Application of comparative strainer clustering as a novel method of high volume of data clustering to optimal power flow problem

Abstract

An increase of the volume of data and uncertain parameters, such as renewable generations and load consumption in power systems, causes new challenges in the optimal power flow (OPF) problem. Clustering is one of the fast and accurate tools, and it is able to deal with them by classifying the unpredictable data into categories based on their similarity. Clustering approaches can be directly applied to the data. However, it requires more time, storage, and processing of high volume data. In this paper, a new clustering method called acomparative strainer (CS) is proposed to cover the aforementioned drawbacks. Then, the efficiency of the proposed clustering method of the OPF problem was evaluated based on technical and economic indices, such as voltage magnitude as well as angle and locational marginal price (LMP). The proposed clustering approach is employed on the IEEE57-bus test system to handle the uncertainty of the active and reactive loads. Then, the optimal clusters were determined. Finally, the results were compared with other clustering algorithms, such as raw data clustering, the famous method of dimension reduction, and principal component analysis (PCA) clustering, in order to reveal that the proposed method is more accurate and faster than others and leads to precise outcomes when it is utilized for the OPF problem.