Substation location quantitative evaluation system driven by artificial intelligence

Abstract

The invention provides an artificial intelligence-driven substation site selection quantitative evaluation system, and the system comprises a site selection index confirmation module which is used for carrying out the evaluation and weighing of decision elements according to the actual demands, and determining the specified decision elements as site selection indexes; the multi-index evaluation module is used for storing a multi-index evaluation model; the multi-index evaluation model is used for calculating a site selection index value; the site selection quantitative scoring module is used for storing a site selection quantitative scoring model; the urban suburb site selection condition channel module is provided with an urban suburb site selection condition channel, and the urban suburb site selection condition channel is respectively communicated with the urban site selection model, the suburb site selection model and the difference site selection model; the potential site selection screening module is used for screening a plurality of potential site selection of the transformer substation; and the scoring module scores each potential site by calling the site quantitative scoring model, and selects the substation address corresponding to the high score as the substation site. According to the invention, the scientificity and accuracy of substation site selection evaluation can be improved.

Description

Technical Field

[0001] The present invention relates to the field of substation site selection, and in particular to an artificial intelligence-driven substation site selection quantitative evaluation system. Background Art

[0002] As an important pillar of the national economy, the power industry bears the core responsibility for national infrastructure and people's livelihood. In terms of power system planning, it is not only the key to the stable operation of the system, but also an important factor in promoting the sustainable development of the power industry. In the face of growing demand, the construction of power grids and their infrastructure has put forward more agile and advanced standards. In this context, the rapid and accurate information acquisition and processing of early planning site selection and production evaluation has become a key link.

[0003] Combining drone technology with artificial intelligence, by analyzing multiple data sources, including earthwork volume, distance to access roads, and the relationship between the proposed site and its surrounding environment, such as resident distribution, agricultural and animal husbandry activities, it is possible to more accurately predict the amount of demolition and other related compensation in the early stage. The image data captured by drones combined with advanced artificial intelligence algorithms can quickly analyze key parameters such as line conditions, difficulty, corridors and channels, thereby more deeply evaluating the pros and cons of each potential site and providing a quantitative analysis basis for site selection.

[0004] Traditional scoring models lack adaptability to complex substation site selection conditions and are difficult to effectively respond to the needs of different regional environments. Therefore, it is necessary to use the present invention to overcome these limitations and improve the scientificity and accuracy of the evaluation. By deeply exploring the decision-making factors of substation site selection, using advanced weight distribution technology, and combining artificial intelligence technology to carefully consider the scientificity of scoring criteria and weight settings, an adaptive scoring model is constructed, and the differences in different site selection conditions in cities and suburbs are analyzed at the same time to ensure that the evaluation system is suitable for site selection needs in different regional environments. Summary of the invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to provide an artificial intelligence-driven substation site selection quantitative evaluation system. By deeply exploring the decision-making factors of substation site selection, adopting advanced weight distribution technology, and combining artificial intelligence technology to carefully consider the scientific nature of scoring criteria and weight setting, an adaptive scoring model is constructed. At the same time, the differences in different site selection conditions in cities and suburbs are analyzed to ensure that the evaluation system is suitable for site selection requirements in different geographical environments, thereby improving the scientificity and accuracy of substation site selection evaluation.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is as follows: The present invention provides an artificial intelligence driven substation site selection quantitative evaluation system, comprising: The site selection index confirmation module is used to evaluate and weigh the decision factors according to actual needs and determine the designated decision factors as site selection indicators; A multi-index evaluation module, used to store a multi-index evaluation model; the multi-index evaluation model is used to calculate the site selection index value; The quantitative scoring module for site selection is used to store the quantitative scoring model for site selection; the quantitative scoring model for site selection is established, including the following steps: according to the relationship between the site selection indicators, a correlation model between the indicators is established, and the weights of the site selection indicators are formulated in combination with the initial weights; based on the historical data of substation site selection, the scoring criteria and the weights of the site selection indicators are dynamically adjusted, and the model parameters of the quantitative scoring model for site selection are adjusted; the site selection indicator values ​​obtained by the multi-indicator evaluation model are input to obtain the site selection score, and a high site selection score represents the substation site selection; The urban suburban site selection condition channel module is provided with an urban suburban site selection condition channel, which connects the urban site selection model, the suburban site selection model and the differential site selection model respectively; the urban site selection model stores key factors of urban site selection, and adjusts the model parameters of the site selection quantitative scoring model according to the key factors of urban site selection and demand; the suburban site selection model stores key factors of suburban site selection, and adjusts the model parameters of the site selection quantitative scoring model according to the key factors of suburban site selection and demand; the differential site selection model adjusts the model parameters of the site selection quantitative scoring model according to the different characteristics and needs of cities and suburbs, so as to ensure that the site selection quantitative scoring model is applicable to different regions; Potential site selection screening module, used to screen several potential sites for substations; The scoring module scores each potential site by calling the quantitative scoring model, and selects the substation address with the highest score as the substation site.

[0007] Furthermore, the decision-making factors include topography, land use policy, electricity demand, earthwork volume, resident distribution, demolition compensation and ecological environmental protection.

[0008] Furthermore, the key factors of city site selection include land utilization rate, population density, traffic convenience, land cost, environmental pollution information, and noise decibels.

[0009] Furthermore, the key factors to be considered in suburban site selection include topographic conditions, communication network conditions, and ecological environment information.

[0010] An artificial intelligence driven substation site selection quantitative assessment method includes the following steps: (1) Explore the decision factors for substation site selection, evaluate and weigh the decision factors according to actual needs, set initial weights, and determine the designated decision factors as site selection indicators; (2) Establish a multi-index evaluation model to calculate the site selection index value; (3) Establishing a quantitative scoring model for site selection, including the following steps: establishing a correlation model between the indicators based on the relationship between the site selection indicators, and formulating the weights of the site selection indicators in combination with the initial weights; dynamically adjusting the scoring criteria and the weights of the site selection indicators based on the historical data of substation site selection, and adjusting the model parameters of the quantitative scoring model for site selection; inputting the site selection indicator values ​​obtained by the multi-indicator evaluation model to obtain the site selection score, where a high site selection score represents a good substation site selection; (4) Establishing an urban-suburban site selection condition channel; the urban-suburban site selection condition channel connects the urban site selection model, the suburban site selection model and the differential site selection model respectively; the urban site selection model stores key factors for urban site selection, and adjusts the model parameters of the site selection quantitative scoring model according to the key factors for urban site selection and demand; the suburban site selection model stores key factors for suburban site selection, and adjusts the model parameters of the site selection quantitative scoring model according to the key factors for suburban site selection and demand; the differential site selection model adjusts the model parameters of the site selection quantitative scoring model according to the different characteristics and needs of cities and suburbs, so as to ensure that the site selection quantitative scoring model is applicable to different regions; (5) Screening several potential sites for substations; (6) Score each potential site through the quantitative scoring model, and select the substation address with the highest score as the substation site.

[0011] Furthermore, in step (1), the specific method for setting the initial weights is: clarifying the target layer, criterion layer and basic layer of the substation site selection problem, and establishing the initial weights with the help of expert experience and domain knowledge.

[0012] Furthermore, after step (4), the method further includes the step of conducting a control experiment and verification with a traditional scoring model, specifically: comparing the site selection quantitative scoring model with the review results of experienced experts; analyzing the performance of the site selection quantitative scoring model in different scenarios through large-scale data verification, evaluating the advantages of the site selection quantitative scoring model's adaptability, and confirming the site selection quantitative scoring model parameters.

[0013] Furthermore, in step (5), screening a number of potential sites for the substation is specifically as follows: determining a number of addresses that meet the requirements as potential addresses based on the preset area, capacity, distribution of electric charge and overall situation of the power grid architecture of the substation.

[0014] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention adopts advanced weight distribution technology and combines artificial intelligence technology to carefully consider the scientific nature of scoring criteria and weight settings, and constructs an adaptive scoring model, so that the model can continuously iterate and learn, and dynamically adjust the scoring criteria and weights according to real-time data and feedback information, so that the scoring system is closer to the actual situation and improves the prediction accuracy and adaptability.

[0015] (2) The present invention takes into account the different site selection conditions of cities and suburbs, fully considers the unique characteristics and needs of the two regions, and ensures the universality of the model. A difference model is established to consider the mutual influence between cities and suburbs. Closely combined with urban and suburban development, the interactive relationship between urban and suburban development is considered, and the pros and cons of substation site selection on the development of both are weighed. In order to ensure the wide applicability of the model, an artificial intelligence algorithm is used, combined with big data analysis, to automatically adjust the model parameters according to the differences between cities and suburbs, so that the model can adapt to the ever-changing situation in cities and suburbs. At the same time, field visits and surveys need to be considered in the model evaluation stage to fully understand the actual local situation, obtain real data support, and conduct comparative analysis in model verification to ensure the applicability and accuracy of the model to different regions, provide more intelligent and reliable decision support for substation site selection, and achieve efficient operation and sustainable development of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0017] Figure 1 4 is a system structure diagram of an embodiment of the present invention.

[0018] Figure 2 4 is a system structure diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0020] The following describes the embodiments of the present invention through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0021] like Figure 1 As shown, an artificial intelligence-driven substation site selection quantitative evaluation system according to an embodiment of the present invention includes: The site selection index confirmation module is used to evaluate and weigh the decision factors according to actual needs and determine the designated decision factors as site selection indicators; the decision factors include topography, land use policy, electricity demand, earthwork volume, resident distribution, demolition compensation and ecological environmental protection.

[0022] The multi-index evaluation module is used to store the multi-index evaluation model. The multi-index evaluation model is used to calculate the site selection index value; The quantitative scoring module for site selection is used to store the quantitative scoring model for site selection; the quantitative scoring model for site selection is established, including the following steps: according to the relationship between the site selection indicators, a correlation model between the indicators is established, and the weights of the site selection indicators are formulated in combination with the initial weights; based on the historical data of substation site selection, the scoring criteria and the weights of the site selection indicators are dynamically adjusted, and the model parameters of the quantitative scoring model for site selection are adjusted; the site selection indicator values ​​obtained by the multi-indicator evaluation model are input to obtain the site selection score, and a high site selection score represents the substation site selection.

[0023] The urban suburban site selection condition channel module is equipped with an urban suburban site selection condition channel, which connects the urban site selection model, the suburban site selection model and the differential site selection model respectively. The urban site selection model stores the key factors of urban site selection, and adjusts the model parameters of the site selection quantitative scoring model according to the key factors and needs of urban site selection. The suburban site selection model stores the key factors of suburban site selection, and adjusts the model parameters of the site selection quantitative scoring model according to the key factors and needs of suburban site selection. The differential site selection model adjusts the model parameters of the site selection quantitative scoring model according to the different characteristics and needs of cities and suburbs to ensure that the site selection quantitative scoring model is applicable to different regions; the key factors of suburban site selection include topography, communication network conditions, and ecological environment information. The key factors of urban site selection include land utilization rate, population density, traffic convenience, land cost, environmental pollution information, and noise decibels.

[0024] The potential site selection screening module is used to screen several potential sites for the substation.

[0025] The scoring module scores each potential site by calling the quantitative scoring model, and selects the substation address with the highest score as the substation site.

[0026] like Figure 2 As shown, an artificial intelligence-driven quantitative evaluation method for substation site selection includes the following steps: (1) Explore the decision factors for substation site selection, evaluate and weigh the decision factors according to actual needs, set initial weights, and determine the designated decision factors as site selection indicators; the specific method for setting initial weights is to clarify the target layer, criterion layer, and basic layer of the substation site selection problem, and establish the initial weights with the help of expert experience and domain knowledge.

[0027] (2) Establish a multi-index evaluation model to calculate the site selection index value; (3) Establishing a quantitative scoring model for site selection, including the following steps: establishing a correlation model between the indicators based on the relationship between the site selection indicators, and formulating the weights of the site selection indicators in combination with the initial weights; dynamically adjusting the scoring criteria and the weights of the site selection indicators based on the historical data of substation site selection, and adjusting the model parameters of the quantitative scoring model for site selection; inputting the site selection indicator values ​​obtained by the multi-indicator evaluation model to obtain the site selection score, where a high site selection score represents a good substation site selection; (4) Establish an urban-suburban site selection condition channel. The urban-suburban site selection condition channel connects the urban site selection model, the suburban site selection model and the differential site selection model. The urban site selection model stores the key factors of urban site selection, and adjusts the model parameters of the site selection quantitative scoring model according to the key factors of urban site selection and demand. The suburban site selection model stores the key factors of suburban site selection, and adjusts the model parameters of the site selection quantitative scoring model according to the key factors of suburban site selection and demand. The differential site selection model adjusts the model parameters of the site selection quantitative scoring model according to the different characteristics and needs of cities and suburbs to ensure that the site selection quantitative scoring model is applicable to different regions; It also includes the steps of conducting control experimental verification with the traditional scoring model, specifically: comparing the site selection quantitative scoring model with the review results of experienced experts; through large-scale data verification, analyzing the performance of the site selection quantitative scoring model in different scenarios, evaluating the advantages of the site selection quantitative scoring model's adaptability, and confirming the site selection quantitative scoring model parameters.

[0028] (5) Screening a number of potential sites for the substation; Screening a number of potential sites for the substation includes: determining a number of addresses that meet the requirements as potential sites based on the preset area, capacity, distribution of electric charge and overall situation of the grid architecture of the substation.

[0029] (6) Score each potential site through the quantitative scoring model, and select the substation address with the highest score as the substation site.

[0030] The above is a detailed introduction to an artificial intelligence-driven substation site selection quantitative evaluation system provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. An artificial intelligence driven substation site selection quantitative evaluation system, characterized in that: include: The site selection index confirmation module is used to evaluate and weigh the decision factors according to actual needs and determine the designated decision factors as site selection indicators; A multi-index evaluation module, used to store a multi-index evaluation model; the multi-index evaluation model is used to calculate the site selection index value; The quantitative scoring module for site selection is used to store the quantitative scoring model for site selection; the quantitative scoring model for site selection is established, including the following steps: according to the relationship between the site selection indicators, a correlation model between the indicators is established, and the weights of the site selection indicators are formulated in combination with the initial weights; based on the historical data of substation site selection, the scoring criteria and the weights of the site selection indicators are dynamically adjusted, and the model parameters of the quantitative scoring model for site selection are adjusted; the site selection indicator values ​​obtained by the multi-indicator evaluation model are input to obtain the site selection score, and a high site selection score represents the substation site selection; The urban suburban site selection condition channel module is provided with an urban suburban site selection condition channel, which connects the urban site selection model, the suburban site selection model and the differential site selection model respectively; the urban site selection model stores key factors of urban site selection, and adjusts the model parameters of the site selection quantitative scoring model according to the key factors of urban site selection and demand; the suburban site selection model stores key factors of suburban site selection, and adjusts the model parameters of the site selection quantitative scoring model according to the key factors of suburban site selection and demand; the differential site selection model adjusts the model parameters of the site selection quantitative scoring model according to the different characteristics and needs of cities and suburbs, so as to ensure that the site selection quantitative scoring model is applicable to different regions; Potential site selection screening module, used to screen several potential sites for substations; The scoring module scores each potential site by calling the quantitative scoring model, and selects the substation address with the highest score as the substation site.

2. According to claim 1, an artificial intelligence driven substation site selection quantitative assessment system is characterized in that: The decision-making factors include topography, land use policy, electricity demand, earthwork volume, resident distribution, demolition compensation and ecological environmental protection.

3. According to claim 1, an artificial intelligence driven substation site selection quantitative assessment system is characterized in that: The key factors to focus on in city site selection include land utilization rate, population density, traffic convenience, land cost, environmental pollution information, and noise decibels.

4. According to claim 1, an artificial intelligence driven substation site selection quantitative assessment system is characterized in that: The key factors to focus on in suburban site selection include topographic conditions, communication network conditions, and ecological environment information.

5. An artificial intelligence-driven quantitative evaluation method for substation site selection, comprising the following steps: (1) Explore the decision factors for substation site selection, evaluate and weigh the decision factors according to actual needs, set initial weights, and determine the designated decision factors as site selection indicators; (2) Establish a multi-index evaluation model to calculate the site selection index value; (3) Establishing a quantitative scoring model for site selection, including the following steps: establishing a correlation model between the indicators based on the relationship between the site selection indicators, and formulating the weights of the site selection indicators in combination with the initial weights; dynamically adjusting the scoring criteria and the weights of the site selection indicators based on the historical data of substation site selection, and adjusting the model parameters of the quantitative scoring model for site selection; inputting the site selection indicator values ​​obtained by the multi-indicator evaluation model to obtain the site selection score, where a high site selection score represents a good substation site selection; (4) Establishing an urban-suburban site selection condition channel; the urban-suburban site selection condition channel connects the urban site selection model, the suburban site selection model and the differential site selection model respectively; the urban site selection model stores key factors for urban site selection, and adjusts the model parameters of the site selection quantitative scoring model according to the key factors for urban site selection and demand; the suburban site selection model stores key factors for suburban site selection, and adjusts the model parameters of the site selection quantitative scoring model according to the key factors for suburban site selection and demand; the differential site selection model adjusts the model parameters of the site selection quantitative scoring model according to the different characteristics and needs of cities and suburbs, so as to ensure that the site selection quantitative scoring model is applicable to different regions; (5) Screening several potential sites for substations; (6) Score each potential site through the quantitative scoring model, and select the substation address with the highest score as the substation site.

6. The method for quantitative evaluation of substation site selection driven by artificial intelligence according to claim 5 is characterized in that: In the step (1), the specific method for setting the initial weights is: clarifying the target layer, criterion layer and basic layer of the substation site selection problem, and establishing the initial weights with the help of expert experience and domain knowledge.

7. The method for quantitative evaluation of substation site selection driven by artificial intelligence according to claim 5 is characterized in that: After step (4), the method further includes the step of conducting a control experiment and verification with a traditional scoring model, specifically: comparing the site selection quantitative scoring model with the review results of experienced experts; analyzing the performance of the site selection quantitative scoring model in different scenarios through large-scale data verification, evaluating the advantages of the site selection quantitative scoring model's self-adaptation, and confirming the site selection quantitative scoring model parameters.

8. The method for quantitative evaluation of substation site selection driven by artificial intelligence according to claim 5 is characterized in that: In the step (5), the screening of several potential sites for the substation is specifically as follows: according to the preset area, capacity, distribution of electric charge of the substation and the overall situation of the power grid architecture, several addresses that meet the requirements are determined as potential addresses.

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