Transformer substation new technology application efficiency evaluation method based on variable weight matter element extension model

Abstract

The invention discloses a transformer substation new technology application efficiency evaluation method based on a variable weight matter element extension model, and the method comprises the steps: building corresponding multi-layer indexes for the application efficiency of each stage of a plurality of new technical schemes of a transformer substation, and enabling the application efficiency of each stage to comprise the application efficiency of a construction stage and the application efficiency of an operation and maintenance stage; determining an integrated subjective weight of each index by using an AHP group decision method; determining the objective weight of each index by using an entropy weight method; using a projection pursuit algorithm to determine the combined weight of each index based on the integrated subjective weight and objective weight of each index, and performing dynamic correction to obtain a variable weight; based on each index corresponding to the application efficiency and the value of each index, constructing an improved matter element extension model; and comprehensively evaluating the application efficiency of each new technical scheme of the transformer substation based on the improved matter-element extension model and the variable weight of each index. According to the invention, the application efficiency of the new technology of the transformer substation can be scientifically and comprehensively evaluated.

Description

Technical Field

[0001] The invention relates to the technical field of substations, and in particular to a method for evaluating the application efficiency of new substation technologies based on a variable-weight matter-element extension model. Background Art

[0002] With the rapid development of China's energy structure transformation and electricity demand, the National Development and Reform Commission issued the "Several Opinions of the National Energy Administration on Accelerating the Development of Energy Digitalization and Intelligence", which emphasized "accelerating the promotion of smart substations to undertake systematic digitalization and intelligence pilot tasks, and conducting in-depth exploration and trial in terms of technological innovation, operation mode, and development formats". State Grid plans to add and transform a total of more than 7,400 smart substations during the 2020-2025 period, and the intelligent transformation of China's smart substations will continue in the future. In order to actively respond to the needs of new power system construction and realize the intelligent transformation of substations, the introduction of new technologies and new equipment in substations has become an inevitable trend for the future development of substations. However, since the new technology is still in the early stage of pilot testing, its commissioning efficiency and its adaptation to substations of different sizes are relatively unknown. Therefore, based on the current status of new technology pilot applications, it is crucial to conduct a scientific and comprehensive comprehensive evaluation of the application efficiency of new substation technologies. This can not only provide decision-making support for the selection of new technology solutions during the design phase of substations, promote the large-scale application of new technologies, and accelerate the transformation of substations to intelligence, but also identify deficiencies and problems in construction and operation, and provide directions for future technological improvements, thereby enhancing the advancement and practicality of smart substation technology and injecting new impetus into the sustainable development of the power industry.

[0003] As an important part of the full life cycle management of new technologies, the evaluation of the application efficiency of new substation technologies is not only an objective test of the achievement of the expected goals of new technologies, but also an important means to promote technology iteration, optimization and promotion. In recent years, certain progress has been made in the evaluation of technology applications, but existing research still has the following limitations: the logical relationship between the indicators of the post-evaluation indicator system for new technologies is not close, and most of them start from a single dimension and a single indicator, and evaluate the entire station plan around the operating characteristics of a new technology. There is a lack of comprehensive horizontal comparison of new technologies from multiple dimensions based on the application characteristics of the introduction, construction and commissioning of various new technologies throughout their life cycle, and the evaluation results are not comprehensive. Secondly, the evaluation method is not dynamic and adaptable enough, and it is difficult to cope with the comprehensive evaluation of technical performance under different scenarios and complex working conditions. In terms of the weight assignment of evaluation indicators, most studies use subjective weighting methods such as G1 and analytic hierarchy process, which rely heavily on expert decision-making and ignore the amount of information in the actual application effect of new technologies; some scholars use objective weighting methods such as coefficient of variation method and standard deviation method. Since the new technology is in the pilot stage and the amount of data is insufficient, there may be large errors in objective weighting. Since the credibility of the evaluation results under a single weighting method is low, the advantages of subjective and objective weights can be integrated through combined weighting to improve the rationality of the evaluation. However, the current combined weight of indicators only considers the influence of different indicators on the evaluation of technical application effectiveness. Based on this, the static weight of the indicator does not consider the change in the influence of the same indicator on the performance when different values ​​are taken, that is, different technologies may get the same result by using the constant weighted sum method, ignoring the differences in the application of different new technologies. Therefore, the emphasis of importance should be different when evaluating application effectiveness. In addition, in multi-criteria evaluation, the existing technology often uses evaluation models such as matter-element extension and gray cloud. Although the traditional maximum membership principle has certain practicality, the selection of membership function is highly subjective, and it only focuses on the maximum matching degree between the sample and a single interval, ignoring the overall relationship between the sample and other intervals; at the same time, for indicators at the interval boundary, the maximum membership principle is difficult to deal with fuzzy problems and is prone to evaluation bias.

[0004] Therefore, a new technology application efficiency evaluation method for substations based on a variable-weight matter-element extension model is needed to solve the above technical problems. Summary of the invention

[0005] To this end, the present invention provides a method for evaluating the application efficiency of new substation technologies based on a variable-weight matter-element extension model to solve or at least alleviate the above problems.

[0006] According to one aspect of the present invention, there is provided a method for evaluating the application efficiency of new technologies in substations based on a variable-weight matter-element extension model, comprising: establishing multi-layer indicators corresponding to the application efficiency of multiple new technology solutions for substations at each stage, wherein the application efficiency at each stage includes the application efficiency at the construction stage and the application efficiency at the operation and maintenance stage, wherein the multi-layer indicators include criterion layer indicators and decision layer indicators located below the criterion layer indicators, wherein each criterion layer indicator corresponds to one or more decision layer indicators, wherein the criterion layer indicators corresponding to the application efficiency at the construction stage include construction stage saving indicators, building efficiency improvement indicators, and construction difficulty reduction indicators, and the criterion layer indicators corresponding to the application efficiency at the operation and maintenance stage include technical performance improvement indicators, technical operation reliability indicators, technical sustainability and replicability indicators, technical investment economic benefit indicators, and social and environmental benefit indicators; and determining the combined weights of each indicator. , including: using the AHP group decision method to subjectively weight each indicator to determine the integrated subjective weight of each indicator; using the entropy weight method to objectively weight each indicator to determine the objective weight of each indicator; using the projection pursuit algorithm, based on the integrated subjective weight and objective weight of each indicator, to determine the combined weight of each indicator; based on the variable weight theory, dynamically correcting the combined weight of each indicator to obtain the variable weight of each indicator; constructing a matter-element matrix based on each indicator corresponding to the application efficiency and the value of each indicator, and normalizing the matter-element matrix to obtain an improved matter-element extension model, wherein the application efficiency has multiple evaluation levels, and the matter-element matrix includes a matter-element matrix to be evaluated, a classical domain matter-element matrix and a node domain matter-element matrix; based on the improved matter-element extension model and the variable weights of each indicator, the application efficiency of each new technology solution of the substation is comprehensively evaluated.

[0007] Optionally, in the new technology application efficiency evaluation method for substations based on the variable-weight matter-element extension model according to the present invention, the AHP group decision method is used to subjectively weight each indicator to determine the integrated subjective weight of each indicator, including: for each expert, based on the importance of each indicator to the upper-level indicator determined according to the expert opinion, the corresponding judgment matrix is ​​constructed using the exponential scaling method, and the maximum eigenvalue and eigenvector of the judgment matrix are determined; based on the maximum eigenvalue of the judgment matrix, the consistency ratio index of the judgment matrix is ​​determined, and the judgment matrix is ​​consistency-checked based on the consistency ratio index, and the consistency ratio index is used to measure the consistency degree of the judgment matrix; the eigenvector is normalized to obtain the relative weight of the same-level indicators of each indicator to the upper-level indicators, and the weight of each indicator relative to the highest-level indicator is determined as the subjective judgment weight of the expert on each indicator; the AHP group decision method is used to determine the integrated subjective weight of each indicator based on the subjective judgment weight of each expert on each indicator.

[0008] Optionally, in the new technology application efficiency evaluation method for substations based on the variable-weight matter-element extension model according to the present invention, the AHP group decision-making method is used to determine the integrated subjective weight of each indicator based on the subjective judgment weight of each expert on each indicator, including: calculating the Pearson correlation coefficient between the opinions of each expert and normalizing it to obtain an expert opinion correlation coefficient matrix; based on the expert opinion correlation coefficient matrix, using a cluster analysis method to classify multiple experts and determine the inter-class weights of each type of expert; determining the intra-class weights of each expert based on a weighting method based on consistency ratio; determining the integrated subjective weights of each indicator based on the inter-class weights of each type of expert and the intra-class weights of each expert.

[0009] Optionally, in the method for evaluating the application efficiency of new substation technologies based on the variable-weight matter-element extension model according to the present invention, the entropy weight method is used to objectively weight each indicator to determine the objective weight of each indicator, including: standardizing each indicator and determining the proportion of each scheme in each indicator; determining the entropy value of each indicator based on the proportion of each scheme in each indicator; determining the information entropy redundancy of each indicator based on the entropy value of each indicator; and determining the objective weight of each indicator based on the information entropy redundancy of each indicator.

[0010] Optionally, in the method for evaluating the application efficiency of new substation technologies based on the variable-weighted matter-element extension model according to the present invention, a projection pursuit algorithm is used to determine the combined weights of each indicator based on the integrated subjective weights and objective weights of each indicator, including: normalizing each indicator of each sample in the sample data set to obtain a standardized data set; establishing a projection indicator function for the standardized data set, and optimizing the projection indicator function to obtain a combined weighted optimization model; using the AO algorithm to solve the combined weighted optimization model to obtain the combined weights of each indicator.

[0011] Optionally, in the new technology application efficiency evaluation method for substations based on the variable-weight matter-element extension model according to the present invention, the combined weights of various indicators are dynamically corrected based on the variable-weight theory to obtain the variable-weight weights of various indicators, including: based on the distance from the indicator evaluation value of each indicator under each evaluation object to the positive bull's eye, the distance from the indicator evaluation value to the negative bull's eye, and the distance between the positive bull's eye and the negative bull's eye, the dominance of each indicator under each evaluation object is determined, and a dominance matrix is ​​constructed; based on the dominance of each indicator under each evaluation object, a state variable weight function based on a penalty-reward mechanism is constructed in combination with a penalty critical value and an incentive critical value; through the state variable weight function, the combined weights of each indicator are dynamically corrected to obtain the variable-weight weights of each indicator.

[0012] Optionally, in the method for evaluating the application efficiency of new substation technologies based on the variable-weight matter-element extension model according to the present invention, a matter-element matrix is ​​constructed based on the various indicators corresponding to the application efficiency and the values ​​of the various indicators, and the matter-element matrix is ​​normalized to obtain an improved matter-element extension model, including: constructing a matter-element matrix to be evaluated corresponding to the application efficiency based on the various indicators corresponding to the application efficiency and the values ​​of the various indicators; constructing a classical domain matter-element matrix corresponding to any evaluation level of the application efficiency based on any evaluation level of the application efficiency, the various indicators corresponding to the application efficiency and the value range of each indicator at any evaluation level; constructing a section domain matter-element matrix corresponding to the application efficiency based on the union of the various indicators corresponding to the application efficiency and the value range of each indicator at each evaluation level; normalizing the matter-element matrix to be evaluated, the classical domain matter-element matrix and the section domain matter-element matrix to obtain an improved matter-element extension model.

[0013] Optionally, in the method for evaluating the application efficiency of new technologies in substations based on the variable-weighted matter-element extension model according to the present invention, the application efficiency of each new technology scheme of the substation is comprehensively evaluated based on the improved matter-element extension model and the variable-weighted weights of each indicator, including: determining the distance between each object-element to be evaluated and each indicator value at each evaluation level based on the improved matter-element extension model, the object-element to be evaluated representing the application efficiency; determining the closeness of each object-element to be evaluated to each evaluation level based on the distance between each object-element to be evaluated and each indicator value at each evaluation level, the variable-weighted weights of each indicator and the number of indicators; and comprehensively evaluating the application efficiency of each new technology scheme of the substation based on the closeness of each object-element to be evaluated to each evaluation level.

[0014] Optionally, in the substation new technology application efficiency evaluation method based on the variable-weight matter-element extension model according to the present invention, the decision-making indicators corresponding to the construction phase saving indicators include floor space saving indicators, earthwork saving indicators, and material saving indicators; the decision-making indicators corresponding to the building efficiency improvement indicators include building utilization coefficient improvement indicators; the decision-making indicators corresponding to the construction difficulty reduction indicators include new technology and new equipment installation difficulty coefficient indicators, new technology and new equipment testing difficulty coefficient indicators, and new technology and new equipment debugging difficulty coefficient indicators; the decision-making indicators corresponding to the technical performance improvement indicators include substation dispatch fault response time reduction indicators, substation equivalent utilization rate improvement indicators, and substation disaster prevention capacity improvement indicators; the decision-making indicators corresponding to the technical operation reliability indicators include substation power supply reliability improvement indicators, substation inspection and repair indicators, and substation power supply reliability improvement indicators. The above indicators include reduction index of power outage times for maintenance, reduction index of maintenance time for new equipment and new technology, maintenance difficulty coefficient index for new equipment and new technology, reduction index of overhaul time for new equipment and new technology, and overhaul difficulty coefficient index for new equipment and new technology; the decision-making indicators corresponding to the technical sustainability and replicability indicators include substation scalability improvement index and provincial promotion ratio index of similar projects; the decision-making indicators corresponding to the technical investment economic benefit indicators include investment and construction cost saving index, substation maintenance cost saving index, substation failure cost saving index, substation decommissioning cost saving index, electricity sales revenue improvement index, investment payback period reduction index, and internal rate of return improvement index; the decision-making indicators corresponding to the social and environmental benefit indicators include substation construction carbon emission reduction index, construction waste comprehensive utilization rate improvement index, and substation operation carbon emission reduction index.

[0015] According to one aspect of the present invention, there is provided a computing device, comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be suitable for execution by the at least one processor, and the program instructions include instructions for executing the method for evaluating the effectiveness of new technology application in a substation based on a variable-weighted matter-element extension model as described above.

[0016] According to one aspect of the present invention, a computer program product is provided, comprising a computer program / instruction, wherein the computer program / instruction implements the method as described above when executed by a processor.

[0017] According to one aspect of the present invention, a readable storage medium storing program instructions is provided. When the program instructions are read and executed by a computing device, the computing device executes the method for evaluating the application efficiency of new substation technologies based on the variable-weight matter-element extension model as described above.

[0018] According to the technical solution of the present invention, a method for evaluating the application efficiency of new substation technologies based on a variable weight matter-element extension model is provided. First, by constructing an application efficiency evaluation index system for new substation technology solutions, the application efficiency of the two stages of construction and operation and maintenance of new substation technology solutions is taken as the core goal and corresponding criterion layer indicators and decision-making layer indicators are established. It can comprehensively consider resource conservation, construction efficiency, and construction difficulty in the construction stage of new substation technology solutions, as well as technical performance, technical operation reliability, technical sustainability and replicability, technical investment economic benefits, and social environmental benefits in the operation and maintenance stage, and then comprehensively evaluate the application efficiency of new substation technology solutions from multiple dimensions to improve benefits. Secondly, the AHP group decision method and the entropy weight method are used for combined weighting, and the combination weight is determined by combining the PP algorithm and the AO algorithm, which can integrate the advantages of subjective and objective weights, improve the rationality of the evaluation, and reduce the random deviation caused by the subjective judgment of the decision maker. Then, the combined weight is dynamically corrected based on the variable weight theory, so that the differences in the application of different new technologies can be considered, and the influence of the index evaluation value on the evaluation result can be more truly reflected, and the credibility of the evaluation result can be improved. In addition, by improving the physical-element extension model, it is possible to significantly distinguish different new technology solutions while taking into account the fuzziness of the evaluation of substations of different sizes, further improving the rationality of the evaluation of new technology solutions for substations.

[0019] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to achieve the above and related purposes, the present invention describes certain illustrative aspects in conjunction with the following description and the accompanying drawings, which indicate various ways in which the principles disclosed in the present invention can be practiced, and all aspects and their equivalents are intended to fall within the scope of the claimed subject matter. By reading the following detailed description in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present disclosure will become more apparent. Throughout the present disclosure, the same reference numerals generally refer to the same parts or elements.

[0021] Figure 1 A schematic diagram of a computing device 100 provided according to an embodiment of the present invention is shown;

[0022] Figure 2 A schematic flow chart of a method 200 for evaluating the effectiveness of new technology application in a substation based on a weighted matter-element extension model according to an embodiment of the present invention is shown;

[0023] Figure 3A schematic diagram of the process flow of the AO algorithm according to some embodiments of the present invention is shown;

[0024] Figure 4 A schematic diagram showing the advantage function corresponding to different β values ​​according to some embodiments of the present invention is shown; DETAILED DESCRIPTION

[0025] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0026] In view of the problems existing in the prior art in evaluating the application efficiency of new substation technologies, an embodiment of the present invention proposes a method for evaluating the application efficiency of new substation technologies based on a variable-weight matter-element extension model, which can achieve a scientific and comprehensive evaluation of the application efficiency of new substation technologies and improve the credibility and rationality of the evaluation results.

[0027] According to the embodiment of the present invention, firstly, by constructing an application efficiency evaluation index system of a new substation technology solution, the application efficiency of the two stages of construction and operation and maintenance of the new substation technology solution is taken as the core goal and the corresponding criterion layer index and decision layer index are established, which can comprehensively consider the resource conservation, construction efficiency, construction difficulty of the new substation technology solution construction stage, and the technical performance, technical operation reliability, technical sustainability and replicability, technical investment economic benefits, social and environmental benefits, etc. in the operation and maintenance stage, and then the application efficiency of the new substation technology solution can be comprehensively evaluated from multiple dimensions to improve the benefits. Secondly, the AHP group decision method and the entropy weight method are used for combined weighting, and the combination weight is determined by combining the PP algorithm and the AO algorithm, which can integrate the advantages of subjective and objective weights, improve the rationality of the evaluation, and reduce the random deviation caused by the subjective judgment of the decision maker. Then, the combined weight is dynamically corrected based on the variable weight theory, which can consider the differences in the application of different new technologies, more truly reflect the influence of the index evaluation value on the evaluation result, and improve the credibility of the evaluation result. In addition, by improving the physical-element extension model, it is possible to significantly distinguish different new technology solutions while taking into account the fuzziness of the evaluation of substations of different sizes, further improving the rationality of the evaluation of new technology solutions for substations.

[0028] In an embodiment of the present invention, a method 200 for evaluating the application efficiency of new substation technologies based on a weighted matter-element extension model can be executed in a computing device to achieve a scientific and comprehensive evaluation of the application efficiency of new substation technologies. The method 200 for evaluating the application efficiency of new substation technologies based on a weighted matter-element extension model of the present invention will be described below.

[0029] A computing device 100 provided by an embodiment of the present invention is introduced below.

[0030] Figure 1 FIG. 1 is a schematic diagram of a computing device 100 provided according to an embodiment of the present invention. Figure 1 As shown, in a basic configuration, the computing device 100 includes at least one processing unit 102 and a system memory 104. According to one aspect, depending on the configuration and type of the computing device, the processing unit 102 can be implemented as a processor. The system memory 104 includes, but is not limited to, volatile storage (e.g., random access memory), non-volatile storage (e.g., read-only memory), flash memory, or any combination of such memories. According to one aspect, the system memory 104 includes an operating system 105.

[0031] According to one aspect, operating system 105 is suitable for controlling the operation of computing device 100, for example. Furthermore, examples are practiced in conjunction with graphics libraries, other operating systems, or any other application programs, and are not limited to any particular application or system. Figure 1 The basic configuration is shown in FIG. 1 by those components within the dashed lines. According to one aspect, computing device 100 has additional features or functionality. For example, according to one aspect, computing device 100 includes additional data storage devices (removable and / or non-removable), such as magnetic disks, optical disks, or tapes. Such additional storage Figure 1 1 is illustrated by a removable storage device 109 and a non-removable storage device 110.

[0032] As stated above, according to one aspect, a program module 103 is stored in the system memory 104. According to one aspect, the program module 103 may include one or more application programs, and the present invention does not limit the type of application program, for example, the application program may include: email and contact application programs, word processing application programs, spreadsheet application programs, database application programs, slide show application programs, drawing or computer-aided application programs, web browser application programs, etc.

[0033] According to one aspect, the program module 103 may include multiple program instructions suitable for executing the substation new technology application efficiency evaluation method 200 based on the variable-weighted matter-element extension model of the present invention, so that the computing device 100 is configured to execute the substation new technology application efficiency evaluation method 200 based on the variable-weighted matter-element extension model of the present invention.

[0034] According to one aspect, the examples may be practiced on a circuit comprising discrete electronic components, a packaged or integrated electronic chip containing logic gates, a circuit utilizing a microprocessor, or a single chip containing electronic components or a microprocessor. Figure 1 Each or many components shown in can be integrated into a system on a chip (SOC) on a single integrated circuit to practice examples. According to one aspect, such a SOC device may include one or more processing units, a graphics unit, a communication unit, a system virtualization unit, and various application functions, all of which are integrated (or "burned") into a chip substrate as a single integrated circuit. When operated via SOC, the functions described in this article can be operated via a dedicated logic integrated with other components of the computing device 100 on a single integrated circuit (chip). Embodiments of the present invention can also be practiced using other technologies capable of performing logical operations (such as AND, OR, and NOT), including but not limited to mechanical, optical, fluid, and quantum technologies. In addition, embodiments of the present invention can be practiced in a general-purpose computer or in any other circuit or system.

[0035] According to one aspect, the computing device 100 may also have one or more input devices 112, such as a keyboard, a mouse, a pen, a voice input device, a touch input device, etc. Output devices 114 may also be included, such as a display, a speaker, a printer, etc. The aforementioned devices are examples and other devices may also be used. The computing device 100 may include one or more communication connections 116 that allow communication with other computing devices 118. Examples of suitable communication connections 116 include, but are not limited to: RF transmitter, receiver and / or transceiver circuits; Universal Serial Bus (USB), parallel and / or serial ports.

[0036] The term computer-readable medium as used herein includes computer storage media. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (e.g., computer-readable instructions, data structures, or program modules 103). System memory 104, removable storage device 109, and non-removable storage device 110 are all examples of computer storage media (i.e., memory storage). Computer storage media may include random access memory (RAM), read-only memory (ROM), electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical storage, cassettes, tapes, disk storage or other magnetic storage devices, or any other products that can be used to store information and can be accessed by computing device 100. According to one aspect, any such computer storage medium can be a part of computing device 100. Computer storage media do not include carrier waves or other propagated data signals.

[0037] According to one aspect, communication media is implemented by computer readable instructions, data structures, program modules 103, or other data in a modulated data signal (e.g., a carrier wave or other transport mechanism), and includes any information delivery media. According to one aspect, the term "modulated data signal" describes a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal. By way of example and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.

[0038] In an embodiment according to the present invention, the computing device 100 is configured to execute a method 200 for evaluating the application effectiveness of new substation technologies based on a variable-weighted matter-element extension model. The computing device 100 includes one or more processors and one or more readable storage media storing program instructions. When the program instructions are configured to be executed by one or more processors, the computing device executes the method 200 for evaluating the application effectiveness of new substation technologies based on a variable-weighted matter-element extension model in an embodiment of the present invention.

[0039] In some embodiments, the computing device 100 executing the method 200 for evaluating the application efficiency of new technology in a substation based on a weighted matter-element extension model in an embodiment of the present invention may be a terminal or a server.

[0040] The following is a detailed description of the new technology application efficiency evaluation method 200 for a substation based on a variable-weight matter-element extension model in an embodiment of the present invention.

[0041] Figure 3FIG. 2 is a flow chart of a method 200 for evaluating the effectiveness of new technology application in a substation based on a weighted matter-element extension model according to an embodiment of the present invention. Figure 3 As shown, the substation new technology application efficiency evaluation method 200 based on the variable weight matter-element extension model includes the following steps 210-250.

[0042] First, in step 210, the computing device 100 can establish multi-layer indicators corresponding to the application performance of each stage of multiple new technology solutions for substations. The application performance of each stage includes the application performance of the construction stage and the application performance of the operation and maintenance stage. The multi-layer indicators can include criterion layer indicators and decision layer indicators located below the criterion layer indicators. Each criterion layer indicator can correspond to one or more decision layer indicators. Based on this, an application performance evaluation indicator system of new technology solutions for substations can be obtained.

[0043] Among them, the criterion-level indicators corresponding to the application efficiency in the construction phase may include the construction phase saving indicators, building efficiency improvement indicators, and construction difficulty reduction indicators. The criterion-level indicators corresponding to the application efficiency in the operation and maintenance phase may include technical performance improvement indicators, technical operation reliability indicators, technical sustainability and replicability indicators, technical investment economic benefit indicators, and social and environmental benefit indicators.

[0044] In this way, according to an embodiment of the present invention, for the application of new technical solutions for substations, the application efficiency of the two stages of construction and operation and maintenance of new technical solutions for substations can be taken as the core goal, and the resource conservation, construction efficiency, and construction difficulty of the new technical solutions for substations in the construction stage, as well as the technical performance, technical operation reliability, technical sustainability and replicability, economic benefits of technical investment, and social and environmental benefits in the operation and maintenance stage are comprehensively considered to construct an application efficiency evaluation index system for new technical solutions for substations, so as to comprehensively evaluate the rationality of the selection of substation technical solutions.

[0045] The application efficiency evaluation index system of the new substation technology solution constructed according to the embodiment of the present invention is shown in Table 1.

[0046] Table 1 Evaluation index system for application efficiency of new technology solutions for substations

[0047]

[0048]

[0049] In the embodiment of the present invention, as shown in Table 1, the criterion layer indicators (first criterion layer indicators) corresponding to the construction phase application efficiency C1 include the construction phase saving indicator C 11 , Building efficiency improvement index C 12 , Construction difficulty reduction index C 13 .

[0050] It should be noted that in the process of evaluating the introduction of new technologies into substations, the application efficiency during the construction phase is a crucial consideration. It is crucial to ensure the economic, social and environmental benefits of the project. It can maximize value through full life cycle management while ensuring safe and reliable power supply. The evaluation of application efficiency during the construction phase can comprehensively consider construction resource conservation, building efficiency improvement and reduction of construction difficulty, which will help optimize resource allocation, reduce cost overruns, improve resource utilization efficiency and ensure the construction of green smart substations.

[0051] It should be noted that in the following embodiments, a conventional substation refers to a substation with a conventional construction plan. A substation after applying new technologies and new equipment refers to a substation after applying new technology solutions.

[0052] In some embodiments, each criterion layer indicator may correspond to one or more decision layer indicators. As shown in Table 1, among the criterion layer indicators corresponding to the application efficiency in the construction phase, the construction phase saving indicator C 11 The corresponding decision-making level indicators can specifically include the floor space saving indicator C 111 , Earthwork volume saving index C 112 , Material saving index C 113 Building efficiency improvement index C 12 The corresponding decision-making level indicators can include the building utilization coefficient improvement index C 121 . Construction difficulty reduction index C 13 The corresponding decision-making level indicators can specifically include the new technology and new equipment installation difficulty coefficient index C 131 , New technology and new equipment test difficulty index C 132 , New technology and new equipment debugging difficulty index C 133 .

[0053] Among them, the area saving index C 111 , considering that saving floor space not only helps improve economic benefits and the digital transformation of substations, but also brings environmental and social benefits and improves the utilization of the environment and assets. The floor space saving index can be expressed as follows:

[0054] ΔLand=Land before -Land new

[0055] In the formula, ΔLand represents the area saved by the substation, Land before represents the area occupied by a conventional substation (i.e., a substation with a conventional construction plan), Land newIt indicates the area occupied by the substation after applying new technologies and new equipment (i.e., the substation after applying new technical solutions).

[0056] About Earthwork Saving Index C 112 , considering that saving earthwork can avoid unnecessary excavation and filling, significantly reduce construction costs, such as earthwork transportation costs, etc., while improving earthwork utilization efficiency and promoting sustainable development. The earthwork saving index can be expressed as follows:

[0057] ΔVoe=Voe before -Voe new

[0058] In the formula, ΔVoe represents the amount of earthwork saved, Voe before represents the earthwork volume of conventional substation, Voe new It indicates the earthwork volume of the substation after applying new technologies and equipment.

[0059] About Material Saving Index C 113 , considering that saving materials can help reduce the engineering costs of enterprises and improve their economic efficiency; at the same time, saving materials can also reduce the exploitation of related resources, protect the ecological environment and achieve sustainable development. The material saving index can be expressed as follows:

[0060] ΔMq=Mq before -Mq after

[0061] In the formula, ΔMq represents the amount of resource saving, Mq before Indicates the material usage of conventional substation, Mq after Indicates the amount of materials used in the substation after applying new technologies and equipment.

[0062] About the building utilization coefficient improvement index C 121 , considering that improving the building utilization factor means more efficient use of space, reducing waste, improving the economic benefits of substations, improving resource utilization efficiency, and reducing environmental impact. In addition, a high building utilization factor supports sustainable development and can meet the needs of intelligent transformation of substations. The building utilization factor improvement index can be expressed as follows:

[0063]

[0064] In the formula, Buf represents the building utilization coefficient, Bv before Represents the building volume of the conventional configuration area, Bv after Represents the building volume of the new technology and new equipment configuration area.

[0065] Regarding the difficulty index C of installing new technologies and new equipment 131The difficulty coefficient of new technology and new equipment installation is considered to be higher installation requirements and risks for construction units, requiring more professional construction teams, etc. Therefore, evaluating the difficulty coefficient of new technology and new equipment installation is helpful to take corresponding technical and management measures in a targeted manner in the subsequent construction stage to ensure the smooth progress of the project. The difficulty coefficient index of new technology and new equipment installation can be expressed as follows:

[0066]

[0067] In the formula, Idc represents the difficulty coefficient of installing new technology and new equipment, Pfi before Indicates the process steps required for conventional equipment installation, Pfi after Indicates the process steps required for the installation of new technologies and new equipment. before The labor required for installation of conventional equipment is after The labor required for installing new technologies and equipment, Tfi before The number of operating tools required for conventional equipment installation, Tfi after The number of tools required to install new technologies and equipment, Sfi before Operating space required for conventional equipment installation, Sfi after Operating space required for installation of new technologies and equipment.

[0068] Regarding the index C of the difficulty coefficient of new technology and new equipment testing 132 , considering that the test of new technology and new equipment can help measure the feasibility of technology, risk management, cost control, etc., a high difficulty coefficient means higher implementation risk and cost, and requires regular maintenance. Therefore, the difficulty coefficient of new technology and new equipment testing helps optimize technology and ensure the economic, safe and effective implementation of the project. The difficulty coefficient index of new technology and new equipment testing can be expressed as follows:

[0069]

[0070] In the formula, Tdc represents the difficulty coefficient of new technology and new equipment testing, Pft before Indicates the process steps required for conventional equipment testing, Pft after Indicates the process steps required for testing new technologies and new equipment, Ll before Indicates the labor required for conventional equipment testing, Ll after Indicates the labor required for testing new technologies and new equipment, Tft before Indicates the number of operating tools required for routine equipment testing, Tft after Indicates the number of tools required for testing new technologies and equipment, Sft before Indicates the operating space required for conventional equipment testing, Sft after Represents the operating space required for testing new technologies and equipment.

[0071] Regarding the difficulty index C of debugging new technologies and new equipment 133 , considering that the debugging difficulty directly affects the stability and safety of the equipment as well as the progress and cost of the project. A high debugging difficulty coefficient may mean that more professional technicians and time are required, which increases the complexity of the project and brings operational risks. In addition, equipment with a high debugging difficulty coefficient may require more frequent maintenance and adjustments, affecting long-term operation and maintenance efficiency and costs. The debugging difficulty coefficient index of new technologies and new equipment can be expressed as follows:

[0072]

[0073] In the formula, Dcd represents the difficulty coefficient of debugging new technologies and new equipment, Drps before Indicates the process steps required for conventional equipment commissioning, Drps after Indicates the process steps required for debugging new technologies and new equipment, Irc before Indicates the labor required for regular equipment debugging, Irc after Trfd represents the labor required for debugging new technologies and new equipment. before Indicates the number of operating tools required for routine equipment commissioning, Trfd after Indicates the number of tools required for debugging new technologies and new equipment Osd before Indicates the operating space required for regular equipment debugging, Osd after Indicates the operating space required for debugging new technologies and new equipment.

[0074] In the embodiment of the present invention, as shown in Table 1, the criterion layer indicators (second criterion layer indicators) corresponding to the application performance C2 in the operation and maintenance phase include the technical performance improvement indicators C 21 , Technical operation reliability index C 22 , Technical Sustainability and Reproducibility Index C 23 , Technical investment economic benefit index C 24 , Social and environmental benefit indicators C 25 .

[0075] It should be noted that in the process of evaluating the introduction of new technologies into substations, the application efficiency in the operation and maintenance phase is also a crucial consideration. The operation and maintenance of substations is the key to ensuring the safe operation of the power grid. Regular maintenance and inspection can ensure the continuity and stability of power supply. By evaluating whether the technical performance of substations has been improved, the reliability of technical operation, the sustainability and replicability of technology, the economic benefits of technical investment, and the social and environmental benefits, preventive maintenance and intelligent monitoring of substations can be carried out to reduce equipment failures and improve operation and maintenance efficiency. At the same time, the impact of the project on the environment, society, and economy can also be considered to ensure the safe, reliable, and efficient operation of the power system.

[0076] In some embodiments, as shown in Table 1, among the various criteria layer indicators corresponding to the application performance C2 in the operation and maintenance phase, the technical performance improvement indicator C 21 The corresponding decision-making layer indicators can specifically include the substation dispatch fault response time reduction indicator C 211 , Substation equivalent utilization rate improvement index C 212 , Substation disaster prevention capability improvement index C 213 . Technical operation reliability index C 22 The corresponding decision-making level indicators can specifically include the substation power supply reliability improvement index C 221 , Substation maintenance outage frequency reduction index C 222 , New equipment and new technology maintenance time reduction index C 223 , New equipment and new technology maintenance difficulty index C 224 , New equipment and new technology maintenance time reduction index C 225 , New equipment and new technology maintenance difficulty index C 226 . Technology Sustainability and Reproducibility Indicator C 23 The corresponding decision-making layer indicators can specifically include the substation scalability improvement index C 231 , the proportion of similar projects in the province that can be promoted C 232 . Technical investment economic benefit index C 24 The corresponding decision-making level indicators can specifically include investment and construction cost saving indicators C 241 , Substation maintenance cost saving index C 242 , Substation failure cost saving index C 243 , Substation decommissioning cost saving index C 244 , Electricity sales revenue improvement index C 245 , Investment payback period reduction index C 246 , Internal rate of return improvement index C 247 . Social and environmental benefit index C 25 The corresponding decision-making level indicators can specifically include the carbon emission reduction index C of substation construction. 251 , Construction waste comprehensive utilization rate improvement index C 252 , Substation operation carbon emission reduction index C 253 .

[0077] Among them, the substation dispatch fault response time reduction index C 211 , is to consider the reduction of substation dispatch fault response time, which helps to optimize the maintenance plan of the substation, reduce unnecessary maintenance and overhaul, reduce maintenance costs and waste of human resources, and improve the operation efficiency of the system. The substation dispatch fault response time reduction index can be expressed as follows:

[0078] ΔAd=Ad before -Adafter

[0079] Where ΔAd represents the reduction in substation dispatch fault response time, Ad before Indicates the average response time of conventional equipment scheduling faults, Ad after Indicates the time required to respond to failures in scheduling new technologies and new equipment.

[0080] About Substation Equivalent Utilization Rate Improvement Index C 212 The improvement of equivalent utilization rate of substations means more efficient use of power resources, which can reduce the demand for new substations and play a vital role in achieving sustainable development of energy and maintaining the ecological environment. 212 It can be expressed as the following formula:

[0081] ΔEur=Eur after -Eur before

[0082] In the formula, ΔEur represents the increase in equivalent utilization rate of substation, Eur after It represents the equivalent utilization rate of substation after applying new technologies and equipment, Eur before Indicates the equivalent utilization rate of conventional substations.

[0083] About Substation Disaster Prevention Capability Improvement Index C 213 This is because improving the disaster prevention capability of substations is crucial to ensuring the stable operation of the power system, which can effectively reduce the impact of natural disasters on the power grid, protect people's lives and property, and ensure the continuity of energy supply. This not only enhances emergency response capabilities and reduces environmental damage, but also maintains and supports the sustainable development of the social economy. Substation disaster prevention capability improvement index C 213 It can be expressed as the following formula:

[0084] ΔDpc=Dpc after -Dpc before

[0085] In the formula, ΔDpc represents the improvement of the disaster prevention capability of the substation, Dpc after Indicates the application of new technologies and equipment to substation disaster prevention capabilities, Dpc before Indicates the disaster prevention capability of conventional substations.

[0086] About Substation Power Supply Reliability Improvement Index C 221 The improvement of power supply reliability of substations is an important manifestation of technological innovation and service quality improvement in the power industry. It symbolizes the steady progress of power technology and the increasing processing capacity of power grids in the face of extreme weather and other situations. At the same time, it can also enhance the public's trust in the power system. The power supply reliability improvement index of substations can be expressed as follows:

[0087] ΔPdr=Pdr after -Pdr before

[0088] Where ΔPdr represents the improvement of substation power supply reliability; Pdr after Indicates the power supply reliability of new equipment and new technology. Specifically, it can indicate the comprehensive voltage qualification rate of substation after applying new technology and new equipment. before It indicates the power supply reliability of conventional equipment and conventional technology, and specifically can be expressed as the comprehensive voltage qualification rate of conventional substations.

[0089] Regarding the reduction index C of the number of power outages for substation maintenance 222 , considering that reducing the number of substation maintenance outages can improve power supply reliability, reduce power outage time, and reduce economic losses caused by power outages. Secondly, by optimizing maintenance plans and improving maintenance efficiency, the power outage operation time and impact range can be reduced, and the stability and security of the power grid can be improved. The substation maintenance outage reduction index can be expressed as the following formula:

[0090] ΔNoo=Noo before -Noo after

[0091] In the formula, ΔNoo represents the reduction in the number of substation maintenance outages, Noo before Indicates the number of annual maintenance outages for conventional substations, Noo after It indicates the number of annual maintenance outages of substations after applying new technologies and equipment.

[0092] Regarding the reduction of maintenance time for new equipment and new technologies, C 223 , considering that shortening the maintenance time of new equipment and new technologies can reduce the number and duration of power outages, thereby improving power supply reliability and ensuring the continuity of social and economic activities. It also means that substations can respond more quickly to changes in power system demand, enhancing the adaptability and resilience of the power grid. The maintenance time reduction index for new equipment and new technologies can be expressed as follows:

[0093] ΔMt=Mt before -Mt after

[0094] In the formula, ΔMt represents the reduction in maintenance time of new equipment and new technology, Mt before Indicates the maintenance time of routine equipment and routine technology, Mt after Indicates the maintenance time for new equipment and new technologies.

[0095] Regarding the maintenance difficulty index C of new equipment and new technology 224, a comprehensive evaluation can be made based on whether the equipment body and the layout of the power distribution device have an impact on daily maintenance, and divided into three maintenance difficulty levels. Among them, the first-level maintenance difficulty is more conducive to daily maintenance, and the score is set to 9 points; the second-level maintenance difficulty has almost no impact on the implementation of daily maintenance work, and is set to 6 points; the third-level maintenance difficulty increases the difficulty of daily maintenance work, and is set to 3 points.

[0096] Regarding the reduction index C of the maintenance time of new equipment and new technology 225 , considering that reducing the maintenance time of new equipment and new technologies can reduce economic losses caused by power outages, improve the economic benefits of power companies, and also help reduce the work pressure of front-line workers, improve job satisfaction, the safety and stability of the power system, and reduce power grid accidents caused by equipment failures. The reduction index of the maintenance time of new equipment and new technologies can be expressed as the following formula:

[0097] ΔRt=Rt before -Rt after

[0098] In the formula, ΔRt represents the reduction in maintenance time for new equipment and new technology, Rt before Indicates the duration of routine equipment and routine technical maintenance, Rt after Indicates the maintenance time of new equipment and new technology.

[0099] Regarding the maintenance difficulty index C of new equipment and new technology 226 , can be divided into three maintenance difficulty levels according to the comprehensive evaluation of whether the equipment body and the layout of the power distribution device have an impact on daily maintenance. Among them, the first-level maintenance difficulty is more conducive to equipment maintenance, and the score is set to 9 points; the second-level maintenance difficulty has almost no impact on the development of equipment maintenance work, and is set to 6 points; the third-level maintenance difficulty increases the difficulty of equipment maintenance work, and is set to 3 points.

[0100] About Substation Scalability Improvement Index C 231 , considering that improving the scalability of substations is crucial to the modernization and sustainable development of the power system. It can improve the efficiency of power grid operation and adapt to the growing power demand, especially in areas with rapid economic development. At the same time, improving scalability can also reduce the power outage time of substation renovation, upgrading, and expansion, promote the construction of new power system substations, support the access of large-scale new energy and power electronic equipment, and meet the needs of system regulation. The substation scalability improvement index can be expressed as follows:

[0101] ΔExp=Exp before -Exp after

[0102] In the formula, ΔExp represents the improvement of substation scalability, Expbefore It indicates the scalability of the substation after applying new technologies and equipment, and specifically indicates the power outage time for its transformation, upgrading, renovation and expansion; Exp after It indicates the scalability of a conventional substation, and specifically indicates the power outage time for its renovation, upgrade, and expansion.

[0103] About the promotion ratio index C of similar projects in the province 232 The proportion of similar projects in the province that can be promoted is of great significance to improving the efficiency and service quality of the power system, which helps to optimize resource allocation, reduce repeated investment, and improve investment efficiency. It can also continuously promote substation technology innovation and intelligent upgrading of power systems. The proportion of similar projects in the province that can be promoted can be expressed as follows:

[0104]

[0105] In the formula, Psp represents the proportion of similar projects in the province that can be promoted, Qow after It indicates the number of projects where new technologies and equipment can be applied, and Qow indicates the total number of projects.

[0106] About the investment and construction cost saving index C 241 , considering that cost saving can improve the project investment return rate and enhance competitiveness; it also continuously promotes technological innovation, promotes technological progress in the power industry, improves the efficiency of substation resource utilization, responds to national policy requirements on the development of smart grids, and promotes the sustainable development of substations. The investment and construction cost saving index can be expressed as follows:

[0107] Ccs=Cc before -Cc after

[0108] In the formula, Ccs represents the amount of savings in investment and construction costs, Cc before represents the investment and construction cost of conventional substation, Cc after It represents the investment and construction cost of substation after applying new technologies and equipment.

[0109] About Substation Maintenance Cost Saving Index C 242 , considering that saving the maintenance cost of substations can directly reduce the economic burden and improve the economic benefits of substations. While saving costs, the introduction of optimized maintenance strategies through new technologies, such as combining condition maintenance with regular maintenance, can also improve the reliability of equipment and the stability of the power system, thereby reducing the power outage time caused by equipment failure and improving the continuity and reliability of power supply. The substation maintenance cost saving index can be expressed as follows:

[0110] Rcs=Rc before -Rc after

[0111] In the formula, Rcs represents the amount of substation maintenance cost savings, Rc before represents the annual maintenance cost of conventional substation, Rc after It represents the annual maintenance cost of the substation after applying new technologies and equipment.

[0112] About Substation Failure Cost Saving Index C 243 This is because the substation is an important part of the power grid, and its failure may affect the stability of the entire power grid. By saving failure costs, strengthening equipment management and technical upgrades of substations, the anti-interference ability of the power grid can be improved and the risk of power grid accidents can be reduced. Saving failure costs can also reduce direct costs caused by equipment damage, repair and replacement. Substation equipment is often expensive. By taking effective measures to reduce failure costs, the economic benefits of the substation can be significantly improved. The substation failure cost saving index can be expressed as follows:

[0113] Fcs=Fc before -Fc after

[0114] Where Fcs represents the amount of cost savings from substation failures, Fc before represents the annual failure cost of a conventional substation, Fc after Represents the annual failure cost of substations after applying new technologies and equipment.

[0115] About Substation Failure Cost Saving Index C 243 , considering that the decommissioning of a substation involves multiple costs such as equipment dismantling, site cleaning, and waste disposal. Saving the decommissioning cost can directly reduce the company's capital expenditure in this regard and improve the company's economic benefits. During the decommissioning process, some equipment and materials may still have a certain use value. Recycling and reusing these resources can bring additional income to the company while also reducing the demand for new resources. The substation failure cost saving index can be expressed as follows:

[0116] Dcs=Dc before -Dc after

[0117] Where Dcs represents the cost savings of substation decommissioning, Dc before represents the conventional equipment decommissioning cost, Dc after Represents the cost of decommissioning new technology and new equipment.

[0118] Regarding the improvement of electricity sales revenue indicator C 245, considering that for power companies, the income from power sales at substations is one of their main sources of income. Stable and continuously growing income from power sales can ensure the normal operation and development of the company, provide funds for the company to be used for equipment updates, technology research and development, personnel training, etc., and realize the continuous strengthening of power infrastructure construction. The power sales income improvement index can be expressed as follows:

[0119] ΔRfe=(Aod×Sal×Aep)-(Nod×Sal×Aep)+Scts×Sal×Aep

[0120] Wherein, ΔRfe represents the increase in electricity sales revenue, Aod represents the annual power outage duration of a conventional substation, Nod represents the annual power outage duration of a substation using new technologies and equipment, Scts represents the time saved in substation construction, Sal represents the average load of the substation, and Aep represents the average electricity price.

[0121] About the payback period reduction index C 246 , considering that the reduction of the payback period means that the company can recover the invested funds faster, so that these funds can be used for investment in other projects or the development of the company. This can improve the turnover speed and utilization efficiency of funds and enhance the economic strength of the company. The payback period reduction index can be expressed as follows:

[0122] Rpp=Rp before -Rp after

[0123] In the formula, Rpp represents the reduction value of the investment payback period, Rp before represents the conventional substation investment payback period, Rp after It indicates the investment payback period of the substation after applying new technologies and equipment.

[0124] About the internal rate of return improvement indicator C 247 , considering that the internal rate of return is an important indicator to measure the profitability of the project. The improvement of the internal rate of return of the substation means that the project can bring higher returns to investors. This can not only increase the profit of the enterprise, but also attract more investment and provide financial support for the sustainable development of the enterprise. The internal rate of return improvement index can be expressed as follows:

[0125] Iir=Irr before -Irr after

[0126] In the formula, Iir represents the improvement in internal rate of return, Irr before represents the internal rate of return of conventional substation, Irr after It represents the internal rate of return of the substation after applying new technologies and equipment.

[0127] About the carbon emission reduction index C of substation construction 251 , considering that reducing carbon emissions is one of the important goals of achieving sustainable development. The reduction of carbon emissions from substations will provide strong support for the sustainable development of society, send positive environmental protection signals to the public, and enhance the public's environmental awareness and sense of responsibility. This will help to form a good atmosphere for the whole society to participate in environmental protection and promote the construction of ecological civilization. The carbon emission reduction index of substation construction can be expressed as follows:

[0128] Cer=Ams×Ce

[0129] In the formula, Cer represents the reduction of carbon emissions from substation construction, Ams represents the amount of various materials saved in substation construction after the application of new technologies and new equipment, and Ce represents the carbon emissions generated by the unit production and consumption of various materials.

[0130] Regarding the improvement index C of comprehensive utilization rate of construction waste 252 , considering that if a large amount of construction waste is randomly piled up or landfilled, it will cause serious pollution to the soil, water and air. Improving the comprehensive utilization rate can reduce the negative impact of construction waste on the environment and reduce the risk of land occupation and environmental pollution. Power companies actively improve the comprehensive utilization rate of construction waste, which further reflects the company's social responsibility and environmental awareness, and is in line with the development of the times. The indicator for improving the comprehensive utilization rate of construction waste can be expressed as follows:

[0131] ΔRmu=Rmu after -Rmu before

[0132] In the formula, ΔRmu represents the improvement of comprehensive utilization rate of construction waste, Rmu after Rmu represents the comprehensive utilization rate of substation construction waste after applying new technologies and equipment. before It indicates the comprehensive utilization rate of construction waste in conventional substations.

[0133] About the carbon emission reduction index C of substation operation 253 This is because reducing carbon emissions helps reduce the concentration of greenhouse gases in the atmosphere, slows down the rate of global warming, and is crucial to protecting the earth's ecological environment; at the same time, it promotes the development of the power industry in a cleaner and more sustainable direction and optimizes the overall energy structure. The carbon emission reduction index of substation operation can be expressed as follows:

[0134] ΔOce=Oce after -Oce before

[0135] In the formula, ΔOce represents the reduction in carbon emissions from substation operation, Oce after represents the annual carbon emissions of conventional substations, Oce beforeIt represents the annual carbon emissions of the substation after applying new technologies and equipment.

[0136] Subsequently, in step 220, the computing device 100 may determine the combined weights of the various indicators. Specifically, first, the AHP group decision method may be used to subjectively weight the various indicators by comprehensively analyzing the opinions of multiple experts to determine the integrated subjective weights of the various indicators. Secondly, the entropy weight method (objective weighting method) may be used to objectively weight the various indicators to determine the objective weights of the various indicators. Furthermore, the projection pursuit algorithm (PP) may be used to determine the combined weights of the various indicators based on the integrated subjective weights and objective weights of the various indicators.

[0137] It should be noted that the AHP group decision-making method is a multi-criteria decision-making method that combines qualitative and quantitative analysis and is highly subjective.

[0138] In some embodiments, the specific process of using the AHP group decision method to subjectively weight each indicator to determine the integrated subjective weight of each indicator is as follows:

[0139] First, a judgment matrix can be constructed. Specifically, for each expert, the corresponding judgment matrix can be constructed using the exponential scaling method based on the importance of each indicator determined according to the expert opinion to the upper-level indicator (upper-level dominant indicator). And the maximum characteristic root λ of the judgment matrix can be determined max and its eigenvector θ.

[0140] Since the consistency test of the nine-level scaling method is contrary to the consistency of thinking, the assignment error is large and the affiliation relationship of different indicators at each level cannot be accurately quantified. The exponential scaling method has significant advantages in consistency and weight fitting, which reduces the subjective judgment error. Therefore, in some embodiments of the present invention, e 0 / 4 ~e 8 / 4 The exponential scaling method is used to construct the judgment matrix. Specifically, for each expert, the relative importance (degree of importance) of each indicator to the upper-level indicator can be determined according to the expert opinion. Then, based on the degree of importance of each indicator to the upper-level indicator determined according to each expert opinion, the judgment matrix can be constructed using the exponential scaling method, and the maximum characteristic root λ of the judgment matrix can be determined. max and its eigenvector θ. See Table 2 for the relevant description of the scaling method.

[0141] Table 2 Scaling method description

[0142]

[0143] Subsequently, the above judgment matrix can be subjected to a consistency check. Among them, the consistency ratio index CR of the above judgment matrix can be determined based on the maximum eigenvalue root of the judgment matrix. The consistency ratio index CR can be used to measure the consistency degree of the above judgment matrix. Then, the above judgment matrix can be subjected to a consistency check based on the consistency ratio index CR of the judgment matrix (to determine whether the above judgment matrix meets the consistency requirements). Among them, when CR < 0.1, it can be determined that the above judgment matrix meets the consistency requirements; otherwise, it is determined that the above judgment matrix does not meet the consistency requirements and the above judgment matrix needs to be modified. The calculation formula of the consistency ratio index of the judgment matrix is ​​as follows:

[0144] CR=CI / RI (1)

[0145] CI=(λ max -n) / (n-1) (2)

[0146] In the formula, CR is the consistency ratio index; CI is the consistency index; RI is the average random consistency index; n is the number of indicators; λ max is the maximum eigenvalue of the judgment matrix.

[0147] Next, the above eigenvectors can be normalized to obtain the relative weight of the same-layer indicators of each indicator to the upper-layer indicators, and then the total hierarchical sorting can be performed, so that the weight of each indicator relative to the highest-layer indicator can be determined based on the hierarchical order of indicators and the relative weight of the same-layer indicators of each indicator to the upper-layer indicators. a,j (j=1,2,…,n), and it can be used as the subjective judgment weight of experts on each indicator.

[0148] Finally, the AHP group decision-making method can be used to determine the integrated subjective weight of each indicator based on the subjective judgment weight of each expert on each indicator.

[0149] It should be understood that based on the judgment matrix corresponding to each expert, a corresponding set of weights (the subjective judgment weights of the experts on each indicator) can be obtained. Therefore, the weighted information can be aggregated into the integrated weights of group decision-making to weaken the subjective uncertainty and cognitive ambiguity of individual experts, thereby weakening the impact of subjective extreme deviations on weights. Assuming that there are s experts in total, the weight matrix W obtained according to the opinion of the xth expert is (x) It can be expressed as:

[0150]

[0151] In some specific embodiments, the AHP group decision method is used to determine the integrated subjective weight of each indicator based on the subjective judgment weight of each expert on each indicator. The specific method is as follows:

[0152] First, the Pearson correlation coefficient between expert opinions can be calculated using the following formula, and then the Pearson correlation coefficient between expert opinions is normalized to obtain the expert opinion correlation coefficient matrix.

[0153]

[0154] Where x, y = 1, 2…s; is the correlation coefficient between the opinions of expert x and expert y; are the subjective judgment weights of expert x and expert y on the jth indicator respectively; are the mean judgment weights of expert x and expert y on n indicators respectively.

[0155] Subsequently, based on the expert opinion correlation coefficient matrix, cluster analysis is used to classify multiple experts (s experts) and determine the inter-class weights of each type of expert. Specifically, cluster analysis can be used to classify s experts. According to the expert opinion correlation coefficient matrix, a threshold κ is selected, and each row is traversed to find the experts whose non-diagonal elements have a correlation coefficient greater than κ with the x-th expert, and cluster them into subsets. Then, the subsets with intersections are combined to obtain the final classification result (including d types of experts in total). Assume that the s experts are divided into d types of experts, and the number of experts in the l-th category is z. l , then the calculation formula of the inter-class weight of each type of expert is as follows:

[0156]

[0157] In the formula, is the inter-class weight of the expert in the lth class.

[0158] Furthermore, the intra-class weight of each expert can be determined based on the weighting method of consistency ratio, as shown in formula (6):

[0159]

[0160] In the formula, gc x , CR x are the intra-class weight of expert x and the consistency ratio index of expert x respectively; τ is the adjustment factor, and τ=10 is taken here.

[0161] Furthermore, the integrated subjective weight of each indicator (i.e., the integrated subjective weight of group decision-making) can be determined based on the inter-class weights of various experts and the intra-class weights of each expert. Specifically, the integrated subjective weight of the jth indicator is The calculation formula is as follows:

[0162]

[0163] It should be noted that the entropy weight method is an objective weighting method. Its basic principle is to reflect the degree of variation of the evaluation of each indicator and the contribution of the indicator to provide effective information by calculating the information entropy, and then determine the role of the indicator in the comprehensive evaluation. According to the idea of ​​information entropy, the greater the difference in the value of the evaluation object on a certain indicator, the smaller the entropy value, the greater the amount of effective information provided by the indicator, and the greater the weight of the indicator; conversely, the smaller the difference in the evaluation object on a certain indicator, the larger the entropy value, indicating that the amount of effective information provided by the indicator is small, and the weight of the indicator should also be small.

[0164] In some embodiments, the entropy weight method is used to objectively weight each indicator to determine the objective weight of each indicator. The specific method is as follows:

[0165] First, since the measurement units of various indicators are not uniform, before calculating the comprehensive indicators based on various indicators, it is necessary to standardize each indicator in advance to convert the absolute value of each indicator into a relative value, so as to solve the homogeneity problem of the values ​​of various indicators of different qualities. In addition, since the positive indicator value and the negative indicator value represent different meanings (the higher the positive indicator value, the better, and the lower the negative indicator value, the better), different algorithms need to be used for standardization of positive indicators and negative indicators. Specifically, the following formula (8) can be used to standardize positive indicators, and the following formula (9) can be used to standardize negative indicators:

[0166]

[0167] After each indicator is standardized, the proportion of each solution (new technology solution) in each indicator can be determined based on the standardized indicators. Specifically, the proportion of the i-th solution value under the j-th indicator in the indicator can be calculated by the following formula (10):

[0168]

[0169] Then, the entropy value of each indicator can be determined based on the proportion of each solution in each indicator. Specifically, the entropy value of the jth indicator can be calculated by the following formula (11):

[0170]

[0171] Among them, k=1 / ln(n)>0, satisfying e j ≥0.

[0172] Furthermore, the information entropy redundancy of each indicator can be determined based on the entropy value of each indicator. Specifically, the information entropy redundancy (difference) of the jth indicator can be calculated by the following formula (12):

[0173] d j=1-e j, j=1,…,m (12)

[0174] Finally, the objective weight of each indicator can be determined based on the information entropy redundancy of each indicator. Specifically, the calculation formula of the objective weight of the jth indicator is as follows:

[0175]

[0176] In an embodiment of the present invention, after the integrated subjective weight of each indicator is determined by the AHP group decision method and the objective weight of each indicator is determined by the entropy weight method, the best projection can be optimized by the projection pursuit algorithm (PP) based on the integrated subjective weight and objective weight of each indicator, and the obtained optimization result can be used as the combined weight of each indicator.

[0177] It should be noted that, compared with the minimum information entropy principle and linear weighting method commonly used in the existing research on the combination weight, the combination weight determined by the method of the present invention is not only more in line with the actual situation, but also can maximize the difference in the evaluation values ​​of each sample. At the same time, according to the projection pursuit algorithm (PP) of the present invention, by projecting high-dimensional data into one-dimensional space, and then analyzing the transformed one-dimensional data, the projection that reflects the structure or characteristics of the original high-dimensional data to the highest degree is compared, thereby realizing the conversion of multi-dimensional to one-dimensional problems.

[0178] In some embodiments, the specific process of determining the combined weights of each indicator based on the integrated subjective weights and objective weights of each indicator using the projection pursuit algorithm (PP) is as follows:

[0179] First, each indicator of each method in the sample data set can be standardized (normalized) to obtain a standardized data set.

[0180]

[0181] In the formula, represents the normalized result of the jth indicator under the eth weighting method in the sample data set; ω max (j) and ω min (j) is the extreme value of the jth indicator. The standardized data set obtained after processing is: {x(e,j)|e=1,2…,f;j=1,2,…,m}.

[0182] Subsequently, a projection index function can be established for the standardized data set to convert the standardized data set into a one-dimensional projection value. Specifically, the standardized data set can be converted into a one-dimensional projection value z(e) with α={α(1), α(2), α(3), ... α(f)} as the projection direction, as shown in formula (15).

[0183]

[0184] Then, the projection index function can be optimized to obtain the optimal projection direction. It should be noted that, when the sample set is fixed, in order to reflect the characteristics of high-dimensional data to the highest degree, the projection index function can be optimized to obtain the optimal projection direction, as shown in formula (16).

[0185]

[0186] In the formula, S z , D z They represent the standard deviation and local density of the one-dimensional projection value z(e), respectively, where the local density D of the one-dimensional projection value z(e) z The calculation method of is shown in formula (18).

[0187]

[0188] Where R is the window radius of the local density, which is 0.1S z ; r(e,j) is the projection value distance. u[Rr(e,j)] is the unit step function, where when Rr(e,j)<0, the unit step function is 0. When Rr(e,j)≥0, the unit step function is 1.

[0189] It should be understood that in the above embodiments, a combined weighted optimization model can be obtained by optimizing the projection index function, as shown in the above formulas (15)-(17).

[0190] Furthermore, by solving the combined weighted optimization model, the combined weights of the various indicators can be obtained. It should be noted that the one-dimensional projection value z(e) obtained by solving the combined weighted optimization model is the combined weight of the various indicators.

[0191] In some embodiments, after the combined weighted optimization model is obtained by using the projection pursuit algorithm (PP), the AO algorithm can be further used to solve the above-mentioned combined weighted optimization model to obtain the combined weights of each indicator. It is worth noting that solving the above-mentioned combined weighted optimization model is a complex nonlinear optimization problem. In the prior art, genetic algorithms, particle swarm algorithms, shark optimization algorithms, etc. are generally used for solving it, which have problems such as poor convergence and easy to fall into local optimality. In view of this, in some embodiments of the present invention, the AO algorithm with the ability to escape local optimality and strong convergence performance is used to solve the above-mentioned combined weighted optimization model.

[0192] It should be noted that the AO algorithm is based on the malaria treatment process and combines the principles of metaheuristic algorithms. By analyzing the malaria treatment strategies at each stage, it is inspired and proposes corresponding strategies for each stage. The strategies for each stage mainly include: comprehensive elimination stage strategy, which encourages the algorithm to conduct global exploration; local clearance stage strategy, which promotes local utilization; and post-consolidation stage strategy, which enhances the algorithm's ability to escape local optimality.

[0193] Figure 3 A schematic flow chart of the AO algorithm in some embodiments of the present invention is shown.

[0194] like Figure 3 As shown, the specific solution of the above combined weighted optimization model using the AO algorithm is as follows:

[0195] First, in the initialization phase, the entire population is initialized to generate an initial population (denoted as A), as shown in the following formula (18). The initialization population includes N search agents, each of which contains multidimensional components, where D can be used to represent the multidimensional components in the search agent (i.e., D represents the number of dimensions of the components in the search agent). Specifically, according to the AO algorithm in the embodiment of the present invention, in the initialization phase, a common method in a metaheuristic algorithm can be used to generate an initial solution (initial population) based on a random number sequence.

[0196]

[0197] In the formula, T and B represent the boundaries of the space; R represents a set of random number sequences with a value range of [0,1].

[0198] Then, the comprehensive elimination stage is entered. In the comprehensive elimination stage, the large-scale decentralized characteristics of the search agent can be used as a guide to explore the complex solution space. Specifically, in the comprehensive elimination stage, the probability coefficient and the decay index are calculated based on the current number of iterations and the maximum number of iterations of the AO algorithm. The probability coefficient can be combined with the evaluation progress of the AO algorithm to simulate the objective scenario. Then, the search agent in the initial population is updated based on the probability coefficient and the decay index. The comprehensive elimination stage can be specifically expressed as the following formula (19):

[0199]

[0200] In the formula, and These are the search agents before and after the update respectively; is the current optimal; r1 is a random number between [0,1]. K is the probability coefficient, and c represents the decay exponent.

[0201] The calculation method of the probability coefficient K is shown in formula (20), and the calculation method of the attenuation index c is shown in formula (21).

[0202]

[0203] Where T and MaxT represent the current number of iterations and the maximum number of iterations respectively.

[0204] Next, we enter the local cleanup phase. In the local cleanup phase, the search agent can be further updated, which helps to retain excellent individuals and provide opportunities for individuals with poor performance. At the same time, it allows the algorithm to utilize and exchange local information to improve the overall performance of the algorithm. The local cleanup phase can be specifically expressed as the following formula (22):

[0205]

[0206] b1,b2,b3~U(1,N),b1≠b2≠b3 (24)

[0207] In the formula, Fit norm (i) represents the normalized fitness value; d represents the coefficient, which is a random number between [0.1, 0.6].

[0208] After that, it enters the post-consolidation stage. In the post-consolidation stage, the possibility of encountering unexpected situations in the post-consolidation stage can be simulated. By simulating this specific situation (the unexpected situation encountered in the post-consolidation stage), the ability of the search agent to escape the local optimum can be enhanced. The post-consolidation stage can be specifically expressed as the following formula (25):

[0209]

[0210] Then, the fitness value of each search agent can be evaluated to determine whether the termination condition is met. If the termination condition is not met, the comprehensive elimination stage can be returned to perform the comprehensive elimination stage and subsequent steps again. If the termination condition is met, the optimal solution (best solution) of the combined weighted optimization model can be obtained and returned, and the algorithm process can be terminated at this time.

[0211] In this way, by iteratively executing the above-mentioned stages of the AO algorithm, the optimal solution of the combined weighted optimization model can be obtained until the termination condition is met, and then the combined weights of each indicator can be determined based on the optimal solution.

[0212] After the combined weights of the various indicators are obtained by combining the projection pursuit algorithm (PP) and the AO algorithm, the following step 230 may be performed.

[0213] In step 230 , the computing device 100 may dynamically modify the combined weights of the various indicators based on the variable weight theory.

[0214] It should be noted that the variable weight idea means that when evaluating plans (new technology plans) according to various indicators, the weights of various indicators (the combined weights of various indicators) are dynamically adjusted according to the indicator values, so that the weights of various indicators after dynamic adjustment (the variable weights of various indicators) can reflect the real impact of the indicator evaluation value on the evaluation results.

[0215] In some specific embodiments, the specific method of dynamically modifying the combined weights of various indicators based on the variable weight theory is as follows:

[0216] First, Definition 1: A set of n-dimensional variable weights refers to n mappings ω j (j=1,2,…,n):[0,1] n →[0,1],(x1,x2,…,x n )∣→ω j (x1,x2,…,x n ), satisfying the following normalization, continuity, and monotonicity:

[0217] Normalization:

[0218] Continuity: ω j (x1,x2,…,x n )(j=1,2,…,n) is continuous with respect to each variable xj;

[0219] Monotonicity: ω j (x1,x2,…,x n )(j=1,2,…,n) monotonically increases (incentive type) or monotonically decreases (penalty type) with respect to the variable xj.

[0220] The combined weights of the various indicators obtained by combining the projection pursuit algorithm (PP) and the AO algorithm can be used as a constant weight vector. For any constant weight vector If the normalization, continuity, and monotonicity in Definition 1 are satisfied, the variable weight vector corresponding to the constant weight vector can be constructed based on the state variable weight function, as shown in the following formula (26):

[0221]

[0222] Where S(X) represents the variable weight vector constructed based on the state variable weight function, S(X) = (S(x1), S(x2), …, S(x n )). W(X) represents the variable weight vector corresponding to S(X).

[0223] Secondly, the dominance of each indicator can be determined, and the dominance of the indicator is used to comprehensively measure the pros and cons of the indicator. In some embodiments, the idea of ​​the gray target decision method can be referred to, and the positive and negative bull's-eyes can be defined as ideal solutions. The distance from the indicator evaluation value to the positive bull's-eye is used as the indicator disadvantage information, and the distance from the indicator evaluation value to the negative bull's-eye is used as the indicator advantage information. The Gaussian criterion is used to convert the projection value of the difference between the indicator disadvantage information and the advantage information into the indicator dominance, and the pros and cons of the indicator are comprehensively measured based on the indicator dominance.

[0224] Definition 2: Let G = (g ij ) m×n is the standardized evaluation matrix, where g ij represents the standardized evaluation value corresponding to the jth indicator of the ith evaluation object. ij ) m×n The jth column of can be expressed as:

[0225] max{g ij |j∈C1,i=1,2,…,m} (27)

[0226] min{g ij |j∈C1,i=1,2,…,m} (28)

[0227] The evaluation value corresponding to the position of formula (27) is say is the positive center of the gray target. The evaluation value corresponding to the position of formula (28) is say It is the negative bull's eye of the grey target.

[0228] Definition 3: Suppose the evaluation object te i , index x j , index x j The corresponding standardized evaluation value g ij , then the evaluation object te i Lower index x j The dominance can be expressed as:

[0229]

[0230] In the formula, R ij For the evaluation object i Lower index x j The dominance, R ij The larger the value, the more obvious the advantage of the indicator evaluation value; on the contrary, R ij The smaller it is, the more obvious the disadvantage of the indicator evaluation value is. Respectively represent the evaluation object te i Lower index x jThe distance from the indicator evaluation value to the positive bull's eye, the distance from the indicator evaluation value to the negative bull's eye, and the distance between the positive bull's eye and the negative bull's eye. β represents the dominance adjustment coefficient, β>0 and the larger β is, the greater the degree of adjustment of the indicator dominance. Among them, Figure 4 A schematic diagram showing advantage functions corresponding to different β values ​​according to some embodiments of the present invention is shown.

[0231] That is to say, in some embodiments of the present invention, the dominance of each indicator under each evaluation object can be determined based on the distance from the indicator evaluation value of each indicator under each evaluation object to the positive bull's eye, the distance from the indicator evaluation value to the negative bull's eye, and the distance between the positive bull's eye and the negative bull's eye.

[0232] In addition, a dominance matrix can be constructed based on the dominance of each indicator under each evaluation object, as shown in the following formula (30):

[0233]

[0234] Next, the state variable weight function can be constructed based on the dominance of each indicator under each evaluation object.

[0235] It should be noted that the state-variable weight function is used to dynamically adjust the indicator weight according to the specific state of the indicator. By analyzing the evaluation characteristics of the command and control system, the setting of the state-variable weight function should meet the following principles:

[0236] (1) Increase the attention paid to extreme indicators. When the dominance of an indicator is very low, even if its constant weight is large, it will significantly reduce the overall efficiency. Similarly, when the dominance of an indicator is very high, even if its constant weight is very low, it will significantly improve the overall system efficiency. Therefore, when changing the weight, the weight of the indicator with low dominance is punished, and the weight of the indicator with high dominance is incentivized.

[0237] (2) When the dominance of a certain indicator is very low, the overall efficiency will be greatly reduced. However, when the dominance of a certain indicator is high, the overall efficiency improvement may not be very obvious. Therefore, the incentive range should be smaller than the punishment range.

[0238] (3) Dynamic variable weights cannot be completely separated from the constraints of constant weights, and the sensitivity of punishment (incentive) must correspond to the size of the constant weights.

[0239] In some embodiments, according to the above principles (1)-(3), based on the dominance of each indicator under each evaluation object, and combined with the penalty threshold and the incentive threshold, a state variable weight function (a local state variable weight function based on the penalty-reward mechanism) can be constructed, as shown in the following formula (31):

[0240]

[0241] In the formula, R L and RU They represent the penalty threshold and incentive threshold respectively. The penalty threshold R can be determined using the K-means algorithm. L and the excitation threshold R U Specifically, the K-means algorithm is used to cluster the dominance of each indicator under each evaluation object into three categories, and the middle value of the adjacent cluster centers is taken as the critical value of penalty and incentive.

[0242] Variable weight function S ij (R ij ) with the index advantage R ij The changes of are shown in Table 3. Among them, δ represents the ratio of incentive to punishment.

[0243] Table 3 Changes in weight function

[0244]

[0245] Furthermore, the combination weights of various indicators can be dynamically modified through the state variable weight function based on the penalty-reward mechanism, so as to obtain the variable weights corresponding to the combination weights of various indicators.

[0246] Specifically, through the state variable weight function (31), the combined weights of each indicator can be dynamically modified in combination with formula (32) to obtain the variable weights corresponding to the combined weights of each indicator. That is, the variable weight of the jth indicator under the i-th evaluation object can be determined by the following formula (32).

[0247]

[0248] According to an embodiment of the present invention, after dynamically correcting the combined weights of various indicators based on variable weight theory to obtain the variable weights of various indicators, the following step 240 can also be executed to construct an improved matter-element extension model. The improved matter-element extension model can be used as a comprehensive evaluation model for comprehensively evaluating new technology solutions for substations.

[0249] In step 240, the computing device 100 can construct a matter-element matrix based on the various indicators (i.e., evaluation indicators) corresponding to the application efficiency of the new technology solution of the substation (application efficiency in the construction phase, application efficiency in the operation and maintenance phase) and the values ​​of each indicator (evaluation indicator), and then normalize the matter-element matrix to obtain an improved matter-element topological model.

[0250] In the embodiment of the present invention, the application effectiveness has multiple evaluation levels. The matter-element matrix includes the matter-element matrix to be evaluated, the classical domain matter-element matrix and the section domain matter-element matrix.

[0251] It should be noted that the Basic Element Model (BEM) is a multidimensional decision analysis tool based on "matter-element theory" and "extension theory", which is widely used to deal with complex, changeable, uncertain and fuzzy problems. The main advantage of this model is that it can effectively describe and deal with fuzziness in the system, especially for complex decision-making problems. It is applied to the post-evaluation of new substation technologies and can handle complex multi-dimensional information during the evaluation.

[0252] In an embodiment of the present invention, based on the matter-element theory, each evaluation index corresponding to the application efficiency in the construction phase and the application efficiency in the operation and maintenance phase can be expressed in the form of matter-element as R=(P, M, V), wherein P represents the object to be evaluated itself, and in the embodiment of the present invention, it is used to represent the application efficiency of the new technology solution of the substation (application efficiency in the construction phase, application efficiency in the operation and maintenance phase). M represents the characteristics of the object to be evaluated (matter-element), and in the embodiment of the present invention, it refers to the evaluation index used to evaluate the application efficiency (i.e., the index in the above embodiment). V represents the value corresponding to the characteristic M, that is, the value of the evaluation index.

[0253] In some specific embodiments, when constructing the matter-element matrix in step 240, the matter-element matrix to be evaluated corresponding to the application effectiveness can be first constructed based on the evaluation indicators corresponding to the application effectiveness and the values ​​of the evaluation indicators. Specifically, assuming that the application effectiveness P of the new technology solution for the substation is x , corresponding to n evaluation indicators m1, m2..., m n , the values ​​of the n evaluation indicators are v 1x ,v 2x ,...,v nx , we can construct the matter-element matrix R corresponding to the application effectiveness x As shown in formula (33):

[0254]

[0255] Assume that application performance has z evaluation levels, then any evaluation level P of application performance j The n evaluation indicators corresponding to the application performance and the value ranges of these evaluation indicators are called the classical domain. For any evaluation level of application performance, any evaluation level P of the application performance can be constructed based on any evaluation level of application performance, each evaluation indicator corresponding to the application performance and the value range of each evaluation indicator under the evaluation level. j The corresponding classical domain matter-element matrix. The jth evaluation level of application effectiveness P j The corresponding classical domain matter-element matrix R j It can be expressed as formula (34):

[0256]

[0257] In the formula, m l ,m2…,m n They represent the n evaluation indicators corresponding to the application performance; v ij ,v 2j ..,v nj They respectively represent the value ranges of the n evaluation indicators corresponding to the jth evaluation level (i.e., the value ranges of the n evaluation indicators at the jth evaluation level); a ij and b ij v ij The value boundary of ; i=1,2,...n, j=1,2,...m.

[0258] Furthermore, the section domain matter-element matrix corresponding to the application performance can also be constructed based on the union of the value ranges of each evaluation indicator and each evaluation indicator at each evaluation level (total value range). It should be noted that the total value range of the evaluation indicator is called the section domain, and the section domain is the union of the value ranges of the evaluation indicator at each evaluation level. p It can be expressed as formula (35):

[0259]

[0260] In the formula, v p ,v p2 ...v pn They are the union of the value ranges of the n evaluation indicators corresponding to application effectiveness at each evaluation level.

[0261] Furthermore, the above-constructed matter-element matrix to be evaluated, the classical domain matter-element matrix and the section domain matter-element matrix can be normalized to obtain an improved matter-element extension model. It should be noted that when using the matter-element extension model for evaluation, if the measured value of the indicator exceeds the section domain range, the correlation degree cannot be calculated through the correlation function. Therefore, it is necessary to improve the matter-element extension model. Specifically, the matter-element matrix to be evaluated, the classical domain matter-element matrix and the section domain matter-element matrix can be normalized to obtain an improved matter-element extension model.

[0262] Among them, the matrix of matter elements to be evaluated R x After normalization, we can get:

[0263]

[0264] For any evaluation level of application performance, the classical domain matter-element matrix R j After normalization, we can get:

[0265]

[0266] Furthermore, the improved matter-element extension model can be obtained according to the above equations (36) and (37).

[0267] Finally, in step 250, the computing device 100 may comprehensively evaluate the application effectiveness of each new technology solution of the substation based on the improved matter-element extension model and the variable weights of each indicator.

[0268] Specifically, in step 250, the distance between each object-element to be evaluated (representing application effectiveness) and each indicator value at each evaluation level can be determined based on the improved object-element extension model constructed according to the embodiment of the present invention. Subsequently, the closeness between each object-element to be evaluated and each indicator value at each evaluation level, the variable weight of each indicator, and the number of indicators can be determined. Furthermore, based on the closeness between each object-element to be evaluated and each evaluation level, the application effectiveness of each new technology solution of the substation can be comprehensively evaluated.

[0269] It should be noted that the object-element extension model usually judges the level of the evaluation object through the maximum membership principle. However, due to the fuzziness of the boundaries of some evaluation objects, this method may lead to information loss and inaccurate evaluation results. In order to obtain more accurate evaluation results, the present invention adopts asymmetric proximity to replace the maximum membership principle in some embodiments. Closeness is a concept in fuzzy mathematics, which is used to measure the degree of closeness between two fuzzy subsets. In some embodiments of the present invention, closeness is used for the application effectiveness of new technology solutions for substations. Specifically, the closeness k between the xth object-element to be evaluated and the jth evaluation level can be calculated by the following formulas (38) and (39): j (x):

[0270]

[0271] In the formula, ω i is the variable weight of the ith indicator (evaluation indicator); n is the number of evaluation indicators; D j (v xi ) is the distance between the xth object-element to be evaluated and the ith index value (evaluation index value) under the jth evaluation level.

[0272] In some embodiments, the closeness between each object element to be evaluated and each evaluation level can be normalized to increase the distinction between each object element to be evaluated and each evaluation level, thereby being able to more accurately determine its evaluation level. Specifically, the closeness between the xth object element to be evaluated and the jth evaluation level can be normalized according to the following formulas (40)-(42).

[0273]

[0274] Where j′ is the eigenvalue of the grade variable of the object-element to be evaluated, and j′ is used to judge the degree to which the xth object-element to be evaluated is biased towards the adjacent evaluation grade of the jth evaluation grade.

[0275] According to the above embodiment of the present invention, the use of asymmetric proximity can achieve differentiated processing of positive and negative distances, and can significantly distinguish the evaluation of different new technology solutions while taking into account the ambiguity of evaluation of substations of different sizes.

[0276] In summary, according to the method 200 for evaluating the application efficiency of new substation technologies based on the variable weight matter-element extension model of the present invention, firstly, by constructing an application efficiency evaluation index system for new substation technology solutions, the application efficiency of the two stages of construction and operation and maintenance of new substation technology solutions is taken as the core goal and corresponding criterion layer indicators and decision layer indicators are established, which can comprehensively consider resource conservation, construction efficiency, construction difficulty in the construction stage of new substation technology solutions, as well as technical performance, technical operation reliability, technical sustainability and replicability, technical investment economic benefits, social and environmental benefits in the operation and maintenance stage, and thus can comprehensively evaluate the application efficiency of new substation technology solutions from multiple dimensions to improve the benefits. Secondly, the AHP group decision method and the entropy weight method are used for combined weighting, and the PP algorithm and the AO algorithm are combined to determine the combined weight method, which can integrate the advantages of subjective and objective weights, improve the rationality of the evaluation, and reduce the random deviation caused by the subjective judgment of the decision maker. Then, the combined weight is dynamically corrected based on the variable weight theory, which can consider the differences in the application of different new technologies, more truly reflect the influence of the index evaluation value on the evaluation result, and improve the credibility of the evaluation result. In addition, by improving the physical-element extension model, it is possible to significantly distinguish different new technology solutions while taking into account the fuzziness of the evaluation of substations of different sizes, further improving the rationality of the evaluation of new technology solutions for substations.

[0277] By way of example and not limitation, readable media include readable storage media and communication media. Readable storage media stores information such as computer readable instructions, data structures, program modules or other data. Communication media generally embody computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and include any information transfer medium. Any combination of the above is also included within the scope of readable media.

[0278] In the description provided herein, algorithms and displays are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the examples of the present invention. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the present invention is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to implement the content of the present invention described herein, and the above description of specific languages ​​is for the purpose of disclosing the best mode of the present invention.

[0279] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0280] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof.

[0281] Those skilled in the art will appreciate that the modules or units or components of the devices in the examples disclosed herein may be arranged in the devices described in the embodiments, or alternatively may be located in one or more devices different from the devices in the examples. The modules in the foregoing examples may be combined into one module or may be divided into a plurality of submodules.

[0282] Unless otherwise specified, the use of ordinal numbers "first", "second", "third", etc. to describe common objects merely indicates that different instances of similar objects are involved and is not intended to imply that the objects so described must have a given order in time, space, order or in any other manner.

Claims

1. A method for evaluating the application efficiency of new technologies in substations based on a variable-weight matter-element extension model, including: For the application efficiency of multiple new technology solutions for substations at each stage, a multi-layer index corresponding to the application efficiency is established. The application efficiency at each stage includes the application efficiency at the construction stage and the application efficiency at the operation and maintenance stage. The multi-layer index includes the criterion layer index and the decision layer index located below the criterion layer index. Each criterion layer index corresponds to one or more decision layer indicators. Among them, the criterion layer index corresponding to the application efficiency at the construction stage includes the construction stage saving index, the building efficiency improvement index, and the construction difficulty reduction index. The criterion layer index corresponding to the application efficiency at the operation and maintenance stage includes the technical performance improvement index, the technical operation reliability index, the technical sustainability and replicability index, the technical investment economic benefit index, and the social and environmental benefit index. Determine the combined weight of each indicator, including: using the AHP group decision method to subjectively weight each indicator to determine the integrated subjective weight of each indicator; using the entropy weight method to objectively weight each indicator to determine the objective weight of each indicator; using the projection pursuit algorithm to determine the combined weight of each indicator based on the integrated subjective weight and objective weight of each indicator; Based on the variable weight theory, the combined weights of each indicator are dynamically modified to obtain the variable weights of each indicator; Based on the various indicators corresponding to the application effectiveness and the values ​​of the various indicators, a matter-element matrix is ​​constructed, and the matter-element matrix is ​​normalized to obtain an improved matter-element extension model, wherein the application effectiveness has multiple evaluation levels, and the matter-element matrix includes a matter-element matrix to be evaluated, a classical domain matter-element matrix, and a section domain matter-element matrix; Based on the improved matter-element extension model and the variable weights of the various indicators, the application efficiency of each new technology solution of the substation is comprehensively evaluated.

2. The method of claim 1, wherein: The AHP group decision-making method is used to subjectively weight each indicator to determine the integrated subjective weight of each indicator, including: For each expert, based on the importance of each indicator to the upper-level indicator determined according to the expert opinion, a corresponding judgment matrix is ​​constructed using the exponential scaling method, and the maximum eigenvalue and eigenvector of the judgment matrix are determined; Determining a consistency ratio index of the judgment matrix based on the maximum eigenvalue of the judgment matrix, and performing a consistency check on the judgment matrix based on the consistency ratio index, wherein the consistency ratio index is used to measure the consistency degree of the judgment matrix; Normalizing the characteristic vectors to obtain the relative weight of the same-layer indicators of each indicator to the upper-layer indicators, and determining the weight of each indicator relative to the highest-layer indicator as the subjective judgment weight of the expert on each indicator; The AHP group decision-making method is used to determine the integrated subjective weight of each indicator based on the subjective judgment weight of each expert on each indicator.

3. The method of claim 2, wherein: The AHP group decision-making method is used to determine the integrated subjective weights of each indicator based on the subjective judgment weights of each expert on each indicator, including: Calculate the Pearson correlation coefficient between each expert opinion and perform normalization to obtain the expert opinion correlation coefficient matrix; Based on the expert opinion correlation coefficient matrix, a cluster analysis method is used to classify multiple experts and determine the inter-class weights of each type of expert; The weighting method based on the consistency ratio determines the intra-class weight of each expert; Based on the inter-class weights of various experts and the intra-class weights of each expert, the integrated subjective weights of each indicator are determined.

4. The method of claim 2, wherein: The entropy weight method is used to objectively weight each indicator to determine the objective weight of each indicator, including: Standardize each indicator and determine the weight of each solution for each indicator; Determine the entropy value of each indicator based on the proportion of each solution in each indicator; Based on the entropy value of each indicator, determine the information entropy redundancy of each indicator; Based on the information entropy redundancy of each indicator, the objective weight of each indicator is determined.

5. The method according to any one of claims 1 to 4, wherein: The projection pursuit algorithm is used to determine the combined weights of each indicator based on the integrated subjective weights and objective weights of each indicator, including: Normalize each indicator of each sample in the sample data set to obtain a standardized data set; A projection index function is established for the standardized data set, and the projection index function is optimized to obtain a combined weighted optimization model; The combined weighted optimization model is solved using the AO algorithm to obtain the combined weights of each indicator.

6. The method according to any one of claims 1 to 5, wherein: Based on the variable weight theory, the combined weights of each indicator are dynamically modified to obtain the variable weights of each indicator, including: Based on the distance from the indicator evaluation value of each indicator under each evaluation object to the positive bull's eye, the distance from the indicator evaluation value to the negative bull's eye, and the distance between the positive bull's eye and the negative bull's eye, the dominance of each indicator under each evaluation object is determined, and a dominance matrix is ​​constructed; Based on the dominance of each indicator under each evaluation object, combined with the penalty threshold and incentive threshold, a state variable weight function based on the penalty-reward mechanism is constructed; The state variable weight function is used to dynamically modify the combined weights of various indicators to obtain the variable weights of various indicators.

7. The method according to any one of claims 1 to 6, wherein: Based on the various indicators corresponding to the application effectiveness and the values ​​of the various indicators, a matter-element matrix is ​​constructed, and the matter-element matrix is ​​normalized to obtain an improved matter-element extension model, including: Based on the indicators corresponding to the application effectiveness and the values ​​of the indicators, a matter-element matrix to be evaluated corresponding to the application effectiveness is constructed; Based on any evaluation level of the application effectiveness, each indicator corresponding to the application effectiveness, and the value range of each indicator under any evaluation level, a classical domain matter-element matrix corresponding to any evaluation level of the application effectiveness is constructed; Based on the union of each indicator corresponding to the application effectiveness and the value range of each indicator at each evaluation level, a node domain matter-element matrix corresponding to the application effectiveness is constructed; The matter-element matrix to be evaluated, the classical domain matter-element matrix and the node domain matter-element matrix are normalized to obtain an improved matter-element extension model.

8. The method according to any one of claims 1 to 7, wherein: Based on the improved matter-element extension model and the variable weights of each indicator, the application efficiency of each new technology solution of the substation is comprehensively evaluated, including: Based on the improved matter-element extension model, determining the distance between each matter-element to be evaluated and each indicator value under each evaluation level, wherein the matter-element to be evaluated represents application effectiveness; Determine the closeness of each object-element to be evaluated to each evaluation level based on the distance between each object-element to be evaluated and each indicator value under each evaluation level, the variable weight of each indicator and the number of indicators; Based on the closeness between each element to be evaluated and each evaluation level, a comprehensive evaluation is conducted on the application performance of each new technology solution for the substation.

9. The method according to any one of claims 1 to 7, wherein: The decision-making indicators corresponding to the saving index in the construction phase include the floor space saving index, the earthwork saving index, and the material saving index; the decision-making indicators corresponding to the building efficiency improvement index include the building utilization coefficient improvement index; the decision-making indicators corresponding to the construction difficulty reduction index include the new technology and new equipment installation difficulty coefficient index, the new technology and new equipment test difficulty coefficient index, and the new technology and new equipment debugging difficulty coefficient index; The decision-making indicators corresponding to the technical performance improvement indicators include the substation dispatch fault response time reduction indicator, the substation equivalent utilization rate improvement indicator, and the substation disaster prevention capacity improvement indicator; the decision-making indicators corresponding to the technical operation reliability indicators include the substation power supply reliability improvement indicator, the substation maintenance outage number reduction indicator, the new equipment and new technology maintenance time reduction indicator, the new equipment and new technology maintenance difficulty coefficient indicator, the new equipment and new technology maintenance time reduction indicator, and the new equipment and new technology maintenance difficulty coefficient indicator; the decision-making indicators corresponding to the technical sustainability and replicability indicators include the substation scalability improvement indicator and the provincial similar project promotion ratio indicator; the decision-making indicators corresponding to the technical investment economic benefit indicators include the investment and construction cost saving indicator, the substation maintenance cost saving indicator, the substation failure cost saving indicator, the substation decommissioning cost saving indicator, the electricity sales revenue improvement indicator, the investment payback period reduction indicator, and the internal rate of return improvement indicator; the decision-making indicators corresponding to the social environmental benefit indicators include the substation construction carbon emission reduction indicator, the construction waste comprehensive utilization rate improvement indicator, and the substation operation carbon emission reduction indicator.

10. A computing device comprising: at least one processor; and A memory storing program instructions, wherein the program instructions are configured to be processed by the at least one processor, and the program instructions include instructions for processing the method according to any one of claims 1 to 9.

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