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Rapid electric vehicle (EV) adoption has increased the need for public charging infrastructure. Public charging station (PCS) placement and size must be determined to enable efficient and sustainable charging services. This work proposes a novel method for optimal design and allocation of hybrid photovoltaic systems (HPVs) and PCSs in reconfigurable feeders. A novel LFHBA combines the strengths of the levy flight (LF) exploration technique and the honey badger algorithm (HBA) to solve the multi-objective function focused on distribution loss reduction, improve voltage profiles, and improve reliability index. To test the suggested technique, modified IEEE 69-bus distribution feeders are simulated with varied EV load penetrations. LFHBA is compared to basic HBA, butterfly optimization algorithm (BOA), pelican optimization algorithm (POA), pathfinder algorithm (PFA) in computing performance. The comparison analysis shows that LFHBA has lower target values and greater convergence. Reconfigurable feeder topology permits distribution network design changes, improving system dependability and minimizing power losses. In comparison to base case, EV penetration causes to raise the losses by 26.65%, by optimal allocation of PCSs alone causes to reduce the losses by 38.82%, optimal allocation of PVs and ONR causes reduce losses by 92.19%, whereas, simultaneous allocation of PCSs, and HPVs results to reduce losses by 96.47%. This study emphasizes the need of optimizing PCS placement and capacity with HPVs for efficient and sustainable charging services. As shown by its greater performance over other optimization methods, the LFHBA algorithm helps achieve these goals. Reconfigurable feeders and renewable energy sources improve system dependability and power losses, increasing EV charging infrastructure. [ABSTRACT FROM AUTHOR]

Renewable energy's (RE) broad acceptance can be attributed to market liberalization, as well as ecological and monetary benefits. Intermittent nature of renewable energy (RE) and unpredictable load behaviour lead to voltage aberrations and harmonic distortions in interconnected hybrid microgrids (HMG). Voltage quality and the harmonic distortion are all metrics used to evaluate power quality. Efficient control approaches are required to reduce harmonic distortions and improve voltage quality for steady power transmission. This research proposes a hybrid Grey Wolf supported sparrow search optimization algorithm (GWSSSOA) method for assessing voltage quality and harmonics in a microgrid that combines renewable energy sources with conventional power generation. To maximize the microgrid's control and operation, guarantee its dependability, and lessen its impact on the grid. Hybrid microgrids can benefit greatly from GWSSSOA's use in voltage quality and harmonic distortion assessment. The goal of this research is to use the GWSSSOA technique in conjunction with the PID controller to achieve real-time optimization of the controller's settings for minimizing harmonic distortions and maintaining stable voltage across the microgrid. The efficiency of the proposed approach is measured against that of alternative optimized controllers. The recommended controller was developed in the MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

The penetration of electric vehicle (EV) loads in the electrical distribution system (EDS) is anticipated to increase dramatically over the next few years in response to rising global warming and fuel price scenarios. Planning for charging infrastructure and renewable energy sources (RES) is urgently required in this context. By carefully combining public charging stations (PCSs) backed by photovoltaic (PVs) systems and distribution static compensators (DSTATCOMs), this study proposes an optimisation strategy to reduce the adverse effects of EV load penetration in EDS. The proposed multi-objective function aims to reduce the voltage deviation index and distribution losses while considering various operational constraints. By combining the voltage stability index (VSI) and opposition-based learning (OBL) strategy with honey badger algorithm (HBA), the optimisation problem is resolved with a smaller search space for global minima. The effectiveness of the proposed technique on IEEE 33-bus EDS was evaluated in various situations. The real power losses are reduced to 242.35 kW, 75.32 kW and 41.76 kW from 350.25 kW by optimally integrating (i) PCSs alone, (ii) simultaneous PCSs and PVs and (iii) simultaneous PCSs, PVs and DSTATCOMs, respectively. Further, the VSI of the EDS is enhanced 0.7057, 0.8727, 0.9018 from 0.6104, respectively. The computational characteristics of HBA were also measured and compared with those of the COA and HBA. In terms of target and computational time, the outcomes produced by IHBA are very competitive with those of COA and superior to those of HBA. Additionally, even with a high EV load penetration, the suggested methodology produces reduced distribution losses and an appropriate voltage profile in EDS. [ABSTRACT FROM AUTHOR]

Several studies in the literature have focused the use of optimum network reconfiguration (ONR) and/or optimal allocation of renewable energy (RE) based distributed generation (DG) methodologies to improve performance in electrical distribution networks (EDNs). However, a few studies have only focused at simultaneous ONR and DG allocation in the context of demand increase, network loading unpredictability, or RE power. Furthermore, there is no much focus on the influence of emerging electric vehicle (EV) load penetration on EDN performance. This study proposes a novel meta-heuristic strategy for solving the simultaneous optimal ONR and REbased DGs allocation problem for minimising distribution losses, improving voltage profile, and lowering greenhouse gas (GHG) emissions while considering the load penetration of electric vehicles (EVs). For solving the complexity inherent in the multi-objective optimization problem with numerous constraints and multi-type variables, a new and efficient honey badger algorithm (HBA) is proposed. On the IEEE 33-bus EDN, simulations are run with various EV load penetration levels. In terms of fitness function, HBA is observed to be superior to COA, TLBO, CSA, and PSO. As observed in the results, for 50% EV load penetration, the losses, AVD, and GHG emissions are increased to 1571.676 kW, 0.02658 p.u., and 25780.05 103 (lb/h), respectively. However, by having DGs optimally and with ONR, the losses, AVD, and GHG emissions are decreased to 77.08 kW, 0.00038 p.u., and 2175.01103 (lb/h), respectively. This indicates that the performance of EDN is improved significantly by having simultaneous DGs allocation and ONR solution irrespective of EV load penetration level. This also indicates the suitability of the proposed methodology for practical applications. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Distribution network is mainly affected by the end user load nature. Ruinous influence on power system operation based on the type of load used by the end user and due to the presence of non-linear loads causes Harmonics in power system. The concept of reducing the harmful effect of harmonics on power system attracted the research attentiveness. OBJECTIVES: To reduce these harmonics and reactive power problems FACTS devices are found reliable. Out of various FACTS devices, Active Power Filter (APF) is one FACTS device which identifies and controls the harmonic contamination in power system with non-linear loads. This work mainly presents a parallel APF concept for the compensation of harmonics to improve the reliability of the system. METHODS: The main contribution of this work is proposal of single control strategy for compensation of Power quality. Corrective back currents (for parallel APF) to compensate the identified harmonics are generated using modified d-q reference-based control methodology. RESULTS: The models are developed, and analysis is presented using MATLAB/SIMULINK software CONCLUSION: d-q reference theory control strategy is explained in detail. Load current, Source Voltage and DC link voltage which are measured using various sensors and send to processor. modified d-q reference the signals to be measured are same [ABSTRACT FROM AUTHOR]

In this paper, we propose a new hybrid Bivariate Complex Fourier Wavelet Regularized Deconvolution (Bi-ComForWaRD) that is an extension to the ComForWaRD algorithm, for medical imaging. This new algorithm is a two-step process, a global blur compensation using generalized Wiener filter and followed by a denoising algorithm using local adaptive Bivariate shrinkage function. It is a low-complexity denoising algorithm using the joint statistics of the wavelet coefficients and considers the statistical dependencies between the coefficients. And also, the performance of this system will be demonstrated on both the orthogonal wavelet transform and the dual-tree complex wavelet transform (DT-CWT) and some comparisons with the best available wavelet-based image denoising results will be given in order to illustrate the effectiveness of the system. [ABSTRACT FROM AUTHOR]