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This paper proposes a combination of neural network and a bandless hysteresis controller, for a switched capacitor active power filter (SCAPF), to improve line power factor and to reduce line current harmonics. The proposed active power filter controller forces the supply current to be sinusoidal, in phase with line voltage, and has low current harmonics. Two main controls are proposed for it: neural network detection of harmonics and bandless digital hysteresis switching algorithm. A mathematical algorithm and a suitable learning rate determine the filter's optimal operation. A digital signal controller (TMS320F2812) verifies the proposed SCAPF, implementing the neural network and bandless hysteresis algorithms. A laboratory SCAPF system is built to test its feasibility. Simulation and experimental results are provided to verify performance of the proposed SCAPF system. Index Terms-Bandless hysteresis, harmonic, neural network, power factor, switched capacitor active power filter (SCAPF).

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This paper presents a solar power modelling method using artificial neural networks (ANNs). Two neural network structures, namely, general regression neural network (GRNN) feedforward back propagation (FFBP), have been used to model a photovoltaic panel output power and approximate the generated power. Both neural networks have four inputs and one output. The inputs are maximum temperature, minimum temperature, mean temperature, and irradiance; the output is the power. The data used in this paper started from January 1, 2006, until December 31, 2010. The five years of data were split into two parts: 2006–2008 and 2009-2010; the first part was used for training and the second part was used for testing the neural networks. A mathematical equation is used to estimate the generated power. At the end, both of these networks have shown good modelling performance; however, FFBP has shown a better performance comparing with GRNN.