Two-Stage Load Profiling of HV Feeders of a Distribution System

Abstract

This paper describes a two-stage unsupervised clustering based on the g-Means approach, which can employ any partitional or crisp clustering algorithm for load profiling of high-voltage (HV) feeders of a distribution system. The methodology proposed can evaluate number of clusters and its respective members from input data. The outcome of g-Means is dependent on a significance level— $\alpha$ . This paper is based on feeder loading data, which are recorded at a transformer secondary at a distribution substation. Real-life active power data of 33-kV feeders acquired through existing supervisory control and data acquisition (SCADA) system of Central Indian state distribution company have been taken as a case study, as it is being processed for the first time, and hence, no prima facie information regarding the number of clusters or classes was available. The first stage entails checking for the randomness in daily load patterns and to extract month-wise working day (weekday) and nonworking day (holidays/Sundays) patterns. The second stage is based on this outcome, where clustering is performed to group similar patterns into clusters to generate month-wise “typical monthly load profiles”. The results are acceptable and are validated by using appropriate validity indices.