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This article describes a method for improving power grid voltage profiles by more effectively regulating reactive power through the integration of hybrid renewable energy systems (HRES) in smart grids. The unpredictable nature of renewable energy sources RES, such as wind turbines and solar systems, causes an unstable voltage profile throughout the grid, underscoring the problem of voltage fluctuation in power grids. This article proposes DSTATCOM, a reactive power adjustment device, to address these voltage fluctuations and provide the grid with the required var. DSTATCOM assists in preserving voltage stability by consistently lowering the voltage drop, which guarantees an increase in active power flow. Therefore, the overall voltage profile throughout the electrical grid gets improved. Convolutional neural networks (CNN) with bidirectional long short-term memory (BiLSTM) combined to form the proposed solution, which controls and maximizes DSTATCOM performance. These advanced artificial intelligence (AI) methods helps in dynamic reactive power management, improving the grid’s voltage profile and DSTATCOM’s performance. In smart grid situations, this method works well for real-time voltage regulation since CNNs are employed for feature extraction and BiLSTM helps capture temporal dependencies in the grid’s power behavior. The CNN-BiLSTM network’s weights are also adjusted using an adaptive parrot optimizer (APO). The proposed approach was implemented using the MATLAB/Simulink environment, and three different scenarios were used to assess its performance. Simulation results confirm that the method achieved up to 33.4% loss reduction, improved voltage stability index (VSI) to 1.02 p.u, minimized total harmonic distortion (THD) below 1.7%, and cut settling time to 0.075 s. The hybrid PV/wind setup ensured superior voltage stability, while the model attained high prediction accuracy with an R 2 of 0.9672 and RMSE of 3.0094. By controlling reactive power balance, the created system assures grid stability, improves the voltage profile, and reduces power loss.

In recent years Intelligent Transportation System (ITS) has been growing interest in the development of vehicular communication technology. The traffic in India shows considerable fluctuations owing to the static and dynamic characteristics of road vehicles in VANET (Vehicular Adhoc Network). These vehicles take up a convenient side lane position on the road, disregarding lane discipline. They utilize the opposing lane to overtake slower-moving vehicles, even when there are oncoming vehicles approaching. The primary objective of this study is to minimize injuries resulting from vehicle interactions in mixed traffic conditions on undivided roads. This is achieved through the implementation of the Modified Manhattan grid topology, which primarily serves to guide drivers in the correct path when navigating undivided roads. Furthermore, the Fuzzy C-Means algorithm (FCM) is applied to detect potential jamming attackers, while the Modified Fisheye State Routing (MFSR) Algorithm is employed to minimize the amount of information exchanged among vehicles. Subsequently, the Particle Swarm Optimization (PSO) algorithm is developed to enhance the accuracy of determining the coordinates of jamming attackers within individual clusters. The effectiveness of the outcomes is affirmed through the utilization of the Fuzzy C-Means algorithm, showcasing a notable 30% reduction in the number of attackers, along with the attainment of a 70% accuracy rate in this research endeavor.

Peer-to-Peer (P2P) electricity trading is a significant research area that offers maximum fulfilment for both prosumer and consumer. It also decreases the quantity of line loss incurred in Smart Grid (SG). But, uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and consumer. In recent times, numerous Machine Learning (ML)-enabled load predictive techniques have been developed, while most of the existing studies did not consider its implicit features, optimal parameter selection, and prediction stability. In order to overcome fulfill this research gap, the current research paper presents a new Multi-Objective Grasshopper Optimisation Algorithm (MOGOA) with Deep Extreme Learning Machine (DELM)-based short-term load predictive technique i.e., MOGOA-DELM model for P2P Energy Trading (ET) in SGs. The proposed MOGOA-DELM model involves four distinct stages of operations namely, data cleaning, Feature Selection (FS), prediction, and parameter optimization. In addition, MOGOA-based FS technique is utilized in the selection of optimum subset of features. Besides, DELM-based predictive model is also applied in forecasting the load requirements. The proposed MOGOA model is also applied in FS and the selection of optimal DELM parameters to improve the predictive outcome. To inspect the effectual outcome of the proposed MOGOA-DELM model, a series of simulations was performed using UK Smart Meter dataset. In the experimentation procedure, the proposed model achieved the highest accuracy of 85.80% and the results established the superiority of the proposed model in predicting the testing data.

Wireless sensor network (WSN) is an emerging technology which find useful in several application areas such as healthcare, environmental monitoring, border surveillance, etc. Several issues that exist in the designing of WSN are node localization, coverage, energy efficiency, security, and so on. In spite of the issues, node localization is considered an important issue, which intends to calculate the coordinate points of unknown nodes with the assistance of anchors. The efficiency of the WSN can be considerably influenced by the node localization accuracy. Therefore, this paper presents a modified search and rescue optimization based node localization technique (MSRO-NLT) for WSN. The major aim of the MSRO-NLT technique is to determine the positioning of the unknown nodes in the WSN. Since the traditional search and rescue optimization (SRO) algorithm suffers from the local optima problem with an increase in number of iterations, MSRO algorithm is developed by the incorporation of chaotic maps to improvise the diversity of the technique. The application of the concept of chaotic map to the characteristics of the traditional SRO algorithm helps to achieve better exploration ability of the MSRO algorithm. In order to validate the effective node localization performance of the MSRO-NLT algorithm, a set of simulations were performed to highlight the supremacy of the presented model. A detailed comparative results analysis showcased the betterment of the MSRO-NLT technique over the other compared methods in terms of different measures.