The increasing demand for green sustainable energy source led to a worldwide increase in the installation of large scale photovoltaic (PV) farms. To ensure the PV farms compliance with safety and operational guidelines, earthing systems are essential component of the design. An adequate earthing system for low, medium, and high voltage areas of the PV farm ensures energy under fault condition and system malfunction are absorbed without exceeding the desirable limits. The work in this paper captures the soil resistivity and the earthing system requirements for each voltage level. The paper highlights the boundaries for combined or separate earthing system between low, medium and high voltage. Furthermore, the works focus on the system cost and maintenance without jeopardizing system integrities and safety compliance. Case study is also included.

Many remote areas around the world are isolated, for various reasons, from electricity networks. They are usually supplied with electricity through diesel generators. The cost of operation and transportation of diesel fuel in addition to its price have led to the procurement of a more efficient and environmentally greener method of supply. Various studies have shown that a wind-diesel hybrid system with compressed air storage (WDCAS) seems to be one of the best solutions, and presents itself as an optimal configuration for the electrification of isolated sites. This system allows significant fuel savings to be made because the stored compressed air is used to supercharge the engine. In order to optimize system performance and minimize fuel consumption, installation of a system for recovering and storing the heat of compression (TES) seems necessary. In addition, the use of hydro-pneumatic energy storage systems that use the same machine as the hydraulic pump and turbine allow us to store energy in tight spaces and, if possible, contribute to power generation. The scrupulous study of this technical approach will be the focus of our research which will validate (or not) the use of such a system for the regulation of frequency of electrical networks. In this article we will skim through the main research that recently examined the wind-diesel hybrid system which addressed topics such as adiabatic compression and hydro-pneumatic storage. Instead, we will offer (based on existing studies) a new ACP-WDCAS (wind-diesel hybrid system with adiabatic air compression and storage at constant pressure), which combines these three concepts in one system for the optimization of wind-diesel hybrid system.

Robot-Assisted Gait Training (RAGT) has emerged as a promising approach to improve motor recovery for stroke survivors. Among RAGT devices, exoskeletons offer precise joint actuation, but they are costly, mechanically complex and present risks related to joint misalignment. End-effector systems present a more affordable and simpler alternative, but face limitations in workspace and adaptability for assist-as-needed therapy. Cable-Driven End-Effector Gait Rehabilitation Robots (CDEGRs) combine the strengths of both approaches, offering low inertia, flexible configurations, and scalable designs. This review systematically examines the current landscape of CDEGRs, encompassing their kinematic classifications, control strategies, and platform configurations. Unlike previous reviews that broadly addressed exoskeletons or upper-limb rehabilitation devices, this work provides a focused and detailed analysis of lower-limb end-effector systems. In doing so, it identifies persistent gaps in design and control frameworks and highlights future research directions toward more efficient and clinically validated CDEGR architectures.

For efficient battery management that ensures lifetime and dependability in applications like electric vehicles, an accurate real‐time assessment of the State of Charge (SOC) and State of Health (SOH) of lithium‐ion (Li‐ion) batteries is essential. To overcome the difficulties presented by aging, unmodeled dynamics, and temperature fluctuations, this study attempts to create a reliable estimation method that improves the precision and robustness of SoC and SoH assessments. To maximize transient responsiveness and guarantee estimator convergence to the actual battery state, the suggested system combines a H _∞ /H ₂ controller with pole placement, which is built using Linear Matrix Inequality (LMI) techniques. Furthermore, this controller is complemented by a sliding mode estimator to assess SoH, which is a novel combination in battery state estimating techniques. By optimizing the disturbance matrix structure and taking into account changes in internal resistances, capacitances, and actual capacity, the H _∞ /H ₂ controller is designed to reduce disturbances caused by things like age and temperature fluctuations. To evaluate SoH, the sliding mode estimator makes use of state variables from the H _∞ /H ₂ controller. The approach is validated under real‐world circumstances, including driving schedules like UDDS, US06, and HWFET, using numerical simulations that consider variations in battery internal properties. The accuracy and dependability of SOC and SOH assessments are significantly improved by the combined estimation technique. By lowering estimating errors, the controller improves resilience to disruptions. The resilience of the approach is shown by simulations conducted under a range of driving circumstances, suggesting that battery management systems might use it in practice.

Because of their great energy density and efficiency, lithium-ion batteries (LIBs) are essential to renewable energy systems and electric vehicles. Effective battery management requires precise estimation of the state of health (SoH) and state of charge (SoC). In order to overcome the difficulties caused by parameter fluctuations and real-world disturbances, this work presents a novel μ-analysis-based methodology designed to improve the resilience and accuracy of online SoC and SoH estimations in LIBs. In contrast to conventional techniques, the suggested strategy successfully manages both structured and unstructured uncertainties in battery systems by combining μ-analysis with model-based estimation. The framework creates an estimator that is resistant to parameter drift and outside perturbations by combining model-based estimation approaches with μ-analysis tools. Simulations using UDDS, US06, and HWFET driving cycles are used to verify its performance. When evaluating battery health and condition in dynamic and uncertain operating scenarios, the μ-analysis-based estimator demonstrates superior accuracy compared to conventional H∞-pole placement filter methods. The proposed approach enhances system robustness, achieving an 8 dB improvement in disturbance attenuation, as verified through MATLAB/Simulink. Stability analysis reveals the μ-analysis controller maintains robust performance up to ‖∆‖∞ = 3.5 at 10 Hz, compared to only ‖∆‖∞ = 1.5 for the H∞-pole placement controller—demonstrating significantly greater tolerance to parameter variations and unmodeled dynamics. These capabilities make the μ-analysis approach particularly suitable for electric vehicle applications requiring next-generation battery management systems.

Load Frequency Control (LFC) is essential for ensuring frequency stability in modern power systems subject to load fluctuations, uncertainties, and increasing renewable penetration. This paper introduces a novel hybrid control framework that unifies H∞ stability guarantees, H₂ performance, and pole placement for transient shaping. Its originality is threefold. First, it models load variation as a measurable disturbance (D₁₂ = 0, D₂₁ ≠ 0), departing from the standard assumption of an unknown input. This enables a low-order H∞ controller that improves transient response, enhances robustness, and reduces energy consumption. Second, the framework explicitly accounts for a wider spectrum of real-world uncertainties, including governor and turbine dynamics and the transmission-line synchronizing power coefficient. Third, it integrates explicit energy optimization to reduce mechanical stress and extend equipment lifespan. This strategy yields substantial energy savings by minimizing fuel use and operational costs. Simulation results confirm its superiority: the proposed H∞/H₂ pole placement controller with measured disturbances achieves a 98% reduction in control energy relative to a standard H∞ controller, along with a 70% reduction in overshoot and a drastic improvement in settling time—from 7 s to 0.2 s—compared to a conventional H∞/H₂ controller. These results establish the proposed framework as a new benchmark for robust, efficient, and high-performance LFC design.