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A New On-Line Parameters Identification Method for IPMSMs Using Current Derivative Measurement
M.X. Bui,D. Xiao,M.F. Rahman 전력전자학회 2019 ICPE(ISPE)논문집 Vol.2019 No.5
The d- and q- axis inductances of an IPMSM, especially Lq, vary with magnitude of the current of each axis due to the magnetic saturation. The existing on-line methods using recursive algorithms fail to estimate the change of the machine inductances during zero speed and fast transient operations. This paper proposes a new on-line method to estimate machine inductances using measurements of current derivatives and the DC-bus voltage of the inverter during each PWM cycle. In addition, the stator resistance and permanent magnet flux linkage, which vary with the operating temperature, are identified by using the recursive least square (RLS) technique. Extensive simulation and experimental studies were conducted to verify the robustness and effectiveness of the proposed on-line parameter identification method which estimates all four electrical parameters of the IPMSM.
L. Nguyen-Ngoc,H. Tran-Ngoc,T. Bui-Tien,A. Mai-Duc,M. Abdel Wahab,Huan X. Nguyen,G. De Roeck 국제구조공학회 2021 Smart Structures and Systems, An International Jou Vol.28 No.1
In this paper, a novel approach to damage identification in structures using Particle Swarm Optimization (PSO) combined with Artificial neural network (ANN) is proposed. With recent substantial advances, ANN has been extensively utilized in a wide variety of fields. However, because of the application of backpropagation algorithms based on gradient descent techniques, ANN may be trapped in local minima when seeking the best solution. This may reduce the accuracy of ANN. Therefore, we propose employing an evolutionary algorithm, namely PSO to deal with the local minimum problems of ANN. PSO is employed to improve the training parameters of ANN consisting of weight and bias ratios by reducing the deviation between calculated and desired results. These training parameters are then used to train the network. Since PSO applies global search techniques to look for the best solution, it can assist the network in avoiding local minima by looking for a beneficial starting point. In order to assess the effectiveness of the proposed approach, both numerical and experimental models with different damage scenarios are employed. The results show that ANN -PSO not only significantly reduces computational time compared to PSO but also possibly identifies damages in the considered structures more accurately than ANN and PSO separately.