This paper presents a comprehensive exploration of a hybrid energy system that integrates wind turbines with photovoltaics (PVs) to address the intermittent nature of electricity production from these sources. The necessity for such technology arises from the sporadic nature of electricity generated by PV cells and wind turbines. The envisioned outcome is an emissions-free, more efficient alternative to traditional energy sources. A variety of optimization techniques are utilized, specifically the Particle Swarm Optimization (PSO) algorithm and Electric Eel Foraging Optimization (EEFO), to achieve optimal power regulation and seamless integration with the public grid, as well as to mitigate anticipated loading issues. The employed mathematical modeling and simulation techniques are used to assess the effectiveness of EEFO in optimizing the operation of grid-connected PV and wind turbine hybrid systems. In this paper, the optimization methods applied to the system’s architecture are described in detail, providing a clear understanding of the intricate nature of the approach. The efficacy of these optimization strategies is rigorously evaluated through simulations of diverse operating scenarios using MATLAB/SIMULINK. The results demonstrate that the proposed optimization strategies are not only capable of precisely and swiftly compensating for linked loads, but also effectively controlling the energy supply to maintain the load’s power at the desired level. The findings underscore the potential of this hybrid energy system to offer a sustainable and reliable solution for meeting power demands, contributing to the advancement of clean and efficient energy technologies. The results demonstrate the capability of the proposed approach to improve system performance, maximize energy yield, and enhance grid integration, thereby contributing to the advancement of renewable energy technologies and sustainable energy systems.

This paper presented enhancing hybrid energy systems, specifically those combining photovoltaic (PV) and wind turbine sources, linked to the electrical grid with the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) control unit during implementation. Our study focuses on the comparative performance of the ANFIS controller against traditional Fuzzy Fraction-Order Proportional-Integral-Derivative, (FOPID) and Fuzzy Proportional-Integral, (PI) controllers. Hybrid energy systems present unique challenges due to renewable energy sources' intermittent and non-linear nature. Conventional controllers, such as Fuzzy FOPID and Fuzzy PI, often struggle to manage these complexities effectively. The ANFIS controller, nevertheless, blends fuzzy logic's qualitative reasoning with neural networks' capacity for adaptive learning, offering a more robust and flexible solution. Through extensive simulations and real-world testing, we demonstrate that the ANFIS controller significantly outperforms both Fuzzy FOPID and Fuzzy PI controllers in key performance metrics. These include improved voltage regulation, lower total harmonic distortion (THD), and enhanced overall system stability and efficiency under varying load and environmental conditions. The findings highlight ANFIS's potential as a better hybrid energy system control method, enabling more dependable and effective grid integration of renewable energy sources. This research contributes to advancing smart grid technologies and promoting sustainable and resilient energy infrastructure. Using the OPAL-RT 4512 platform, this paper generates a thorough real-time simulation and investigation of a hybrid PV/wind energy system. Real-time integration of intricate MATLAB/Simulink models is made possible by the OPAL-RT 4512, which makes it easier to accurately simulate real-world operational situations. At the beginning time the values and percentage modified ANFIS and Fuzzy FOPID give good controller compared Fuzzy PI, at time (0 to 0.4 sec) Fuzzy PI decrease in most results values percentage 20% compared to two controller ANFIS and Fuzzy FOPID.

This study presents an enhanced, adaptive, and dynamic surface sliding mode control (SMC), a cutting-edge method for improving grid-connected photovoltaic (PV) system performance. The suggested control approach uses dynamic SMC and adaptive approaches to enhance the robustness and efficiency of a system. Proportional–integral (PI) and SMC, two control systems for maximum power point tracking (MPPT) in PV systems, are compared in this paper. This study finds that the SMC system is a more effective and efficient MPPT approach for PV systems compared to the conventional PI control system. The SMC system’s unique feature is the capacity to stabilize grid voltage and attain a modulation index of less than one. An important component of power electronic system control is the index, which acts as a parameter representing the relationship between the output signal’s amplitude and the reference signal’s amplitude. The SMC method demonstrates improved robustness, efficiency, and stability, especially in dynamic operating settings with load and solar radiation changes. Compared to the PI control, the SMC exhibits a noteworthy 75% reduction in voltage fluctuations and an improvement in the power output of 5% to 10%. Regarding output power optimization, voltage stability, and accurate current tracking, the SMC system performs better than the PI control system. Furthermore, the SMC technique maintains a modulation index below one and guarantees grid voltage stability, both of which are essential for the efficiency and stability of power electrical systems.

Author(s): Saad A Mohamed Abdelwahab a,b, Hosam Youssef Hegazy c, Wael I Mohamed ca Electrical Department, Facul