A New Immersion and Invariance Control and Stable Deep Learning Fuzzy Approach for Power/Voltage Control Problem

Abstract

Background: The use of renewable energies is extended due to their valuable features such as abundant and clarity. The microgrids that include the renewable energies are widely used in various applications such as power supplying of remote areas, increasing the network reliability, reducing the greenhouse gas emission, reducing the consumption demand, eliminating the consumption peaks, and so on. But, energy management in the these systems in an challenging problem. Because, there are some natural perturbations such as variation output load, grid-side faults and changes of irradiation and temperature. Aim and Objective: The problem is to design a controller to regulate the output voltage/energy under aforementioned disturbances. Methods: The paper presents a new approach for energy management in Photovoltaic (PV)/Battery/Fuel Cells (FC) systems. The uncertainties are compensated by the new optimization rules based on Immersion and Invariance (II) theorem and proposed deep learning type-2 fuzzy logic compensator (T2FLC). The objective function of T2FLC is to minimize the tracking error in presence of perturbations. The adaptation rules are derived such that the II stabilization criterions are satisfied. Both rules and fuzzy sets (FSs) of T2FLCs are optimized by guaranteed stability rules to tackle the effect of perturbations and estimation errors. Results and Discussion: It is shown that a well voltage/energy regulation performance is achieved under variation of temperature, suddenly changes of load and variation of irradiation. A comparison with similar controllers demonstrates the superiority of the suggested approach. Conclusion: The suggested regulator do not depend on the mathematical models, and results in good accuracy under difficult conditions, then it can be used in various applications.