Electrical

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

This paper presents a modeling and performance evaluation of a five-phase PMBLDC motor with different controllers to analyze its transients and dynamic response with time domain specifications. Four different types of speed controllers, namely an Adaptive Neuro-Fuzzy Inference System (ANFIS), an Adaptive Hybrid fuzzy-PI, a Proportional Integral (PI) control and a Fuzzy Logic Control (FLC), were considered and compared for this purpose. The mathematical model of the five-phase Permanent Magnet Brushless Direct Current (PMBLDC) motor was developed and simulated using MATLAB/Simulink to analyze its performance. The simulation results for all controllers with step and linear load fluctuations were evaluated. It was evident that the ANFIS controller provided a better dynamic time domain response than the other controllers. It gave eight times the peak torque at starting, and settled at 92.6% of the rated speed, with negligible overshoot, a very low rise time and a quick settling time compared to other controllers, which are ideally suited for an electric vehicle (EV). A real-time experimental setup was also developed, and experiments were carried out to validate the simulation results.

School of Electrical Engineering

Chennai Campus

World University root node

Applied Sciences

Time Domain Investigation of Hybrid Intelligent Controllers Fed Five-Phase PMBLDC Motor Drive

Abstract: Power losses (PL) are one of the most—if not the most—vital concerns in power distribution networks (DN). With respect to sustainability, distribution network reconfiguration (DNR) is an effective course of action to minimize power losses. However, the optimal DNR is usually a nonconvex optimization process that necessitates the employment of powerful global optimization methods. This paper proposes a novel hybrid metaheuristic optimization (MO) method called the chaotic golden flower algorithm (CGFA) for PL minimization. As the name implies, the proposed method combines the golden search method with the flower pollination algorithm to multiply their benefits, guarantee the best solution, and reduce convergence time. The performance of the algorithm has been evaluated under different test systems, including the IEEE 33-bus, IEEE 69-bus, and IEEE 119-bus systems and the smart city (SC) network, each of which includes distributed-generation (DG) units and energy storage systems (ESS). In addition, the locations of tie-switches in the DN, which used to be considered as given information in previous studies, are assumed to be variable, and a branch-exchange adaption is included in the reconfiguration process. Furthermore, uncertainty analysis, such as bus and/or line fault conditions, are studied, and the performance of the proposed method is compared with other pioneering MO algorithms with minimal standard deviations ranging from 0.0012 to 0.0101. The case study of SC is considered and the obtained simulation results show the superiority of the algorithm in finding higher PL reduction under different scenarios, with the lowest standard deviations ranging from 0.012 to 0.0432

Energies

Distribution Network Reconfiguration Based on Hybrid Golden Flower Algorithm for Smart Cities Evolution

The aim of demand response concept is to mitigate the consumption of electric utility consumers to a reasonable consumption level so as to balance the demand-supply requirements. In this current research paper, demand response is considered as a multi-objective optimal economic emission dispatch issue to be addressed. The authors propose a novel nature-inspired optimization algorithm in the name of improved circle search algorithm (ICSA) to find the optimum combination between two generations, such as traditional generators, and demand response as a demand resource. The proposed ICSA was incorporated in MATLAB environment whereas the proposed optimization algorithm was evaluated using two standard test systems such as IEEE-30 bus system and 6-bus system. The optimization results substantiate that the proposed optimization algorithm is capable of mitigating both operation costs as well&nbsp;…

AI Techniques for Renewable Source Integration and Battery Charging Methods in Electric Vehicle Applications

<a href="https://www.igi-global.com/chapter/environmental-economic-load-dispatch-considering-demand-response-using-a-new-heuristic-optimization-algorithm/318637">Environmental Economic Load Dispatch Considering Demand Response Using a New Heuristic Optimization Algorithm</a>

Recently, the Distributed Generation (DG) structure has become an important aspect of daily planning and has achieved a variety of functions while maximizing the functionality of utilities. It should balance the energy-equilibrium of the system components and the production limit. Uncertainties arise during the operation of the DG, represented by generator power, load requirements and fluctuations in electricity prices. The reactive power problem is the major concern associated with the Distributed Generation (DG) system. This article analyzes both DG and an energy storage system along with their optimal performance using a specific algorithm. For optimal analysis of DG and ESS, an Improved Artificial Bee Colony Algorithm (IABC) is proposed. The IABC evaluates the performance of distribution network. It takes DG and ESS into account for analyzing optimal design problems, the main goal of which is to minimize&nbsp;…

International Journal of Modeling, Simulation, and Scientific Computing

<a href="https://www.worldscientific.com/doi/abs/10.1142/S1793962324500065">Energy Storage Systems with Distributed Generation in Power Network reconfiguration using Improved Artificial Bee Colony Algorithm</a>

This paper provides a meta-heuristic hybridized version called multi-objective golden flower pollination algorithm (MOGFPA) as the best method for choosing the optimal reconfiguration for distribution networks (DNs) in order to reduce power losses (PLs). Aside from PLs, another parameter is considered: the load balance index (LBI). The expression for the LBI is stated using real and reactive indices. It makes the optimal distributed generation (DG) placement and DN routing of the multi-objective (MO) problem have PLs and the LBI as the main parameters that need to be optimized. For that purpose, the MOGFPA is proposed in this paper. The MOGFPA consists of a golden search (GS) and tangent flight with Pareto distribution that only needs a few tuning parameters. Therefore, it is simple to alter these parameters to reach the best values compared to other existing methodologies. Its performance is predicted using different case studies on multiple test bus systems, namely the IEEE systems such as 33, 69, 119, and Indian 52 bus. Through simulation outcomes, the MOGFPA computes the optimum distribution of DG units and reconfigures the DNs with the aim of minimal PLs and LBI. Furthermore, another state-of-the-art technology and comparing convergence charts provide optimal outputs in less time, with minimum iterations.

Fulltext

Axioms

Multi-Objective Golden Flower Optimization Algorithm for Sustainable Reconfiguration of Power Distribution Network with Decentralized Generation

Optimal energy management has become a challenging task to accomplish in today’s advanced energy systems. If energy is managed in the most optimal manner, tremendous societal benefits can be achieved such as improved economy and less environmental pollution. It is possible to operate the microgrids under grid-connected, as well as isolated modes. The authors presented a new optimization algorithm, i.e., Oppositional Gradient-based Grey Wolf Optimizer (OGGWO) in the current study to elucidate the optimal operation in microgrids that is loaded with sustainable, as well as unsustainable energy sources. With the integration of non-Renewable Energy Sources (RES) with microgrids, environmental pollution is reduced. The current study proposes this hybrid algorithm to avoid stagnation and achieve premature convergence. Having been strategized as a bi-objective optimization problem, the ultimate aim of this model’s optimal operation is to cut the costs incurred upon operations and reduce the emission of pollutants in a 24-h scheduling period. In the current study, the authors considered a Micro Turbine (MT) followed by a Wind Turbine (WT), a battery unit and a Fuel Cell (FC) as storage devices. The microgrid was assumed under the grid-connected mode. The authors validated the proposed algorithm upon three different scenarios to establish the former’s efficiency and efficacy. In addition to these, the optimization results attained from the proposed technique were also compared with that of the results from techniques implemented earlier. According to the outcomes, it can be inferred that the presented OGGWO approach outperformed other methods in terms of cost mitigation and pollution reduction

Energies-B(Electrical Engineering)

Multi-Objective Optimal Scheduling of a Microgrid Using Oppositional Gradient-Based Grey Wolf Optimizer

The modest objective is to check the integrated effect of energy storage systems (ESSs) and distributed generations (DGs) and compare the optimization of the size and location of ESS and DG to explore its challenges for smart grids (SGs) modernization. The research enlisted different algorithms for cost-effectiveness, security, voltage control, and less power losses. From this perspective, optimization of the distribution network’s energy storage and capacity are being performed using a variety of methods, including the particle swarm, ant-lion optimization, genetic, and flower pollination algorithms. The experimental outcomes demonstrate the effectiveness of these techniques in lowering distribution network operating costs and controlling system load fluctuations. The efficiency and dependability of the distribution network (DN) are both maximized by these strategies.

Modernized Planning of Smart Grid Based on Distributed Power Generations and Energy Storage Systems Using Soft Computing Methods

The importance of integrating distributed generation (DG) units into the distribution network (DN) was recently developed. To decrease power losses (PL), this article presents a meta-heuristic population‐based tangent golden flower pollination algorithm (TGFPA) as an optimization technique for selecting the ideal site for DG. Furthermore, the proposed algorithm also finds the optimal routing configuration for power flow. TGFPA requires very few tuning parameters and is comprised of a golden section and a tangent flight algorithm (TFA). Hence, it is easy to update these parameters to obtain the best values, which provide highly reliable results compared to other existing techniques. In different case studies, the TGFPA’s performance was assessed on four test bus systems: IEEE 33‐bus, IEEE 69‐bus, IEEE 119‐bus, and Indian‐52 bus. According to simulation results, TGFPA computes the optimal reconfigured DN embedded along with DG, achieving the goal of minimal power loss

Optimized Network Reconfiguration with Integrated   Generation Using Tangent Golden Flower Algorithm

The rapid exhaustion of fossils shaded concern about environmental problems which enforce researchers to embed this renewable distribution generation into power system networks. Electric vehicles are increasing threat to reliability of power grid by overloaded equipment and disturbed stability in voltage. However, it is important that electric vehicles must provide support to grids through vehicle to grid optimized V2G scheduling to minimize load variances. The purpose of this study is to employ a hybrid approach to improve energy storage system scheduling and sizing for electric vehicles. The hybrid version of Flower Pollination and golden section search is used. This research considers peak shaving, valley filling and priority charging mode of Energy Storage System for effective load management. Results indicated that optimized and scheduled energy storage system can fulfill the proper load allocation at grid

IEEE Xplore

Optimal Scheduling and Sizing of Energy Storage System using Hybrid Algorithm for Electric Vehicles

The minimization of annual operating costs in radial distribution networks with the optimal selection and siting of fixed-step capacitor banks is addressed in this research by means of a two-stage optimization approach. The first stage proposes an approximated mixed-integer quadratic model to select the nodes where the capacitor banks must be installed. In the second stage, a recursive power flow method is employed to make an exhaustive evaluation of the solution space. The main contribution of this research is the use of the expected load curve to estimate the equivalent annual grid operating costs. Numerical simulations in the IEEE 33- and IEEE 69-bus systems demonstrate the effectiveness of the proposed methodology in comparison with the solution of the exact optimization model in the General Algebraic Modeling System software. Reductions of 33.04%<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics>&nbsp;</semantics></math>&nbsp;and 34.29%<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics>&nbsp;</semantics></math>&nbsp;with respect to the benchmark case are obtained with the proposed two-stage approach, with minimum investments in capacitor banks. All numerical implementations are performed in the MATLAB software using the convex tool known as CVX and the Gurobi solver. The main advantage of the proposed hybrid optimization method lies in the possibility of dealing with radial and meshed distribution system topologies without any modification on the MIQC model and the recursive power flow approach.

Mathematics

Annual Operating Costs Minimization in Electrical Distribution Networks via the Optimal Selection and Location of Fixed-Step Capacitor Banks Using a Hybrid Mathematical Formulation