Photovoltaic (PV) systems are crucial to the production of electricity for a newly established community in Egypt, especially in grid-tied systems. Power quality (PQ) issues appear as a result of PV connection with the power grid (PG). PQ problems cause the PG to experience faults and harmonics, which affect consumers. A series compensator dynamic voltage restorer (DVR) is the most affordable option for resolving the abovementioned PQ problems. To address PQ difficulties, this paper describes a grid-tied PV combined with a DVR that uses a rotating dq reference frame (dqRF) controller. The main goal of this study is to apply and construct an effective PI controller for a DVR to mitigate PQ problems. The artificial rabbits optimization (ARO) is used to obtain the best tune of the PI controller. The obtained results are compared with five optimization techniques (L-SHADE, CMAES, WOA, PSO, and GWO) to show its impact and effectiveness. Additionally, Lyapunov’s function is used to analyze and evaluate the proposed controller stability. Also, a mathematical analysis of the investigated PV, boost converter, and rotating dqRF control is performed. Two fault test scenarios are examined to confirm the efficacy of the suggested control approach. The parameters’ (voltage, current, and power) waveforms for the suggested system are improved, and the system is kept running continuously under fault periods, which improves the performance of the system. Moreover, the findings demonstrate that the presented design successfully keeps the voltage at the required level with low THD% values at the load side according to the IEEE standards and displays a clear enhancement in voltage waveforms. The MATLAB/SIMULINK software is used to confirm the proposed system’s performance.

Owing to the consistent development in the high energy permanent magnet materials and in the power semiconductors along with control methodologies, permanent magnets brushless DC motors (PM-BLDCs) had been used widely in miscellaneous industrial applications recently. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) controller for a Six-Step BLDC motor drive. This ANFIS controller for BLDC motor drive is used for improving the performance characteristics of the BLDC motor by supporting the profile of the torque, current, speed, and inverter voltages. The effective performance of the proposed ANFIS controller is evaluated through a comparison with the classical proportional-integral-derivative (PID) and Fuzzy controllers. Results disclosed the preeminence of the proposed ANFIS controller over both of the classical PI and Fuzzy controllers. All these simulations are performed using MATLAB/SIMULINK.

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Climate change, environmental impact and the limited natural resources urge scientific research and novel technical solutions. The monograph series Green Energy and Technology serves as a publishing platform for scientific and technological approaches to "green"-i.e. environmentally friendly and sustainable-technologies. While a focus lies on energy and power supply, it also covers "green" solutions in industrial engineering and engineering design. Green Energy and Technology addresses researchers, advanced students, technical consultants as well as decision makers in industries and politics. Hence, the level of presentation spans from instructional to highly technical. **Indexed in Scopus**. **Indexed in Ei Compendex**.More information about this series at https://link.springer.com/bookseries/8059

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