In recent times, artificial intelligence applications in the field of dynamic systems control have proved to be an efficient tool for the improvement of processes. Programmable Logic Controller-based control systems are found in the vast majority of industrial automation systems. In this paper, a fuzzy controller approach is proposed to enhance the design of industrial control systems using commercial PLCs. The fuzzy controller is tuned by artificial intelligent techniques, and the performances of different fuzzy controller-tuning methods are compared, including the metaheuristic ones. The results shown in this paper validate the proposal that metaheuristics tuning methods of fuzzy controller have better performance in intelligent automation.
Electric vehicle (EV) charging may impose a substantial power demand on existing low voltage (LV) and medium voltage (MV) networks, which are usually not prepared for high power demands in short time intervals. The influx of E-mobility may require an increase in grid reinforcements, but these can be reduced and optimized by a combination of new technologies, tools, and strategies, such as the deployment of solar PV generation integrated with aggregated energy storage systems. One of the challenges in the implementation of charging infrastructures in public stations is coupling the projected sizes of energy demand and power requirements in each location for each charger. This paper describes a method to estimate projected values for energy consumption and power demand in EV fast charging stations (CS). The proposed ideas were applied in a concept facility located in Campinas, Brazil, in a structure equipped with two 50 kW DC Fast Chargers, local 12.5 kW/13.2 kWp PV generation (to reduce energy impacts to the grid), and a 100 kW/200 kWh storage system, using electrochemical batteries (to minimize peak power requirements).
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