The effective integration of renewable energy sources (RES), such as solar and wind power, into smart grids is essential for advancing sustainable energy management. Hybrid inverters play a pivotal role in the conversion and distribution of this energy, but conventional approaches, including Static Resource Allocation (SRA) and Fixed Threshold Inverter Control (FTIC), frequently encounter inefficiencies, particularly in managing fluctuating renewable energy inputs and adapting to variable load demands. These inefficiencies lead to increased energy loss and a reduction in overall system performance. In response to these challenges, the Optimized Energy Storage and Hybrid Inverter Management Algorithm (OESHIMA) has been developed, employing machine learning for real-time data analysis and decision-making. By continuously monitoring energy production, storage capacity, and consumption patterns, OESHIMA dynamically optimizes energy allocation and inverter operations. Comparative analysis demonstrates that OESHIMA enhances energy efficiency by 0.25% and reduces energy loss by 0.20% when benchmarked against conventional methods. Furthermore, the algorithm extends the lifespan of energy storage systems by 0.15%, contributing to both sustainable and cost-efficient energy management within smart grids. These findings underscore the potential of OESHIMA in addressing the limitations of traditional energy management systems (EMSs) while improving hybrid inverter performance in the context of renewable energy integration.

To address the challenges in detecting surface defects on insulator iron caps, particularly due to the complex backgrounds that hinder accurate identification, an improved defect detection algorithm based on YOLOv8n, whose full name is You Only Look Once version 8 nano, was proposed. The C2f convolutional layers in both the backbone and neck networks were replaced by the C2f-Spatial and Channel Reconstruction Convolution (SCConv) convolutional network, which strengthens the model's capacity to extract detailed surface defect features. Additionally, a Convolutional Block Attention Module (CBAM) was incorporated after the Spatial Pyramid Pooling - Fast (SPPF) layer, enhancing the extraction of deep feature information. Furthermore, the original feature fusion method in YOLOv8n was replaced with a Bidirectional Feature Pyramid Network (BiFPN), significantly improving the detection accuracy. Extensive experiments conducted on a self-constructed dataset demonstrated the effectiveness of this approach, with improvements of 2.7% and 2.9% in mAP@0.5 and mAP@0.95, respectively. The results confirm that the proposed algorithm exhibits strong robustness and superior performance in detecting insulator iron cap defects under varied conditions.

The classification of fruit ripeness and detection of defects are critical processes in the agricultural industry to minimize losses during commercialization. This study evaluated the performance of three Convolutional Neural Network (CNN) architectures—Extreme Inception Network (XceptionNet), Wide Residual Network (Wide ResNet), and Inception Version 4 (Inception V4)—in predicting the ripeness and quality of tomatoes. A dataset comprising 2,589 images of beef tomatoes was assembled from Golden Fingers Farms and Ranches Limited, Abuja, Nigeria. The samples were categorized into six classes representing five progressive ripening stages and a defect class, based on the United States Department of Agriculture (USDA) colour chart. To enhance the dataset's size and diversity, image augmentation through geometric transformations was employed, increasing the dataset to 3,000 images. Fivefold cross-validation was conducted to ensure a robust evaluation of the models' performance. The Wide ResNet model demonstrated superior performance, achieving an average accuracy of 97.87%, surpassing the 96.85% and 96.23% achieved by XceptionNet and Inception V4, respectively. These findings underscore the potential of Wide ResNet as an effective tool for accurately detecting ripeness levels and defects in tomatoes. The comparative analysis highlights the effectiveness of deep learning (DL) techniques in addressing challenges in agricultural automation and quality assessment. The proposed methodology offers a scalable solution for implementing automated ripeness and defect detection systems, with significant implications for reducing waste and improving supply chain efficiency.

The traditional K-means clustering algorithm has unstable clustering results and low efficiency due to the random selection of initial cluster centres. To address the limitations, an improved K-means clustering algorithm based on adaptive guided differential evolution (AGDE-KM) was proposed. First, adaptive operators were designed to enhance global search capability in the early stages and accelerate convergence in later stages. Second, a multi-mutation strategy with a weighted coefficient was introduced to leverage the advantages of different mutation strategies during various evolutionary phases, balancing global and local search capabilities and expediting convergence. Third, a Gaussian perturbation crossover operation was proposed based on the best individual in the current population, providing individuals with superior evolution directions while preserving population diversity across dimensions, thereby avoiding the local optima of the algorithm. The optimal solution output at the end of the algorithm implementation was used as the initial cluster centres, replacing the cluster centres randomly selected by the traditional K-means clustering algorithm. The proposed algorithm was evaluated on public datasets from the UCI repository, including Vowel, Iris, and Glass, as well as a synthetic dataset (Jcdx). The sum of squared errors (SSE) was reduced by 5.65%, 19.59%, 13.31%, and 6.1%, respectively, compared to traditional K-means. Additionally, clustering time was decreased by 83.03%, 81.33%, 77.47%, and 92.63%, respectively. Experimental results demonstrate that the proposed improved algorithm significantly enhances convergence speed and optimisation capability, significantly improving the clustering effectiveness, efficiency, and stability.