Data-Driven Multi-Hidden Markov Model-Based Power Quality Disturbance Prediction That Incorporates Weather Conditions

Power quality (PQ) disturbance in power systems has been a concern for operators and customers. The purpose is to locate and forecast the presence of PQ disturbances to suppress or avoid their negative effects on power grid and appliances. This study, using a multi-hidden Markov model (MHMM), motivates data-driven tools to achieve situation awareness of PQ disturbance. We first design a modified adaptive-sorted neighborhood method that consists of blocking and merging phases to locate PQ disturbance sources from a large volume of PQ records. We then group and discretize the data on disturbance sources and weather conditions according to regions. The capability of MHMM-based tools to predict future PQ disturbance level can be improved by clustering the training set of time series of PQ disturbance levels based on weather conditions. A Hadoop-based PQ analysis framework is proposed to reduce computational times, considering the volume of PQ data in a realistic power grid is large. We utilize numerical case studies that use real data collected from a power system of a Chinese city to investigate the correctness and feasibility of the proposed method.