Performance analysis of dynamic voltage restorer using improved PSO technique

Kassarwani, Neelam; Ohri, Jyoti; Singh, Alka

doi:10.1080/00207217.2018.1519859

Cited by 19 publications

(15 citation statements)

References 23 publications

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“…As shown in Figure , a three‐phase voltage source converter acts as DVR, and it is used to transform DC into required AC by properly controlling the switches present in the VSC. In this paper, SRFT is utilized to extract harmonics and oscillating components and to generate reference voltage for switching of Voltage Source Converter (VSC) …”

Section: Dvr For Power Quality Improvementmentioning

confidence: 99%

“…In this paper, SRFT is utilized to extract harmonics and oscillating components and to generate reference voltage for switching of Voltage Source Converter (VSC). 5 3 | MODELING OF DVR Figure 2 shows the DVR's identical circuit. DVR is a controllable source of voltage and series-connected impedance branch.…”

Section: Dvr For Power Quality Improvementmentioning

confidence: 99%

“…There are several papers that propose PI for DVR. Kassarwani et al proposed PSO‐optimized PI controller for DVR implementation. Here, a capacitor‐connected DVR is simulated through MATLAB Simulink.…”

Section: Introductionmentioning

confidence: 99%

“…The weight of inertia is a critical factor that regulates the previous velocity impact of every particle. At first, the weight of inertia is set aside large and becomes less with the rise in the iteration number .…”

Section: Introduction About Pso Algorithmmentioning

confidence: 99%

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Development and performance analysis of PSO‐optimized sliding mode controller–based dynamic voltage restorer for power quality enhancement

Jeyaraj

Devaraj

Velusamy

2019

Int Trans Electr Energ Syst

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Summary In the distribution power system, voltage sag and swell are the critical issues. The impact of this issue creates major economic loss and power loss in the distribution system. There are several detection methods available, mainly, connecting the FACTS devices such as Static Compensator (STATCOM), Unified Power Quality Conditioner (UPQC), and dynamic voltage restorer (DVR) at the point of sag or swell. DVR is one of the custom power devices to mitigate the voltage sag and swell. Sliding mode controller (SMC) is proposed in the paper, which is used to regulate the DVR to mitigate the voltage sag issue. Particle swarm optimization (PSO) algorithm is utilized in this work to find the optimum value of SMC parameters, namely, Kp and Ki, with the objective of minimizing the integral square error (ISE). A simple two feeder distribution network with DVR is developed in MATLAB‐SIMULINK to assess the performance of DVR under various faults and disturbed situations. The mitigation level and THD values of PSO‐optimized SMC‐based DVR results are compared with the PI controller–based DVR, PSO‐optimized PI controller DVR, and SMC‐based DVR. At last, in the proposed method, the trial is taken by adding a renewable energy resource, that is, PV farm at 400‐kW power rating. The solar energy is integrated with the existing distribution system, and the results are incorporated. Simulation results show that the PSO‐optimized SMC‐based DVR can compensate the voltage sag and swell and also reduce the load voltages efficiently.

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Section: Dvr For Power Quality Improvementmentioning

confidence: 99%

Section: Dvr For Power Quality Improvementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introduction About Pso Algorithmmentioning

confidence: 99%

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Development and performance analysis of PSO‐optimized sliding mode controller–based dynamic voltage restorer for power quality enhancement

Jeyaraj

Devaraj

Velusamy

2019

Int Trans Electr Energ Syst

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“…Particle swarm optimization is a social inspired evolutionary algorithm, which has been implemented for solving various engineering problems [14][15][16]. Past studies considered particle swarm optimization (PSO) based task scheduling is more efficient for dynamic task scheduling.…”

Section: Fig1 Flow Process Of Task Scheduling In Cloud Environmentmentioning

confidence: 99%

Teaching-Learning Based Task Scheduling Optimization in Cloud Computing Environments

Goddu¹,

Reddi²

2019

IJRTE

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 Abstract: Generating optimal task scheduling plans in cloud environments is a tedious task as it is a np-hard problem. The optimal resource allocation in cloud environments involves more search space and time consuming. Therefore, recent researchers are focused on implementation of artificial intelligence to solve task scheduling problem. In this paper, a new and efficient evolutionary algorithm named teaching-learning based algorithm has been implemented first time to solve the task scheduling problem in cloud environments. The current research work considers the task scheduling problem as a multi-objective optimization problem. The proposed algorithm finds the best solution by minimizing the execution time and response time while maximizing the throughput of all resources to complete the assigned tasks.Index Terms: teaching-learning based optimization, task scheduling, cloud computing, multi-objective optimization, resource allocation and throughput.environments. The results showed that the proposed TLBO algorithm obtains the best solution in less number of iterations even for the cloud environments with huge search space.

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Comparative performance of dynamic voltage restorer using adaptive control algorithms with optimized error regulator gains

Naidu

Arya

Al‐Durra

et al. 2020

Int Trans Electr Energ Syst

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Summary This paper describes the performance of a dynamic voltage restorer (DVR) using various adaptive controls. The implemented control algorithms are; the amplitude adaptive notch filter (AANF), the quasi newton (QN), and the sliding mode observer (SMO) based control algorithms equipped with optimized PI tuning for three leg voltage sources converter‐based topology. These control algorithms are assessed and compared using key factors including computational parameters complexity, system response, dynamic and steady state performance etc. The control of a DVR is mainly dependent on the error minimization of the DC voltage and the load terminal voltages, respectively, which directly affects the generation reference load voltage for a DVR. Accordingly, optimization algorithms have been implemented for estimating the PI controllers' gains along with the control algorithms under investigation. The optimization method Harris Hawks Optimization algorithm (HOA) has been implemented for PI tuning for all aforementioned control algorithms. The estimation technique of the PI controller gains using the aforementioned optimization algorithm is elaborated mathematically and pictorially described. A comparative analysis of the performance of the HOA optimization method in estimating the gains of PI controllers of the adaptive control algorithms for three phase DVR are simulated using MATLAB. Moreover, all control algorithms have been developed and tested on the Micro Lab Box based experimental setup of DVR in the laboratory and their performance were compared while considering unbalance for compensation and satisfactory results were obtained.

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Performance analysis of dynamic voltage restorer using improved PSO technique

Cited by 19 publications

References 23 publications

Development and performance analysis of PSO‐optimized sliding mode controller–based dynamic voltage restorer for power quality enhancement

Development and performance analysis of PSO‐optimized sliding mode controller–based dynamic voltage restorer for power quality enhancement

Teaching-Learning Based Task Scheduling Optimization in Cloud Computing Environments

Comparative performance of dynamic voltage restorer using adaptive control algorithms with optimized error regulator gains

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