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In this work a fuzzy reinforcement learning (RL) based intelligent classifier for power transformer incipient faults is proposed. Fault classifiers proposed till date have low identification accuracy and do not identify all types of transformer faults. Herein, an attempt has been made to design an adaptive, intelligent transformer fault classifier that progressively learns to identify faults on-line with high accuracy for all fault types. In the proposed approach, dissolved gas analysis (DGA) data of oil samples collected from real power transformers (and from credible sources) has been used, which serves as input to a fuzzy RL based classifier. Typically, classification accuracy is heavily dependent on the number of input variables chosen. This has been resolved by using the J48 algorithm to select 8 most appropriate input variables from the 24 variables obtained using DGA. Proposed fuzzy RL approach achieves a fault identification accuracy of 99.7%, which is significantly higher than other contemporary soft computing based identifiers. Experimental results and comparison with other state-of-the-art approaches, highlights superiority and efficacy of the proposed fuzzy RL technique for transformer fault classification. • A novel RL based classifier for Transformer faults diagnosis. • Uses J48 decision tree for pruning the data. • Achieves much higher classification accuracy than conventional techniques. • Comparisons made with other state of art approaches. • Uses Actual or Physical data from Electricity Board in India for simulations. [ABSTRACT FROM AUTHOR]

This paper presents optimal power flow management in real time for grid connected photovoltaic (PV)–battery system. The objective is the real time, dynamic and optimal scheduling of battery storage which reduces grid power consumption and achieves the peak load shaving. For this purpose, two strategies are developed; one is rule-based and other is optimization algorithm based. Two strategies are compared, pros and cons of those strategies are established. Though rule-based technique is simple & easy to implement, the optimization technique provides more optimal regulation of battery which provides the lesser electricity cost. Forecasted PV/load powers are used to get optimal value of battery power. In order to handle uncertainties due to forecasted data, fuzzy-based corrective strategy is developed to modify the reference battery power as per the present values of PV/load powers. The proposed analysis is carried out for over one exemplary day. The simulations are presented using typical load/PV data of India and Finland as a part of research collaboration. Experimental results are also carried out using proposed scheme. [ABSTRACT FROM AUTHOR]