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SUMMARY This paper proposes an efficient method to obtain the equivalent fundamental positive-sequence voltage for determining the shunt active power filter (APF) reference compensation currents, where the source voltage magnitude and phase angle at the fundamental frequency are detected by a simplified adaptive linear combiner neural network. The APF reference current calculator is developed and is realized on the DSP board through processor-in-the-loop (PIL) simulation for validating the effectiveness of the proposed APF control strategy, where the source voltages and the load currents are both distorted and unbalanced. In addition, results obtained based on the true positive-sequence fundamental source current after APF compensation and another classic approach are included for comparison. It is shown that results obtained by the PIL simulations with the proposed and compared methods agree very well. The proposed APF reference compensation current strategy is simple and practical, which provides a cost-effective and computationally efficient manner for the APF controller implementation with PIL simulations. Copyright © 2013 John Wiley & Sons, Ltd.

SUMMARY In this contribution, a control strategy in phase coordinates abc is proposed as the reference current generator to be used by the shunt active power filter (APF) compensation device. The development of the proposed reference current generator is described in detail. Its control design is kept simple, and transformation between different frameworks is not needed, thus allowing the control strategy to be applicable for both three-phase and single-phase electric systems maintaining theoretical and practical advantages. The proposed control strategy for the reference current generator is tested using Matlab/Simulink® with the incorporation of the Real-Time Workshop® for the simulation of the shunt APF compensation in real time. Based on the reported real-time application, it is possible to obtain simulation conditions that are close to the true real-time environment under study using only a single computer, giving accurate results with relatively low-cost hardware and providing a more complete evaluation of the applied control strategy. The dynamic behavior and performance of the shunt APF, using the proposed reference current calculator, have shown remarkable results. Copyright © 2012 John Wiley & Sons, Ltd.

Summary The wind power forecast has attracted much attention in recent years because of the significantly increasing number of large-scale integrations of the wind power plants in the electric grid. In this paper, a Gaussian mixture model-based neural network model is proposed to forecast the short-term wind power generation. Actual measured wind power output data are adopted to train the proposed model. Test results of wind power obtained by autoregressive integrated moving average, radial basis function neural network, support vector regression, and the proposed method are then under comparisons. It shows that the proposed method can provide more accurate forecast of short-term wind power output than other compared methods.