Optimizing Solar Energy Production in Partially Shaded PV Systems with PSO-INC Hybrid Control

Partial shading, from obstacles such as buildings or trees, is a major challenge for photovoltaic systems, causing unpredictable fluctuations in solar energy production and underlining the need for advanced energy management strategies. In this paper, we propose an innovative approach that combines hybrid metaheuristic optimization with maximum power point tracking control (MPPT), using particle swarm optimization (PSO) in conjunction with the incremental conductance (IC) algorithm. We compare the proposed method with the conventional Perturb and Observation (PO) algorithm. The choice of PO as a comparison method is due to its simplicity, its familiarity with the scientific literature, its low cost of implementation. The main objective of swarm optimization combined with the IC algorithm lies in its ability to overcome the challenges posed by partial shading, ensuring accurate and efficient tracking of the point of maximum power, thanks to dynamic adaptation to variations in solar irradiation, thus enhancing the performance and resilience of the photovoltaic system. This approach  is of crucial importance, offering considerable potential for solving the complex challenges associated with partial shading. Our results show that this hybrid MPPT algorithm offers superior tracking efficiency 98% , faster convergence 500ms , better stability and increased robustness compared to traditional MPPT approaches. The system is composed of a PV and a boost converter that connects the input to the resistive load. The algorithms were implemented with MATLAB/Simulink as the simulation tool. These results not only reinforce the viability of sustainable energy solutions, but also open the way for the development of more sustainable energy solutions.The perspectives of this work are oriented towards a practical and extended integration of the proposed hybrid approach in real photovoltaic systems, with a particular emphasis on experimental validation

Enhancing fault ride-through capacity of DFIG-Based WPs by adaptive backstepping command using parametric estimation in non-linear forward power controller design

The principal issue associated with wind parks (WPs) based on doubly-fed induction generators (DFIGs) is their vulnerability to network faults. This paper presents a novel nonlinear forward power controller design with an adaptive backstepping command using parametric estimation (NFPC_ABC-PE) to enhance fault ride-through (FRT) capacities in WP utilizing DFIGs. The suggested NFPC_ABC-PE manupiles both rotor and network-side power converters (i.e., RSPCs and NSPCs). Specifically, RSPCs are manipulated to maintain the targeted voltage at dc-bus terminals, while NSPCs are manipulated to supply the reactive energy (power) necessary if the network is disturbed. As a result, the NFPC_ABC-PE proposed precisely supplies reactive energy to ensure the smooth execution of FRT ability. The method developed comprehends the dynamics of RSPC, NSPC-side filters, and dc-bus terminal voltage in the form of electrical active and reactive output power. The parameters of the RSPC and NSPC-side filters, including those associated with the dc-bus capacitor, are regarded as entirely unknown. To estimate and regulate these parameters, adaptation algorithms are utilized. The NFPC_ABC-PE employs parameter adaptation algorithms and switching control inputs designed to safeguard the overall stability of WP. The stability analysis of the DFIG-based WPs with the proposed NFPC_ABC-PE involves applying stability in the sense of the Lyapunov function (LF). To validate its efficacy, simulations are carried out on a single 10 MW power generation unit. The results of the simulation highlight a clear enhancement in the stability and FRT capability of WP, contrasting with the nonlinear forward power controller employing the sliding mode command (NFPC-SMC)

Enhancing battery capacity estimation accuracy using the bald eagle search algorithm

Accurate assessment of metrics such as state of health (SOH) is of paramount importance in effective battery management systems (BMS), given the propensity of batteries to experience capacity degradation with aging. This research endeavors to significantly enhance the precision of battery capacity estimation by effectively mitigating the inherent uncertainties associated with state of charge (SOC) estimation and measurement. To address this challenge, we introduce an innovative approach leveraging the bald eagle search algorithm (BES), a method inspired by the systematic hunting behavior of bald eagles. BES strategically navigates the search space, identifying and selecting promising solutions through fitness evaluations. Our principal aim, utilizing the inherent capabilities of BES, is to pinpoint the optimal candidate that minimizes a designated cost function, while ensuring real-time cell capacity updates facilitated by the incorporation of a memory forgetting factor. The distinctiveness of this study is twofold: firstly, the strategic integration of the BES algorithm within the context of battery capacity optimization, and secondly, the inclusion of a memory forgetting factor to enhance real-time capacity estimations. The efficacy of our approach is rigorously substantiated through validation using NASA’s Prognostic Data, along with three battery scenarios for plug-in hybrid and electric vehicles. BES consistently outperformed four aggressive algorithms, demonstrating heightened accuracy with a peak error rate of only 1.06% in the most demanding scenario. Furthermore, the predictive performance measures remained consistently below 0.41%, underscoring the robustness of our proposed methodology