In recent years, there has been a remarkable increase in the usage of Deep Neural Networks (DNNs) for addressing and solving electrical field problems. This research primarily aims to present an advanced approach to classify different motor faults based on their time-series data by implementing a new Recurrent Neural Network (RNN) model that consists of mixed Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and two Fully Connected (FC) layers. The main idea of this study centers on developing one comprehensive model capable of categorizing primary motor faults. The proposed model is supposed to classify 10 different classes, extracted from the Machinery Fault Database (MaFaulDa), which are normal (no fault), vertical misalignment, horizontal misalignment, imbalance, overhang-ball, overhang-cage, overhang-outer race, underhang-ball, underhang-outer race, and underhang-cage. Classifying 10 different situations can be considered as a notable classification problem. Additionally, the learning period did not include any data augmentation, which reflects the model’s power in training over the available data. Significantly, the accuracy of the model is enhanced through setting precise values for hyperparameters, including network structure (number of layers and neurons), learning rate, regularization, optimizer type, number of epochs, and more. The obtained train-validation-test accuracies from the proposed model are 99.87%, 99.599%, and 99.48%, respectively. The accuracy of the model represents the highest accuracy between other publications. This advanced approach offers numerous advantages, including early-stage fault detection, improved robustness in industrial maintenance, and generating fast and intelligent alerts, thereby reducing the possible damages to the electrical instruments.

The highest piezoelectric performance of ZnO can be achieved by utilizing the piezoelectric operation along the c-axis due to its anisotropic permanent dipole moment. However, a 1-dimensional ZnO crystal is vulnerable to mechanical stress along the c-axis because it is susceptible to breaking. The synthesis of mechanically stable ZnO hexagonal, nanopyramidal arrays is achieved by modulating the premature oxidation of a Zn precursor on a fluorine-doped tin oxide (FTO) substrate during the chemical vapor depositon (CVD) process. The as-synthesized ZnO step-wise nanostructures show a piezoelectric charge coefficient of d₃₃ = 13.20 pm V⁻¹ measured by piezoresponse force microscopy (PFM). Post-synthesis annealing under an oxygen condition improves the piezoelectric response by 98% to a value of d₃₃ = 26.10 pm V⁻¹. The growth mechanism, morphology, crystal structure, and crystal defects are characterized and discussed in this paper.

This study highlights the intricate relationship between Gray-Level Co-occurrence Matrix (GLCM) metrics and machine learning model performance in the context of plant disease identification. It emphasizes the importance of rigorous dataset evaluation and selection protocols to ensure reliable and generalizable classification outcomes. Through a comprehensive examination of publicly available plant disease datasets, focusing on their performance as measured by GLCM metrics, this research identified dataset_2 (D2), a database of leaf images, as the top performer across all GLCM analyses. These datasets were then utilized to train the DarkNet19 deep learning model, with D2 exhibiting superior performance in both GLCM analysis and DarkNet19 training (achieving about 91% testing accuracy) according to performance metrics such as accuracy, precision, recall, and F1-score. The datasets other than dataset_1 and 2 exhibited significantly low classification performance, particularly in supporting GLCM analysis. The findings underscore the need for transparency and rigor in dataset selection, particularly given the abundance of similar datasets in the literature and the growing trend of utilizing deep learning methods in future scientific research.

The optimal design of a proportional-integral-derivative controller with two cascaded first-order low-pass filters (PID-FF) for non-ideal buck converters faces significant challenges, including effective disturbance rejection, robustness to parameter variations, and the mitigation of high-frequency signal noise, with existing approaches often struggling and leading to suboptimal performance in practical applications. This study addresses these challenges by introducing a constraint on the open-loop crossover frequency to mitigate high-frequency noise and ensuring the controller prioritizes maintaining constant output voltage and robust responsiveness to input voltage and load current variations. This study also introduces an innovative metaheuristic algorithm, the opposition-based snake optimizer with pattern search (OSOPS), designed to address these limitations. OSOPS enhances the Snake Optimizer (SO) by integrating opposition-based learning (OBL) and Pattern Search (PS), thereby improving its exploration and exploitation capabilities. The proposed algorithm design includes a crossover frequency constraint aimed at counteracting high-frequency noise and ensuring robust performance under diverse disturbances. The efficacy of the OSOPS algorithm is demonstrated through rigorous statistical box plot analysis and convergence response comparisons with the original SO algorithm. Additionally, we systematically compare the performance of the OSOPS-based PID–FF–controlled non-ideal buck converter system against systems utilizing the original SO algorithm and the classical pole placement (PP) method. This evaluation encompasses transient and frequency responses, disturbance rejection, and robustness analysis. The results reveal that the OSOPS-based system outperforms the SO- and PP-based systems with 14.21 % and 32.10 % faster rise times, along with 15.38 % and 84.95 % faster settling times, respectively. The OSOPS and SO systems also exhibit higher bandwidths, exceeding the PP-based system by 18.74 % and 17.03 %, respectively. By addressing the key challenges in PID-FF controller design for non-ideal buck converters, this study provides a substantial advancement in control strategy, promising enhanced performance in practical applications.

This paper presents a thorough investigation into the implementation of Digital Twin technology for the fault detection of Photovoltaic (PV) panels. With the increasing deployment of PV systems worldwide, it is crucial to ensure their reliable performance and early detection of faults. Digital Twin technology offers a promising approach to replicate and simulate the behavior of physical PV panels in real-time, enabling accurate fault detection and predictive maintenance. The paper explores the principles of Digital Twin, its application in the context of PV panels, and the development of an efficient fault detection framework. The proposed methodology is validated using real-world data and compared with traditional fault detection techniques, showcasing the potential of Digital Twin technology in improving the reliability and performance of PV systems.

An electric power network can be evolved into smart grids, which are measured by providing energy efficiency and improving the available resources. With the development of software and hardware elements, the decision-making mechanism of existing smart grids is transformed into more robust uninterrupted and economical energy management systems. In this study, a cognitive-based algorithm using dynamic energy management flexibility, storage and energy management algorithm and cloud computing architecture is proposed. Using this approach, an uninterrupted and economical energy management system can be planned. In addition, the proposed approach provides the optimization of supply and demand sides.