Design of optimal power supply parameters for industrial expansion

Abstract

The business expansion installation can only simply record the most basic business information, which leads to the problems of complex power supply procedures and low efficiency. Therefore, a study on the optimal power supply parameters of the business expansion installation based on grey correlation degree and fuzzy C-means clustering algorithm is proposed. Firstly, the grey correlation degree is used to process the optimal power supply parameter data of industrial expansion and installation, and the parameters of fuzzy C-means clustering algorithm are set. On this basis, an intelligent management system for the optimal power supply process of industrial expansion and installation is constructed, and the system development conditions are set up; According to the four business links of project reserve, business acceptance, collaborative operation and performance evaluation, the customer business expansion and installation function module is constructed, so as to realize the calculation of the optimal power supply line of the business expansion and installation and complete the research on the optimal power supply parameters. The experimental results show that the output stability, output throughput performance and parameter optimization ability of this method for the line impedance characteristic control of the power supply of the industrial expansion device are good and are always on the rise. At 3 cm, the output throughput reaches 1.9%, and the parameter analysis ability can reach 350 pixels, which has certain application value.

Keywords

Grey correlation degree fuzzy C-means clustering algorithm business expansion newspaper installation Optimal power supply parameters

1 Introduction

Business expansion refers to the expansion of power supply business, which is the general term for handling new power consumption and change of power consumption business [1], including new installation, capacity increase, capacity reduction, suspension, recovery, household change, etc. For the power supply company, the business expansion installation includes all the business processes from the customers’ power application to the actual power transmission process [2]. As the expansion of the industry is directly related to customers, it can directly reflect the future changes in electricity and economy as the root cause of customers’ changes in electricity consumption [3]. And it is also the main part of the refined analysis model of the power market, which can link the industry expansion and installation data, household electricity data, so as to explore the relationship between industry expansion and installation, electricity [4, 5].

At present, the analysis of the power sales market is more about the overall macro control, and the detailed analysis is only limited to the eight power consumption categories or the eight power consumption of national economic industries, and there is little quantitative analysis of the micro market. Reference [6] proposes the design of redundant information extraction system for power enterprise industry expansion and installation. The system adopts a b/s three-tier structure design framework. The framework is mainly composed of integration layer, communication layer and extraction processing layer. The integration layer is mainly composed of data warehouse integrated chip and communication interface, which is responsible for centralizing the information of power enterprise industry expansion and installation; There is only one nRF2401 chip in the communication layer, which is responsible for transmitting the integrated data information to the extraction processing layer; The extraction processing layer is mainly composed of MCU and internal and external storage carriers, which is mainly responsible for extracting redundant information and storing the processed information. The results show that the working frequency of this system is 66.9 Hz higher than that of the traditional system, and the redundancy rate is reduced by 6.3%. Reference [7] puts forward the research on verification technology of application materials for business expansion and packaging based on feature intelligent recognition. Through the improved LBP algorithm, the digital features of image and convolution neural network identification number are recognized, and the feature similarity matching algorithm is used to realize feature comparison. The validity and reliability of the system are verified by the verification of the core application material ID card. The research content is to promote the digital development of verification of application materials for business expansion and packaging, It provides effective theoretical and practical guidance.

Although the above research has made some progress, it cannot adapt to the changes in the market situation. At present, the power market analysis should focus on refining the power market analysis, forming a “micro macro” comprehensive analysis system. The so-called micro market refers to the external factors directly related to the marketing activities of enterprises. For power supply enterprises, it specifically includes the internal environment of power supply enterprises, power suppliers, power customers, competitors and the public. In order to better analyze the power market and improve the data analysis throughput and parameter analysis capability, this paper proposes a study on the optimal power supply parameters for industrial expansion installation based on the grey correlation and fuzzy c-means clustering algorithm, and obtains the effectiveness conclusion through experiments.

2 Grey correlation degree and parameters under fuzzy C-means clustering algorithm

2.1 Grey relational degree processing optimal power supply parameter data of industry expansion and installation

Grey correlation degree is a kind of grey system analysis method, which is a method to measure the degree of correlation between factors according to the degree of similarity or difference of development trends between factors [8, 9]. The optimal strategy will regard the optimal power supply parameter data as additional variables, and the optimization operation will be carried out on the additional variables, so as to obtain the minimum possible value of the objective function of industrial expansion and installation, and improve the power supply efficiency of industrial expansion and installation. The data processing flow of optimal power supply parameters of industrial expansion and installation based on grey correlation is shown in Fig. 1.

Fig. 1

Data processing flow of optimal power supply parameters of industrial expansion and installation based on grey correlation degree.

The specific implementation steps of the processing flow are as follows:

Step 1: Sort out the optimal power parameter data of industrial expansion installation, collect n optimal power parameter data samples of industrial expansion installation, each with multiple characteristics, and add them to the optimal power parameter data set of industrial expansion installation.

Step 2: Set the initial value of the data to be processed based on the gray correlation, initialize the data to be processed in the optimal power parameter data set of the industrial expansion device, and obtain the initial processing value of the data to be processed [10].

Step 3: Randomly classify the optimal power supply parameter data of industrial expansion and installation, randomly divide the optimal power supply parameter data matrix of industrial expansion and installation into C classes, and randomly obtain the initial clustering center of each class.

Step 4: Initialize the number of iterations and calculate the correlation matrix. For each of the optimal power supply parameter data elements x_ij of the optimal power supply parameter data matrix of the industrial expansion installation, calculate the correlation matrix of the optimal power supply parameter data of the industrial expansion installation by using formula (1): $R_{u} = \frac{1}{x_{ij}} \times P_{O}$ (1)

In formula (1), P_O represents the preset correlation factor.

Step 5: Update the data clustering center of the optimal power supply parameters of the industry expansion report installation, and update the data clustering center according to formula (2). $J_{K} = \frac{\sum E_{D}}{R_{u}}$ (2)

In formula (2), ∑E_D represents the total value of optimal power supply parameters for industrial expansion and installation.

Step 6: Calculate the correlation degree of the optimal power supply parameter data of the industry expansion and installation to be processed. For each of the optimal power supply parameter data y_ij of the industry expansion and installation to be processed in the data set to be processed, use formula (3) to calculate the correlation degree of the optimal power supply parameter data of the industry expansion and installation to be processed. $W_{ab} = \frac{\sum y_{ij}}{J_{K}}$ (3)

Step 7: Judge whether the iterative calculation meets the final conditions. If not, go to step 8, otherwise return to step 4 for data processing.

Step 8: Get the data processing results of the optimal power supply parameters of the industry expansion report installation.

Above, according to the data processing flow of the optimal power supply parameters of the industrial expansion, the optimal power supply parameters of the industrial expansion are sorted out and the initial values are set. According to the random classification of the optimal power supply parameters of the industrial expansion, the correlation matrix of the optimal power supply parameters of the industrial expansion is calculated, and the end condition of the correlation of the optimal power supply parameters of the industrial expansion is used to complete the processing of the optimal power supply parameters of the industrial expansion.

2.2 Parameter setting of fuzzy C-means clustering algorithm

Fuzzy C-means clustering algorithm is one of the most widely used and successful fuzzy clustering algorithms. The membership degree of each sample point to all class centers can be obtained by optimizing the objective function, so as to determine the class of sample points, so as to achieve the purpose of automatic classification of sample data [11, 12]. The fuzzy C-means clustering algorithm restricts the transmission authority of various load information in the electric thermal interconnected integrated energy system. In the given disposal cycle, the mean clustering parameters always maintain the same change trend with the performance of data density estimation, that is, the increase or decrease of the value level of the latter directly affects the actual parameter value of the former. If the load offset of the load forecasting data of the electric thermal interconnected integrated energy system is not considered, the setting result of the mean clustering parameter is affected by the simultaneous action of three physical quantities: the authority of the fuzzy C-means clustering algorithm, the information processing conditions and the load forecasting duration of the energy data [13]. The authority of the fuzzy C-means clustering algorithm restricts the average transmission rate of the application data of the electric thermal interconnected integrated energy system in the converter device, which is often expressed as $\dot{q}$ . With the extension of the system connection time, the ability of this physical quantity to limit the mean clustering parameters continues to increase until the numerical characteristic expression is equal to the physical coefficient “1".

The information processing conditions describe the scope of authority of load forecasting data in the integrated energy system of electric thermal interconnection, which is often expressed as β. Compared with other information processing parameters, this physical coefficient directly affects the final load forecasting result, and will not have too strong response behavior with the change of processing time, so it always has strong application stability in the whole process of forecasting and processing. The duration of energy data load forecasting can be expressed as T, which directly reflects the definition accuracy of mean clustering parameters. Different from the information transmission action time t₁ and information application time t₂, this physical quantity belongs to the theoretical application value, is not affected by any load forecasting behavior, and has absolute execution stability in terms of order of magnitude. The parameter setting result of fuzzy C-means clustering algorithm can be defined as: $d = \frac{min (β \times T - \dot{q} \times W)}{p \sqrt{t_{1}^{2} - t_{2}^{2}} / | m_{1} \times n_{1} |}$ (4)

In formula (4), W represents the total amount of electric thermal interconnection data in the energy system, p represents the termination application condition of clustering processing, m₁ represents the conversion implementation coefficient between electric thermal interconnection data, and n₁ represents the constant clustering processing index. Thus, the parameters of fuzzy C-means clustering algorithm are set.

2.3 Intelligent management system of optimal power supply process for industry expansion and installation

2.3.1 System development conditions

Before creating an intelligent management system for the optimal power supply process of industry expansion, it is necessary to have a clear understanding of the overall performance of the system, and the system should meet the following development conditions:

(1) Expandability

The expandability of the system indicates the supporting ability of the system to the changes of technology and business needs. For the intelligent management system of optimal power supply process for industry expansion, expansibility is quite critical. Appropriate management should be used to meet the needs of business categories, provide reasonable space for future system upgrading, and facilitate the completion of secondary development to improve system performance.

(2) Maintainability

The maintainability of the system means that it has the ability to revise the existing functional defects without interfering with other functions of the system.

(3) Ease of use

The ease of use representative system uses a simple and friendly man-machine interface to enable users to quickly implement business processing, and provides a variety of auxiliary functions to enhance business processing efficiency.

(4) Reliability and safety

System reliability is an important element of operation, which directly determines the work process of industry expansion, so this point should be fully considered in the initial stage of design. The system should have corresponding fault tolerance during operation, and give corresponding prompts to users.

System security can prevent unauthorized users from logging in the LAN, maintain the information and infrastructure in the system from malicious attacks, and provide authentication links such as identity authentication for the system. In order to optimize the design of optimal power supply parameters in the process of industry expansion, it is necessary to analyze the relevant data of industry expansion power supply marketing services, that is, collect the data of industry expansion power supply marketing services. For the integration detection of marketing mix elements of marketing services, the optimal power marketing service process for design industry expansion and installation is shown in Fig. 2.

Fig. 2

Flow chart of optimal power supply marketing service for industry expansion and installation.

According to the design process of the optimal power supply marketing service for industry expansion and installation shown in Fig. 2, combined with the reference characteristics of the capacity characteristics of power generation equipment [14], the joint state parameters of the power supply marketing service for industry expansion and installation are obtained as follows: $E_{AB} = S_{Z} + B_{N} + θ$ (5)

In formula (5), S_Z represents the capacity characteristic parameters of power generation equipment, B_N represents the control parameters of power supply marketing service of industrial expansion installation, and θ represents the data collection samples. Based on the results of actual line impedance to power distribution, under the condition of adding additional hardware, the line impedance characteristic component of power supply marketing service of industrial expansion installation can be obtained. The calculation formula is: $R_{1} = [H (a) + H (b)] + E_{AB}$ (6)

In formula (6), H (a) and H (b) both represent the joint characteristic components of the optimal power supply marketing service of the industry expansion and installation. The initial marketing service control function and phased marketing service control function are designed to complete the data collection of the optimal power supply marketing service of the industry expansion and installation.

2.3.2 Customer industry expansion report installation function module

On the basis of the work flow and characteristics of the expansion and installation of the power industry, combined with the concept of software engineering and the design concept of realizing each function by segment, an interactive platform between customers and power supply companies is built. In order to meet the power supply users with different needs at different levels, different functional modules of the system are designed, which are mainly divided into four business links: project reserve, business acceptance, collaborative operation and performance evaluation.

(1) Project reserve system:

Connect with the project construction approval platform of governments at all levels, and collect project information through multiple channels, such as government platform push, resident collection of administrative service centers, acquisition of relevant government meetings, visits of customer managers, and connection during temporary power utilization applications. For the projects included in the industrial expansion reserve, the supporting scheme for industrial expansion shall be prepared and reviewed in advance, incorporated into the power grid planning, and the construction of supporting projects shall be started in advance to meet the formal power consumption conditions.

1) Acquisition of industry expansion reserve information: get through the data interface with the government engineering construction project approval platform. The government platform directly pushes the project power demand information to the company and directly initiates the industry expansion project reserve process in the system [15].

Fully stationed in administrative service centers at all levels, open communication channels with relevant departments, actively participate in relevant government meetings, and actively obtain information on industrial expansion projects. The account manager initiates the project reserve process in the system.

2) On site service: for the industrial expansion projects obtained in advance, the customer manager should timely provide on-site service and inquire the customers’ electricity demand and installation intention face to face. For customers who have made it clear that they want to submit construction or formal power use applications, the customer manager should collect funds on site and turn the reserve process of industrial expansion projects into a formal installation application process.

3) Preparation and review of power supply plan in advance: for projects that are not formally accepted temporarily, the preparation and review of temporary power consumption plan and formal power industry expansion supporting plan can be carried out in advance. The exclusive customer manager organizes the technical team to conduct field research, comprehensively consider the customer demand, industry category, building area and other information, determine the scale of power consumption, and combine the on-site power supply situation to develop the supporting scheme for industrial expansion, determine the power supply point, property right demarcation point, project construction content, etc. The projects that have completed the compilation and review of the plan are included in the management of the industrial expansion reserve. The industrial expansion reserve projects generate a summary table on a monthly basis, and push the key information of the project to the development, transportation inspection and other departments, which are included in the main distribution network planning on a rolling basis.

4) Pre implementation of supporting projects: for projects included in the industrial expansion reserve, the system can automatically generate a scheme notice, which is delivered to the customer by the territorial customer manager to explain the relevant terms face to face. For major projects (key projects), in principle, agreements should be signed according to the supporting scheme of industrial expansion, focusing on the demarcation point of property rights, the responsibility of project construction, as well as the completion time of the implementation part of the customer, the completion time of the implementation part of the power supply company, the intended power connection time of the customer and other key nodes.

If the scheme notice or agreement has been signed, it will be automatically included in the power grid construction project reserve. The construction of supporting projects will be carried out in advance and included in the whole process control according to the milestone plan.

5) Answer of power supply scheme: for the projects included in the industry expansion reserve, when the marketing system formally accepts the business, it directly calls and generates the power supply scheme according to the project number to realize the answer of the scheme.

(2) Business acceptance system:

Optimize online business drainage and improve the power handling rate of online channels; Open up information barriers with government agencies and various public service industries, broaden service channels, and realize “third-party channels"; Strengthen government data sharing, optimize business processes, improve service quality, and achieve “one certificate handling".

1) Business expansion standard template: establish a standard template for business expansion reporting and installation, provide it to the handling personnel for reference and modification, help speed up the handling speed and improve customer satisfaction.

2) Offline business hall drainage: add online channel drainage QR code one click generation and push function in business hall, self-service equipment and on-site mobile operation terminal. Guide customers to use “online state grid” app, wechat and other online channels to handle business.

For businesses that are being accepted offline at the counter of the business hall, the external screen of the counter automatically generates the corresponding business application form (electronic version) to provide customer interactive QR code drainage services. The system sends the login name, initial login password, account number and query password to the user’s; mobile phone to guide customers to download, register and log in to online channels; After the customer scans the QR code, the application form information will be synchronized automatically, and the system will automatically push the business progress.

3) Third party service channel acceptance: connect the municipal (county) administrative service center government service network, street, community and village convenience service center with the data interface of the power business system, and handle the power business with the government affairs comprehensive self-service terminal at the “undifferentiated acceptance” window of the government service network. Other third-party service channels realize business expansion through H5 embedding, and unify typical business acceptance interfaces for third-party channel integration.

4) Realize “one certificate handling": when applying for business, the ID card information is read with the help of OCR scanning technology, and the information is directly filled in the application work order. With the consent of the head of household, the business processing platform can obtain the associated real estate certificate information, household register information and low-income insurance household information from the government data platform through the ID card of the head of household, and the customer does not need to provide other relevant paper materials.

For enterprise customers, reduce the types of capital collection through information sharing. The business processing platform can obtain the associated business license, organization code certificate, property right certificate, land certificate and other information from the government data platform through the unified social credit code certificate, and customers do not need to provide corresponding paper materials.

(3) Collaborative operation system:

Further optimize the on-site operation mode of low-voltage customers, and realize the “three zero” service of zero door-to-door, zero approval and zero investment. The transformation and integration of information systems should be carried out with customer demand as the center, so as to effectively integrate the business processes, information flows and data flows in the whole process of industrial expansion, and support the process control, analysis, judgment and work evaluation in the whole process of industrial expansion.

1) Low voltage industry expansion “three zero” operation: determine the installation location of electricity meters by self-service, and innovate the application of mobile operating terminals;

2) Intelligent scheduling of business expansion work orders: order application and intelligent scheduling;

3) Auxiliary preparation of power supply scheme: grid resource sharing and modularization of power supply scheme;

4) Standardized construction of customer projects: typical design application and customer project management and control;

5) Support project information sharing: optimize the support process for industrial expansion and synchronize project construction information;

6) Intelligent management of industrial expansion access; Optimize the intelligent balance of outage (transmission) management and outage plan.

(4) Performance evaluation system:

1) Evaluation of the whole process of industrial expansion: according to the time limit requirements of the State Grid, relying on the data sharing of various systems, the whole process tracking control is realized on the whole process control platform of industrial expansion. Introduce the time limit control mode of customer contract system, automatically reverse the calendar time of each link to be completed according to the customers’ intended power connection time in the contract, and give early warning in advance.

2) Customer experience evaluation: build an evaluation system covering the whole process of power operation.

3) The interactive system of power distribution: customers “order” power distribution, open and transparent information of power distribution, customer interaction “trace” and accurate promotion of related businesses.

2.4 Realize the calculation of optimal power supply and optimal line for industry expansion and installation

In the process of application and installation of industry expansion, engineering construction is the most important link. When users apply for a large installation capacity and need to set up special lines, the power supply department can not quickly design a feasible installation route, can not identify the best line, consume a lot of material and financial resources, and bring more capital investment to customers. In order to achieve a win-win situation for customers and power supply departments, the industry expansion installation line is selected as the optimal path, that is, the shortest path problem with the minimum weight is explored between the two nodes of the weighted graph.

There are two kinds of key investment costs for the power supply department: one is the capital construction investment amount, including the cost of adding equipment, the cost of requisitioning line corridors, etc. among all the engineering costs required for key equipment from ordering to installation, the construction amount of distribution network lines is an uncertain value, which changes relatively according to the different route materials and line diameters; The second is the annual operation cost, including equipment depreciation cost, equipment maintenance cost and power consumption cost. Describe the construction cost of each section of lines in the distribution network as: $V_{abc} = M_{m} \times L_{k} \times P_{I}$ (7)

In formula (7), M_m represents the investment amount of the line, L_k represents the length of the path, and P_I represents the investment amount of the line per unit length. Record the mathematical model of the process of user industry expansion as: $min C = \frac{C_{k} + R_{k}}{G_{H} + D_{F}} \times V_{ABC}$ (8)

In formula (8), C_k represents the current of the line, R_k represents the maximum allowable current of the line, G_H represents the power supply load point, and D_F represents the node voltage limit.

The goal of the calculation is a multiple lines with many constraints, that is, a complex path optimization problem. In this paper, the grey correlation degree and fuzzy C-means clustering algorithm are used to solve the optimal power supply parameters of industry expansion and installation, so as to reduce the complexity of intelligent management and operation of the system.

The optimization of optimal power supply parameters for business expansion and installation is a relatively complex optimization problem. When calculating the optimal path using grey correlation degree and fuzzy C-means clustering algorithm, the following problems usually occur: the search is very easy to fall into the local optimal solution and the time to converge to the global optimal solution is too long. Calculating the volatilization residue of the pheromone substituted into the current street pheromone can better solve the problem that the search falls into the local optimal solution. In order to converge the optimal solution at the fastest speed and give street line predictability, the predictability of random lines in geographic information is: $O_{QW} = \frac{min C}{d_{k} \times λ}$ (9)

In formula (9), d_k represents the path length and λ represents the geographical coefficient. Thus, the research on the optimal power supply parameters of industrial expansion and installation based on grey correlation degree and fuzzy C-means clustering algorithm is realized.

3 Experimental analysis

In order to verify the application performance of the research on the optimal power supply parameters of industrial expansion and installation based on grey correlation degree and fuzzy C-means clustering algorithm, simulation tests are carried out. The data source is the customer service center of a high-voltage industry expansion and installation power supply marketing company. From the customers’ handling of new installation, capacity increase, and change of electricity related business procedures, to the formulation and reply of power supply schemes, and the design review of customer power receiving projects, the corresponding data samples are obtained. At this time, the information collection sample size of the high-voltage industry expansion and installation power supply marketing service is 1024, and the capacity load ratio is 8.12%. The selection of other experimental indicators, As shown in Table 1.

Table 1
Experimental index setting table

Index sorting Indicator name Correlation index

1 Sequence length 200

2 Load size 16.9 kW

3 Power supply inlet 5000000

4 Annual power consumption per capita 831.3 kW·h

5 Maximum operating frequency 2.8MHz

Index sorting	Indicator name	Correlation index
1	Sequence length	200
2	Load size	16.9 kW
3	Power supply inlet	5000000
4	Annual power consumption per capita	831.3 kW·h
5	Maximum operating frequency	2.8MHz

Firstly, aiming at the problem that the main network infrastructure model based on operation data cannot be accurately quantified and has a great impact on the optimal power supply parameter data, the grey correlation degree and fuzzy C-means clustering algorithm are used to calculate the correlation degree of each optimal power supply parameter data index, formulate the data feature selection strategy, and select the optimal power supply parameter data as the training feature for the experiment. Then, the optimal power supply parameter data is regarded as the optimal power supply parameter data of the same level, and data training is carried out to expand the optimal power supply parameter data. Finally, the grey correlation degree and fuzzy C-means clustering algorithm are used to train the selected optimal power supply parameter data. Through the classifier, the characteristics of different types of optimal power supply parameter data are mapped to the corresponding correlation degree value, and the power supply efficiency of the main network is recorded.

Debug the electronic detection equipment of the experimental group, close the control switch of the electric thermal interconnection integrated energy conversion equipment, and record the actual value of the index of the experimental group (method in this paper); Then debug the electronic detection equipment of the control group, turn off the control switch of the electric thermal interconnection integrated energy conversion equipment, and record the actual values of various indicators of the control group (refer to [6] method and [7] method); Finally, the real physical difference between the three groups of values is compared. The electric thermal interconnection integrated energy conversion equipment is shown in Fig. 3:

Fig. 3

Electric thermal interconnection integrated energy conversion equipment.

The methods of this paper, reference [6] and reference [7] are used to connect the power supply thermal interconnection integrated energy conversion equipment, and then the specific change behavior of relevant experimental indicators is analyzed with the help of software processing device. According to the above parameter settings, the analysis results of the impedance characteristics of the power line of the industrial expansion device are shown in Fig. 4.

Fig. 4

Line impedance characteristic results of power supply for industrial expansion installation.

According to the analysis of Fig. 4, the output stability of the line impedance characteristic control of the industry expansion installation power supply using this method is good, which is conducive to obtaining the optimal power supply parameters of the industry expansion installation. According to the obtained results, the industry expansion installation power supply marketing service evaluation is carried out, and the load switching state of the industry expansion installation power supply is obtained, as shown in Fig. 5.

Fig. 5

Load switching state of power supply for industrial expansion and installation.

It can be seen from the analysis of Fig. 5 that the load switching state of power supply by using the method in this paper is expanded into the industry. From the beginning of the load switching, the three methods show different states. The method in this paper shows an upward trend, and the methods in reference [6] and reference [7] show a downward trend; After development, the method in this paper has no interruption change at the communication interruption line resistance, and the methods in reference [6] and reference [7] have interruption changes. Test the output throughput performance of the optimal power supply parameters of the expanded power supply, and the comparison results are shown in Fig. 6.

Fig. 6

Output throughput performance comparison results.

According to Fig. 6, this method analyzes the reliability detection and related characteristics of the optimal power supply parameters of industrial expansion devices. It can be seen from Fig. 6 that although the throughput of wenxiaj [6] and literature [7] methods is high at the beginning, it is in a fast decline state, dropping to 0 at 3cm. Although the throughput of this design method is low at the beginning, it is always in a rising trend. The throughput reaches 1.9% at 3cm, and the output throughput performance is good, It is beneficial to improve the reliability and stability of the optimal power supply parameters of industrial expansion devices.

In order to further verify the performance of the method in this paper, based on the grey correlation degree and fuzzy C-means clustering algorithm, the optimal power supply parameter error compensation control of industry expansion installation is carried out, and the parameter analysis results are shown in Fig. 7.

Fig. 7

Analysis results of optimal power supply parameters for industry expansion and installation.

It can be seen from Fig. 7 that the parameter analysis capability of the method adopted in this paper is always on the rise, up to 350 pixels. It has good parameter analysis capability and strong parameter optimization capability, and can perform error compensation control on the optimal power supply parameters of industrial expansion devices. The reason is that this method has a clear understanding of the overall performance of the system before designing the intelligent management system for the optimal power supply process installed in the expansion of the industry. The system meets the development conditions of scalability, maintainability, ease of use, reliability and security. To some extent, it is beneficial to improve the analysis ability of the optimal power supply parameters for industry expansion and installation.

4 Conclusion and prospect

4.1 Conclusion

This paper presents the research on the optimal power supply parameters of industrial expansion and installation based on grey correlation degree and fuzzy c-means clustering algorithm. The following conclusions are obtained through experiments:

Using this method to control the line impedance characteristics of the power supply of the industrial expansion installation, the output stability is good, which is conducive to obtaining the optimal power supply parameters of the industrial expansion installation.

Using this method to expand the load switching state of power supply in the industry, it gradually shows an upward trend. After development, this method has no interruption change at the communication interruption line resistance.

The output throughput performance of the method in this paper is good, which is conducive to improving the reliability and stability of the optimal power supply parameters of power supply for industrial expansion.

Using the optimal power supply parameters of industry expansion and installation based on grey correlation degree and fuzzy c-means clustering algorithm, the error compensation control of the optimal power supply parameters of industry expansion and installation has good parameter analysis ability and strong parameter optimization ability.

4.2 Prospect

The research process of this paper still has some limitations, and its analysis and research process still need to be further explored. The details are as follows:

The seasonal changes of different cities, different industry categories and different power consumption categories are quite different. Whether there is a large error in the seasonal adjustment of installed capacity and power sales reported by the whole industry needs further study.

The customer group to which new installed customers belong shall be strictly screened, and the influence of city, industry, power consumption category and voltage level shall be excluded, so that the growth law of new installed capacity can be directly incorporated into new customers. However, due to the limitation of existing data, too few customers have access to newly installed power from the customer group. How to solve this contradiction remains to be further explored.

The fluctuation of installed capacity reported by the customer base has risen from the whole industry to prefecture level cities. It can be seen that if the industries of prefecture level cities need to be further classified, the reported installed capacity will fluctuate more obviously, and the prediction may be inaccurate. At the same time, the most relevant month cannot be found due to the limitation of the existing data volume. Therefore, in order to further understand the stimulating effect of power sales growth on industry expansion and installation of optimal power supply parameter capacity, it is necessary to take other methods or seek earlier industry expansion and installation data for further discussion.

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