Abstract
Wind generator (WG) output is a function of wind speed and three-phase terminal voltage. Distribution systems are predominantly unbalanced. A WG model that is purely a function of wind speed is simple to use with unbalanced three-phase power flow analysis but the solution is inaccurate. These errors add up and become pronounced when a single three-phase feeder connects several WGs. Complete nonlinear three-phase WG models are accurate but are slow and unsuitable for power flow applications. This paper proposes artificial neural network (ANN) models to represent type-3 doubly-fed induction generator and type-4 permanent magnet synchronous generator. The proposed approach can be readily applied to any other type of WGs. The main advantages of these ANN models are their mathematical simplicity, high accuracy with unbalanced systems and computational speed. These models were tested with the IEEE 37-bus test system. The results show that the ANN WG models are computationally ten times faster than nonlinear accurate models. In addition, simplicity of the proposed ANN WG models allow easy integration into commercial software packages such as PSS®E and PSS®SINCAL and implementations are also shown in this paper.
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