The unpredictable nature of renewable energy sources, such as wind and solar, makes them unreliable sources of energy for the power system. Nevertheless, with the advancement in the field of artificial intelligence (AI), one can predict the availability of solar and wind energy in the short, medium, and long term with fairly high accuracy. As such, this research work aims to develop a machine-learning-based framework for forecasting global horizontal irradiance (GHI) for Muscat, Oman. The proposed framework includes a data preprocessing stage, where the missing entries in the acquired data are imputed using the mean value imputation method. Afterward, data scaling is carried out to avoid the overfitting/underfitting of the model. Features such as the GHI cloudy sky index, the GHI clear sky index, global normal irradiance (GNI) for a cloudy sky, GNI for a clear sky, direct normal irradiance (DNI) for a cloudy sky, and DNI for a clear sky are extracted. After analyzing the correlation between the abovementioned features, model training and the testing procedure are initiated. In this research, different models, named Linear Regression (LR), Support Vector Machine (SVR), KNN Regressor, Decision Forest Regressor, XGBoost Regressor, Neural Network (NN), Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Random Forest Regressor, Categorical Boosting (CatBoost), Deep Autoregressive (DeepAR), and Facebook Prophet, are trained and tested under both identical features and a training–testing ratio. The model evaluation metrics used in this study include the mean absolute error (MAE), the root mean squared error (RMSE), R², and mean bias deviation (MBD). Based on the outcomes of this study, it is concluded that the Facebook Prophet model outperforms all of the other utilized conventional machine learning models, with MAE, RMSE, and R² values of 9.876, 18.762, and 0.991 for the cloudy conditions and 11.613, 19.951 and 0.988 for the clean weather conditions, respectively. The mentioned error values are the lowest among all of the studied models, which makes Facebook Prophet the most accurate solar irradiance forecasting model for Muscat, Oman.

With respect to battery management and safe operation and maintenance scheduling of electric vehicles (EVs), it is very important to predict the remaining useful life (RUL) of lithium-ion batteries (LIBs). Accurate prediction of RUL can bring secure working conditions, avert internal and external failure, and, last, avoid any undesirable consequences. However, achieving accurate prediction of RUL is complicated for EV applications due to various reasons such as the complex operational characteristics, dynamic changes in the model parameters during the aging process, extraction of battery parameters, data preparation, and hyper-parameter tuning of the predictive model. This research proposes a novel approach that integrates Particle Swarm Optimization (PSO) with a multi-model technique for RUL prediction. The framework integrates many machine learning (ML) models and deep learning (DL) models. Combining domain knowledge, advanced optimization techniques, and learning models to make high-accuracy RUL predictions reduces maintenance costs and improves battery management systems. This study uses domain-driven feature engineering to extract battery-specific indicators, including voltage drops, charging time, and temperature fluctuations, to increase model accuracy. Among the evaluated models, LSTM demonstrates superior performance, achieving a mean absolute error (MAE) of 0.34, a root mean square error (RMSE) of 0.76, and an R² of 0.93, providing the best results in RUL prediction. The proposed research uniquely integrates PSO-based optimization with domain-driven feature engineering across multiple machine learning and deep learning models, demonstrating a unified and novel approach that significantly improves the prediction accuracy of RUL in LIBs.

The parasitic currents issue arises in induction machines operating at higher frequencies. The parasitic current flow is the main cause of premature degradation of winding insulation deterioration and bearing damage. The effect of electromagnetic interference (EMI) requires investigating an equivalent per-phase model of an induction motor (IM) at higher frequencies. The per-phase induction motor mathematical model of the IM drive for common mode (CM) configuration is developed to perform high-frequency analysis for drive operation. The high-frequency per-phase induction motor model MATLAB (2021b) is developed to generate the transfer function of IM drive and generate bode plots for both CM and differential mode (DM) impedance configuration at higher frequency. Initially, the IM typical frequency response without considerations of stray and parasitic effects is presented for normal behavior of the IM. In order to verify the simulated impedance parameters of 0.3 kW and 38 kW IMs with stray and parasitic components, a high-frequency response for magnitude and phase response is generated and compared to analyze per-phase induction motor model performance before and after resonance frequencies. The comparison of CM and DM bode plots validates the dominance of inductance and parasitic capacitance before and after the occurrence of resonance frequency, respectively. The analysis suggests that 38 kW IM resonance occurs in MHz range which exhibits much better performance compared to the 0.3 kW IM model.

Electricity theft in power grids results in significant economic losses for utility companies. While machine learning (ML) methods have shown promising results in detecting such frauds, they often suffer from low detection rates, leading to excessive physical inspections. In this study, we attempted to solve the above-mentioned problem using a novel approach. The proposed framework utilizes the intelligence of Siamese network architecture with the Triplet Loss function to detect electricity theft using a labeled dataset obtained from Multan Electric Power Company (MEPCO), Pakistan. The proposed method involves analyzing and comparing the consumption patterns of honest and fraudulent consumers, enabling the model to distinguish between the two categories with enhanced accuracy and detection rates. We incorporate advanced feature extraction techniques and data mining methods to transform raw consumption data into informative features, such as time-based consumption profiles and anomalous load behaviors, which are crucial for detecting abnormal patterns in electricity consumption. The refined dataset is then used to train the Siamese network, where the Triplet Loss function optimizes the model by maximizing the distance between dissimilar (fraudulent and honest) consumption patterns while minimizing the distance among similar ones. The results demonstrate that our proposed solution outperforms traditional methods by significantly improving accuracy (95.4%) and precision (92%). Eventually, the integration of feature extraction with Siamese networks and Triplet Loss offers a scalable and robust framework for enhancing the security and operational efficiency of power grids.

The parasitic currents issue arises at high frequency operation of induction motor. The flow of parasitic currents is the main cause of premature deterioration of winding insulation and damage to the ball bearing. In order to comprehend the effect of electromagnetic interference (EMI) an equivalent per-phase model of an induction motor should be investigated at higher frequencies. The per-phase induction motor mathematical model of induction motor drive for common mode (CM) configuration is developed to perform high frequency analysis for drive operation. The high frequency per phase induction motor model MATLAB model is developed to generate the transfer function of IM and generate bode plots for CM and differential mode (DM) impedance configuration for high frequency analysis. The induction motor typical frequency response without considerations of stray and parasitic effects is presented for normal behavior of IM. In order to verify the simulated impedance parameters of 0.3 kW and 38kW induction motor with stray and parasitic components. High frequency response for magnitude and phase response is generated and compared to analyze per phase induction motor model performance before and after resonance frequencies. The comparison of CM and DM bode plots validates the dominance of inductance and parasitic capacitance before and after the occurrence of resonance frequency respectively.