The electricity production via these sources of energy, offers a bigger safety of supply to the consumers while respecting the environment. For that reason, the principal objective of this paper is to study and control the photovoltaic lighting energy storage system. We presented the study of the whole PV system such as solar panels, DC chopper, batteries with account of all conditions of the sites of installation (period of sunshine and temperature). This study analyzed the integration of a photovoltaic power plant, super capacitor energy storage system, and lightning system. Our approach of sizing is based on the modeling of various components describing the functioning of every part of the installation among others. We can mention the production, the consumption and the storage of energy through solar battery.

The main aim of the research work presented in this paper consists of proposing an effective control scheme for a grid-connected single-phase photovoltaic (PV) system to enhance not only the power quality at the point of common coupling (PCC) but also to operate with a maximum power point tracking (MPPT) controller. Moreover, an orthogonal signal generator (OSG) module for effective grid synchronization, a current reference generation controller, and a PWM generating block have also been designed and included in this paper. The proposed control strategy allows the MPPT controller to switch to faulty mode and maintains the voltage according to network requirements using an adaptive neuro-fuzzy inference system (ANFIS)-based control whenever a fault occurs at the PCC. The performance of the analyzed control strategy, which is based on the static compensation of the DC-link voltage fluctuations in a grid-connected inverter powered by PV, is further explored through simulations in MATLAB, and the results are included in this paper. Moreover, the control scheme is implemented experimentally using a dSPACE DS 1104 control board and then assessed on a small laboratory-scale single-phase PV system that is subjected to some fault scenarios. The simulation and experimental results have shown improved power quality and robustness against grid fluctuations, resulting in better dynamic performance.

The growing concern about the energy crisis and environmental protection has caused a growing interest in wind power generation systems. Researchers and engineers urgently need to create new multiphase induction machines for the production of wind energy, since they are essential parts of wind turbines. This study offers control and stability analysis of a multiphase induction machine based on the entropy stability requirements for its linearized model. The generated model was used to assess the on-load properties of the multiphase induction machine and calculate its steady-state parameters under each operating circumstance. According to the analysis, the eigenvalues depend on the machine parameters, with the excitation capacitance and speed variation being the most important. Stabilization of the multiphase induction machine is the main focus of the singular values, which vary according to its variables. The simulated results include an examination of a multiphase induction machine steady state for voltage build-up at various types of load.

Electric Vehicles (EVs) are becoming more and more common. In addition to their ecological advantages, EVs have demonstrated their efficiency in curtailing travel expenses by transitioning from gasoline to electricity, which is significantly less costly. This study proposes a Brushless Permanent Magnet Motor (BPMM) design for EVs, models the influence of average and ripple torque, and evaluates its efficiency compared to other designs. The validity of the proposed design was supported by simulation results.