Optimal Design of Microgrids in Autonomous and Grid-Connected Modes Using Particle Swarm Optimization

Hassan, Mohamed A.; Abido, M. A.

doi:10.1109/tpel.2010.2100101

Cited by 272 publications

(163 citation statements)

References 30 publications

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“…While nominal system frequency denoted by ωn is used to obtain the reference values (Equations (6) and (7)). The other values denoted by ω0 and ωCOM are the value of the frequency at time zero and the common system reference frame frequency, respectively [3]. As shown in Figure 4, each DG has to be modeled on its local rotating (dqi) reference frame, and then all DGs will have common reference frames (DQ).…”

Section: Autonomous Microgrid Modelmentioning

confidence: 99%

“…Controller gains may cause poor damped responses and even the instability of the microgrid [3]. To enhance the microgrid transient performance, the controller parameters of inverters and active load, PLL gains, and power-sharing coefficients would be tuned carefully.…”

Section: Problem Formulationmentioning

confidence: 99%

“…To operate a microgrid at the distribution level, constant impedance loads (CILs) and constant power loads (CPLs) are connected with DGs [2][3][4][5][6][7][8][9][10][11][12][13][14]. For inverter-based microgrids, stability analysis is one of the most important concerns [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…It was stated that load demand and the controller power-sharing coefficients dominantly distress the low frequency modes; while filter parameters, load dynamics, and the controller parameters of the inner voltage and outer current controllers distress the medium and high frequency damped modes [5]. Moreover, constant load perturbation and load dynamics significantly distress microgrid stability [3,21]. Considering CPLs and/or CILs, it is essential to understand microgrid dynamic behavior [22].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, different computational intelligence techniques, like particle swarm optimization (PSO), have been used for different power system problems with impressive achievement [34]. Although, complexity of the control system will be increased with these algorithms, PSO advantages-like computational efficiency, simplicity, and robustness-will enhance the microgrid transient performance [3,35,36]. To study and analysis the power system dynamics, offline simulation such as MATLAB is not enough especially if the power converters with high switching frequency are included.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Optimal Design and Real Time Implementation of Autonomous Microgrid Including Active Load

2018

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Abstract:Controller gains and power-sharing parameters are the main parameters affect the dynamic performance of the microgrid. Considering an active load to the autonomous microgrid, the stability problem will be more involved. In this paper, the active load effect on microgrid dynamic stability is explored. An autonomous microgrid including three inverter-based distributed generations (DGs) with an active load is modeled and the associated controllers are designed. Controller gains of the inverters and active load as well as Phase Locked Loop (PLL) parameters are optimally tuned to guarantee overall system stability. A weighted objective function is proposed to minimize the error in both measured active power and DC voltage based on time-domain simulations. Different AC and DC disturbances are applied to verify and assess the effectiveness of the proposed control strategy. The results demonstrate the potential of the proposed controller to enhance the microgrid stability and to provide efficient damping characteristics. Additionally, the proposed controller is compared with the literature to demonstrate its superiority. Finally, the microgrid considered has been established and implemented on real time digital simulator (RTDS). The experimental results validate the simulation results and approve the effectiveness of the proposed controllers to enrich the stability of the considered microgrid.

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Section: Autonomous Microgrid Modelmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimal Design and Real Time Implementation of Autonomous Microgrid Including Active Load

2018

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Dynamic response enhancement of grid‐tied ac microgrid using salp swarm optimization algorithm

Jumani

Mustafa

Rasid

et al. 2019

Int Trans Electr Energ Syst

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Summary To alleviate the overloads in the power system and to reduce the exponential growth in carbon dioxide (CO2) emissions, deployment of the renewable energy sources (RES) into the power system is the need of the hour. However, injecting these RES into the current power system network causes large voltage and power overshoots hence deteriorate the transient response and power quality of the overall power system. In this paper, an efficient solution of the above‐mentioned issues is explored by developing an optimal microgrid (MG) controller using one of the most modern and intelligent artificial intelligence (AI) techniques named the salp swarm optimization algorithm (SSA). The intelligence of the SSA is exploited to select the optimal controller gains and dc‐link capacitance value by minimizing a time integrating error fitness function (FF) which in‐turn enhances the dynamic response and power quality of the studied MG system. The proposed grid‐tied MG controller is designed to achieve the preset active and reactive power sharing ratio between distributed generator (DG) and utility grid during DG and load switching conditions. To validate the superiority of the proposed controller, its performance is compared with that of its precedent grasshopper optimization algorithm (GOA)‐based controller for the identical operating conditions and system configuration. The outcomes of the study show that the proposed MG controller outperforms its competitor in terms of transient response and quality of power.

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Enhancement of small signal stability in inverter‐dominated microgrid with optimal internal model controller

Vemula

Parida

2020

Int Trans Electr Energ Syst

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Summary The distributed generations (DGs) of an islanded microgrids (MGs) comprises of interconnecting primary and secondary control layers. The interactions among these controllers may yield new oscillatory modes with low damping, which decrease the stability margins. As the distribution network is dynamic in nature, the maintenance of system stability is of great concern, mainly while operating in autonomous mode. This paper proposes the optimal internal model control (IMC)‐based droop control scheme to improve the small‐signal stability and transient‐response of an inverter‐based MGs (IMGs) under different operating conditions. The proposed optimal scheme is achieved with the following approach: (a) first, comprehensive small signal model is prepared for the study system, and the interaction of the control parameters which influence the system stability are investigated through eigenvalue analysis, (b) the critical values of the significant parameters of the IMC and droop controllers are sorted out, and their corresponding stability domain is formulated, (c) based on the stability domain, PSO optimization technique is employed to generate optimal values, which delivers effective coordination among the crucial control parameters to improve the stability, (d) the developed optimal control scheme is cross‐validated by comparing with the existing methods from the literature. The effectiveness of the proposed optimal scheme is subsequently assessed quantitatively with the help of time‐domain specifications.

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Optimal Design of Microgrids in Autonomous and Grid-Connected Modes Using Particle Swarm Optimization

Cited by 272 publications

References 30 publications

Optimal Design and Real Time Implementation of Autonomous Microgrid Including Active Load

Optimal Design and Real Time Implementation of Autonomous Microgrid Including Active Load

Dynamic response enhancement of grid‐tied ac microgrid using salp swarm optimization algorithm

Enhancement of small signal stability in inverter‐dominated microgrid with optimal internal model controller

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