<p>Meta Heuristic methods have made a deep impact in the area of optimization in different streams of engineering. The performance of these algorithms is of importance because the hardware implementation of these algorithms is to be carried out for different engineering applications. As an important application in High Voltage DC (HVDC) transmission and Industrial Drives the multilevel inverter fault diagnosis is carried out using the different meta-heuristic methods with Neural Network as the decision making algorithm. The optimization of the weight and the bias values in the neural network diagnosis system is carried out in order to analyze the performance by means of comparing the Mean Square Error (MSE) while the Neural Network is getting trained for different fault conditions in the multilevel inverter. Matlab based implementation is carried out and the results are tabulated and inferred for a Multilevel Inverter fed from the Photovoltaic power generation system. In order to increase the robustness of the fault detection, with renewable energy based power generation system as the source for the Multilevel Inverter, the feature extracted from the multilevel inverter are positive, negative and zero sequence voltage along with the THD of the output voltage. The optimization algorithm used is Particle Swarm Optimization (PSO), Cuckoo Search Algorithm(CSA), Genetic Algorithm(GA) and Tabu Search Algorithm (TSA).</p>
Fault diagnosis on Multilevel Inverter (MLI) has been a subject of research for about a decade. This paper is an attempt to deliver a performance analysis of Genetic Algorithm (GA) and the Modified Genetic Algorithm (MGA) working to optimize Artificial Neural Network (ANN) that trains itself on the fault detection and reconfiguration of the Cascaded Multilevel Inverters (CMLI). The open circuit (OC) faults occurring in the CMLI is considered for this comparative analysis of the performance. The parameters that are taken for the performance evaluation are elapsed time of recovery, Mean Square Error (MSE) and the computational budgets of ANN. Matlab/Simulink is used to develop the CMLI and M-files are used to develop the ANN and optimization algorithms like GA and MGA. The results are obtained and tabulated and performance evaluation carried out.
Multilevel Inverters (MLI) gains importance in Distribution systems, Electrical Drive systems, HVDC systems and many more applications. As Multilevel Inverters comprises of number of power switches the fault diagnosis of MLI becomes tedious. This paper is an attempt to develop and analyze the fault diagnosis method that utilizes Artificial Neural Network to get it trained with the fault situations. A performance analysis of Genetic Algorithm (GA) and the Modified Genetic Algorithm (MGA), which optimizes the Artificial Neural Network (ANN) that trains itself on the fault detection, and reconfiguration of the Cascaded Multilevel Inverters (CMLI) is attempted. The Total Harmonic Distortion (THD) occurring due to switch failures or driver failures occurring in the CMLI is considered for this comparative analysis. Elapsed time of recovery, Mean Square Error (MSE) and the computational budgets of ANN are the performance parameters considered in this comparative analysis. Optimization is involved in the process of updating the weight and the bias values in the ANN network. Matlab based simulation is carried out and the results are obtained and tabulated for the performance evaluation. It was observed that Modified Genetic Algorithm performed better than the Genetic Algorithm while optimizing the ANN training.
Keyword:Artificial [3]. CMLI gets rid of the capacitor voltage balancing method, which is a major challenge in the Flying Capacitor Multilevel Inverter as; it needs very complex voltage balancing techniques [4]. Modular topology of the CMLI facilitates the topology for the location of fault and the reconfiguration applied in a simpler manner [5]. Whereas the Diode Clamped MLI and the Flying Capacitor MLI has switches continuously connected according to the number of voltage levels it can deliver, which introduces complexity while fault location and reconfiguration. Application of CMLI ranges from STATCOM [6] to Active Power Filter, which demands fault free operation since it involves power quality solutions [7]. The fault diagnosis and the reconfiguration of the MLI by the use of Artificial Neural Network as the fault diagnosis tool was developed which exhibited 95% accuracy in diagnosis of the faults was tabulated [6]. The reconfiguration method proposed in [8] cleared both the open circuit and the closed circuit faults in six periodic cycles. In order to develop the fault diagnosis the development of a mathematical model
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