A robust H∞ power system stabilizer design using reduced-order models

Hardiansyah, Hardiansyah; Furuya, Seizo; Irisawa, Juichi

doi:10.1016/j.ijepes.2005.09.002

Cited by 48 publications

(17 citation statements)

References 12 publications

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“…One of the first examples of PSO is presented in [9]. The results obtained in [10][11][12][13][14][15] suggest that the sequential linear programing such as Genetic Algorithm (GA), chaotic algorithm, bacteria foraging and H∞. A key limitation of this research is that their simple structure, need to an accurate model and its non-optimal damping in different cases of power system.…”

Section: Introductionmentioning

confidence: 99%

A New Adaptive Neuro-Fuzzy Inference System (ANFIS) and PI Controller to Voltage Regulation of Power System Equipped by Wind Turbine

Griche¹,

Messalti²,

Saoudi³

et al. 2019

EJEE

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In this paper, new Adaptive Neuro-Fuzzy Inference System (ANFIS) and PI controller have been proposed and investigated which the power system is equipped by Static Var Compensator (SVC) and small wind turbine. The SVC is controlled by PI controller optimized by genetic algorithm (GA) to regulate the voltage profile. To demonstrate the efficiency of proposed controller, a Single Machine Infinite Bus (SMIB) has been considered, in which small fluctuation of mechanical damped has been applied to improve the transient stability and has been evaluated using a relative rotor criteria. Obtained results have demonstrated a better performance with ANFIS and PI controller in which both voltage and transient stability have been controlled perfectly.

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Section: Introductionmentioning

confidence: 99%

A New Adaptive Neuro-Fuzzy Inference System (ANFIS) and PI Controller to Voltage Regulation of Power System Equipped by Wind Turbine

Griche¹,

Messalti²,

Saoudi³

et al. 2019

EJEE

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“…To overcome these oscillations, power system stabilizer is widely used. Recent research is related to lead/lag compensators [1,2,3].…”

Section: Introductionmentioning

confidence: 99%

Online Tuning of Power System Stabilizers Using Fuzzy Logic Network with Fuzzy C-Means Clustering Prediction: A Case Study

Kanafgorabi¹,

Karami²

2014

IJEPE

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Abstract:Power system stabilizers (PSS) has been widely used to enhance damping due to the electromechanical low frequency oscillations occurrence in power systems. In this paper, a new method is used for the online tuning of parameters of conventional power system stabilizers (CPSS) using fuzzy logic. Fuzzy logic enables mathematical modeling and computation of some nonlinear parameters of the system, which are usually derived empirically by utilization of expert knowledge rules. Various literatures has shown that fuzzy logic controller is one of the most useful methods for expert knowledge utilization. This type of controller is adaptive in nature and can be used successfully as a power system stabilizer. The design of fuzzy logic controllers is mainly based on fuzzy rules and input/output membership functions. Simple and efficient clustering algorithms allow data classification in distinct groups using distance and/or similarity functions. In the present paper, the optimum generation of fuzzy rules base using Fuzzy C-means (FCM) clustering technique is used. In fact, data are classified and the number of fuzzy rules which depends on convergence radius is determined. Finally, the performance of proposed FCM controller is compared with that of conventional controller. The active power, reactive power and bus voltages used as inputs to the fuzzy logic network based power system stabilizer and the parameters of the optimum stabilizer , i.e. gain factor as well as time constants of the lead/lag compensator, are the outputs of the proposed system. The design method has been successfully implemented on a single machine power system connected to an infinite bus over various operating conditions.

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“…Few years ago, some approaches, for example, H ∝ control [10,11], have appeared to enhance the tuning of classical controllers [3][4][5][6][7][8][9]. However, these techniques were developed again using linear models.…”

Section: Introductionmentioning

confidence: 99%