Fuzzy Generalized Predictive Control of Power Converter in DC Microgrids with Constant Power Load

This paper develops a Takagi-Sugeno Generalized Predictive Controller (TS-GPC) to adjust the power converters of a class of direct current (DC) Microgrids (MGs) feed linear resistive loads and nonlinear Constant Power Loads (CPLs). Since the overall DC microgrid has a nonlinear behavior, it is vital to model the behavior of the system with an accurate model and design the control input. In this regard, a Takagi-Sugeno fuzzy model is utilized to model a nonlinear system with multi-linear models accurately; and, a GPC is applied on the multi-linear models based on the obtained model and defined cost function. The numerical results show that the model can predict the behavior of the nonlinear system and the proposed controller can effectively track the desired reference

Intelligent Multiobjective NSBGA-II Control of Power Converters in DC Microgrids

In this article, we develop a novel multiobjective controller to regulate the power converters of a class of dc microgrids connected to nonlinear constant power loads and linear resistive loads. The suggested control approach uses the nondominating sorting binary genetic algorithm (NSBGA-II) to directly design the on/off switching signal of the converters without using the pulsewidth modulation technique. The multiobjective controller minimizes the tracking error of the dc bus voltage and at the same time tries to reduce the total number of switching actions. Thereby, the developed controller tracks the desired reference with a reduced converter switching action and power loss by using a proper Pareto solution. Moreover, by employing the NSBGA-II algorithm, it is feasible to involve the switching frequency in the design procedure to enhance the performance. Exploiting the binary genetic algorithm instead of the conventional genetic algorithm (GA) turns a continuous surface searching into a binary one, which not only makes it more compatible with the nature of the power converter control but also decreases the online computational burden. To illustrate the superiority of the proposed approach, real-time OPAL results are provided