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An intelligent wind power smoothing control using recurrent fuzzy neural network (RFNN) is proposed in this study. First, the modeling of wind power generator and the designed battery energy storage system (BESS) are introduced. The BESS is consisted of a bidirectional interleaved DC/DC converter and a 3-arm 3-level inverter. Then, the network structure of the RFNN and its online learning algorithms are described in detail. Moreover, actual wind data is adopted as the input to the designed wind power generator model. Furthermore, the three-phase output currents of the wind power generator are converted to dq-axis current components. The resulted q-axis current is the input of the RFNN power smoothing control and the output is a gentle wind power curve to achieve the effect of wind power smoothing. The difference of the actual wind power and smoothed power is supplied by the BESS. The minimum energy capacity of the BESS with a small fluctuation of the grid power can be achieved by the RFNN power smoothing control. A digital signal processor (DSP) based BESS is built using two TMS320F28335. From the experimental results of various wind variation sceneries, the effectiveness of the proposed intelligent wind power smoothing control is verified.

Continuous conduction mode power factor correction AC-DC converters are widely employed as the front stage in power supplies with medium or high output power. The second-order reactive input power in single-phase systems causes second-order ripple in the DC-link voltage. The speed of voltage regulation is thus limited by the second-order frequency in seeking to achieve low distorted input current. This study presents a fast second-order voltage estimation method, wherein an integrator is used for the estimation of second-order voltage ripple to obtain rapid voltage feedback without ripple and thereby cope with the limitations imposed by the second-order frequency. This approach greatly expands the bandwidth of the voltage control loop beyond the second-order frequency while reducing the total harmonic distortion associated with the input current. This also makes it possible to reduce the DC capacitance to reduce costs. The authors opted for full digital control using a TI F28335 DSP IC, in conjunction with feedback plus feedforward control to facilitate the design of the current loop. A feedback current corrector is used to reduce distortion associated with the input current over a wide load range. The effectiveness of the proposed control method was confirmed with some simulation and experimental results.

This paper outlines the modeling and controller design of a novel two-stage photovoltaic (PV) micro inverter (MI) that eliminates the need for an electrolytic capacitor (E-cap) and input current sensor. The proposed MI uses an active-clamped current-fed push-pull DC-DC converter, cascaded with a full-bridge inverter. Three strategies are proposed to cope with the inherent limitations of a two-stage PV MI: (i) high-speed DC bus voltage regulation using an integrator to deal with the 2nd harmonic voltage ripples found in single-phase systems; (ii) inclusion of a small film capacitor in the DC bus to achieve ripple-free PV voltage; (iii) improved incremental conductance (INC) maximum power point tracking (MPPT) without the need for current sensing by the PV module. Simulation and experimental results demonstrate the efficacy of the proposed system.