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Predictive neural network based adaptive controller for grid-connected PV systems supplying pulse-load
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DOI：10.1016/j.solener.2019.09.018.png)
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Abstract
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This paper presents an adaptive controller for grid-tie DC-AC inverter in grid-connected Photovoltaic (PV) power system supplying a pulse AC load. The proposed controller oversees regulating the dc-bus voltage, managing the injected power to the grid, and minimizing the injected harmonics. The controller parameters are optimized and adaptively tuned using predictive neural network controller (PNNC). The PNNC predicts the control parameters by tracking the mean square errors of grid currents and dc-bus voltage and eliminating these errors in a very short finite time. The proposed controller was implemented in MATLAB environment and tested under different dynamic conditions, including step variation of irradiance level and the application of pulse loads. The controller performance was investigated in comparison with a base-case, in which the controller parameters are arbitrarily tuned. The results showed that the proposed adaptive controller offers faster dynamic response with less settling time and maximum overshoot for both current and voltage variables. Furthermore, the injected harmonics to the grid were significantly reduced showing a 1.97% total harmonic distortion (THD) in comparison with the conventional controller with 5.06% THD, which makes the PV system compatible with the requirements in the IEEE 519 international standard for harmonic control in electrical power systems.
Keywords:
Adaptive control
DC-AC inverter
Grid-connected
Photovoltaic (PV) system
Predictive neural network controller (PNNC)
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