Active filter for power quality improvement by artificial neural networks technique

Use of nonlinear loads and devices in power systems is expected to grow rapidly. These loads inject harmonic currents into the power system. Active filtering of electric power has now become a mature technology for harmonic and reactive power compensation in two-wire (single phase), three-wire (three phase without neutral), and four-wire (three phase with neutral) ac power networks with nonlinear loads. Current harmonics are one of the most common power quality problems and are usually resolved by the use of shunt passive or active filters. In this paper, a new control design using an artificial neural networks is proposed to make conventional shunt active adaptive filter. The control design is based on artificial neural networks that use a modified Widrow-Hoff weight-updating algorithm. With the use of this artificial neural network algorithm, the functionality of the shunt active filter are enhanced. The proposed shunt active filter can compensate for balanced and unbalanced nonlinear load currents, adapt itself to compensate for variations in nonlinear load currents or nonlinear load types, and correct power factor of the supply side near to unity. The integrity of the proposed scheme is fulfilled through simulation studies and results obtained are discussed. Moreover, the superiority of its performance is proved by comparison to other active filter approaches. The results show an appreciable improvement in the source current waveforms and a near unity power factor operation is achieved. The presented control system of the active power filter exhibits robustness and a high dynamic response.