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Usually, consumers experience power failures due to problems in the distribution systems. Distribution systems have evolved to an extent where smart technologies are needed for their smooth operation. Network reconfiguration is one of the functions of distribution automation that improves the performance of the system by altering the structure of the distribution network. In literature, different network reconfiguration problems have been resolved with various methods. However, the active power loss minimisation problem is considered most of all, because if these losses are less than the standard, the distribution companies can benefit monetarily. In addition, during the last few decades, distribution system reliability has been developed to provide interruption free electricity services. With improved system reliability, power distribution companies can save the expenses that are spent on a distribution system's maintenance and operation after a power failure. This paper thus gives a general background, literature review and comparative analysis of methods used for network reconfiguration for reliability enhancement and loss minimisation in the distribution network, so that new researchers can easily read the literature, especially in this area. Moreover, this is the first effort to collect the previous and the most recent research works on the field of reliability of distribution system through network reconfiguration. Therefore, this work will serve as a one stop source for the guidance of the new researchers in this field. [ABSTRACT FROM AUTHOR]

A hybrid model is designed by combining the genetic algorithm (GA), radial basis function neural network (RBF-NN) and Sugeno fuzzy logic to determine the optimal parameters of a proportional-integral-derivative (PID) controller. Our approach used the rule base of the Sugeno fuzzy system and fuzzy PID controller of the automatic voltage regulator (AVR) to improve the system sensitive response. The rule base is developed by proposing a feature extraction for genetic neural fuzzy PID controller through integrating the GA with radial basis function neural network. The GNFPID controller is found to possess excellent features of easy implementation, stable convergence characteristic, good computational efficiency and high-quality solution. Our simulation provides high sensitive response (∼0.005 s) of an AVR system compared to the real-code genetic algorithm (RGA), a linear-quadratic regulator (LQR) method and GA. We assert that GNFPID is highly efficient and robust in improving the sensitive response of an AVR system. [ABSTRACT FROM AUTHOR]

This study presents a new method for reactive power tracing in a pool-based power system by introducing the hybrid genetic algorithm and least squares support vector machine (GA-LSSVM). The idea is to use GA to obtain the optimal values of regularisation parameter, γ, and kernel radial basis function (RBF) parameter, σ2, and adopt a supervised learning approach to train the LSSVM model. The technique that uses proportional sharing method (PSM) is used as a teacher. To obtain a lossless system, the concept of virtual load is proposed. Prior to that, the equivalent transmission line model is introduced. It integrates the nodal reactive power with the power produced by shunt admittances. Based on power-flow solution and reactive power tracing procedure by PSM, the description of inputs and outputs for training and testing data is created. The generators' shares to reactive loads in the test system are expected can be determined accurately by proposed GA-LSSVM model. In this study, five-bus system is used to illustrate the concept of virtual load and equivalent transmission line model whereas the 25-bus equivalent system of southern Malaysia is used to illustrate the effectiveness of the proposed GA-LSSVM model compared to PSM and artificial neural network. [ABSTRACT FROM AUTHOR]

Energy monitoring is one of the important aspects of the energy management, as such there is a need to monitor the power consumption of a premises before planning some of the technical measures to minimize the energy consumption. This paper presents the current state of art of appliances’ energy management through Intrusive Load Monitoring (ILM) and Non-intrusive Load Monitoring (NILM), ILM is referred to as distributive sensing and NILM is based on single point sensing. In an effort to explore the recent trend in Energy Management for researchers in the area, various techniques of Home Energy Management (HEM) using ILM and NILM have been analyzed and categorized. Different contributions made by the researchers have been highlighted together with some techniques of reducing power consumption of a building for cost saving and greener environment. This study reveal that there are issues concerning load monitoring and load management that need the attention; issues such as more accurate recognition and the need for monitoring system that can recognize as many kind of loads as possible. More efforts to apply NILM in appliances’ energy management is also required. Finally there is need for promoting the energy management culture among the electricity consumers whether in homes, offices or industries. The study will help the researchers in the area to have a clear vision of the recent trends in the area. [ABSTRACT FROM AUTHOR]