The proliferation of not only power electronics supported consumption technologies but also the expansion of the renewable-based distributed generation (DG) systems has given rise to severe power quality (PQ) phenomena in consort with the offered technical, economic and environmental benefits under deregulated environment. The forthcoming complexity of distribution power networks caused by incorporation of a large number of DG units in deregulated electricity market unquestionably makes PQ assessment procedure a quite cumbersome one. In present work, an analytic hierarchy process (AHP) inspired methodology is proposed for PQ assessment of distorted distribution power systems under the presence of renewable-based DGs. The proposed PQ assessment approach is based on formulating a unified power quality index (UPQI) for assessing the overall PQ performance of individual buses of the network along with the entire distribution network (DN) considered taking four PQ phenomena, viz. voltage harmonics, voltage sags, voltage unbalance and steady-state voltage profile at each bus into account. The proposed PQ assessment approach can streamline the PQ performance assessment as well as benchmarking procedure of modern DNs up to a thoroughgoing extent. The application significance of the presented methodology is established by utilizing it on an IEEE 13 bus test distribution system modified through incorporating the nonlinear loads and DG systems based on three types of RES namely, photovoltaic (PV), wind and fuel cell, in MATLAB/Simulink environment. The results achieved validates the efficacy of the presented approach in assessing the overall PQ performance of each of the buses and the entire DN along with benchmarking it with respect to the threshold level of unity. Based on obtained results, also the comparative analysis is performed among PQ performances of DN with selected three RES based DGs. Moreover, the impact of the employing the custom power devices (CPDs) as well as excessive penetration level of renewable energy over PQ performance of distribution network, are also investigated by the application of the formulated index.

The main intent of the cloud computing to provide utilities to the demands of the users that are booming day by day. To meet the requirements, existing scheduling algorithms focus on the improving the performance and neglecting the energy consumed to fulfill those demands. Hence, we propose a new Hybrid Scheduling Algorithm (HS) which is based on the Genetic Algorithm (GA) and Flower Pollination based Algorithm (FPA) for cloud environments. The proposed scheduling algorithm has surpassed in terms of performance across various parameters, i.e. completion time, resource utilization, cost of computation, and energy consumption for both cloud environments than the existing scheduling algorithms (GA and FPA). The simulation results revealed that HS has demonstrated maximum resource utilization with minimum energy consumption in less completion time for the execution of the tasks as compared to the existing scheduling algorithms in both environments. The simulation results have shown that HS has utilization of the resources, 36% better than GA and 16% better than FPA in homogeneous environment whereas in heterogenous environment, HS has performed 12% better than GA and 3.8% better than FPA. The performance of HS has an improvement of 2.6% from FPA and 6.9% from the GA for completion time in homogeneous environment whereas the completion time of the HS is reduced by 17.8% from FPA and 33.7% from GA in heterogeneous environment. For energy consumption, HS has improved 22% than FPA and 11% from GA in the homogeneous environment and HS is 4% better than FPA and 14% from GA in heterogeneous environment.

Speech impairment limits a person’s capacity for oral and auditory communication. Improvements in communication between the deaf and the general public can be progressed by a real-time sign language detector. Recent studies have contributed to make progress in motion and gesture identification processes using Deep Learning (DL) methods and computer vision. But the development of static and dynamic sign language recognition (SLR) models is still a challenging area of research. The difficulty is in obtaining an appropriate model that addresses the challenges of continuous signs that are independent of the signer. Different signers’ speeds, durations, and many other factors make it challenging to create a model with high accuracy and continuity. This study mainly focused on SLR using a modified DL and hybrid optimization approach. Notably, spatial and geometric-based features are extracted via the Visual Geometry Group 16 (VGG16), and motion features are extracted using the optical flow approach. A new DL model, CNNSa-LSTM, is a combination of a Convolutional Neural Network (CNN), Self-Attention (SA), and Long-Short-Term Memory (LSTM) to identify sign language. This model is developed for feature extraction by combining CNNs for spatial analysis with SA mechanisms for focusing on relevant features, while LSTM effectively models temporal dependencies. The proposed CNNSa-LSTM model enhances performance in tasks involving complex, sequential data, such as sign language processing. Besides, a Hybrid Optimizer (HO) is proposed using the Hippopotamus Optimization Algorithm (HOA) and the Pathfinder Algorithm (PFA). The proposed model has been implemented in Python, and it has been evaluated over the existing models in terms of accuracy (98.7%), sensitivity (98.2%), precision (98.5%), Word Error Rate (WER) (0.131), Sign Error Rate (SER) (0.114), and Normalized Discounted Cumulative Gain (NDCG) (98%) as well. The proposed model has recorded the highest accuracy of 98.7%.

A photovoltaic-powered electric vehicle is a complex system that necessitates the use of a high-performance control algorithm. This paper aims to boost the performance of a photovoltaic system by employing a suitable algorithm to control the power interface. The main goal is to find an effective and optimal control law that will enable the photovoltaic generator (GPV) to generate the maximum amount of power possible. The main facts dealt with in this article are the mathematical simulation of the photovoltaic system, its function, and its characteristics, considering the synthesis of the step-up converter and the analysis of the maximum power point tracking algorithm. This study examines and compares two control techniques for extracting full power from the solar energy system. These two techniques are the classical “perturbation and observation” (P&O) method and the intelligent solution “particle swarm optimization (PSO) method.” The PSO solution is tested for two versions: the online PSO version and the table PSO version. The Simulink/MATLAB tool is used for simulation and comparative experiments based on the performance metrics provided. The study revealed that smart technology delivers improved efficiency than the classic edition.

Shunt active power filter (SAPF) belongs to the class of custom power devices (CPDs) and offers compensation to harmonics originated owing to customer side nonlinear loads, reactive power and unbalance in the distribution power networks functioning in current control mode (CCM). The performance of a SAPF as a harmonic compensator entirely relies on the control technique i.e. the precise detection of the harmonic current components of load that are necessary to be compensated. In the present work, a 3-phase SAPF, inspired by a Lyapunov function based control approach, has been designed for compensation of harmonics resulted in the feeder current owing to the customer side nonlinearity. A control law is determined in the proposed strategy which makes the derivative of the Lyapunov function consistently a negative one for an entire set of stable states. The DC-link capacitor voltage is regulated at constant reference through the proportional-integral (PI) controller. In this method rating of the shunt active power filter is considerably reduced than the other two broadly employed conventional methods. Furthermore, the harmonic compensation efficacy of the proposed Lyapunov function based SAPF is compared with the one based on other two conventional approaches under four different system scenarios namely a simple nonlinear load with and without utility side voltage distortion, a modified IEEE 13 bus test distribution system loaded with a 3-phase chopper fed direct current (DC) motor drive at a single bus and last especially for increasing the harmonic-constrained penetration level of renewable energy. Results obtained through simulation performed in MATLAB/Simulink shows that total harmonic distortion (THD) of source current and dynamic, as well as steady-state performance with Lyapunov function based controller, is significantly improved than the other two conventional methods. Also, the robust compensation performance of the SAPF empowers it to deal with the high penetration of renewable energy.