In the era of millennium, the electric vehicle (EV) has a high demand from many sector which is to replace the existing internal combustion vehicle since it has given a negative side impacts towards the environment and also due to the increasing of the price of the fossil fuels that decreasing day by day. The electric vehicle is one of the alternative way to reduce pollution by moving the electric vehicle by using the energy that stored in the battery's car and after the battery has reach its limit, only then the petroleum will continue the role of the energy to move the electric vehicle. The energy that required by the battery's car are generated from the charging station which it connected to the distribution network. The charging or discharging of the electric vehicle could cause some power quality issues in a few terms such as voltage profile, power losses etc. This paper presents the Evolutionary Programming Based Technique for Plug-In-Hybrid Electric Vehicle Charging System. The proposal technique has been tested on the IEEE 33-bus distribution system. The results shown that the proposed technique managed to maximize the voltage level in the system in the plug-in-hybrid electric vehicle charging system environment.
<div>Multilayer perceptron (MLP) optimization is carried out to investigate the classifier's performance in discriminating the uniformity of reduced Graphene Oxide(rGO) thin-film sheet resistance. This study used three learning algorithms: resilient back propagation (RP), scaled conjugate gradient (SCG) and levenberg-marquardt (LM). The dataset used in this study is the sheet resistance of rGO thin films obtained from MIMOS Bhd. This work involved samples selection from a uniform and non-uniform rGO thin-film sheet resistance. The input and output data were under going data pre-processing: data normalization, data randomization and data splitting. The data were dividedin to three groups; training, validation and testing with a ratio of 70%: 15%: 15%, respectively. A varying number of hidden neurons optimized the learning algorithms in MLP from 1 to 10. Their behavior helped establish the best learning algorithms in discriminating MLP for rGO sheet resistance uniformity. The performances measured were the accuracy of training, validation and testing dataset, mean squared errors (MSE) andepochs. All the analytical work in this study was achieved automatically via MATLAB software version R2018a. It was found that the LM is dominant inthe optimization of a learning algorithm in MLP forrGO sheet resistance.The MSE for LM is the most reduced amid SCG and RP.</div><div> </div>
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