Medium-voltage power distribution network weak link identification method based on risk assessment

A technology of weak links and identification methods, applied in data processing applications, instruments, calculations, etc., can solve problems such as limited statistical records, insufficient statistical records, and difficulty in modeling alone

Active Publication Date: 2019-12-03
ELECTRIC POWER RES INST OF GUANGXI POWER GRID CO LTD
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Problems solved by technology

However, modeling alone is not easy due to limited statistical records
In addition, this ambiguity may exist in raw fault data that cannot be represented by ...

Method used

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  • Medium-voltage power distribution network weak link identification method based on risk assessment
  • Medium-voltage power distribution network weak link identification method based on risk assessment
  • Medium-voltage power distribution network weak link identification method based on risk assessment

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Embodiment Construction

[0079] In order to better understand the present invention, the present invention will be further described below in conjunction with accompanying drawing and specific embodiment:

[0080] A method for identifying weak links in a medium-voltage distribution network based on risk assessment, comprising the following steps:

[0081] S1: Establish a fuzzy model of distribution network component failure parameters according to historical statistical data; the fuzzy model of distribution network component failure parameters includes a fuzzy model of maintenance time, a fuzzy model of failure rate, and a fuzzy model of unavailability; the specific steps include:

[0082] S11: Establish a fuzzy model of maintenance time:

[0083] The sample mean of the fault duration can be obtained by the following formula:

[0084]

[0085] in, is the average value of the fault duration and is also a point estimate of the fault duration; r i is the fault duration of the i-th fault; n is the ...

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Abstract

The invention belongs to the technical field of power distribution risk evaluation and power distribution risk identification, and particularly relates to a medium-voltage power distribution network weak link identification method based on risk evaluation. Compared with a traditional risk assessment method, the invention innovatively provides a new modeling method combining a fuzzy model and a traditional probability distribution model. A hybrid fuzzy probability model of a system peak load is provided aimed at the problems of heavy overload of a distribution network medium-voltage line, heavyoverload of a transformer area distribution transformer and the like. On the basis of the traditional Monte Carlo simulation method, a risk index fuzzy membership function construction method and risk index defuzzification are provided, and the risk degree of the distribution network system is measured through the specific quantity value of the risk index after defuzzification, so that the identification of the weak link of the distribution network is realized. The method provided by the invention can help a power supply company to analyze the risk degree of the urban distribution network system and identify the weak link of the distribution network system, thereby providing a valuable theoretical basis for distribution network generation decision.

Description

technical field [0001] The invention belongs to the technical field of power distribution risk assessment and power distribution risk identification, and specifically relates to a method for identifying weak links in a medium-voltage distribution network based on risk assessment. Background technique [0002] In the entire power system, the distribution network, as a link directly connected to power customers, directly affects the power supply of power customers. Therefore, the evaluation and risk assessment of power system operation status has become the focus of attention of power supply companies and power customers. The power system risk assessment method commonly used in the past is the probability assessment of power system risk based on statistical analysis of theoretical probability. Confirm changes. In fact, there are two types of uncertainty in power systems: randomness and fuzziness. Probabilistic models can be used for randomness, but not fuzziness. In traditi...

Claims

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/06
CPCG06Q10/06393G06Q10/0635G06Q10/067G06Q50/06Y04S10/50
Inventor 梁朔陈绍南袁智勇高立克秦丽文李珊周杨珺俞小勇于力白浩欧阳健娜欧世锋李克文陈千懿
Owner ELECTRIC POWER RES INST OF GUANGXI POWER GRID CO LTD
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