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This work presents a methodology for distribution system feeder reconfiguration considering different model of DGs with an objective of minimizing real power loss. The distributed generation availability of wind turbines, solar photovoltaic panels and fuel cell etc., are classified into different models according to their operation modes and control characteristics. Decimal coded quantum particle swarm optimization (DQPSO) has been applied to solve feeder reconfiguration of DGs. The method applies decimal encoding to quantum particle swarm optimization (QPSO), which can decrease the particle length, generate few infeasible solutions and have better search efficiency. Aiming to the problem that the reactive power output of PV model sometimes exceeds the limit, this paper researches the impact factors of the output reactive power during reconfigurations, such as rated active power and rated voltage magnitude. The method has been tested on IEEE 33-bus and PG&E 69-bus radial distribution systems to demonstrate the performance and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: A novel method for fault diagnosis in analog circuits using S-transform (ST) as a preprocessor and a quantum neural network (QNN) as a classifier is proposed in this paper. The ST provides a frequency-dependent resolution and the features obtained from ST are distinct, and easy to understand. The QNN identifier, a computational tool for fuzzy classification combining the advantages of neural modeling and fuzzy-theoretic principles, has the ability to autonomously detect the presence of uncertainty, adaptively learn the existing uncertainty, properly approximate any membership profile, and autonomously quantify uncertainty in sample data. The comparison between the ST-based method and the wavelet-transform-based method, and comparison between the QNN method and the traditional NN method for analog fault diagnosis is provided. Simulation results show that the proposed method is effective in enhancing the efficiency of the training phase and the performance of the fault diagnostic system. The results clearly indicate more than 97.61% correct classification of fault classes in the example circuits of various sizes in the presence of similar faults. [Copyright &y& Elsevier]