The performance of a photovoltaic (PV) power generation system could be improved through the optimal control and operation of a PV module which is one of the fundamental components of this system. Thus, an appropriate PV module model along with precise knowledge of its parameters is necessary. This paper proposes a novel technique to estimate the source current, the saturation current of diodes, the shunt resistance, the series resistance, the ideality coefficient of diodes and the maximum power points (MPPs) of PV modules at the same time. This estimation problem can be described by the minimization of the root mean squared error (RMSE) of the powers obtained from the PV module through estimation and experiment. The improved stochastic fractal search (ISFS) algorithm is proposed to solve this minimization with two modifications. The first replaces the logarithmic function with the exponential function in the standard deviation of the diffusion technique to improve the exploration ability efficiently in the search space. The second utilizes the sine map instead of the uniform distribution in both the diffusion and update techniques for improving the performance of the ISFS algorithm. Numerical results demonstrate the remarkable ability of the ISFS algorithm in obtaining both the model parameters and MPPs of the PV module with high accuracy. The comparison shows that the ISFS algorithm outperforms other meta-heuristic algorithms such as a stochastic fractal search (SFS) algorithm, a particle swarm optimization (PSO) algorithm, and an improved particle swarm optimization (IPSO) algorithm in the proposed parameter estimation application.

A novel strategy is proposed to tackle an optimal dispatch of a microgrid in response to dynamic conditions, utilizing a water wave optimization (WWO) algorithm and considering a day-ahead load forecasting. Amongst meta-heuristic algorithms, the WWO algorithm stands out in terms of population size, parameter tuning, exploitation and exploration, convergence speed, as well as optimization mechanism. It leverages its ability to efficiently explore solution spaces and adapt to changing conditions. It is applied to the dynamic optimal dispatch of a microgrid with the uncertainty of load power considered and solved by day-ahead load forecasting. It dynamically adjusts the microgrid operation in response to these inputs, ensuring optimal decision-making in the face of varying load scenarios. With the competition of various day-ahead load forecasting techniques in the microgrid, a multi-variate linear regression (MLR) model shows its advantage features, being more transparent, more effective, and more robust than other techniques, especially transparent explainability, as well as simple and fast in model training. These are requirements to achieve the result of day-ahead load forecasting. Thus, the MLR model is proposed to forecast day-ahead load in the microgrid in this paper. The simulation results show that the percentage error ( PE ) between the MLR model-based forecasted and actual load powers is always less than 4.42%, the mean absolute percentage error ( MAPE ) of the forecasting result is 3.33%, and the execution time is 49 (s). These achievements meet the accurate and fast requirements. They are completely competitive with the results of using other techniques such as convolutional neural networks (CNN) and long short-term memory (LSTM), especially in the execution time. This has contributed to improving the efficiency of the dynamic optimal dispatch in the microgrid. Then, the diesel generation, battery energy storage, and total microgrid generation costs are 68.76 ($), 5.09 ($), and 73.85 ($) respectively by using the WWO algorithm which are better than those by using a genetic algorithm (GA), a non-dominated sorting genetic algorithm-II (NSGA-II), a particle swarm optimization (PSO) algorithm, and a transient search optimization (TSO) algorithm in the microgrid. The findings offer valuable insights for microgrid operators, energy planners, and policymakers seeking sustainable and cost-effective solutions for distributed energy resource management.

This paper proposes an integrated framework for reliability assessment, load growth evaluation, and optimal reconfiguration of islanded microgrids under increasing electricity demand. The studied microgrid consists of solar photovoltaic (PV) panels, wind turbines, and diesel generators, operating independently from the main grid. First, as load demand grows over time, the microgrid must be reassessed to ensure that reliability metrics such as Loss of Load Probability (LOLP), Energy Not Supplied (ENS), and Loss of Power Supply Probability (LPSP) remain within acceptable limits. Second, a maximum allowable load growth is determined under current generation capacity constraints. Third, a long-term load forecasting model based on the Long Short-Term Memory (LSTM) technique is developed to predict future demand accurately. Fourth, if reliability thresholds are violated, the microgrid is reconfigured by optimally expanding generation capacity, focusing on diesel generators and battery storage units. To address the complex, mixedvariable nature of the reconfiguration problem, the Fick’s Law Optimization (FLO) algorithm is proposed. The FLO algorithm is compared with other metaheuristic algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) algorithm, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Artificial Bee Colony (ABC) algorithm, Grey Wolf Optimizer (GWO) algorithm, Equilibrium Optimizer (EO) algorithm, Runge-Kutta Optimizer (RUN) algorithm, and Honey Badger Algorithms (HBA) demonstrating superior performance in convergence speed, exploration–exploitation balance, and global optimality. Numerical results validate the proposed approach, confirming its effectiveness in ensuring reliable and cost-efficient microgrid operation under load growth scenarios.

Wind energy has been extensively studied worldwide to advance technology, reduce operating costs, and improve performance. A key challenge in this field is ensuring the optimal performance of wind turbines through proactive and effective maintenance strategies. In particular, wind turbine blade inspection and fault detection play an important role in minimizing the risk of unexpected failures, downtime, and operational disruptions. Although predictive maintenance methods based on machine learning, deep learning, and traditional visual inspection have been widely studied, detecting small faults from aerial images remains a major challenge. The main obstacles include data shortages, high computational complexity, limited labelled datasets, and difficulty in accurately identifying faults under real-world conditions. Notably, one of the most pressing problems that modern deep learning models face today is the detection of small-sized objects in images. To address these challenges, we propose an improved model based on the You Only Look Once version 12n model, which enhances the accuracy of wind turbine blade surface damage detection while maintaining real-time processing capability. The improvements are made by adding a very small target Head and removing the two Heads for medium and large targets. In addition, in the backbone part, we also propose to remove a Convolution module and an Area Attention Concatenate-Convolution-Fusion module and add an improved SoftPool Feature Spatial Pyramid Pooling - Fast module to increase the feature extraction ability while maintaining the complexity of the model. The proposed model not only optimizes wind turbine maintenance efficiency but also contributes to advancements in the field of computer vision.