Court line loss rate prediction calculation method using K-means clustering algorithm

Abstract

The invention discloses a court line loss rate prediction calculation method using a K-means clustering algorithm, the method comprises the following steps: 1, selecting an active power supply quantity X1, a reactive power supply quantity X2, a total length X3 of a power supply line, a power supply radius X4 and a total resistance X5 of the line as electrical characteristic parameters; 2, standardizing the original data of the electrical characteristic parameters; 3, establishing a performance index function PI (i) of that stage area through electrical characteristic parameters, and selecte aninitial cluster center point and a cluster numb K; Step 4, using the improved K-means clustering algorithm to predict the court line loss rate. The invention utilizes the index function established by the electric characteristic parameters of the court as the principle of judging the initial clustering center by clustering analysis, and improves the accuracy of a clustering result. And the original clustering mode algorithm is changed, which not only provides reasonable sample data for the prediction calculation of the court line loss rate, but also makes the line loss rate prediction resultmore accurate, and improves the calculation speed.

Description

Technical field: [0001] The invention belongs to the line loss prediction technology of station area, in particular to a method for predicting and calculating the line loss rate of station area by using K-means clustering algorithm. Background technique [0002] Line loss rate is an important economic and technical indicator for comprehensively evaluating the planning and design, production operation, technical management, and operation level of the power system, and it is also an important content for evaluating the management of the power sector. However, due to the characteristics of large scale, many nodes, long lines and wide area in China's distribution network, the power loss of low-voltage distribution network accounts for about 40% of the entire power network loss, and it is urgent to solve the problem of line loss. Therefore, the accurate and simple line loss calculation and analysis method has important guiding significance for the actual management work. It is be...

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Problems solved by technology

Zou Yunfeng and others published "Research on the Reasonable Line Loss Prediction Model Based on Data Mining Technology" in the Journal of Power Demand Side Management. In this paper, the initial clustering centers of the K-means clustering algorithm are randomly generated, and the clustering results have randomness, imprecise

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