A power equipment optimal service life calculation method, system, device and medium
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- STATE GRID HENAN ELECTRIC POWER
- Filing Date
- 2026-04-27
- Publication Date
- 2026-08-07
AI Technical Summary
[0005]本发明提供一种电力设备最佳使用年限计算方法、系统、设备及介质，以至少解决现有技术中无法根据不同设备类型准确计算出电力设备的最佳使用年限的问题
本申请提供的电力设备最佳使用年限计算方法中，通过执行获取待评估电力设备的投运成本、退役处置成本，以及历史运行年限内各年的年运行成本数据和年维修成本数据的步骤，为最佳使用年限计算提供了全面、结构化的数据基础，确保了评估不仅考虑初始投资，还纳入了设备运行期间持续发生的各类成本及最终的处置收益或支出，从而支持了从全寿命周期视角进行更完整、更贴近实际的经济性分析。
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Abstract
Description
Technical Field
[0001] This invention belongs to the field of power technology, and in particular relates to a method, system, equipment and medium for calculating the optimal service life of power equipment. Background Technology
[0002] With the continuous expansion of power system scale and the increasing asset intensity, the refined and economical management of power equipment in the power grid has become a key link in ensuring the safe and stable operation of the power grid, optimizing asset allocation, and achieving cost reduction and efficiency improvement. Scientifically assessing the optimal service life of equipment, instead of the traditional fixed design life, is of great significance for rationally planning equipment upgrades and replacements and avoiding resource waste caused by excessive maintenance or premature retirement.
[0003] In existing technologies, the retirement of power equipment is based on the design life indicated by the manufacturer. However, this approach increases the maintenance cost of the equipment, resulting in a disproportionate investment and output, and causing a certain degree of waste of funds.
[0004] Therefore, the present invention provides a method, system, device and medium for calculating the optimal service life of power equipment. Summary of the Invention
[0005] This invention provides a method, system, device, and medium for calculating the optimal service life of power equipment, thereby at least solving the problem in the prior art that the optimal service life of power equipment cannot be accurately calculated according to different equipment types.
[0006] In a first aspect, embodiments of this application provide a method for calculating the optimal service life of electrical equipment, the method comprising the following steps: Step S1: Obtain the commissioning cost, decommissioning cost, and historical operating years of the power equipment to be evaluated. Historical operating cost data and historical maintenance cost data for each year; Step S2: Based on the equipment category of the power equipment to be evaluated, determine the benchmark calculation model for the change of its operating cost and maintenance cost over time; Step S3: Input the acquired historical annual operating cost data and historical annual maintenance cost data into the pre-built health status assessment model, and output the current health status score of the power equipment to be assessed; Step S4: Based on the health status score, correct the key growth parameters in the baseline calculation model to obtain the operating cost calculation model and the maintenance cost calculation model; Step S5: Based on the obtained calculation models for commissioning costs, decommissioning costs, operating costs, and maintenance costs, construct a system based on the number of years of operation. A multi-objective optimization function with variables; Step S6: Solve the multi-objective optimization function using an optimization algorithm to obtain the optimal service life of the power equipment to be evaluated.
[0007] Further, in step S2, based on the equipment category of the power equipment to be evaluated, a calculation model for the changes in its operating and maintenance costs over time is determined, specifically as follows: Power equipment is classified into transformers, generating units, wind turbines, power electronics, low-voltage power distribution, and transmission lines. The calculation models include linear growth cost calculation models, exponential growth cost calculation models, and power growth cost calculation models. The calculation model for the operating costs of power equipment such as transformers, wind turbines, and transmission lines is a linear growth cost calculation model; The calculation model for the operating cost of power electronic equipment is an exponential growth cost calculation model; The calculation model for the operating cost of generator sets is a power-law-based cost calculation model; The calculation model for maintenance costs of low-voltage power distribution and transmission line equipment is a linear growth cost calculation model; The calculation model for maintenance costs of transformers and power electronic equipment is an exponential growth cost calculation model; The calculation model for maintenance costs of power equipment such as generating units and wind turbines is a power-law-increasing cost calculation model.
[0008] Furthermore, the expression for the linear growth cost calculation model is as follows:
[0009] in, In the linear growth cost calculation model, the first... Annual maintenance cost or annual operating cost; This indicates the initial maintenance cost or initial operating cost; This is an unknown parameter, representing the annual cost increase; Indicates the service life of electrical equipment.
[0010] Furthermore, the expression for the exponential growth cost calculation model is as follows:
[0011] in, In the exponential growth cost calculation model, the first... Annual maintenance cost or annual operating cost; This is an unknown parameter, representing a fixed increment in annual maintenance costs or a fixed increment in annual operating costs.
[0012] Furthermore, the expression for the power-law cost calculation model is:
[0013] in, In the power-law cost calculation model, the first... Annual maintenance cost or annual operating cost; The parameter is unknown, representing the growth index.
[0014] Further, in step S3, the acquired historical annual operating cost data and historical annual maintenance cost data are input into a pre-built health status assessment model, and the current health status score of the power equipment to be assessed is output, specifically including: S31: Based on the historical annual operating cost data and historical annual maintenance cost data, construct a time-series feature sequence for model input; S32: Extract feature indicators reflecting the deterioration pattern of equipment costs from the time-series feature sequence. The feature indicators include at least the average annual growth rate of operating costs and maintenance costs over the past N years, the volatility index of cost change rate, and the ratio of cumulative cost to operating years. S33: The extracted feature indicators are combined into a feature vector and input into the pre-trained health status assessment model. The model calculates an assessment value representing the overall health level of the device based on the input feature vector. S34: Normalize the evaluation value and map it to the [0,1] interval. The output is the current health status score of the power equipment to be evaluated, where a higher score indicates a better health status of the equipment.
[0015] Furthermore, in step S5, the expression for the multi-objective optimization function is:
[0016] in, Indicates the service life of the electrical equipment to be evaluated. The average annual total cost; Indicates the number of years of operation; When the electrical equipment to be evaluated is a transformer-type electrical equipment for: ; When the power equipment to be evaluated is a generator unit type power equipment for: ; When the power equipment to be evaluated is a wind turbine type power equipment for: ; When the electrical equipment to be evaluated is a power electronic device for: ; When the electrical equipment to be evaluated is a low-voltage power distribution equipment This is a fixed value and only includes inspection fees. When the power equipment to be evaluated is a power transmission line, for: ; When the electrical equipment to be evaluated is a transformer-type electrical equipment for: ; When the power equipment to be evaluated is a generator set power equipment. for: ; When the power equipment to be evaluated is a wind turbine type power equipment. for: ; When the electrical equipment to be evaluated is a power electronic device for: ; When the electrical equipment to be evaluated is a low-voltage power distribution equipment for: ; When the power equipment to be evaluated is a power transmission line type power equipment. for: .
[0017] Secondly, embodiments of this application also provide a system for calculating the optimal service life of power equipment as described in the above aspects, the system comprising: The commissioning costs, decommissioning costs, and historical operating years of the power equipment to be evaluated. Historical operating cost data and historical maintenance cost data for each year; The model determination module is used to determine a benchmark calculation model for the changes in operating costs and maintenance costs over time based on the equipment category of the power equipment to be evaluated. The parameter fitting module is used to input the acquired historical annual operating cost data and historical annual maintenance cost data into the pre-built health status assessment model and output the current health status score of the power equipment to be assessed. The modeling module is used to correct key growth parameters in the baseline calculation model based on the health status score, so as to obtain the operating cost calculation model and the maintenance cost calculation model. The model optimization module is used to construct a model based on the acquired commissioning costs, decommissioning costs, and dynamic cost calculations, taking into account the number of years of operation. A multi-objective optimization function with variables; The optimal service life output module uses an optimization algorithm to solve a multi-objective optimization function to obtain the optimal service life of the power equipment to be evaluated.
[0018] Thirdly, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps of the method for calculating the optimal service life of electrical equipment as described in the preceding aspects.
[0019] Fourthly, a storage medium storing a computer program that, when executed by a processor, implements the steps of the method for calculating the optimal service life of electrical equipment as described in the preceding aspects.
[0020] As can be seen from the above technical solutions, the present invention has the following advantages: The method for calculating the optimal service life of power equipment provided in this application provides a comprehensive and structured data foundation for calculating the optimal service life by performing steps to obtain the commissioning cost, decommissioning and disposal cost of the power equipment to be evaluated, as well as the annual operating cost data and annual maintenance cost data for each year within the historical operating period. This ensures that the evaluation not only considers the initial investment, but also includes various costs that continue to occur during the operation of the equipment and the final disposal revenue or expenditure, thereby supporting a more complete and realistic economic analysis from a life cycle perspective.
[0021] By determining the benchmark calculation model for the changes in operating and maintenance costs over time based on the equipment category of the power equipment to be evaluated, the model matching problem is specifically solved. Based on the inherent technical characteristics and typical aging patterns of different types of equipment, the most suitable growth model for their operating and maintenance costs is preset, so that the cost prediction can more accurately reflect the actual deterioration pattern of specific equipment, and improve the scientificity and applicability of the model.
[0022] By using the obtained commissioning cost, decommissioning cost, and operation cost calculation models and maintenance cost calculation models, the average annual total cost of the power equipment to be evaluated under the operating years t is calculated, and a unified economic evaluation index is constructed. The one-time cost, time-varying cost and final disposal cost are evenly distributed to each operating year, forming an average annual total cost curve that can be compared horizontally. This clearly reveals the trade-off between the decrease in average annual fixed cost and the increase in average annual operation and maintenance cost as the operating time increases, providing a direct quantitative basis for finding the cost-optimal solution.
[0023] By calculating the average annual total cost corresponding to different operating years t, and determining the operating years corresponding to the minimum average annual total cost as the optimal service life of the power equipment, the lowest point of the average annual total cost curve is automatically located through mathematical calculation, thereby obtaining a specific retirement year that minimizes the average annual cost throughout the entire life cycle. This replaces subjective experience judgment, providing quantifiable and verifiable data support for equipment retirement decisions and enhancing the reliability of the decisions. Attached Figure Description
[0024] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0025] Figure 1 This is a flowchart of the method for calculating the optimal service life of power equipment according to the present invention.
[0026] Figure 2 The annual average cost curves of various calculation models for power equipment based on the optimal service life calculation method for power equipment described in this invention; Figure 3 This is a total cost and average annual cost curve for Example 1 of the method for calculating the optimal service life of power equipment according to the present invention; Figure 4 This is a total cost and average annual cost curve for Example 2 of the method for calculating the optimal service life of power equipment according to the present invention; Figure 5 Example 3 of the method for calculating the optimal service life of power equipment according to the present invention is a graph showing the total cost and average annual cost. Detailed Implementation
[0027] To make the purpose, features, and advantages of this application more apparent and understandable, specific embodiments and accompanying drawings will be used to clearly and completely describe the technical solution protected by this application. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.
[0028] This application provides a method, system, device, and medium for calculating the optimal service life of power equipment, addressing the urgent technical problem of needing a precise quantitative assessment of the optimal service life of various types of power equipment based on a classification cost model.
[0029] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.
[0030] Figure 1 A flowchart illustrating a method for calculating the optimal service life of electrical equipment, provided as an embodiment of this application. Figure 1 As shown in the embodiment of this application, a method for calculating the optimal service life of electrical equipment specifically includes the following steps: Step S1: Obtain the commissioning cost, decommissioning cost, and historical operating years of the power equipment to be evaluated. Historical operating cost data and historical maintenance cost data for each year; Step S2: Based on the equipment category of the power equipment to be evaluated, determine the benchmark calculation model for the change of its operating cost and maintenance cost over time; Step S3: Input the acquired historical annual operating cost data and historical annual maintenance cost data into the pre-built health status assessment model, and output the current health status score of the power equipment to be assessed; Step S4: Based on the health status score, correct the key growth parameters in the baseline calculation model to obtain the operating cost calculation model and the maintenance cost calculation model; Step S5: Based on the obtained calculation models for commissioning costs, decommissioning costs, operating costs, and maintenance costs, construct a system based on the number of years of operation. A multi-objective optimization function with variables; Step S6: Solve the multi-objective optimization function using an optimization algorithm to obtain the optimal service life of the power equipment to be evaluated.
[0031] In an exemplary embodiment, in step S2, a benchmark calculation model for the changes in operating and maintenance costs over time is determined based on the equipment category of the power equipment to be evaluated, specifically as follows: Power equipment is classified into transformers, generating units, wind turbines, power electronics, low-voltage power distribution, and transmission lines. The calculation models include linear growth cost calculation models, exponential growth cost calculation models, and power growth cost calculation models. The calculation model for the operating costs of power equipment such as transformers, wind turbines, and transmission lines is a linear growth cost calculation model; The calculation model for the operating cost of power electronic equipment is an exponential growth cost calculation model; The calculation model for the operating cost of generator sets is a power-law-based cost calculation model; The calculation model for maintenance costs of low-voltage power distribution and transmission line equipment is a linear growth cost calculation model; The calculation model for maintenance costs of transformers and power electronic equipment is an exponential growth cost calculation model; The calculation model for maintenance costs of power equipment such as generating units and wind turbines is a power-law-increasing cost calculation model.
[0032] According to another embodiment of the present invention, the expression for the linear growth cost calculation model is as follows:
[0033] in, In the linear growth cost calculation model, the first... Annual maintenance cost or annual operating cost; This indicates the initial maintenance cost or initial operating cost; This is an unknown parameter, representing the annual cost increase; Indicates the service life of electrical equipment.
[0034] The expression for the exponential growth cost calculation model is:
[0035] in, In the exponential growth cost calculation model, the first... Annual maintenance cost or annual operating cost; This is an unknown parameter, representing a fixed increment in annual maintenance costs or a fixed increment in annual operating costs.
[0036] The expression for the power-law cost calculation model is:
[0037] in, In the power-law cost calculation model, the first... Annual maintenance cost or annual operating cost; The parameter is unknown, representing the growth index.
[0038] Transformer-type power equipment includes 110kV / 220kV main transformers, distribution transformers, and converter transformers; generator-type power equipment includes thermal power units and photovoltaic inverters; wind turbine-type power equipment includes wind turbine units; power electronic equipment includes inverters, SVG, high-voltage frequency converters, and DC converter valves; low-voltage distribution power equipment includes low-voltage switchgear, circuit breakers, contactors, and distribution boxes; transmission line-type power equipment includes 110kV / 220kV overhead lines and cable lines. The calculation model for the operating cost of transformer-type power equipment is as follows: The operating costs of transformer-type power equipment include cooling and inspection fees, with only core losses showing a steady increase.
[0039] The calculation model for the operating cost of generator sets is as follows: The operating cost of generator sets includes the fact that coal consumption of generator sets accelerates slightly with aging.
[0040] The calculation model for the operating cost of wind turbine-type power equipment is as follows: The operating cost of wind turbine power equipment includes wind turbine operating fees, which are linearly related to power generation.
[0041] The calculation model for the operating cost of power electronic equipment is as follows: The operating costs of power electronic equipment include heat dissipation and software. As IGBTs age, heat dissipation decreases, leading to a faster rate of energy consumption.
[0042] The operating cost of low-voltage power distribution equipment only includes inspection fees, which is a fixed value with no energy consumption expenditure and no room for growth.
[0043] The calculation model for the operating cost of power transmission line equipment is as follows: The operating costs of power transmission line equipment include inspection fees and corridor maintenance fees, with maintenance fees increasing slightly.
[0044] The calculation model for the maintenance cost of transformer-type power equipment is as follows: In the early stages, only oil samples were tested for transformer-type power equipment. Later, due to a surge in faults such as insulation aging and oil leakage, maintenance costs rose rapidly.
[0045] The calculation model for the maintenance cost of generator sets and other power equipment is as follows: Faults in generator sets are concentrated in bearings / gearboxes, and wear gradually increases with operating time. The cost of failure is between linear and exponential.
[0046] The calculation model for the maintenance cost of wind turbine-type power equipment is as follows: Failures in wind turbine-type power equipment are concentrated in bearings / gearboxes, and wear gradually increases with operating time. The cost of failure is between linear and exponential.
[0047] The calculation model for the maintenance cost of power electronic equipment is as follows: In the later stages of the repair process for power electronic equipment, issues such as capacitor bulging and IGBT failure lead to a surge in maintenance costs.
[0048] The calculation model for the maintenance cost of low-voltage power distribution equipment is as follows: For low-voltage power distribution equipment, the failures are limited to contact wear / loose wiring, and the maintenance cost increases by a small amount of spare parts each year, with no accelerating growth trend.
[0049] The calculation model for the maintenance cost of power transmission line equipment is as follows: Faults in power transmission line equipment are mainly due to insulator aging and tower corrosion, and maintenance costs rise steadily with the number of years of operation.
[0050] In step S3, the acquired historical annual operating cost data and historical annual maintenance cost data are input into the pre-built health status assessment model, and the current health status score of the power equipment to be assessed is output, specifically including: S31: Based on the historical annual operating cost data and historical annual maintenance cost data, construct a time-series feature sequence for model input; S32: Extract feature indicators reflecting the deterioration pattern of equipment costs from the time-series feature sequence. The feature indicators include at least the average annual growth rate of operating costs and maintenance costs over the past N years, the volatility index of cost change rate, and the ratio of cumulative cost to operating years. S33: The extracted feature indicators are combined into a feature vector and input into the pre-trained health status assessment model. The model calculates an assessment value representing the overall health level of the device based on the input feature vector. S34: Normalize the evaluation value and map it to the [0,1] interval. The output is the current health status score of the power equipment to be evaluated, where a higher score indicates a better health status of the equipment.
[0051] According to embodiments of this application, the commissioning cost of transformer-type power equipment includes equipment procurement cost and equipment installation cost; The commissioning cost of power generating units includes equipment procurement costs, infrastructure construction costs, grid connection and commissioning costs, and auxiliary material costs. The commissioning cost of wind turbine-type power equipment includes equipment procurement cost, infrastructure construction cost, grid connection and commissioning cost, and auxiliary material cost; The commissioning cost of power electronic equipment includes equipment procurement cost, heat dissipation system modification cost, software cost, and debugging cost; The commissioning cost of low-voltage power distribution equipment includes equipment procurement cost, installation and wiring cost, and cabinet modification cost; The commissioning cost of power equipment such as transmission lines includes the cost of purchasing poles / cables and the cost of erecting / laying them; The formula for calculating the decommissioning and disposal costs of power equipment such as transformers, generator sets, wind turbines, power electronics, low-voltage distribution equipment, and transmission lines is: dismantling cost + environmental treatment cost + transportation cost - residual value recovery income.
[0052] In one embodiment, the expression for the multi-objective optimization function is:
[0053] in, Indicates the service life of the electrical equipment to be evaluated. The average annual total cost; Indicates the number of years of operation; When the electrical equipment to be evaluated is a transformer-type electrical equipment for: ; When the power equipment to be evaluated is a generator unit type power equipment for: ; When the power equipment to be evaluated is a wind turbine type power equipment for: ; When the electrical equipment to be evaluated is a power electronic device for: ; When the electrical equipment to be evaluated is a low-voltage power distribution equipment This is a fixed value and only includes inspection fees. When the power equipment to be evaluated is a power transmission line, for: ; When the electrical equipment to be evaluated is a transformer-type electrical equipment for: ; When the power equipment to be evaluated is a generator set power equipment. for: ; When the power equipment to be evaluated is a wind turbine type power equipment. for: ; When the electrical equipment to be evaluated is a power electronic device for: ; When the electrical equipment to be evaluated is a low-voltage power distribution equipment for: ; When the power equipment to be evaluated is a power transmission line type power equipment. for: .
[0054] In step S6, the step of solving the multi-objective optimization function using an optimization algorithm to obtain the optimal service life of the power equipment to be evaluated specifically includes the following steps: S61: Within a preset timeframe, iterate through a series of candidate running years; S62: For each candidate operating period, substitute the candidate operating period into the multi-objective optimization function constructed in step S5 to calculate the corresponding average annual total cost; S63: Compare all the calculated average annual total cost values and find the minimum value among them; S64: Output the candidate operating years corresponding to the minimum average total cost as the optimal service life of the power equipment to be evaluated.
[0055] According to an embodiment of the present invention, a flowchart of a method for calculating the optimal service life of electrical equipment is provided, and the steps of the flowchart are as follows: Obtain historical operating data for the target type of power equipment. This includes all decommissioned and operating power equipment of that type. Historical operating data includes the commissioning year, decommissioning year, service life, and all costs incurred for all power equipment of the target type. These costs include commissioning costs, operating costs, maintenance costs, and decommissioning disposal costs.
[0056] Select the corresponding cost calculation formula based on the equipment type, substitute the data into the formula, and calculate the model parameters. The main models include linear functions, exponential functions, and power functions, etc. The parameters that need to be calculated for linear functions include... Parameters, exponential functions Parameters, power functions Parameters, etc.
[0057] By combining the power equipment cost curve, the average annual total cost curve is obtained, and the equipment operating life or range corresponding to the lowest average annual cost curve is determined. Since the average annual cost is lowest within this range, this operating life or range can be considered the optimal range for the optimal service life of the power equipment.
[0058] Table 1 Annual Costs for Example 1
[0059] Depend on Figure 2 As can be seen, the total cost of power equipment includes fixed expenditures (such as commissioning costs and decommissioning costs) and non-fixed expenditures (such as operating costs and maintenance costs). Since fixed costs do not change with the length of operation, they decrease annually with longer operating time. However, annual operating costs and maintenance costs increase with the length of operation, so there is a minimum point for the average total annual cost. The following specific examples illustrate this.
[0060] Example 1: A standard 10kV switchgear; The 10kV switchgear is calculated using a low-voltage power distribution calculation model. Based on historical data, the equipment's investment cost is 50,000 yuan, and the annual operating cost is a fixed 2,300 yuan per year. Its annual maintenance cost is calculated using a linear model: 0.1 + 0.04 × (t) 1) (Unit: RMB 10,000), the cost of decommissioning and disposal is -RMB 0.07 million (net income). See Table 1 for details of the expenses.
[0061] As shown in Table 1, the average annual operating cost was lowest between 2014 and 2018, at 0.94 million yuan. Therefore, it is advisable to consider retiring the equipment within this operating period.
[0062] Example 2: A 220kV main transformer; The calculation is based on the equipment model for category 1 in Table 1. According to historical data, the equipment's commissioning cost is 9 million yuan, and the operating cost is calculated using a linear model: 20 + 1 × (t) 1) (Unit: RMB 10,000), Maintenance cost calculated using the index model: 5 × 1.12t 1 (Unit: RMB 10,000), Decommissioning disposal costs: -RMB 522,500 (net income). See Table 2 for details of expenses.
[0063] Table 2 Annual Costs for Example 2
[0064] As shown in Table 2, the average annual operating cost was lowest in 2021, at 898,200 yuan. Therefore, it is advisable to consider retiring the equipment around this point in its service life.
[0065] Example 3: A 1.5MW wind turbine; Calculations are performed using a power equipment calculation model for transformers. Based on historical data, the equipment's commissioning cost is 15 million yuan, and the operating cost is calculated using a linear model: 8 + 2 × (t) 1) (Unit: RMB 10,000), maintenance cost is calculated using the index model: 2 × t1.4 (Unit: RMB 10,000), and decommissioning disposal cost is RMB 50,000 (net expenditure). See Table 3 for details of the expenses.
[0066] Table 3 Annual Costs for Example 3
[0067] As shown in Table 3, the average annual operating cost was lowest in 2019, at 1.58 million yuan. Therefore, it is advisable to consider retiring the equipment around this point in its service life.
[0068] As can be seen from the above three specific embodiments, once the relevant costs of the power equipment are obtained, the calculation models for various costs can be further obtained by calculating its model parameters, ultimately yielding the average annual total cost. The operating years or range with the lowest average annual total cost are then taken as the optimal retirement age for the power equipment. Therefore, this application can calculate and guide the optimal service life of power equipment.
[0069] The present invention also provides a system for calculating the optimal service life of power equipment, the system comprising: The commissioning costs, decommissioning costs, and historical operating years of the power equipment to be evaluated. Historical operating cost data and historical maintenance cost data for each year; The model determination module is used to determine a benchmark calculation model for the changes in operating costs and maintenance costs over time based on the equipment category of the power equipment to be evaluated. The parameter fitting module is used to input the acquired historical annual operating cost data and historical annual maintenance cost data into the pre-built health status assessment model and output the current health status score of the power equipment to be assessed. The modeling module is used to correct key growth parameters in the baseline calculation model based on the health status score, so as to obtain the operating cost calculation model and the maintenance cost calculation model. The model optimization module is used to construct a model based on the acquired commissioning costs, decommissioning costs, and dynamic cost calculations, taking into account the number of years of operation. A multi-objective optimization function with variables; The optimal service life output module uses an optimization algorithm to solve a multi-objective optimization function to obtain the optimal service life of the power equipment to be evaluated.
[0070] The method for calculating the optimal service life of electrical equipment provided in this application can be applied to electronic devices. Those skilled in the art will understand that the electronic device structure involved in the embodiments of this invention does not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. In the embodiments of this invention, electronic devices include, but are not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.
[0071] Electronic devices may include processors, external memory interfaces, internal memory, universal serial bus (USB) interfaces, charging management modules, power management modules, batteries, wireless communication modules, audio modules, speakers, microphones, sensor modules, buttons, cameras, displays, and SIM card interfaces, etc.
[0072] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the electronic device. In other embodiments of this application, the electronic device may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.
[0073] A processor may include one or more processing units, such as: a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors.
[0074] The processor can serve as the nerve center and command center of an electronic device. The controller can generate operation control signals based on the instruction opcode and timing signals to control the fetching and execution of instructions.
[0075] The processor may also include memory for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can store instructions or data that the processor has just used or that are used repeatedly. If the processor needs to use the instruction or data again, it can retrieve it directly from this memory. This avoids repeated accesses, reduces processor latency, and thus improves system efficiency.
[0076] An external storage interface (ESI) can be used to connect external memory cards, such as microSD cards, to expand the storage capacity of electronic devices. The external memory card communicates with the processor through the ESI to perform data storage functions, such as saving music and video files on the external memory card.
[0077] Internal memory can be used to store computer executable program code, which includes instructions. The processor executes various functional applications and data processing of electronic devices by running the instructions stored in internal memory. Internal memory can include a program storage area and a data storage area. Internal memory can include high-speed random access memory, and can also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.
[0078] Wireless communication functionality in electronic devices can be achieved through antennas, wireless communication modules, modem processors, and baseband processors.
[0079] Wireless communication modules can provide solutions for wireless communication applications in electronic devices, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies.
[0080] Electronic devices can implement audio functions through audio modules, speakers, receivers, microphones, headphone jacks, and application processors.
[0081] Electronic devices can achieve shooting functions through ISPs, cameras, video codecs, GPUs, displays, and application processors.
[0082] Electronic devices can achieve display functions through GPUs, displays, and application processors.
[0083] A GPU is a microprocessor for image processing, connected to the display screen and application processor. GPUs are used to perform mathematical and geometric calculations for graphics rendering. A processor may include one or more GPUs, which execute program instructions to generate or modify display information.
[0084] A display screen is used to display images, videos, etc. A display screen includes a display panel.
[0085] 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, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. 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.
[0086] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
[0087] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of 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 couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, apparatuses, or units, or they may be electrical, mechanical, or other forms of connection.
[0088] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a full understanding of embodiments of the invention. However, those skilled in the art will recognize that the technical solutions of the invention can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of the invention.
[0089] The aforementioned electronic equipment enables the acquisition of the optimal service life calculation method for power equipment as described in this application, including the commissioning cost, decommissioning cost, and historical operating years of the power equipment to be evaluated. Historical annual operating cost data and historical annual maintenance cost data for each year are used to determine the benchmark calculation model for the changes in operating and maintenance costs over time. Based on a pre-built health status assessment model, a health status score is output. Based on the health status score, the benchmark calculation model is revised to obtain the operating cost calculation model and the maintenance cost calculation model. Based on the obtained commissioning cost, decommissioning cost, operating cost calculation model, and maintenance cost calculation model, a multi-objective optimization function is constructed. Solving the multi-objective optimization function yields the optimal service life of the power equipment to be evaluated. This accurately quantifies the optimal service life of the power equipment.
[0090] The storage medium provided in this application stores a program product capable of implementing a method for calculating the optimal service life of electrical equipment.
[0091] The method for calculating the optimal service life of electrical equipment includes: Step S1: Obtain the commissioning cost, decommissioning cost, and historical operating years of the electrical equipment to be evaluated. Historical operating cost data and historical maintenance cost data for each year; Step S2: Based on the equipment category of the power equipment to be evaluated, determine the benchmark calculation model for the change of its operating cost and maintenance cost over time; Step S3: Input the acquired historical annual operating cost data and historical annual maintenance cost data into the pre-built health status assessment model, and output the current health status score of the power equipment to be assessed; Step S4: Based on the health status score, correct the key growth parameters in the baseline calculation model to obtain the operating cost calculation model and the maintenance cost calculation model; Step S5: Based on the obtained calculation models for commissioning costs, decommissioning costs, operating costs, and maintenance costs, construct a system based on the number of years of operation. A multi-objective optimization function with variables; Step S6: Solve the multi-objective optimization function using an optimization algorithm to obtain the optimal service life of the power equipment to be evaluated.
[0092] This application calculates the average annual total cost corresponding to different operating years t, and determines the operating years corresponding to the minimum average annual total cost as the optimal service life of the power equipment. By automatically locating the lowest point of the average annual total cost curve through mathematical calculation, a specific retirement year that minimizes the average annual cost throughout the entire life cycle is obtained. This replaces subjective experience judgment, providing quantifiable and verifiable data support for equipment retirement decisions and enhancing the reliability of the decisions.
[0093] In some possible implementations, the method for calculating the optimal service life of electrical equipment disclosed herein can be implemented as a program product comprising program code that, when run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.
[0094] The storage medium disclosed herein may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.
[0095] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
[0096] Any changes, modifications, substitutions, and variations made to the embodiments without departing from the principles and spirit of the present invention still fall within the protection scope of the present invention.
Claims
1. A method for calculating the optimal service life of electrical equipment, characterized in that, The method includes the following steps: Step S1: Obtain the commissioning cost, decommissioning cost, and historical operating years of the power equipment to be evaluated. Historical operating cost data and historical maintenance cost data for each year; Step S2: Based on the equipment category of the power equipment to be evaluated, determine the benchmark calculation model for the change of its operating cost and maintenance cost over time; Step S3: Input the acquired historical annual operating cost data and historical annual maintenance cost data into the pre-built health status assessment model, and output the current health status score of the power equipment to be assessed; Step S4: Based on the health status score, correct the key growth parameters in the baseline calculation model to obtain the operating cost calculation model and the maintenance cost calculation model; Step S5: Based on the obtained calculation models for commissioning costs, decommissioning costs, operating costs, and maintenance costs, construct a system based on the number of years of operation. A multi-objective optimization function with variables; Step S6: Solve the multi-objective optimization function using an optimization algorithm to obtain the optimal service life of the power equipment to be evaluated.
2. The method as described in claim 1, characterized in that, In step S2, based on the equipment category of the power equipment to be evaluated, a calculation model for the changes in its operating and maintenance costs over time is determined, specifically as follows: Power equipment is classified into transformers, generating units, wind turbines, power electronics, low-voltage power distribution, and transmission lines. The calculation models include linear growth cost calculation models, exponential growth cost calculation models, and power growth cost calculation models. The calculation model for the operating costs of power equipment such as transformers, wind turbines, and transmission lines is a linear growth cost calculation model; The calculation model for the operating cost of power electronic equipment is an exponential growth cost calculation model; The calculation model for the operating cost of generator sets is a power-law-based cost calculation model; The calculation model for maintenance costs of low-voltage power distribution and transmission line equipment is a linear growth cost calculation model; The calculation model for maintenance costs of transformers and power electronic equipment is an exponential growth cost calculation model; The calculation model for maintenance costs of power equipment such as generating units and wind turbines is a power-law-increasing cost calculation model.
3. The method as described in claim 2, characterized in that, The expression for the linear growth cost calculation model is: in, In the linear growth cost calculation model, the first... Annual maintenance cost or annual operating cost; This indicates the initial maintenance cost or initial operating cost; This is an unknown parameter, representing the annual cost increase; Indicates the service life of electrical equipment.
4. The method as described in claim 3, characterized in that, The expression for the exponential growth cost calculation model is: in, In the exponential growth cost calculation model, the first... Annual maintenance cost or annual operating cost; This is an unknown parameter, representing a fixed increment in annual maintenance costs or a fixed increment in annual operating costs.
5. The method as described in claim 4, characterized in that, The expression for the power-law cost calculation model is: in, In the power-law cost calculation model, the first... Annual maintenance cost or annual operating cost; The parameter is unknown, representing the growth index.
6. The method as described in claim 5, characterized in that, In step S3, the acquired historical annual operating cost data and historical annual maintenance cost data are input into the pre-built health status assessment model, and the current health status score of the power equipment to be assessed is output, specifically including: S31: Based on the historical annual operating cost data and historical annual maintenance cost data, construct a time-series feature sequence for model input; S32: Extract feature indicators reflecting the deterioration pattern of equipment costs from the time-series feature sequence. The feature indicators include at least the average annual growth rate of operating costs and maintenance costs over the past N years, the volatility index of cost change rate, and the ratio of cumulative cost to operating years. S33: The extracted feature indicators are combined into a feature vector and input into the pre-trained health status assessment model. The model calculates an assessment value representing the overall health level of the device based on the input feature vector. S34: Normalize the evaluation value and map it to the [0,1] interval. The output is the current health status score of the power equipment to be evaluated, where a higher score indicates a better health status of the equipment.
7. The method as described in claim 6, characterized in that, In step S5, the expression for the multi-objective optimization function is: in, Indicates the service life of the electrical equipment to be evaluated. The average annual total cost; Indicates the number of years of operation; When the electrical equipment to be evaluated is a transformer-type electrical equipment for: ; When the power equipment to be evaluated is a generator unit type power equipment for: ; When the power equipment to be evaluated is a wind turbine type power equipment for: ; When the electrical equipment to be evaluated is a power electronic device for: ; When the electrical equipment to be evaluated is a low-voltage power distribution equipment This is a fixed value and only includes inspection fees. When the power equipment to be evaluated is a power transmission line, for: ; When the electrical equipment to be evaluated is a transformer-type electrical equipment for: ; When the power equipment to be evaluated is a generator set power equipment. for: ; When the power equipment to be evaluated is a wind turbine type power equipment. for: ; When the electrical equipment to be evaluated is a power electronic device for: ; When the electrical equipment to be evaluated is a low-voltage power distribution equipment for: ; When the power equipment to be evaluated is a power transmission line type power equipment. for: .
8. A system applied to the method for calculating the optimal service life of power equipment as described in any one of claims 1-7, characterized in that, The system includes: The commissioning costs, decommissioning costs, and historical operating years of the power equipment to be evaluated. Historical operating cost data and historical maintenance cost data for each year; The model determination module is used to determine a benchmark calculation model for the changes in operating costs and maintenance costs over time based on the equipment category of the power equipment to be evaluated. The parameter fitting module is used to input the acquired historical annual operating cost data and historical annual maintenance cost data into the pre-built health status assessment model and output the current health status score of the power equipment to be assessed. The modeling module is used to correct key growth parameters in the baseline calculation model based on the health status score, so as to obtain the operating cost calculation model and the maintenance cost calculation model. The model optimization module is used to construct a model based on the acquired commissioning costs, decommissioning costs, and dynamic cost calculations, taking into account the number of years of operation. A multi-objective optimization function with variables; The optimal service life output module uses an optimization algorithm to solve a multi-objective optimization function to obtain the optimal service life of the power equipment to be evaluated.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method for calculating the optimal service life of power equipment as described in any one of claims 1-7.
10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for calculating the optimal service life of power equipment as described in any one of claims 1-7.