A new technique for unbalance current and voltage estimation with neural networks

In this paper, a new measurement procedure based on neural networks for the estimation of harmonic powers and current/voltage-symmetrical components is presented. The theory foundation is the Park vectors representation of a three-phase voltage/current. The measurement system scheme is built with three neural network blocks. The first block is a feedforward neural network that computes the Park vectors and the zero-phase sequence components. The second block is an adaptive linear neuron (ADALINE) that estimates the harmonic complex coefficients of the current/voltage Park vectors. A third block is another feedforward neural network that obtains symmetrical components of current/voltage harmonics and harmonic active/reactive powers. Finally, to check the measurement method performance, the digital simulation results of a practical case are presented.