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2 Journal of Power Technologies

2 2019

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Proposing an efficient wind forecasting agent using adaptive MFDFA

Mozaffarilegha Mahdi, Farjah Ebrahim

Journal of Power Technologies

2019

Vol. 99, nr 2

152--162

High penetration by distributed energy sources (DERs) such as wind turbines (WT) and various types of consumer have triggered a need for new approach to coordination and control strategy to meet the stochastic wind speed of the environment. Here, a Multi Agent System is used to deliver strengthened, distributed, self-governing energy management of a multiple micro-grid to adapt to changes in the environment. Prediction of wind speed is crucial for various aspects, such as control and planning of wind turbine operation and guaranteeing stable performance of multiple micro-grids. The main purpose of the proposed system is to account for wind variability in the energy management of a multiple micro-grid based on a hierarchical multi-factor system. In this study, the prediction is based on adaptive multifractal detrended fluctuation analysis (Adaptive MFDFA). A genetic algorithm is used to solve the optimization problem. Eventually, the proposed strategy is applied to a typical MG which consists of micro turbine (MT), wind turbine (WT) and energy storage system (ESS). Evaluation of the results show that the proposed strategy works well and can adapt the level of confidence interval in various situations.

Open-switch fault diagnosis system based on one current sensing for a matrix converter using neural network

Iraji Farzad, Nateghi Alireza, Farjah Ebrahim

Journal of Power Technologies

2019

Vol. 99, nr 4

289--294

It is difficult to diagnose a three-phase matrix converter using a mathematical model, because a matrix converter consists of nine switches with various nonlinear factors. Since a neural network does not require any mathematical system model, it is a suitable technique for fault classification in matrix converters. In this manuscript, a fault diagnostic system for three-phase to three-phase matrix converters using a neural network is proposed to detect a fault and identify its location. The proposed diagnostic system can detect faults using just one phase current waveform which is very efficient in terms of cost of sensors and system complexity. This method was evaluated using simulation and experimental data sets in two scenarios. The results confirm that in different normal and abnormal situations the system achieves performance levels in excess of 98%.