Power distribution network reliability evaluation method based on big data mutual information attribute reduction

Abstract

The invention relates to the field of power distribution network planning, and provides a power distribution network reliability evaluation method based on big data mutual information attribute reduction. The method utilizes a mutual information concept in a rough set to measure correlation among basic indexes from big data, the indexes which relate to the index with high reliability and are mutually independent are screened from various types of mass indexes, and the indexes are taken as input so as to develop power distribution network reliability evaluation work by a BP (Back Propagation) neural network model based on a genetic algorithm. The method breaks through the limitation of a traditional Monte Carlo simulation and analytical method, aims at power big data and realizes the power distribution network reliability evaluation based on the big data mutual information attribute reduction.

Description

technical field [0001] The invention relates to the field of distribution network planning, in particular to a distribution network reliability evaluation method based on big data mutual information attribute reduction. Background technique [0002] With the development of technologies such as the Internet and databases and the automation of production environments, the fields of finance, electricity, meteorology and other fields have produced a large amount of various and rapidly growing data, which is called big data. Nowadays, big data has penetrated into various fields and has become an important production factors, and because of its huge utilization value is becoming a new engine to promote industrial change. Only by mining and analyzing big data, extracting its main information and using it reasonably can the value of big data be realized. Distribution network reliability is a technical indicator that is strongly related to many factors. Among them, the reliability of...

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Problems solved by technology

Traditional reliability indicators are generally evaluated by multiple indicators through modeling or sampling simulation, such as load point indicators, outage time indicators, outage economic indicators, etc., but the analytical method has great limitations when dealing with complex power systems. Due to the state redundancy of the Luo sampling method, it takes a long time. Big data technology provides a new idea for the distribution network reliability assessment.

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