Big data analysis and processing method for distribution network non-power-cut operation

Abstract

The invention discloses a big data analysis and processing method for power-on operation of a distribution network. The method comprises the following steps: constructing a hierarchical evaluation index system; constructing a judgment matrix; calculating and sorting importance degrees of influence factors; checking the consistency; and outputting an evaluation result. The method has the beneficial effects that the method provides powerful support for accurately evaluating the importance degrees of various influence factors of the power distribution network non-power-cut operation capability and optimizing decision-making of the power distribution network non-power-cut operation, the power distribution network non-power-cut operation capability evaluation and analysis method based on the AHP-Delphi is provided, various influence factors are comprehensively and reasonably evaluated, and the power distribution network non-power-cut operation capability evaluation and analysis method is suitable for the power distribution network non-power-cut operation capability evaluation and analysis of the power distribution network. Therefore, key factors influencing the power-off-free operation capability are mastered, so that existing resources are utilized efficiently, and the power-off-free operation capability of the distribution network is improved to the maximum extent. According to the method, the influence factors of the power distribution network non-outage operation capability can be effectively evaluated, the key factors are accurately judged, a scientific basis is provided for accurate decision making of a decision maker, and the overall benefit is maximized.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution network, and in particular to a big data analysis and processing method for non-stop power supply operation of distribution network. Background Art

[0002] With the rapid development of economy and society, all walks of life have higher and higher requirements for uninterrupted power supply, and power supply reliability is also one of the important indicators to measure the power supply capacity of power supply enterprises. In order to meet the requirements of all walks of life for power supply reliability, power supply enterprises usually take the following measures: continuously transform old equipment, improve grid structure, optimize power grid operation mode, and actively carry out non-stop power supply operations.

[0003] As the final link in the power system where the power network supplies power to users, the importance of the distribution network is self-evident. The efficiency of its operation and management directly affects the reliability and quality of the power supply of the power grid. Most distribution networks are built near industrial and residential areas, with the characteristics of dense line networks, scattered equipment distribution, complex terrain and environment, etc. Therefore, the failure rate of the distribution network is relatively high. Non-stop operation is a special operation method that directly performs maintenance, replaces parts, and handles defects without power outages for users. It is a technical measure to ensure the stable operation of the power supply system and an important way to improve the reliability of power supply.

[0004] Under the current situation where the requirements for power supply reliability management are becoming more and more stringent and users are more sensitive to power outages, non-stop power supply operations in distribution networks have become an important means to reduce the frequency and duration of power outages and reduce users' perception of power outages. Actively explore various innovative technical means, focus on improving the application of new technologies and informationization level of non-stop power supply operations, so as to gradually promote the distribution network from power outage operations to non-stop power supply operations, and realize the comprehensive transformation from power outage maintenance to non-stop power supply maintenance. However, the distribution network lines are complex, with the characteristics of dense distribution facilities and narrow operation scope, which makes it easy for operators to cause safety accidents due to inadequate protective measures or illegal violations in the case of non-stop power supply operations.

[0005] At present, the safety of the distribution network non-stop operation site mainly relies on on-site supervision by staff or control by video recording. Although the management system is relatively complete, the risk level of distribution network non-stop operation is generally high, the tasks are of various types, the operation methods are diverse, the operation areas are complex and scattered, the operation process is short and fast, the abilities and qualities of the practitioners are different, the management level of the construction units is uneven, and it is difficult for on-site safety supervisors to achieve comprehensive monitoring. This often leads to inadequate or loopholes in the safety measures at the operation site (such as: on-site re-survey, protective wear, layout of isolation area warning signs, parking and operation of boom trucks, insulation shielding and standardization of operator actions, etc.).

[0006] However, in terms of the distribution network's non-stop operation technology itself, its operating capacity is restricted by many factors such as personnel, tools, special vehicles and operating methods. Existing methods are mostly based on subjective experience to judge weaknesses, making it difficult to accurately and efficiently utilize existing resources, maximize the distribution network's non-stop operation capacity, and achieve overall benefit maximization.

[0007] Therefore, it is necessary to propose a big data analysis and processing method for non-stop power supply operation in distribution network in view of the above technical solution. Summary of the invention

[0008] The object of the present invention is to provide a big data analysis and processing method for non-stop operation of distribution network.

[0009] To achieve the above object, the present invention provides the following technical solutions:

[0010] A big data analysis and processing method for non-stop power distribution network operation, the method steps are as follows:

[0011] S1. Construct a hierarchical evaluation indicator system;

[0012] S2, construct a judgment matrix;

[0013] S3. Calculate and rank the importance of influencing factors;

[0014] S4, consistency check;

[0015] S5. Output of evaluation results.

[0016] Among them, step S1 determines the evaluation target, i.e., the non-stop operation capability of the distribution network, and lists the influencing factors of the non-stop operation capability of the distribution network, including f1, f2, f3..., fk, a total of k influencing factors. Following the above principles, the influencing factors {F} are screened, classified and summarized to form a recursive hierarchical structure, and a hierarchical evaluation index system for the influencing factors of the non-stop operation capability of the distribution network is constructed.

[0017] In step S2, the Delphi method is used to compare the importance of the influencing factors at the same level, and a judgment matrix of the influencing factors at this level is formed. The mathematical expression of the judgment matrix A is:

[0018] The value range of xij is [1, 9], where 1, 3, 5, 7, and 9 respectively indicate that xi is equally important, slightly important, obviously important, strongly important, and extremely important compared to xj, and 2, 4, 6, and 8 indicate that the importance is in the middle of the adjacent scales.

[0019] In step S3, the matrix A is judged, the maximum eigenvalue λmax and its corresponding eigenvector ω are calculated, and the eigenvector ω is normalized to obtain ω', and the maximum eigenvalue λmax and its corresponding eigenvector ω of the judgment matrix A are calculated. The calculation formula is:

[0020] Aω=λ max ω (2)

[0021] Normalize the required eigenvector ω to get ω', which is the importance ranking of the factors at this level. The calculation formula is:

[0022] Where: ωi is the i-th element of the eigenvector ω; n is the order of the judgment matrix.

[0023] Step S4 is to determine the rationality of the ω' weight, and a consistency check is required. If the consistency index does not meet the requirements, go to step 2 to reconstruct the judgment matrix; otherwise, go to step 5;

[0024] Calculate the consistency index CI, the calculation formula is

[0025]

[0026] Calculate the consistency ratio CR, the calculation formula is

[0027]

[0028] Where RI is the average random consistency ratio.

[0029] Among them, S5 outputs the evaluation results, outputs the judgment matrix and the weights of its corresponding influencing factors, that is, the importance of the influencing factors at this level.

[0030] The analysis system comprises a voltage acquisition module, a current acquisition module, a temperature acquisition module, a humidity acquisition module, a wind speed acquisition module and a main control module, wherein the voltage acquisition module, the current acquisition module, the temperature acquisition module, the humidity acquisition module and the wind speed acquisition module are all connected to the main control module.

[0031] Preferably, the main control module includes a main control board, a liquid crystal display screen, a data line interface, a USB interface and an early warning device, and the liquid crystal display screen, the data line interface, the USB interface and the early warning device are all connected to the main control board.

[0032] The humidity collection module adopts an NTC thermistor, and the humidity collection module adopts a humidity-sensitive capacitor.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. In order to accurately evaluate the importance of various influencing factors of the non-stop operation capability of the distribution network and provide strong support for the optimization decision-making of the non-stop operation of the distribution network, the present invention proposes an evaluation and analysis method for the non-stop operation capability of the distribution network based on AHP-Delphi, and conducts a comprehensive and reasonable evaluation of various influencing factors, so as to grasp the key factors affecting the non-stop operation capability, so as to efficiently utilize existing resources and maximize the non-stop operation capability of the distribution network.

[0035] 2. The present invention can effectively evaluate the factors affecting the non-stop operation capability of the distribution network and accurately determine the key factors, thus providing a scientific basis for decision makers to implement precise policies and maximize the overall benefits.

[0036] 3. The present invention can determine whether the five major factors on site meet the conditions for safe operation without power outages. Secondly and more importantly, when live operations have been carried out, it can solve the problem that sudden changes in the live operation site environment cannot be predicted in the first time, provide real-time and accurate environmental operation data for the work supervisor and live operation personnel, and promptly alarm when it exceeds the prescribed range, further improving the safety of live operation personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the big data analysis and processing method of the present invention;

[0038] Figure 2 It is a schematic diagram of the Delphi algorithm flow of the present invention;

[0039] Figure 3 is a schematic diagram of an analysis system of the present invention; DETAILED DESCRIPTION

[0040] The embodiments of the present invention are described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0041] The following is combined with Figures 1 to 3 The present invention is described in further detail.

[0042] like Figure 1-3 As shown, an embodiment of the present invention is: a big data analysis and processing method for non-stop power supply operation of a distribution network, and the method steps are as follows:

[0043] S1. Construct a hierarchical evaluation indicator system;

[0044] S2, construct a judgment matrix;

[0045] S3. Calculate and rank the importance of influencing factors;

[0046] S4, consistency check;

[0047] S5. Output of evaluation results.

[0048] Among them, step S1 determines the evaluation target, i.e., the non-stop operation capability of the distribution network, and lists the influencing factors of the non-stop operation capability of the distribution network, including f1, f2, f3..., fk, a total of k influencing factors. Following the above principles, the influencing factors {F} are screened, classified and summarized to form a recursive hierarchical structure, and a hierarchical evaluation index system for the influencing factors of the non-stop operation capability of the distribution network is constructed.

[0049] In step S2, the Delphi algorithm is used to compare the importance of the influencing factors at the same level, and a judgment matrix of the influencing factors at this level is formed. The mathematical expression of the judgment matrix A is:

[0050] The value range of xij is [1, 9], where 1, 3, 5, 7, and 9 respectively indicate that xi is equally important, slightly important, obviously important, strongly important, and extremely important compared to xj, and 2, 4, 6, and 8 indicate that the importance is in the middle of the adjacent scales.

[0051] In step S3, the matrix A is judged, the maximum eigenvalue λmax and its corresponding eigenvector ω are calculated, and the eigenvector ω is normalized to obtain ω', and the maximum eigenvalue λmax and its corresponding eigenvector ω of the judgment matrix A are calculated. The calculation formula is:

[0052] Aω=λ max ω (2)

[0053] Normalize the required eigenvector ω to get ω', which is the importance ranking of the factors at this level. The calculation formula is:

[0054] Where: ωi is the i-th element of the eigenvector ω; n is the order of the judgment matrix.

[0055] Step S4 is to determine the rationality of the ω' weight, and a consistency check is required. If the consistency index does not meet the requirements, go to step 2 to reconstruct the judgment matrix; otherwise, go to step 5;

[0056] Calculate the consistency index CI, the calculation formula is

[0057]

[0058] Calculate the consistency ratio CR, the calculation formula is

[0059]

[0060] Where RI is the average random consistency ratio.

[0061] Among them, S5 outputs the evaluation results, outputs the judgment matrix and the weights of the corresponding influencing factors, that is, the importance of the influencing factors at this level;

[0062] The value of the normalized eigenvector ω' in the output result represents the importance of the factor on the uninterrupted operation capability of the distribution network. Decision makers can focus on the existing advantageous resources to solve the important influencing factors based on the evaluation results, and gradually formulate measures to deal with other unimportant factors, so as to continuously improve the uninterrupted operation capability of the distribution network and maximize the overall benefits.

[0063] The analysis system comprises a voltage acquisition module, a current acquisition module, a temperature acquisition module, a humidity acquisition module, a wind speed acquisition module and a main control module, wherein the voltage acquisition module, the current acquisition module, the temperature acquisition module, the humidity acquisition module and the wind speed acquisition module are all connected to the main control module.

[0064] Preferably, the main control module includes a main control board, a liquid crystal display screen, a data line interface, a USB interface and an early warning device, and the liquid crystal display screen, the data line interface, the USB interface and the early warning device are all connected to the main control board.

[0065] The humidity collection module adopts an NTC thermistor, and the humidity collection module adopts a humidity-sensitive capacitor.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] 1. In order to accurately evaluate the importance of various influencing factors of the non-stop operation capability of the distribution network and provide strong support for the optimization decision-making of the non-stop operation of the distribution network, the present invention proposes an evaluation and analysis method for the non-stop operation capability of the distribution network based on AHP-Delphi, and conducts a comprehensive and reasonable evaluation of various influencing factors, so as to grasp the key factors affecting the non-stop operation capability, so as to efficiently utilize existing resources and maximize the non-stop operation capability of the distribution network.

[0068] 2. The present invention can effectively evaluate the factors affecting the non-stop operation capability of the distribution network and accurately determine the key factors, thus providing a scientific basis for decision makers to implement precise policies and maximize the overall benefits.

[0069] 3. The present invention can determine whether the five major factors on site meet the conditions for safe operation without power outages. Secondly and more importantly, when live operations have been carried out, it can solve the problem that sudden changes in the live operation site environment cannot be predicted in the first time, provide real-time and accurate environmental operation data for the work supervisor and live operation personnel, and promptly alarm when it exceeds the prescribed range, further improving the safety of live operation personnel.

[0070] Determine whether the five major factors on site meet the requirements for safe operation without power outages. If not, alarm. Alarm in time when the voltage or current (reverse power supply on the user side) suddenly increases and affects the safety of the operation. Alarm in time when the temperature or humidity on site suddenly changes and affects the safety of the operation. Alarm in time when the wind speed on site is greater than level 5 and affects the safety of the operation.

[0071] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A big data analysis and processing method for non-stop power supply operation of distribution network, characterized by: The method steps are: S1. Construct a hierarchical evaluation indicator system; S2, construct judgment matrix; S3. Calculate and rank the importance of influencing factors; S4, consistency check; S5. Output of evaluation results.

2. A big data analysis and processing method for non-stop power supply operation of distribution network as claimed in claim 1, characterized in that: Step S1 determines the evaluation target, i.e., the non-stop operation capability of the distribution network, and lists the influencing factors of the non-stop operation capability of the distribution network, including f1, f2, f3, ..., fk, a total of k influencing factors. Following the above principles, the influencing factors are screened, classified and summarized to form a recursive hierarchical structure, and a hierarchical evaluation index system for the influencing factors of the non-stop operation capability of the distribution network is constructed.

3. A big data analysis and processing method for non-stop power supply operation of distribution network as claimed in claim 1, characterized in that: In step S2, the Delphi method is used to compare the importance of the influencing factors at the same level, and a judgment matrix of the influencing factors at this level is formed. The mathematical expression of the judgment matrix A is: Among them, x ij The value range is [1, 9], which means 1, 3, 5, 7, 9 represent x respectively. i With x j Compared with equally important, slightly important, obviously important, strongly important, and extremely important, 2, 4, 6, and 8 indicate that the importance is in the middle of the adjacent scale.

4. A big data analysis and processing method for non-stop power supply operation of distribution network as claimed in claim 1, characterized in that: In step S3, the matrix A is judged, the maximum eigenvalue λmax and its corresponding eigenvector ω are calculated, and the eigenvector ω is normalized to obtain ω', and the maximum eigenvalue λmax and its corresponding eigenvector ω of the judgment matrix A are calculated. The calculation formula is: Aω=λ max oh (2) Normalize the required eigenvector ω to get ω', which is the importance ranking of the factors at this level. The calculation formula is: Where:ω i The i-th element of the eigenvector ω; n is the order of the judgment matrix.

5. The big data analysis and processing method for non-stop operation of distribution network according to claim 1, characterized in that: Step S4 is to determine the rationality of the ω' weight, and a consistency check is required. If the consistency index does not meet the requirements, go to step S2 to reconstruct the judgment matrix; otherwise, go to step S5; Calculate the consistency index CI, the calculation formula is Calculate the consistency ratio CR, the calculation formula is Where RI is the average random consistency ratio.

6. The big data analysis and processing method for non-stop power supply operation of distribution network according to claim 1, characterized in that: Step S5 outputs the evaluation results, outputs the judgment matrix and the weights of the corresponding influencing factors, that is, the importance of the influencing factors at this level.

7. The big data analysis and processing method for non-stop power supply operation of a distribution network according to claim 1, characterized in that: It also includes an analysis system, which includes a voltage acquisition module, a current acquisition module, a temperature acquisition module, a humidity acquisition module, a wind speed acquisition module and a main control module, and the voltage acquisition module, current acquisition module, temperature acquisition module, humidity acquisition module and wind speed acquisition module are all connected to the main control module.

8. A big data analysis and processing method for non-stop power supply operation of distribution network as claimed in claim 7, characterized in that: The main control module comprises a main control board, a liquid crystal display screen, a data line interface, a USB interface and an early warning device, and the liquid crystal display screen, the data line interface, the USB interface and the early warning device are all connected to the main control board.

9. A method for analyzing and processing big data for non-stop power supply operation of a distribution network as claimed in claim 7, characterized in that: The humidity collection module adopts an NTC thermistor, and the humidity collection module adopts a humidity-sensitive capacitor.

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