Abstract
In this work, a photovoltaic (PV) system integrated with a non-inverting DC-DC buck-boost converter to extract maximum power under varying environmental conditions such as irradiance and temperature is considered. In order to extract maximum power (via maximum power transfer theorem), a robust nonlinear arbitrary order sliding mode-based control is designed for tracking the desired reference, which is generated via feed forward neural networks (FFNN). The proposed control law utilizes some states of the system, which are estimated via the use of a high gain differentiator and a famous flatness property of nonlinear systems. This synthetic control strategy is named neuro-adaptive arbitrary order sliding mode control (NAAOSMC). The overall closed-loop stability is discussed in detail and simulations are carried out in Simulink environment of MATLAB to endorse effectiveness of the developed synthetic control strategy. Finally, comparison of the developed controller with the backstepping controller is done, which ensures the performance in terms of maximum power extraction, steady-state error and more robustness against sudden variations in atmospheric conditions.
Highlights
Entire world electricity demand is rising continuously, which motivates the researchers to focus on those energy resources which are efficient, environment-friendly and costeffective [1]
The simulation results are performed using Simulink environment of MATLAB (R2018b) environment to check the applicability of the developed High Gain Differentiator (HGD) based AOSMC
The generated reference voltage Vre f using feed forward neural networks (FFNN) for changing irradiance profile was tracked by the proposed AOSMC
Summary
Entire world electricity demand is rising continuously, which motivates the researchers to focus on those energy resources which are efficient, environment-friendly and costeffective [1]. The ANN-based method requires a training data set to train the output-input relation, but when it develops, it becomes efficient and robust under abruptly changing input parameters This method of extracting maximum power has fast-tracking speed and low computation requirement, but they require a large memory size and training to track MPP. To resolve the problems state-above, an adaptive nonlinear Sliding Mode Control (SMC) based method is presented, which is insensitive to parameter uncertainties, and internal/external disturbances [30] They show low converging time compared to conventional techniques. Results validate the applicability of the developed model for control law in terms of maximum power extraction, steady-state error, and robustness against abrupt variations in atmospheric conditions compared to the conventional MPPT methods.
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