Low-voltage distribution network cascading failure early warning method based on risk assessment model

A risk assessment model and low-voltage distribution network technology, applied in forecasting, electrical digital data processing, instruments, etc., can solve problems such as complex dynamic behavior of the power grid, successive failures of components, and deterioration of system operation status, to reflect real-time safe operation level , Improvement can not effectively eliminate redundancy and realize the effect of real-time early warning

CN114254818APending Publication Date: 2022-03-29JIANGSU ELECTRIC POWER CO +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2022-03-29

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Abstract

The invention discloses a low-voltage power distribution network cascading failure early warning method based on a risk assessment model, and the method comprises the steps: data collection and processing: collecting load data, weather data and fault data of a low-voltage power distribution network, and carrying out the data preprocessing of the collected data; fault feature selection: adopting an improved G-ReliefF algorithm to carry out optimal selection of fault features of the low-voltage power distribution network; performing cascading failure search: performing subsequent failure search according to the line outage model and the key line model, generating an accident chain and evaluating the accident chain; and fault early warning: calculating a risk assessment coefficient of the element and the line, determining a fault occurrence probability through the risk assessment coefficient, finally carrying out comprehensive risk assessment on the fault, and calculating a risk level. The method is used for predicting and mastering the safe operation state of the low-voltage power distribution network, discovering potential dangers and judging the fault development trend so as to improve the accuracy of fault early warning and further avoid large-area power failure.
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Description

technical field

[0001] The invention belongs to the technical field of low-voltage distribution network fault early warning, and relates to a low-voltage distribution network cascading fault early warning method based on a risk assessment model. Background technique

[0002] With the rapid development of economy and society, more stringent requirements are put forward for the reliability of power supply of low-voltage distribution network. However, with the large-scale interconnection of the power grid, while improving the reliability and economy of the system, it also makes the dynamic behavior of the power grid more complex. Some faults in the local power grid may also spread to adjacent regional power grids, causing cascading failures, and then leading to power outages. Accidents and even breakdowns in the power grid. Large-scale power outages in low-voltage distribution networks are almost always caused by cascading failures, that is, after the primary failure of the sy...

Examples

Embodiment Construction

[0080] The application will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solutions of the present invention more clearly, but not to limit the protection scope of the present application.

[0081] like figure 1 As shown, the present invention is realized by adopting the following methods, including:

[0082] Step 1: Data collection and processing

[0083] The load data, weather data and fault data of the low-voltage distribution network are collected, and data preprocessing is performed on the collected data. Data preprocessing includes four steps: data cleaning, data transformation, data integration and outlier sample elimination.

[0084] a. Data cleaning: including data blank value processing, data abnormal value processing, and data repeated value processing. The processing of data gaps is mainly to eliminate or supplement the missing records in the original data and the missin...