A self-tuning power system stabilizer based on artificial neural network

Segal, Ravi; Sharma, Aneesh; Kothari, Mangal

doi:10.1016/j.ijepes.2003.11.010

Cited by 69 publications

(34 citation statements)

References 6 publications

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“…Therefore, the linear, small perturbation model of the machine-infinite bus system, originally developed by Heffron and Philips [20], can be used in the process of developing TS model intended for power stability applications. Linearization of the model (7) gives well-known Heffron and Filips model [18] given by equations (8) and graphically depicted in Fig. 2.…”

Section: Physical Mathematical Model Of the Generator Unitmentioning

confidence: 99%

“…Some of the commonly used methods are gain scheduling, indirect and direct adaptive control using artificial intelligence (mostly neural networks and fuzzy logic) [2]. The largest group of the proposed adaptive PSSs consists of indirect selftuning power system stabilizers [2], [3][4][5][6][7][8]. Those PSSs provide better dynamic performance over a wide range of operating conditions, but they suffer from the significant drawback of requiring model parameter identification, state observation and feedback gain calculations in realtime.…”

Section: Introductionmentioning

confidence: 99%

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A Takagi-Sugeno Fuzzy Model of Synchronous Generator Unit for Power System Stability Application

Tečec¹,

Petrović

Matuško

2010

Automatika

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Original scientific paperThis paper presents a Takagi-Sugeno (TS) synchronous generator unit model intended for application in an autotuning power system stabilizer. The model takes into account all three process variables that can affect synchronous machine dynamics regarding stability -beside usually used active and reactive power (P and Q) it includes also line reactance (xm). It is shown that the proposed model gives very good results in spite of simple third order local model used as the consequent part of the proposed TS structure. This makes the model appropriate for implementation on simple microprocessor platforms. Because P, Q and xm are included as TS model premises, it is enough to identify parameters of models in consequent part of TS model off-line. In this way possible numerical instability is avoided, which is common to adaptive PSSs that calculate controller's parameters directly from on-line identified plant parameters.Key words: Modelling, Electrical machines, Takagi-Sugeno model, Power system stabilizer Takagi-Sugeno model sinkronog generatora namijenjen primjenama u samopodesivim stabilizatorima elektroenergetskog sustava. U ovome je radu predstavljen Takagi-Sugeno (TS) model sinkronog generatora namijenjen primjenama u samopodesivim stabilizatorima elektroenergetskog sustava. Model uzima u obzir sve tri procesne varijable koje utječu na stanje sinkronog stroja s obzirom na njegovu stabilnost -uz uobičajeno korištene radnu i jalovu snagu (P i Q) model takoer uključuje i ekvivalentnu mrežnu reaktanciju (xm). Pokazano je da predloženi model daje vrlo dobre rezultate unatoč jednostavnom modelu trećeg reda u posljedičnom dijelu TS strukture. Točini predloženi model prikladnim za primjenu na jednostavnim mikroprocesorskim platformama. Budući da su P, Q i xm uzročne varijable TS modela, dovoljno je off-line identificirati njegov posljedični dio. Na taj su način izbjegnuti problemi numeričke nestabilnosti, karakteristični za adaptivne strukture PSS-a koji parametre regulatora proračunavaju na temelju on-line identificiranog modela procesa.

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Section: Physical Mathematical Model Of the Generator Unitmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Takagi-Sugeno Fuzzy Model of Synchronous Generator Unit for Power System Stability Application

Tečec¹,

Petrović

Matuško

2010

Automatika

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“…In general, the PSS problem can be divided into two major categories: 1) optimal allocation and 2) optimal design. Several methods to determine the best location for PSS installation have been studied [6][7][8][9] including sequential methods such as outlined in [10] taking into account the presence of PSSs that were already installed. The sequential placement does not always determine the best multiple PSS placements because the placement order can affect the results.…”

Section: Introductionmentioning

confidence: 99%

Robust Bi-level Model for Optimal Allocation and Design of Power System Stabilizer in MultiMachine Power Systems

Gazijahani¹,

Abadi²,

Safari³

2017

IJCA

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“…The design of PSS has an ample significance to ensure the oscillatory stability. The Conventional Power System Stabilizer (CPSS) is a lead lag compensator [6]. For designing the damping controller, a designer has to find an accurate set of parameters (Gain & time constant).Proper tuning of the Lead Lag loop presents an adequate amount of damping to the system which helps the system to overcome with oscillatory instability [7].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Power System Stabilizer Parameters Using Swarm Intelligence Techniques to Improve Small Signal Stability of Power System

Soleymani¹,

Hasanvand²

2017

Adv. sci. technol. eng. syst. j.

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A self-tuning power system stabilizer based on artificial neural network

Cited by 69 publications

References 6 publications

A Takagi-Sugeno Fuzzy Model of Synchronous Generator Unit for Power System Stability Application

A Takagi-Sugeno Fuzzy Model of Synchronous Generator Unit for Power System Stability Application

Robust Bi-level Model for Optimal Allocation and Design of Power System Stabilizer in MultiMachine Power Systems

Estimation of Power System Stabilizer Parameters Using Swarm Intelligence Techniques to Improve Small Signal Stability of Power System

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