TY - JOUR ID - 103376 TI - Identification of Wind Turbine using Fractional Order Dynamic Neural Network and Optimization Algorithm JO - International Journal of Engineering JA - IJE LA - en SN - 1025-2495 AU - Aslipour, Z. AU - Yazdizadeh, A. AD - Department of Electrical Egineering, Shahid Beheshti University, Tehran, Iran Y1 - 2020 PY - 2020 VL - 33 IS - 2 SP - 277 EP - 284 KW - Dynamic Neural Network KW - Fractional Order KW - system identification KW - Particle Swarm Optimization KW - Wind Energy System DO - 10.5829/ije.2020.33.02b.12 N2 - In this paper, an efficient technique is presented to identify a 2500 KW wind turbine operating in Kahak wind farm, Qazvin province, Iran. This complicated system dealing with wind behavior is identified by using a proposed fractional order dynamic neural network (FODNN) optimized with evolutionary computation. In the proposed method, some parameters of FODNN are unknown during the process of identiļ¬cation, so a particle swarm optimization (PSO) algorithm is employed to determine the optimal values by which a fractional order nonlinear system can be completely identified with a high degree of accuracy. These parameters are very effective to achieve high performance of FODNN identifier and they include fractional order, initial values of states and weights of FODNN, and numerical algorithm step size for solving FODNN equation. Simulation results confirm the efficiency of the proposed scheme in term of accuracy. Furthermore, comparison of the results achieved by the proposed method and those of the integer order dynamic neural network (IODNN) depicts higher accuracy of the proposed FODNN. UR - https://www.ije.ir/article_103376.html L1 - https://www.ije.ir/article_103376_c0565e553e4925a44a4e42dd18fb9402.pdf ER -