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This study presents a scheme for improvement of power quality in electrical distribution systems using a static VAr compensator (SVC). The switching of the SVC has been controlled with an objective to improve the overall power quality at the distribution level. The reduction of the degree of unbalance and improvement of power factor along with minimisation of harmonics injected into the system due to the operation of SVC are identified as the objectives of the SVC switching scheme. The proposed scheme can minimise a wider range of harmonics injected into the system without any external filter. The optimised switching angles have been calculated using the particle swarm optimisation technique. An artificial neural network has been designed to predict the optimum triggering angles and the performance of the scheme for fast changing loads has been evaluated in the laboratory using a data acquisition system. Practical evaluation of power quality has been carried out at each stage of simulation and experimentation to highlight the effectiveness of the proposed scheme. The results obtained from the proposed scheme have been compared with other recent works to prove the efficacy of the system. [ABSTRACT FROM AUTHOR]

This study presents a novel technique for automated power quality (PQ) disturbance detection and classification in power distribution system using cross‐correlation‐based approach in conjunction with fuzzy logic. The proposed method requires minimum number of features when compared with conventional approaches for identification of disturbances. Total 17 types of PQ disturbances including eight basic and nine combinations which are very close to real situations are considered for the classification. The scheme is immune to real life uncorrelated noises due to incorporation of cross spectrum analysis in the feature extraction stage. Experimentation under real operating conditions is carried out in the laboratory using data acquisition system in order to test the proposed technique. The proposed scheme is also applied in IEEE 33‐bus distribution system and validated by a real‐time simulator. The developed classifier achieved 100% accuracy and could comfortably outperform several contemporary methods for PQ disturbance classification. [ABSTRACT FROM AUTHOR]