Elman neural network-based nonlinear state estimation for induction motors

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Atıf İçin Kopyala

Aksoy S., Mühürcü A.

Turkish Journal of Electrical Engineering and Computer Sciences, cilt.19, sa.4, ss.1-15, 2011 (SCI-Expanded)

• Yayın Türü: Makale / Tam Makale
• Cilt numarası: 19 Sayı: 4
• Basım Tarihi: 2011
• Doi Numarası: 10.3906/elk-1002-406
• Dergi Adı: Turkish Journal of Electrical Engineering and Computer Sciences
• Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED)
• Sayfa Sayıları: ss.1-15
• İstanbul Ticaret Üniversitesi Adresli: Hayır

Özet

This study presents a recurrent neural network (RNN)-based nonlinear state estimator that uses an Elman

neural network structure (ENN) for state estimation of a squirrel-cage induction motor. The proposed

algorithm only uses the measurements of the stator currents and the rotor angular speed, and it learns the

dynamic behavior of the state observer from these measurements through prediction error minimization.

A squirrel-cage induction motor was fed from sinusoidal, 6-step, and pulse-width modulation (PWM)

supply sources at different times in order to observe the performance of the proposed estimator for different

operation conditions. Estimation results showed that the proposed algorithm is capable of estimating the

states of an induction motor and performs better than extended Kalman filtering (EKF) in terms of accuracy

and convergence speed.