The study explores the application of the nodal analysis approach to the analysis of distributed electric power networks using the IEEE-30 bus system as a case study. Since nodal analysis aids in the creation of voltage contour maps, power flow, and system stability, it has become standard practice for all power systems engineers to be proficient in it. The primary objective is to evaluate the suitability of both the traditional and advanced nodal approaches for application in power systems dynamic models, with a focus on dispersed networks and renewable resource utilization. Nodal analysis, for instance, can be used to model systems with various configurations, including those with interfaced multiple variable generation sources, such as wind and solar, within a distributed system. This in turn introduces additional factors, such as scalability, timely processing, and information management. In this regard, the study’s conclusions also concentrated on power flow calculations and matrix treatment techniques for the stability of operational systems. Furthermore, in relation to potential smart grid functions, it discusses the integration of RE technologies and the ensuing impacts on power quality and system stability. The study concludes by highlighting the most pertinent areas for further investigation, including the use of machine learning methods and other strategies to address the difficulties presented by emerging power systems.
Photovoltaic (PV) systems exhibit nonlinear behavior and are highly affected by environmental variations such as solar irradiance and temperature. These effects become more pronounced under non-uniform irradiance conditions. In this work, a high-efficiency maximum power-point tracking method is proposed using a Type-2 fuzzy logic controller integrated with a modified Self-Lift Luo DC–DC converter. The Type-2 fuzzy MPPT controller manages system uncertainties and generates an adaptive control signal to regulate the converter duty-cycle, thereby enabling the PV system to operate at its maximum power-point. The modified Luo converter, based on a switched-inductor structure, provides high voltage gain, improved efficiency, and reduced power losses. The complete system is developed and evaluated in MATLAB/Simulink under various operating environments, including partial shading scenarios. Simulation results indicate that the proposed method achieves improved tracking capability, faster dynamic response, increased voltage gain, reduced settling time, and enhanced power quality with low total harmonic distortion.
India’s transitional shift towards electric mobility, which necessitates the strategically planning and requires expandable charging infrastructures. The present scenarios of charging network, however, is asymmetrically spread mainly serving two- and three-wheelers. This inequality limits the accessibility for four-wheeler users and complicates the intercity travel. Contrasting the previous studies on EV charging infrastructure, which are primarily descriptive or limited to siting/optimization models, this study offers a reproducible, data-driven classification and validation stream. It is (i) statistically validates station groupings using the Silhouette, Davies-Bouldin, and Calinski-Harabasz indices along the Elbow method, (ii) rigorously tests classifier generalizability through stratified cross-validation on a leakage-free feature set, and (iii) correlates station-level operational attributes with their geospatial spreading to identify regional infrastructure constriants, which are only partially addressed, if at all, in the studies reviewed in Table 1. The study presents data-driven methodologies for classifying and analyzing the electric vehicle (EV) charging stations in India using K-means clustering and supervised machine learning methods. Data is taken from a publicly accessible repository of 2705 charging-station sessions across 25 major Indian cities—including power rating, cost per unit, charging duration, and vehicle compatibility—were refined to identify four distinct operational categories of charging stations: Level-1 slow charging, Level-2 fast AC charging, Level-3 DC fast charging, and battery swapping. Cluster quality was assessed using the Silhouette, Davies-Bouldin, and Calinski-Harabasz indices. A Random Forest classifier trained on autonomously measured, non-definitional features (capacity, cost per unit, and vehicle type) is evaluated using a stratified holdout test set and 5-fold cross-validation to ensure generalizability. The analysis specifies that approximately 74.9
Reliable fault detection and classification in electrical transmission lines are essential for maintaining power-system stability, reliability, and uninterrupted energy delivery. However, conventional transmission line fault diagnosis methods often suffer from reduced accuracy, sensitivity to noise, and limited capability to handle nonlinear and non-stationary fault signals. To address these challenges, this research proposes a Hybrid Artificial Neural Network with Osprey Optimization Algorithm (HANOA) integrated with Ensemble Empirical Mode Decomposition and Wavelet Transform (EEMD-WT) for intelligent transmission-line fault diagnosis. EEMD-WT are employed to preprocess current and voltage signals, respectively, enabling effective extraction of fault-sensitive time–frequency features while suppressing noise and disturbances. The extracted features are subsequently classified using the proposed HANOA model, in which network parameters are optimally tuned to enhance convergence behavior, minimize classification error, and improve fault-classification performance. The proposed framework is implemented in MATLAB and evaluated using a synthesized transmission-line fault dataset containing multiple fault categories and operating conditions. Experimental results demonstrate that the proposed framework achieves an accuracy of 99.87
The presented paper conducts a comprehensive analysis of several Demand Response (DR) programs, advanced devices, and load planning algorithms, offering a detailed assessment based on insights derived from previous studies on Energy Management Systems (EMS). Over the past decade, there has been a noteworthy increase in the generation of electric power from renewable sources, with solar energy experiencing remarkable development in electricity production within the broader landscape of available renewable energy options. Given the pivotal role of solar photovoltaic (PV) energy in ensuring the stability and security of the grid, especially in the context of expansionary monetary policies, the paper underscores the essential nature of reliable PV power generation forecasting. The study references a substantial number of research endeavors that have evaluated PV electricity production from various perspectives. The growing utilization of renewable power is positioned as a strategy to mitigate the adverse environmental impacts of greenhouse gas emissions. Different modeling processes have been explored and adopted in an effort to enhance the predictive accuracy of sustainable energy. The research highlights the increasing demand for renewable energy forecasts in facilitating transmission system supply and demand predictions. Natural events are discussed to elucidate their impact on the performance of PV systems. The paper systematically evaluates photovoltaic power production forecasts, emphasizing the importance of associating incoming data and modeling it before analysis. Various power generation modeling techniques, categorized across multiple classifications, are scrutinized for their effectiveness. The research critically reviews empirical investigations, encompassing quantitative and machine-learning models based on statistical methodologies.
