This paper investigated the performance of the sliding mode control technique for dc/dc converter using frequency response method. The applications of the step down type switching regulator) buck converter (are found in the devices that use batteries as power source like laptop, cell phones, electric vehicle, and recently, it has also been used in the renewable energy processing, as a maximum output power can be achieved at higher efficiency. In order to optimize the efficiency and for convenient power management, the issues like power on transients, the effect of load variation, Switching and Electromagnetic interference (EMI) losses has to be overcome for which controllers are used. In the proposed method, pulse width modulation (PWM) based on proportional-integral-derivative sliding mode voltage controller (PID SMVC) is designed for a buck converter and the response for appropriate control parameters has been obtained. The system stability has been examined and analyzed from the performance characteristics, which shows clearly that the buck converter controlled by the sliding mode controller has fast dynamic response and it’s very efficient for various applications.

The digital twin (DT) technology implementation in photovoltaic (PV) systems provides an innovative approach to real-time performance monitoring and predictive maintenance. In this paper, an end-to-end DT framework for real-time performance analysis, fault detection, and optimization of a 250 W PV system is proposed. A physics-based equation and AI-based prediction hybrid DT model is developed through MATLAB/Simulink, trained from real data acquired by means of a testbed. The DT simulates the dynamic physical PV system behavior and adjusts itself using self-correcting algorithms to enhance precision in prediction and forecast power output at high fidelity. Results indicate that the DT gives the true response of the PV system with very small differences attributable to model approximations and sensor faults, 95% error minimization after compensation, and a root mean square error (RMSE) of 2.8 W, indicating its applicability for real-time monitoring and predictive main-maintenance. The work here focuses on the feasibility of applying DTs towards the autonomous optimization of distributed renewable energy systems.

Nowadays, several electronic devices as field programmable gate arrays (FPGA) and dSPACE controllers are used to implement the maximum power point tracking (MPPT) for the photovoltaic (PV) systems applications. In contrast, the PIC microcontrollers are recommended to be used due to its low cost and simplicity compared with the other hardware devices. In this paper, the Arduino microcontroller is used to validate the function of perturb and observe (P&O) MPPT controller used with a stand-alone PV system under constant climate conditions and step variations in the connected load. From the comparison of experimental and simulation results, the available maximum power of the solar panel is successfully extracted by using Arduino Uno board irrespective of the variable load conditions.

There is an increasing shortage of water in dry and semiarid areas due to population growth, climate variability, and a lack of fresh water. Combining clean energy with desalination technology is an effective way to sustainably provide clean water in these areas, and this paper proposes a method for operating a renewable energy management system (EMS) with a combined wind turbine/battery‐powered reverse osmosis (RO) desalination system under these constraints. The RO will be modelled as a flexible electric load and water storage for buffering long‐term supply reliability. A detailed simulation of operations will be carried out using actual wind data from dry areas, similar to those in Kuwait and Iraq, under seasonal conditions. Two example scenarios will be studied: one for low‐wind (January) and another for high‐wind (July). The proposed EMS is evaluated against a conventional constant‐load baseline strategy using key performance indicators, including battery state‐of‐charge (SOC) stability, degradation index, wind energy utilisation, and freshwater production. Simulation studies have shown that the smart strategy method offers substantial improvements in a system's overall performance. For instance, in winter scenarios, this strategy reduces battery degradation by 49% while maintaining a comparable water volume from the facility. Similar improvements were observed during summer periods with high winds, with degradation reduced by 74%. The proposed strategy provided much greater stability in battery SOC variations and improved the operational lifespan of the batteries.

Reverse osmosis (RO) desalination is a major technology for freshwater production in arid regions, but its energy demand becomes more challenging when the system is supplied by variable renewable energy. This study presents an offline digital-twin-assisted decision-support framework for dynamic RO under Kuwait solar-availability conditions. Within this framework, the predictive models are driven primarily by the dynamic RO process variables, while NASA Prediction Of Worldwide Energy Resources (POWER) data provide the Kuwait solar-availability context, and the PV power margin serves as a scenario-level energy indicator. The purpose is to predict instantaneous permeate flow rate, estimate specific energy consumption, and identify energy-efficient operating conditions using machine learning. Kuwait City was used as the solar case-study location. Hourly solar and meteorological data were obtained from NASA POWER, while dynamic RO membrane data were obtained from the open experimental wave desalination dataset published by the National Renewable Energy Laboratory (NREL) through Data.gov and the Marine and Hydrokinetic Data Repository. The RO dataset includes steady-state, ramp, sinusoidal, and Wave Energy Converter SIMulator (WEC-Sim) pressure/flow experiments. The process-flow image used in the system description was also taken from the same NREL dataset and is cited in the figure caption. The raw RO files were cleaned, harmonized, and transformed into a process-informed modeling dataset. Derived features included pressure rate, recovery ratio, salt rejection, estimated pump power, specific energy consumption (SEC), PV power margin, and rolling pressure/flow features. Three supervised regression models were tested: Gradient Boosting, Random Forest, and XGBoost. A representative subset of 60,000 records was used to preserve the main experimental conditions while reducing redundancy in the densely sampled sequential data. Results show that permeate flow rate can be predicted with high accuracy using Gradient Boosting (R² = 0.981; RMSE = 0.161 L/min). The moderate energy prediction performance yielded an R² of 0.654 and RMSE of 7.570 kWh/m³ for Random Forest. The accuracy of permeate conductivity predictions was lower (R² = 0.257; RMSE = 245.44 µS/cm) because membrane and feed characterizing parameters should be included for an adequate water quality control. The proposed approach is best suited as an offline decision-support framework for dynamic RO process analysis.

This paper presents study, modeling and simulation of the piezoelectric material works as transformer (piezoelectric transformer (PT)) in power electronic circuits, comparisons are made with the regular transformer (iron core) works in the same circuit, the tested circuit is the full bridge converter which used in the simulation as dc power supply circuit. As a result, a detailed simulation for both the piezoelectric transformer and traditional transformer are achieved, as well as the output voltage from the dc power supply is tested by varying the load resistance. The dc power supply circuit has been simulated using PSIM (V9.1) power electronic circuit simulation software.