A novel maximum torque per ampere (MTPA) method based on power perturbation for a field-oriented control (FOC) interior permanent magnet synchronous motor (IPMSM) drive system is proposed in this study. The proposed MTPA method is designed based on the power perturbation resulting from the signal injection in the current angle. Moreover, the influence of current and voltage harmonics to the MTPA control can be effectively eliminated. Furthermore, to enhance the robustness of the control system, a real-time design scheme for the integral–proportional (IP) speed controller using a recursive least square (RLS) estimator with disturbance torque feedforward control is developed. The disturbance torque is obtained from an improved disturbance torque observer with online parameters updated. Finally, some experimental results using an IPMSM drive system based on a low-price digital signal processor (DSP) are presented. From the experimental results, the proposed control approach can guarantee the control performance of a speed loop even under a cyclic fluctuating load.
An intelligent wind power smoothing control using fuzzy neural network (FNN) 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 FNN 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 FNN 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. Comparing to the other smoothing methods, a minimum energy capacity of the BESS with a small fluctuation of the grid power can be achieved by the FNN power smoothing control. In the experimentation, 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.
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