KEYWORDS: Data modeling, Power grids, Data acquisition, Power supplies, Detection and tracking algorithms, Neural networks, Model-based design, Databases, Chemical analysis, Analytical research
Station line loss is directly related to the operating efficiency of power grid enterprises. Traditional low-voltage station line loss management directly distinguishes abnormal line loss stations according to the index value, which is relatively extensive management. Based on the theory of station line loss, this paper proposes a method of station line loss anomaly analysis based on K-means clustering algorithm. Firstly, the LOF algorithm is used to eliminate the outliers in the platform area, and then the line losses in the platform area are clustered. Finally, the abnormal line loss platform area is identified by combining the value interval of the average line loss rate and the distance from the clustering center.
With the deepening of the construction of electricity information collection for power grid enterprises, the scale of power load data continues to increase. It is of great significance to carry out power load data mining and analysis for the production and operation of power grid enterprises. In this paper, the shortcomings of traditional load classification methods are studied. Firstly, the power load characteristics and related indicators are compared, and the load curve is selected to carry out K-means clustering algorithm to realize the power load pattern recognition. According to the clustering algorithm process, the power load clustering data of 1000 households were selected to carry out data normalization and data dimension reduction. After comparison, the elbow method was used to select K value, and the simulation test was carried out. It has been proved that the K-means clustering algorithm can realize accurate identification and classification of user load, and the effect is obvious.
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