A power grid old equipment technical transformation strategy optimization adjustment method

Abstract

A kind of power grid old equipment technical transformation strategy optimization adjustment method, comprising the following steps: obtaining the historical data and real-time data of old equipment life cycle cost;Establish regionalization classification model to preprocess data;The historical data and real-time data are compared and evaluated, and the difference value range of each data is obtained;Determine evaluation index according to the cost of old equipment and the difference value range;Test old equipment, compare test data with real data of old equipment to obtain differential data, and determine the influence factors of bad working condition through differential data;Set different types of technical transformation strategies by the influence of different working conditions of old equipment through influence factors;Calculate the evaluation index weight of each influence factor of old equipment, and then obtain the evaluation value of the technical transformation strategy of old equipment, and take the technical transformation strategy with the highest evaluation value as the optimal scheme.The design is not only conducive to the optimization adjustment of technical transformation strategy, but also reduces the technical transformation cost.

Description

Technical Field

[0001] This invention relates to the field of power grid equipment technology, and in particular to a method for optimizing and adjusting the technical upgrade strategy of aging power grid equipment, which is mainly applicable to reducing the cost of technical upgrades. Background Technology

[0002] Electrical equipment mainly includes two categories: power generation equipment and power supply equipment. Power generation equipment mainly includes power plant boilers, steam turbines, gas turbines, water turbines, generators, transformers, etc. Power supply equipment mainly includes transmission lines of various voltage levels, instrument transformers, contactors, etc. There are many electrical devices in a power system. Based on their different roles in operation, they are usually divided into primary electrical equipment and secondary electrical equipment. Equipment that directly participates in the production, transformation, transmission, distribution, and consumption of electrical energy is called primary electrical equipment. This mainly includes: equipment for generating and transforming electrical energy, such as generators, motors, and transformers; switching devices for connecting and disconnecting circuits, such as circuit breakers, disconnecting switches, contactors, and fuses; current-carrying conductors and gas-insulated equipment, such as busbars, power cables, insulators, and wall bushings; equipment that limits overcurrent or overvoltage, such as current-limiting reactors and surge arresters; and instrument transformers, which reduce high voltage and high current in the primary circuit for use by measuring instruments and relay protection devices, such as voltage transformers and current transformers. To ensure the normal operation of primary electrical equipment, the equipment used to measure, monitor, control, and regulate its operating status is called secondary electrical equipment. This mainly includes various measuring instruments, various relay protection and automatic devices, DC power supply equipment, etc.

[0003] The planned maintenance system, which is prevalent in most power systems, suffers from serious flaws, such as frequent ad-hoc repairs, insufficient or excessive maintenance, and indiscriminate maintenance. This results in enormous annual costs for equipment maintenance. Therefore, how to rationally schedule power equipment maintenance, save on maintenance expenses, reduce maintenance costs, and simultaneously ensure high system reliability is a crucial issue for system operators. With the application of integrated intelligent systems such as sensing technology, microelectronics, computer hardware and software, digital signal processing technology, artificial neural networks, expert systems, and fuzzy set theory in condition monitoring and fault diagnosis, research on condition-based maintenance based on equipment condition monitoring and advanced diagnostic technologies has developed and become an important research area in power systems.

[0004] Currently, the financial investment required for the corresponding technical transformation and repair of old equipment is increasing year by year. As the use of old equipment increases over time, its service life and operating performance decrease year by year. There is a lack of unified specifications and inspection standards. Furthermore, the performance parameters of old equipment will change accordingly due to aging and deterioration. It is difficult to achieve the goal of reshaping the value of equipment through factors such as poor operating conditions and condition evaluation, which is not conducive to the optimization and adjustment of technical transformation strategies. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings and problems of high technical transformation costs in the existing technology, and to provide a method for optimizing and adjusting technical transformation strategies for old power grid equipment with low technical transformation costs.

[0006] To achieve the above objectives, the technical solution of the present invention is: a method for optimizing and adjusting the technical upgrading strategy of aging power grid equipment, the method comprising the following steps:

[0007] S1. Obtain the full life cycle cost data of old equipment, which includes historical data and real-time data;

[0008] S2. Establish a regional classification model to preprocess the life cycle cost data of old equipment;

[0009] S3. Compare and evaluate historical data with real-time data, establish a differential evaluation model, and calculate the differential value range of each data item.

[0010] S4. Determine the evaluation indicators based on the set cost and differential value range of the old equipment;

[0011] S5. Test the old equipment to obtain test data, compare the test data with the actual data of the old equipment to obtain differential data, and use the differential data to determine the influencing factors of the poor working conditions.

[0012] S6. Set different types of technical renovation strategies by considering the impact of influencing factors on the different operating conditions of old equipment;

[0013] S7. Calculate the evaluation index weights of each influencing factor of old equipment, and then obtain the evaluation value of the technical transformation strategy of old equipment. The technical transformation strategy with the highest evaluation value is taken as the optimal solution.

[0014] In step S1, the total life cycle cost of the old equipment includes the old equipment commissioning cost, the old equipment operating cost, the old equipment maintenance cost, and the old equipment unexpected failure cost.

[0015] In step S1, real-time data is acquired through online or offline reception.

[0016] Online real-time data reception involves information entry on a daily basis.

[0017] Offline reception of real-time data involves transmitting and recording data that is currently offline in a predefined format, either as a database or a word processing program.

[0018] Step S2 specifically includes the following steps:

[0019] S21. Use the acquired full life cycle cost data of old equipment as training samples, and each training sample includes a single attribute feature.

[0020] S22. Preprocess the training samples according to the preset data classification and grading standards, convert each training sample and its contained standard data into a corresponding data model, and determine the data category and data level corresponding to each of the N training samples.

[0021] S23. Based on the data entities contained in each training sample and their attribute features, encode the data model corresponding to each training sample to obtain the feature matrix corresponding to each training sample.

[0022] S24. Input the feature matrices of N training samples into the data classification and grading library to obtain the prediction category and prediction level corresponding to each of the N training samples;

[0023] S25. Based on the data category and data level corresponding to each of the N training samples, and the prediction category and prediction level corresponding to each of the N training samples, determine the modular layout matrix corresponding to the data classification and grading library.

[0024] S26. Repeat the above steps until the modular layout matrix corresponding to the data classification and grading library meets the preset data standards, and obtain the trained data regionalization classification model.

[0025] In step S3, the differentiated evaluation model is as follows:

[0026]

[0027] In the above formula, S represents the range of differential values ​​for each data point, and x... i 'x' represents the comparison parameters for historical data entry, 'n' represents the total number of comparisons, and 'i' represents the number of comparisons where data differences exist.

[0028] In step S5, the old equipment is tested before and after the test using the short-circuit impedance method, low-voltage pulse method, capacitance method, and frequency response method.

[0029] In step S5, a short-circuit current test is performed on the old equipment, and the damage status of the old equipment is determined based on the test results.

[0030] The current is adjusted according to the threshold of the old equipment parameters to test the old equipment, determine the real-time short-circuit withstand capability of the old equipment, and then the old equipment is disassembled to verify the test results.

[0031] Step S6 specifically includes the following steps:

[0032] S61. Calculate the average annual cost AC of upgrading old equipment:

[0033]

[0034] In the above formula, C1 is the total cost of upgrading and using old equipment, K0 is the original value of old equipment; S0 is the differential value, which is the current residual value of old equipment; r is the annual increase in operating cost, and T is the value of the service years of old equipment;

[0035] S62. Calculate the technological upgrading costs Gl0, Gl1, ..., Gl for each influencing factor. n :

[0036]

[0037] In the above formula, K0 represents the original value of the old equipment, and AC m L represents the total cost of upgrading old equipment over m years. m C represents the residual value of old equipment after m years of use. t The operating cost of the old equipment in year t;

[0038] S63. The technical upgrade costs Gl0, Gl1, ..., Gl of each influencing factor are calculated. n By matching each evaluation indicator with the total cost of upgrading old equipment, a matrix is ​​formed between the evaluation indicators and the upgrading strategy, thus creating a set of evaluation indicators.

[0039] Step S7 specifically includes the following steps:

[0040] S71. Calculate the weights of the evaluation indicators:

[0041]

[0042] In the above formula, P j Let L be the weight of the j-th evaluation indicator, m be the number of evaluation indicators, and L be the weight of the j-th evaluation indicator. j Different evaluation values ​​for all evaluation indicators;

[0043] S72. Calculate the subjective weight value W of the evaluation index. m :

[0044]

[0045] S73. Arrange the subjective weight values ​​in ascending order to form a list of solutions corresponding to the subjective weight values ​​and technical improvement strategies, and select the top-ranked technical improvement strategies.

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0047] 1. In the present invention, a method for optimizing and adjusting the technical renovation strategy of aging power grid equipment, historical data and real-time data are compared and evaluated to establish a differentiated evaluation model and calculate the differentiated value range of various data. Evaluation indicators are determined based on the set cost of the aging equipment and the differentiated value range. The aging equipment is tested to obtain test data, which is compared with the actual data of the aging equipment to obtain differentiated data. The influencing factors of adverse operating conditions are judged through the differentiated data. Different types of technical renovation strategies are set based on the impact of the influencing factors on different operating conditions of the aging equipment. The evaluation index weights of each influencing factor of the aging equipment are calculated to obtain the evaluation value of the technical renovation strategy of the aging equipment. The technical renovation strategy with the highest evaluation value is taken as the optimal solution. The above design effectively achieves the purpose of reshaping the value of aging equipment through adverse operating conditions, status evaluation, and various influencing factors. The strategy is adjusted according to the technical renovation evaluation value of the aging equipment, providing a decision-making basis for technical renovation and overhaul. It can conduct a comprehensive technical life evaluation of aging equipment, which is conducive to optimizing and adjusting the technical renovation strategy and reducing the technical renovation cost.

[0048] 2. In the present invention, a method for optimizing and adjusting the technical upgrade strategy of aging power grid equipment, short-circuit current tests are conducted on the aging equipment. The damage status of the aging equipment is judged based on the test results. The current is adjusted according to the threshold of the equipment parameters to test the equipment and determine the real-time short-circuit withstand capability of the aging equipment. The aging equipment is disassembled to verify the test results. The test data is compared with the actual data of the equipment. The influencing factors of adverse operating conditions are judged through the differential data. Different types of technical upgrade strategies are set according to the impact of the influencing factors on different operating conditions of the aging equipment. It is necessary to judge whether the technical upgrade cost of the aging equipment can be minimized based on the range of differential values ​​and the technical upgrade cost of the corresponding influencing factors. The adverse operating conditions of the aging equipment are reflected by parameter detection. The weight of the evaluation index is calculated according to the influencing factors affecting the adverse operating conditions, which helps to improve the accuracy of assessing the comprehensive technical life of the aging equipment. Attached Figure Description

[0049] Figure 1 This is a flowchart of a method for optimizing and adjusting the technical upgrading strategy of aging power grid equipment according to the present invention. Detailed Implementation

[0050] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0051] See Figure 1 A method for optimizing and adjusting the technical upgrading strategy of aging power grid equipment, the method includes the following steps:

[0052] S1. Obtain the full life cycle cost data of old equipment, which includes historical data and real-time data;

[0053] S2. Establish a regional classification model to preprocess the life cycle cost data of old equipment;

[0054] S3. Compare and evaluate historical data with real-time data, establish a differential evaluation model, and calculate the differential value range of each data item.

[0055] S4. Determine the evaluation indicators based on the set cost and differential value range of the old equipment;

[0056] S5. Test the old equipment to obtain test data, compare the test data with the actual data of the old equipment to obtain differential data, and use the differential data to determine the influencing factors of the poor working conditions.

[0057] S6. Set different types of technical renovation strategies by considering the impact of influencing factors on the different operating conditions of old equipment;

[0058] S7. Calculate the evaluation index weights of each influencing factor of old equipment, and then obtain the evaluation value of the technical transformation strategy of old equipment. The technical transformation strategy with the highest evaluation value is taken as the optimal solution.

[0059] In step S1, the total life cycle cost of the old equipment includes the old equipment commissioning cost, the old equipment operating cost, the old equipment maintenance cost, and the old equipment unexpected failure cost.

[0060] In step S1, real-time data is acquired through online or offline reception.

[0061] Online real-time data reception involves information entry on a daily basis.

[0062] Offline reception of real-time data involves transmitting and recording data that is currently offline in a predefined format, either as a database or a word processing program.

[0063] Step S2 specifically includes the following steps:

[0064] S21. Use the acquired full life cycle cost data of old equipment as training samples, and each training sample includes a single attribute feature.

[0065] S22. Preprocess the training samples according to the preset data classification and grading standards, convert each training sample and its contained standard data into a corresponding data model, and determine the data category and data level corresponding to each of the N training samples.

[0066] S23. Based on the data entities contained in each training sample and their attribute features, encode the data model corresponding to each training sample to obtain the feature matrix corresponding to each training sample.

[0067] S24. Input the feature matrices of N training samples into the data classification and grading library to obtain the prediction category and prediction level corresponding to each of the N training samples;

[0068] S25. Based on the data category and data level corresponding to each of the N training samples, and the prediction category and prediction level corresponding to each of the N training samples, determine the modular layout matrix corresponding to the data classification and grading library.

[0069] S26. Repeat the above steps until the modular layout matrix corresponding to the data classification and grading library meets the preset data standards, and obtain the trained data regionalization classification model.

[0070] In step S3, the differentiated evaluation model is as follows:

[0071]

[0072] In the above formula, S represents the range of differential values ​​for each data point, and x... i 'x' represents the comparison parameters for historical data entry, 'n' represents the total number of comparisons, and 'i' represents the number of comparisons where data differences exist.

[0073] In step S5, the old equipment is tested before and after the test using the short-circuit impedance method, low-voltage pulse method, capacitance method, and frequency response method.

[0074] In step S5, a short-circuit current test is performed on the old equipment, and the damage status of the old equipment is determined based on the test results.

[0075] The current is adjusted according to the threshold of the old equipment parameters to test the old equipment, determine the real-time short-circuit withstand capability of the old equipment, and then the old equipment is disassembled to verify the test results.

[0076] Step S6 specifically includes the following steps:

[0077] S61. Calculate the average annual cost AC of upgrading old equipment:

[0078]

[0079] In the above formula, Cl is the total cost of upgrading and using old equipment, K0 is the original value of old equipment; S0 is the differential value, which is the current residual value of old equipment; r is the annual increase in operating cost, and T is the value of the service life of old equipment.

[0080] S62. Calculate the technological upgrading costs Gl0, Gl1, ..., Gl for each influencing factor. n :

[0081]

[0082] In the above formula, K0 represents the original value of the old equipment, and AC m L represents the total cost of upgrading old equipment over m years. m C represents the residual value of old equipment after m years of use. t The operating cost of the old equipment in year t;

[0083] S63, the technical upgrade costs Gl0, Gl1, ..., Gl of each influencing factor n By matching each evaluation indicator with the total cost of upgrading old equipment, a matrix is ​​formed between the evaluation indicators and the upgrading strategy, thus creating a set of evaluation indicators.

[0084] Step S7 specifically includes the following steps:

[0085] S71. Calculate the weights of the evaluation indicators:

[0086]

[0087] In the above formula, P j Let L be the weight of the j-th evaluation indicator, m be the number of evaluation indicators, and L be the weight of the j-th evaluation indicator. j Different evaluation values ​​for all evaluation indicators;

[0088] S72. Calculate the subjective weight value W of the evaluation index. m :

[0089]

[0090] S73. Arrange the subjective weight values ​​in ascending order to form a list of solutions corresponding to the subjective weight values ​​and technical improvement strategies, and select the top-ranked technical improvement strategies.

[0091] The principle of this invention is explained as follows:

[0092] By classifying the received data from older equipment and laying it out in a modular fashion, the regionalized classification and storage method can efficiently store the equipment data for easy searching and querying.

[0093] Different types of technical upgrade strategies should be set by considering the impact of various influencing factors on the different operating conditions of old equipment. It is necessary to determine whether to minimize the technical upgrade cost of old equipment and increase its value based on the range of differentiated values ​​and the technical upgrade cost of the corresponding influencing factors. Furthermore, the required costs for each type of factor should be visualized to form cost evaluation indicators for relevant personnel to make judgments and adjustments.

[0094] Example:

[0095] See Figure 1 A method for optimizing and adjusting the technical upgrading strategy of aging power grid equipment, the method includes the following steps:

[0096] S1. Obtain the full life cycle cost data of old equipment, which includes historical data and real-time data;

[0097] The total life cycle cost of the old equipment includes the commissioning cost, operating cost, maintenance cost, and unexpected failure cost of the old equipment.

[0098] Acquire real-time data through online or offline reception;

[0099] Online real-time data reception involves information entry on a daily basis.

[0100] Offline reception of real-time data involves transmitting and recording data that is currently offline in a predefined format, such as a database or word processing program.

[0101] S2. Establish a regionalized classification model to preprocess the life-cycle cost data of old equipment; specifically including the following steps:

[0102] S21. Use the acquired full life cycle cost data of old equipment as training samples, and each training sample includes a single attribute feature.

[0103] S22. Preprocess the training samples according to the preset data classification and grading standards, convert each training sample and its contained standard data into a corresponding data model, and determine the data category and data level corresponding to each of the N training samples.

[0104] S23. Based on the data entities contained in each training sample and their attribute features, encode the data model corresponding to each training sample to obtain the feature matrix corresponding to each training sample.

[0105] S24. Input the feature matrices of N training samples into the data classification and grading library to obtain the prediction category and prediction level corresponding to each of the N training samples;

[0106] S25. Based on the data category and data level corresponding to each of the N training samples, and the prediction category and prediction level corresponding to each of the N training samples, determine the modular layout matrix corresponding to the data classification and grading library.

[0107] S26. Repeat the above steps until the modular layout matrix corresponding to the data classification and grading library meets the preset data standards, and the trained data regionalization classification model is obtained.

[0108] S3. Compare and evaluate historical data with real-time data, establish a differential evaluation model, and calculate the differential value range of each data item.

[0109] The differentiated evaluation model is as follows:

[0110]

[0111] In the above formula, S represents the range of differential values ​​for each data point, and x... i Here, x represents the comparison parameters for historical data entry, n represents the comparison parameters for real-time data entry, and i represents the total number of comparisons.

[0112] S4. Determine the evaluation indicators based on the set cost and differential value range of the old equipment;

[0113] S5. Test the old equipment to obtain test data, compare the test data with the actual data of the old equipment to obtain differential data, and use the differential data to determine the influencing factors of the poor working conditions.

[0114] Before and after testing, the old equipment was tested using the short-circuit impedance method, low-voltage pulse method, capacitance method, and frequency response method.

[0115] Perform short-circuit current tests on old equipment and determine the extent of damage based on the test results;

[0116] The current is adjusted according to the threshold of the old equipment parameters to test the old equipment, determine the real-time short-circuit withstand capability of the old equipment, and then the old equipment is disassembled to verify the test results.

[0117] S6. Develop different types of technical upgrade strategies based on the impact of influencing factors on the different operating conditions of aging equipment; this includes the following steps:

[0118] S61. Calculate the average annual cost AC of upgrading old equipment:

[0119]

[0120] In the above formula, Cl is the total cost of upgrading and using old equipment, K0 is the original value of old equipment; S0 is the differential value, which is the current residual value of old equipment; r is the annual increase in operating cost, and T is the value of the service life of old equipment.

[0121] S62. Calculate the technological upgrading costs Gl0, Gl1, ..., Gl for each influencing factor. n :

[0122]

[0123] In the above formula, K0 represents the original value of the old equipment, and AC m L represents the total cost of upgrading old equipment over m years. m C represents the residual value of old equipment after m years of use. t The operating cost of the old equipment in year t;

[0124] S63, the technical upgrade costs Gl0, Gl1, ..., Gl of each influencing factor n Matching with each evaluation indicator, and based on the judgment of each evaluation indicator and the total cost of upgrading old equipment, the evaluation indicators and the upgrading strategy form a corresponding matrix, thus forming a set of evaluation indicators;

[0125] S7. Calculate the evaluation index weights of each influencing factor of aging equipment, and then obtain the evaluation value of the technical renovation strategy for aging equipment. The technical renovation strategy with the highest evaluation value is taken as the optimal solution. This includes the following steps:

[0126] S71. Calculate the weights of the evaluation indicators:

[0127]

[0128] In the above formula, P j Let L be the weight of the j-th evaluation indicator, m be the number of evaluation indicators, and L be the weight of the j-th evaluation indicator. j Different evaluation values ​​for all evaluation indicators;

[0129] S72. Calculate the subjective weight value W of the evaluation index. m :

[0130]

[0131] S73. Arrange the subjective weight values ​​in ascending order to form a list of solutions corresponding to the subjective weight values ​​and technical improvement strategies, and select the top-ranked technical improvement strategies.

Claims

1. A method for optimizing and adjusting the technical upgrading strategy of aging power grid equipment, characterized in that, The method includes the following steps: S1. Obtain the full life cycle cost data of old equipment, which includes historical data and real-time data; S2. Establish a regionalized classification model to preprocess the life-cycle cost data of old equipment; specifically including the following steps: S21. Use the acquired full life cycle cost data of old equipment as training samples, and each training sample includes a single attribute feature. S22. Preprocess the training samples according to the preset data classification and grading standards, convert each training sample and its contained standard data into a corresponding data model, and determine the data category and data level corresponding to each of the N training samples. S23. Based on the data entities contained in each training sample and their attribute features, encode the data model corresponding to each training sample to obtain the feature matrix corresponding to each training sample. S24. Input the feature matrices of N training samples into the data classification and grading library to obtain the prediction category and prediction level corresponding to each of the N training samples; S25. Based on the data category and data level corresponding to each of the N training samples, and the prediction category and prediction level corresponding to each of the N training samples, determine the modular layout matrix corresponding to the data classification and grading library. S26. Repeat the above steps until the modular layout matrix corresponding to the data classification and grading library meets the preset data standards, and the trained data regionalization classification model is obtained. S3. Compare and evaluate historical data with real-time data, establish a differential evaluation model, and calculate the differential value range of each data item. The differentiated evaluation model is as follows: In the above formula, S represents the range of differential values ​​for each data point, and x... i Here, x represents the comparison parameters for historical data entry, n represents the comparison parameters for real-time data entry, and i represents the total number of comparisons. S4. Determine the evaluation indicators based on the set cost and differential value range of the old equipment; S5. Test the old equipment to obtain test data, compare the test data with the actual data of the old equipment to obtain differential data, and use the differential data to determine the influencing factors of the poor working conditions. Before and after testing, the old equipment was tested using the short-circuit impedance method, low-voltage pulse method, capacitance method, and frequency response method. Perform short-circuit current tests on old equipment and determine the extent of damage based on the test results; The current is adjusted according to the threshold of the old equipment parameters to test the old equipment, determine the real-time short-circuit withstand capability of the old equipment, and then the old equipment is disassembled to verify the test results. S6. Develop different types of technical upgrade strategies based on the impact of influencing factors on the different operating conditions of aging equipment; this includes the following steps: S61. Calculate the average annual cost AC of upgrading old equipment: In the above formula, C1 is the total cost of upgrading and using old equipment, K0 is the original value of old equipment; S0 is the differential value, which is the current residual value of old equipment; r is the annual increase in operating cost, and T is the value of the service years of old equipment; S62. Calculate the technological upgrading costs G10, G11, ..., G1 for each influencing factor. n : In the above formula, K0 represents the original value of the old equipment, and AC m L represents the total cost of upgrading old equipment over m years. m C represents the residual value of old equipment after m years of use. t The operating cost of the old equipment in year t; S63. The technical upgrade costs G10, G11, ..., G1 of each influencing factor. n Matching with each evaluation indicator, and based on the judgment of each evaluation indicator and the total cost of upgrading old equipment, the evaluation indicators and the upgrading strategy form a corresponding matrix, thus forming a set of evaluation indicators; S7. Calculate the evaluation index weights of each influencing factor of aging equipment, and then obtain the evaluation value of the technical renovation strategy for aging equipment. The technical renovation strategy with the highest evaluation value is taken as the optimal solution. This includes the following steps: S71. Calculate the weights of the evaluation indicators: In the above formula, P j Let L be the weight of the j-th evaluation indicator, m be the number of evaluation indicators, and L be the weight of the j-th evaluation indicator. j Different evaluation values ​​for all evaluation indicators; S72. Calculate the subjective weight value W of the evaluation index. m : S73. Arrange the subjective weight values ​​in ascending order to form a list of solutions corresponding to the subjective weight values ​​and technical improvement strategies, and select the top-ranked technical improvement strategies.

2. The method for optimizing and adjusting the technical upgrading strategy of aging power grid equipment according to claim 1, characterized in that: In step S1, the total life cycle cost of the old equipment includes the old equipment commissioning cost, the old equipment operating cost, the old equipment maintenance cost, and the old equipment unexpected failure cost.

3. The method for optimizing and adjusting the technical upgrading strategy of aging power grid equipment according to claim 1, characterized in that: In step S1, real-time data is acquired through online or offline reception. Online real-time data reception involves information entry on a daily basis. Offline reception of real-time data involves transmitting and recording data that is currently offline in a predefined format, either as a database or a word processing program.

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