Improve MPPT in Organic Photovoltaics with Chaos-Based Nonlinear MPC

Abstract

Environmental pollution, climate changes such as melting of natural glaciers and rising sea levels are only instances of the challenges of using fossil fuels. Therefore, increasing use of clean renewable energy sources such as photovoltaic systems has a great importance. In this paper, the chaotic-based nonlinear model predictive control approach is used for extracting the maximum power of organic photovoltaic cells, which has not only a suitable tracking speed but also in fault conditions, can be useful to improve the operation level of the distribution network. This approach is a feedback-based recursive control strategy which capable of predict the proper operating state that minimizes its cost function. The proposed approach consists of two stages of estimating the reference point and regulating the operatimg point according to it. In this regard, the Lagrange function is used for managing the performance of the estimator and chaotic neural network model predictive controller to control the operation of boost converter. By using the chaos-based nonlinear model predictive controller, the amount of overvoltage is reduced by more than 1.3%. In fact, without using of control methods, the voltage range exceeds its allowable values with increasing of the OPV panels penetration. According to the obtained results, with the reduction of network losses, the capacity of distribution feeders is increased and the level of system efficiency is also improved.

Keywords

• Chaos Theory
• Model predictive controller
• MPP tracking
• Nonlinear MPC
• Organic Photovoltaic

References

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