the first aspect or any possible implementation of the first aspect.

[0061] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method as described in the first aspect or any possible implementation thereof.

[0062] This invention provides a method, apparatus, device, and storage medium for optimizing the operation and maintenance strategy of power equipment. First, a failure rate prediction model for the power equipment is constructed based on its initial degradation rate and real-time operating parameters. Then, based on a fault prediction module and the power equipment's downtime, failure loss costs, maintenance time, maintenance costs, and load loss caused by maintenance under various maintenance levels, a real-time monitoring-based operation and maintenance strategy optimization model is constructed. Next, the optimization model is solved based on preset constraints to determine the optimal operation and maintenance scheme. Finally, under the optimal operation and maintenance scheme, the first degradation rate of the power equipment under that scheme is determined. If the first degradation rate is less than the initial degradation rate, the optimal operation and maintenance scheme is executed; otherwise, the initial degradation rate of the power equipment is reset, and the optimization model is solved again.

[0063] When formulating operation and maintenance plans, this invention makes full use of the real-time monitoring status parameters of power equipment and combines them with the initial degradation rate of power equipment. It closely integrates the operation and maintenance plan of power equipment with the real-time status parameters of power equipment, thereby improving the accuracy of the equipment operation and maintenance plan and further improving the reliability of power equipment operation. Attached Figure Description

[0064] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1 This is a flowchart illustrating the implementation of the device operation and maintenance strategy optimization method provided in this embodiment of the invention.

[0066] Figure 2 This is a flowchart of a device operation and maintenance strategy optimization method provided in an embodiment of the present invention;

[0067] Figure 3 This is a schematic diagram of the structure of the equipment operation and maintenance strategy optimization device provided in the embodiment of the present invention;

[0068] Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0069] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0070] To make the objectives, technical solutions, and advantages of the present invention clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0071] The construction of a new power system under the "dual carbon" target requires safer and more efficient operation and maintenance of power equipment. Power equipment operation and maintenance is a crucial guarantee for improving the reliability of power system operation and the utilization efficiency of equipment. With the construction of smart grids, digital grids, and the energy internet, online monitoring technology for power equipment has been greatly developed, and equipment operation and maintenance is shifting from periodic maintenance to condition-based maintenance. A condition-based maintenance plan for power equipment first needs to assess the real-time status of the equipment, and then formulate an operation and maintenance plan based on the safety, reliability, and economy of equipment operation.

[0072] However, current operation and maintenance plans for power equipment lack real-time monitoring of the equipment's status, leading to inaccurate final operation and maintenance plans.

[0073] To address the problems of existing technologies, embodiments of the present invention provide a method, apparatus, device, and storage medium for optimizing equipment operation and maintenance strategies. The method for optimizing equipment operation and maintenance strategies provided by embodiments of the present invention will be described first.

[0074] The execution entity of the equipment operation and maintenance strategy optimization method can be an equipment operation and maintenance strategy optimization device. This device can be an electronic device with a processor and memory, such as a mobile electronic device or a non-mobile electronic device. This embodiment of the invention does not impose specific limitations.

[0075] See Figure 1 The document illustrates a flowchart of the implementation of the device operation and maintenance strategy optimization method provided in this embodiment of the invention, which is described in detail below:

[0076] Step S110: Based on the initial degradation rate of the power equipment and the real-time operating parameters, construct a failure rate prediction model for the power equipment.

[0077] The operating parameters include both electrical and non-electrical parameters of the power equipment. Electrical parameters include current and voltage, while non-electrical parameters include temperature, pressure, and vibration.

[0078] Electrical and non-electrical parameters can be monitored using relevant monitoring equipment, such as ammeters to collect real-time current, voltmeters to collect real-time voltage, and temperature sensors, pressure sensors, and vibration sensors to detect temperature, pressure, and vibration, respectively.

[0079] The initial degradation rate can be set to 1. This setting can be adjusted according to the condition of the power equipment and is not limited here.

[0080] In some embodiments, to accurately construct a failure rate prediction model for power equipment, a health assessment model can be constructed first. The health level of the power equipment is then assessed based on the health assessment model, and a failure prediction model for the power equipment is constructed in conjunction with the health level. For example, step S110 may include:

[0081] Step S1110: Construct a health assessment model for power equipment.

[0082] There are many operational parameters that need to be monitored, so it is necessary to calculate the corresponding health index for each monitored operational parameter.

[0083]

[0084] H n 'x' represents the health index of the nth operating parameter of the power equipment. i This represents the real-time monitoring value of the operating parameters for the nth state. These represent the upper and lower limits of the normal state, slight degradation, and severe degradation of the nth state operating parameters of the power equipment, respectively.

[0085] For example, if the collected temperature is 50 degrees Celsius, the setting is... The health index is then calculated using the formula in the second row.

[0086] Step S1120: Determine the health index of the power equipment based on the initial deterioration rate, real-time operating parameters, and health assessment model.

[0087] In this embodiment, the target state operating parameters are first input into the health assessment model to obtain the health index of the target state operating parameters.

[0088] Assuming there are k state operation parameters, by inputting each of the k state operation parameters into the health assessment model, the health index of these k state operation parameters can be obtained.

[0089] Then, the health index with the largest value among all the health indices of the target state operating parameters is determined as the real-time health index of the power equipment.

[0090] H′=max{H1′,H2′,…,Hn ′,…,H k ′};

[0091] Finally, the ratio of the real-time health index to the initial degradation rate is determined as the health index of the power equipment.

[0092]

[0093] Step S1130: Based on the real-time health index, the historical failure rate of power equipment, and the historical health assessment index, determine the failure rate prediction model of power equipment.

[0094] λ(t)=K(t)e H·C(t) ;

[0095] Where t represents the operating years of the power equipment; λ(t) represents the failure rate of the power equipment; H represents the health index of the power equipment; and K and C are undetermined coefficients. K and C can be derived from historical data of the power equipment. The values ​​of K and C can be determined by using multiple sets of known historical failure rates and historical health assessment indices of power equipment.

[0096] In this embodiment, by fully considering the initial degradation rate and real-time operating parameters of the power equipment when constructing the failure rate prediction model, the operating status of the power equipment can be more accurately reflected, making the constructed failure rate prediction model of the power equipment more accurate.

[0097] Step S120: Based on the fault prediction module and the fault downtime, fault loss cost, operation and maintenance time, operation and maintenance cost, and load loss caused by operation and maintenance of power equipment under various operation and maintenance levels, construct an operation and maintenance strategy optimization model for real-time monitoring of power equipment.

[0098] Power equipment has various operation and maintenance levels, and these different levels correspond to different handling methods, thus forming different operation and maintenance strategies.

[0099] Specifically, it can be divided into 5 maintenance levels, each corresponding to different downtime, failure loss costs, maintenance and repair time, maintenance and repair costs, and load loss caused by maintenance.

[0100] According to the "State Grid Corporation of China Distribution Network Condition-Based Maintenance System Standard", the operation and maintenance level is divided into five categories: A, B, C, D, and E. Category A is the overall operation and maintenance of equipment, Category B is the partial operation and maintenance of equipment, Category C is the general operation and maintenance of equipment, Category D is the testing and maintenance of equipment, and Category E is the troubleshooting and maintenance of equipment.

[0101] For example, step S120 may include the following steps S1210 to S1230:

[0102] Step S1210: Based on the failure rate prediction model and the failure downtime and failure loss cost of power equipment under various operation and maintenance levels, construct the failure loss model of power equipment.

[0103] E F1,f =∑ i∈f(t) C i,l ΔT i,l ;

[0104] E F2,f =P f(t) c F ΔT f(t) ;

[0105]

[0106] Among them, E F For losses due to power equipment failures; E F1,f E F2,f These represent the equipment failure cost itself caused by the failure state f(t) and the load loss cost caused by the equipment failure, respectively; C i,l ΔT i,l These represent the maintenance costs and maintenance time for power equipment i at maintenance level l; ΔT f(t) P f(t) These represent the downtime and load loss caused by the fault state f(t), respectively; c F Let f(t) be the unit loss of load caused by equipment failure, and N be the set of all failure states. T This is the sum of downtime and maintenance time.

[0107] Step S1220: Based on the operation and maintenance time and cost of power equipment under various operation and maintenance levels, construct an operation and maintenance loss model for power equipment.

[0108] E M1 =∑ i∈m(t) C i,j ΔT i,j ;

[0109] E M2 =P m(t) c M ΔT m(t) ;

[0110]

[0111] Among them, E M For losses incurred during the operation and maintenance of power equipment; E M1 E M2 These are the costs of losses incurred due to the operation and maintenance of power equipment and the costs of planned load shedding; Ci,j ΔT i,j For equipment i, the maintenance and repair costs and time are determined according to maintenance level j; P m(t) ΔT represents the planned load loss caused by equipment during a certain maintenance period t. m(t) c is the total maintenance time for the equipment; M The unit loss-of-load cost caused by planned operation and maintenance of equipment, m(t) represents the set of all electrical equipment, and N T This is the sum of downtime and maintenance time.

[0112] Step S1230: Based on the difference in loss costs of power equipment before and after operation and maintenance, construct the operation and maintenance benefits of power equipment.

[0113]

[0114] E i,t =E 1i,t +E 2i,t =ΔT i,l P i c F +C i,l ΔT i,l ;

[0115]

[0116] Where, ΔE F E represents the difference in loss costs before and after equipment maintenance; i,t Let λ be the cost of equipment i failing during a certain maintenance period t; B be the maintenance benefits of the power equipment; λ i,t Let λ′ be the failure rate of power equipment i before operation and maintenance. i,t Let N be the failure rate of power equipment i after maintenance, and N be the total number of power equipment.

[0117] Step S1240: Based on the objectives of minimizing power equipment failure losses, minimizing operation and maintenance losses, and maximizing operation and maintenance benefits, construct an operation and maintenance strategy optimization model for real-time monitoring of power equipment.

[0118] Specifically, the operation and maintenance strategy optimization model Z max for:

[0119] Z max =[ω1B-ω2(E)] F +E M )] max ;

[0120] Where B represents the operation and maintenance efficiency of the power equipment, and E... F For losses due to power equipment failures, E MThe operation and maintenance losses of power equipment are represented by ω1 and ω2, which are the weights of the operation and maintenance benefits and losses of the power equipment, respectively. ω1 and ω2 are determined by the importance of the power equipment in the power system. The higher the importance of the power equipment, the greater its corresponding weight; however, no specific limit is imposed here.

[0121] Step S130: Based on preset constraints, solve the operation and maintenance strategy optimization model to determine the optimal operation and maintenance solution.

[0122] Based on the maintenance time, maintenance resources, and grid security conditions reported by the power grid, establish the constraints of the operation and maintenance strategy optimization model.

[0123] The constraint on maintenance time is: t zao ≤t mi ≤t wan ;

[0124] t zao The earliest permissible maintenance time for electrical equipment i; t mi The maintenance time for power equipment i; t wan The latest permitted maintenance time for electrical equipment i.

[0125] The constraints on operation and maintenance resources are:

[0126]

[0127] In the formula: D ε,i (t) represents the demand of power equipment i for operation and maintenance resources ε during time period t; Q ε (t) represents the maximum available amount of resource ε during time period t.

[0128] The power grid security constraints are:

[0129]

[0130] In the formula: P i P represents the actual transmission power of line i; imax U represents the maximum power allowed to be transmitted through line i. i u imax u imin Let be the voltage at node i and its upper and lower limits.

[0131] Based on the above constraints, the operation and maintenance strategy optimization model is solved to determine the optimal operation and maintenance solution.

[0132] Specifically, the Yalmip toolbox in the MATLAB environment can be used to optimize the operation and maintenance strategy model, obtain the operation and maintenance time and level of power equipment that meet the optimization objectives, and form an operation and maintenance plan for the equipment.

[0133] Step S140: Determine the first degradation rate of the power equipment under the optimal operation and maintenance scheme. If the first degradation rate is less than the initial degradation rate, then execute the optimal operation and maintenance scheme; otherwise, reset the initial degradation rate of the power equipment and solve the operation and maintenance strategy optimization model again.

[0134] By solving the operation and maintenance strategy optimization model, the optimal operation and maintenance scheme for the power equipment can be obtained, and thus the first degradation rate of the power equipment under the optimal operation and maintenance scheme can be determined.

[0135] In some embodiments, firstly, real-time operating parameters of the power equipment are obtained when it is running according to the optimal operation and maintenance plan. Then, based on a pre-built equipment degradation rate model, real-time operating parameters, and historical operating parameters of the power equipment under a specified operation and maintenance plan, a first degradation rate is determined.

[0136] In this embodiment, by monitoring the power equipment in real time, it can be determined whether the power equipment has deteriorated under the optimal operation and maintenance plan.

[0137] The equipment degradation rate model is as follows:

[0138]

[0139] v i Let α be the degradation rate of power equipment i within the optimal operation and maintenance scheme cycle; α be the real-time status level of the power equipment; x i This refers to the real-time monitoring value of the power equipment's status within the optimal operation and maintenance plan cycle; x i 'These are the status monitoring values ​​for power equipment when developing operation and maintenance plans; x' α These are the real-time status limits for the equipment.

[0140] Among them, the real-time status level α is related to the real-time status monitoring value x within the optimal operation and maintenance plan cycle. i It depends on the scope. For example, in the health assessment model of electrical equipment, x... i Taking the range as an example, if α can be 1; if or α can be 1.1; if or α can be 1.2; if or α can be 1.3.

[0141] By comparing the calculated first degradation rate with the initial degradation rate, if the first degradation rate is less than the initial degradation rate, the optimal operation and maintenance plan is executed. If the first degradation rate is greater than or equal to the initial degradation rate, the initial degradation rate needs to be updated, and then the operation and maintenance strategy optimization model needs to be solved again.

[0142] When formulating operation and maintenance plans, this invention makes full use of the real-time monitoring status parameters of power equipment and combines them with the initial degradation rate of power equipment. It closely integrates the operation and maintenance plan of power equipment with the real-time status parameters of power equipment, thereby improving the accuracy of the equipment operation and maintenance plan and further improving the reliability and safety of power equipment operation.

[0143] The following details the equipment operation and maintenance strategy optimization method provided by this invention, such as... Figure 2 As shown,

[0144] First, collect the operating parameters of electrical and non-electrical quantities of the power equipment, such as the values ​​of voltage, current, temperature, pressure, and vibration.

[0145] Then, based on the initial degradation rate of the power equipment, a health assessment model and a failure rate prediction model are constructed. Considering the operation and maintenance level and parameters of the power equipment, a loss model and operation and maintenance benefits model are constructed. The operation and maintenance parameters include: downtime due to failure, cost of failure, operation and maintenance time, operation and maintenance cost, and load loss caused by operation and maintenance. The loss model includes a failure loss model and an operation and maintenance loss model.

[0146] Next, based on the reported maintenance time, maintenance resources, and grid safety conditions, constraints are established for the operation and maintenance strategy optimization model. With the objectives of minimizing power equipment failure losses, minimizing operation and maintenance losses, and maximizing operation and maintenance benefits, a real-time monitoring operation and maintenance strategy optimization model for power equipment is constructed.

[0147] Next, the objective function of the operation and maintenance strategy optimization model is solved to obtain the equipment operation and maintenance time and operation and maintenance level that meet the optimization objective, thus forming an operation and maintenance plan.

[0148] Finally, based on the first degradation rate of the power equipment under the optimal operation and maintenance scheme, if the first degradation rate is less than the initial degradation rate, the optimal operation and maintenance scheme shall be executed; otherwise, the initial degradation rate of the power equipment shall be reset, and the operation and maintenance strategy optimization model shall be solved again.

[0149] By evaluating the health status of power equipment, optimizing equipment operation and maintenance strategies, and effectively utilizing equipment status monitoring information to track equipment status changes in real time, and further adjusting operation and maintenance plans, it is of great significance to improve the reliability and economy of equipment operation and maintenance.

[0150] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0151] Based on the device operation and maintenance strategy optimization method provided in the above embodiments, the present invention also provides a specific implementation of a device operation and maintenance strategy optimization device applied to the device operation and maintenance strategy optimization method. Please refer to the following embodiments.

[0152] like Figure 3 As shown, a device operation and maintenance strategy optimization device 300 is provided, the device comprising:

[0153] A first model module 310 is constructed to build a failure rate prediction model for power equipment based on the initial degradation rate of the power equipment and the real-time operating parameters of the power equipment.

[0154] A second model module 320 is constructed to build an operation and maintenance strategy optimization model for real-time monitoring of power equipment based on the fault prediction module and the fault downtime, fault loss cost, operation and maintenance time, operation and maintenance cost and load loss caused by operation and maintenance under various operation and maintenance levels of power equipment.

[0155] The operation and maintenance plan determination module 330 is used to solve the operation and maintenance strategy optimization model based on preset constraints to determine the optimal operation and maintenance plan;

[0156] The execution module 340 is used to determine the first degradation rate of the power equipment under the optimal operation and maintenance scheme. If the first degradation rate is less than the initial degradation rate, the optimal operation and maintenance scheme is executed; otherwise, the initial degradation rate of the power equipment is reset and the operation and maintenance strategy optimization model is solved again.

[0157] In one possible implementation, a first model module 310 is constructed for constructing a health assessment model for power equipment;

[0158] The health index of power equipment is determined based on the initial deterioration rate, real-time operating parameters, and health assessment model.

[0159] Based on the health index of power equipment, the historical failure rate of power equipment, and the historical health assessment index of power equipment, a failure rate prediction model for power equipment is determined.

[0160] In one possible implementation, a first model module 310 is constructed to input the target state operating parameters into the health assessment model to obtain the health index of the target state operating parameters;

[0161] The health index with the highest value among all the health indices of the target state operating parameters is determined as the real-time health index of the power equipment.

[0162] The ratio of the real-time health index to the initial degradation rate is determined as the health index of the power equipment.

[0163] In one possible implementation, the health assessment model H for electrical equipment is:

[0164]

[0165] Where H′=max{H1′,H2′,…,H n ′,…,H k ′},

[0166]

[0167] H represents the health index of electrical equipment; H n 'This represents the health index of the nth operating parameter of the power equipment; v i x represents the initial degradation rate of the power equipment, with an initial value of 1; i This represents the real-time monitoring value of the operating parameters for the nth state. These represent the upper and lower limits for the normal state, slight degradation, and severe degradation of power equipment, respectively.

[0168] In one possible implementation, a second model module 320 is constructed to build a failure loss model for power equipment based on a failure rate prediction model and the failure downtime and failure loss cost of power equipment under various different operation and maintenance levels.

[0169] Based on the operation and maintenance time and cost of power equipment under various operation and maintenance levels, an operation and maintenance loss model for power equipment is constructed.

[0170] Based on the difference in loss costs before and after operation and maintenance of power equipment, the operation and maintenance benefits of power equipment are constructed;

[0171] Based on the objectives of minimizing power equipment failure losses, minimizing operation and maintenance losses, and maximizing operation and maintenance benefits, an optimization model for operation and maintenance strategies for real-time monitoring of power equipment is constructed.

[0172] In one possible implementation, the operation and maintenance strategy optimization model Z max for:

[0173] Z max =[ω1B-ω2(E)] F +E M )] max ;

[0174] Where B represents the operation and maintenance efficiency of the power equipment, and E... F For losses due to power equipment failures, E M The operation and maintenance losses of power equipment are represented by ω1 and ω2, which are the weights of the operation and maintenance benefits and losses of power equipment, respectively.

[0175] In one possible implementation, the execution module 340 is used to obtain real-time status operating parameters of the power equipment when it is running according to the optimal operation and maintenance plan;

[0176] The first degradation rate is determined based on a pre-built equipment degradation rate model, real-time operating parameters, and historical operating parameters of power equipment under a specified maintenance plan.

[0177] Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Figure 4 As shown, the electronic device 4 in this embodiment includes: a processor 40, a memory 41, and a computer program 42 stored in the memory 41 and executable on the processor 40. When the processor 40 executes the computer program 42, it implements the steps in the above-described embodiments of the operation and maintenance strategy optimization method for each device, for example... Figure 1 Steps 110 to 140 are shown. Alternatively, when the processor 40 executes the computer program 42, it implements the functions of each module in the above-described device embodiments, for example... Figure 3 The functions of modules 310 to 340 are shown.

[0178] For example, the computer program 42 can be divided into one or more modules, which are stored in the memory 41 and executed by the processor 40 to complete the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 42 in the electronic device 4. For example, the computer program 42 can be divided into... Figure 3 Modules 310 to 340 are shown.

[0179] The electronic device 4 may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will understand that... Figure 4 This is merely an example of electronic device 4 and does not constitute a limitation on electronic device 4. It may include more or fewer components than shown, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0180] The processor 40 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0181] The memory 41 can be an internal storage unit of the electronic device 4, such as a hard disk or memory. The memory 41 can also be an external storage device of the electronic device 4, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 41 can include both internal and external storage units of the electronic device 4. The memory 41 is used to store the computer program and other programs and data required by the electronic device. The memory 41 can also be used to temporarily store data that has been output or will be output.

[0182] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0183] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0184] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0185] In the embodiments provided by this invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0186] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0187] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0188] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the above-described embodiments of the operation and maintenance strategy optimization method for each device. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0189] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for optimizing the operation and maintenance strategy of equipment, characterized in that, include: Based on the initial degradation rate of the power equipment and its real-time operating parameters, a failure rate prediction model for the power equipment is constructed. Based on the failure rate prediction model and the failure downtime, failure loss cost, operation and maintenance time, operation and maintenance cost, and load loss caused by operation and maintenance of the power equipment under various operation and maintenance levels, an operation and maintenance strategy optimization model for real-time monitoring of the power equipment is constructed. Based on preset constraints, the operation and maintenance strategy optimization model is solved to determine the optimal operation and maintenance solution; Under the optimal operation and maintenance scheme, determine the first degradation rate of the power equipment under this scheme. If the first degradation rate is less than the initial degradation rate, then execute according to the optimal operation and maintenance scheme. Otherwise, reset the initial degradation rate of the power equipment and re-solve the operation and maintenance strategy optimization model; The method for constructing a failure rate prediction model for power equipment based on its initial degradation rate and real-time operating parameters includes: Construct a health assessment model for the power equipment; The target state operating parameters are input into the health assessment model to obtain the health index of the target state operating parameters; the largest health index among all the health indices of the target state operating parameters is determined as the real-time health index of the power equipment; the ratio of the real-time health index to the initial deterioration rate is determined as the health index of the power equipment. Based on the health index of the power equipment, the historical failure rate of the power equipment, and the historical health assessment index of the power equipment, a failure rate prediction model for the power equipment is determined. The health assessment model for the power equipment H for: ； in, , H A health index for electrical equipment; H n ' For power equipment n Health index of each state's operating parameters; v i This represents the initial degradation rate of the power equipment, with an initial value of 1. x i For the first n Real-time monitoring values ​​of each state's operating parameters. , , , , , The first of each power equipment n The upper and lower limits of the normal state, mild degradation, and severe degradation of each state's operating parameters.

2. The operation and maintenance strategy optimization method as described in claim 1, characterized in that, Based on the failure rate prediction model and the failure downtime, failure loss cost, maintenance time, maintenance cost, and load loss caused by maintenance of the power equipment under various maintenance levels, a real-time monitoring maintenance strategy optimization model for the power equipment is constructed, including: Based on the failure rate prediction model and the failure downtime and failure loss cost of the power equipment under various different operation and maintenance levels, a failure loss model for the power equipment is constructed. Based on the maintenance time and maintenance costs of the power equipment under various maintenance levels, a maintenance loss model for the power equipment is constructed. Based on the difference in loss costs before and after the operation and maintenance of the power equipment, the operation and maintenance benefits of the power equipment are constructed. Based on the objectives of minimizing fault losses, minimizing operation and maintenance losses, and maximizing operation and maintenance benefits of the power equipment, an operation and maintenance strategy optimization model for real-time monitoring of the power equipment is constructed.

3. The operation and maintenance strategy optimization method as described in claim 2, characterized in that, The operation and maintenance strategy optimization model Z max for: ； in, B To improve the operation and maintenance efficiency of the aforementioned power equipment, For the failure loss of the aforementioned power equipment, The maintenance losses of the aforementioned power equipment, and These are the weights for the operation and maintenance benefits of the power equipment and the losses of the power equipment, respectively.

4. The operation and maintenance strategy optimization method as described in claim 1, characterized in that, Determining the first degradation rate of the power equipment under the optimal operation and maintenance scheme includes: Obtain the real-time status operating parameters of the power equipment when it is running according to the optimal operation and maintenance plan; The first degradation rate is determined based on a pre-built equipment degradation rate model, the real-time operating parameters, and the historical operating parameters of the power equipment under a specified maintenance plan.

5. A device for optimizing the operation and maintenance strategy of equipment, characterized in that, include: A first model module is constructed to build a failure rate prediction model for the power equipment based on the initial degradation rate and real-time operating parameters of the power equipment. A second model module is constructed to build an operation and maintenance strategy optimization model for real-time monitoring of the power equipment based on the failure rate prediction model and the failure downtime, failure loss cost, operation and maintenance time, operation and maintenance cost, and load loss caused by operation and maintenance under various different operation and maintenance levels. The operation and maintenance plan determination module is used to solve the operation and maintenance strategy optimization model based on preset constraints to determine the optimal operation and maintenance plan; The execution module is used to determine the first degradation rate of the power equipment under the optimal operation and maintenance scheme. If the first degradation rate is less than the initial degradation rate, the optimal operation and maintenance scheme is executed; otherwise, the initial degradation rate of the power equipment is reset and the operation and maintenance strategy optimization model is solved again. The first model building module is also used to build a health assessment model for the power equipment. The target state operating parameters are input into the health assessment model to obtain the health index of the target state operating parameters; the largest health index among all the health indices of the target state operating parameters is determined as the real-time health index of the power equipment; the ratio of the real-time health index to the initial deterioration rate is determined as the health index of the power equipment. Based on the health index of the power equipment, the historical failure rate of the power equipment, and the historical health assessment index of the power equipment, a failure rate prediction model for the power equipment is determined. The health assessment model for the power equipment H for: ； in, , H A health index for electrical equipment; H n ' For power equipment n Health index of each state's operating parameters; v i This represents the initial degradation rate of the power equipment, with an initial value of 1. x i For the first n Real-time monitoring values ​​of each state's operating parameters. , , , , , The first of each power equipment n The upper and lower limits of the normal state, mild degradation, and severe degradation of each state's operating parameters.

6. An electronic device, characterized in that, The method includes a memory and a processor, the memory being used to store a computer program, and the processor being used to call and run the computer program stored in the memory to perform the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 4.

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