Reaching phase free adaptive fuzzy synergetic power system stabilizer

Bouchama, Z.; Essounbouli, Najib; Harmas, M. N.; Hamzaoui, Abdelaziz; Saoudi, Kamel

doi:10.1016/j.ijepes.2015.11.017

Cited by 45 publications

(29 citation statements)

References 15 publications

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“…Power system stabilizer (PSS) provides an additional input signal to generate supplementary control signals for the excitation control system in order to damp slow mode oscillations of the power system [43,44]. Some commonly used input signals are rotor speed deviation, accelerating power, and frequency deviation.…”

Section: Power System Stabilizermentioning

confidence: 99%

Power System Stabilizer Application for Load Frequency Control in Hydro-Electric Power Plant

Shahgholian¹

2017

IJTAM

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Abstract:Renewable energy provides 20% of electricity generation worldwide. Hydroelectric power is the cheapest way to generate electricity today. It provides almost onefifth of the electricity in world. Hydropower is produced in more than 150 countries. The main objective of this paper is modelling and analysis of the effects of the power system stabilizer (PSS) for load frequency control (LFC) system in hydro-electric power plant to damp the frequency oscillations due to different load distributions. LFC is the mechanism by which the energy balance is maintained. PSS is used to increase the system positive damping. The results of small signal stability analysis have been represented employing eigenvalue as well as time domain response.

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Section: Power System Stabilizermentioning

confidence: 99%

Power System Stabilizer Application for Load Frequency Control in Hydro-Electric Power Plant

Shahgholian¹

2017

IJTAM

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“…that means the RBF neural network (8) can approximate smooth nonlinear functions with the arbitrary small error. In general cases, f (x), h(x) are not identical to f (x), h(x).…”

Section: Tracking Control Based On Rbf Neural Networkmentioning

confidence: 99%

“…Four trends will appear, the first trend consists in introducing a direct adaptive fuzzy control combined with sliding mode control [5] and [6] based on Lyapunov theory. The second trend is carried out in controller design based on universal approximation [7], [8] and [9], the indirect adaptive fuzzy control (IAFC) combined with sliding mode control (SMC) has attracted much attention. The combining the two achieving more superior performances such as overcoming some limitations of the traditional SMC.…”

Section: Introductionmentioning

confidence: 99%

New Adaptive Neural Network for Uncertain Nonlinear System with Disturbance

Pham

Lai²

2017

REV J. Electron. Commun.

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Abstract-The paper deals with the problem of tracking control for a class of nonlinear systems in presence of the disturbances. The developed formation for the tracking control is taken into account as an adaptive neural network. The set of controller's parameter, which is a satisfy Hurwitz polynomial, is then updated by adaptive laws via a model reference system. In addition, the unknown nonlinear functions are estimated by radial basis functions neural network. The adaptive updated law based on radial basis functions neural network and a feed-forward correction is proposed to estimate both estimation errors of nonlinear functions and external disturbances, which is called lumped disturbances. The feed-forward correction term is calculated by the algebraic equation regarding the parameters of controller and the radial basis function. Moreover, this estimator (using estimating the lumped disturbance) is also used both in class of SISO nonlinear and MIMO nonlinear system. Thanks to Lyapunov's theory, asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, two examples of coupled tank liquid system and an active magnetic bearing system, are presented to illustrate the our proposed methods.

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“…The FPSS has many advantages, e.g., it is simple in structure, and there is no need to know the mathematical model of the controlled system. [7][8][9] Changes in operating condition do not significantly affect the performance of the FPSS. 10 A FPSS can adjust its parameters online according to the conditions and can provide a good damping over a wide range of operating conditions.…”

Section: Introductionmentioning

confidence: 97%

An enhanced and auto-tuned power system stabilizer based on optimized interval type-2 fuzzy PID scheme

Shokouhandeh

Jazaeri

2017

Int Trans Electr Energ Syst

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SummaryIn this paper, an innovative optimized type-2 fuzzy PID power system stabilizer is proposed to enhance the stability of the power system. This stabilizer is a combination of PID controller and type-2 fuzzy set whose parameters have been optimized with Hybrid Big Bang-Big Crunch (HBB-BC) algorithm. This combination makes the stabilizer to be flexible in face of changing in operating points of the power system and also encountering to power system nonlinearities and uncertainties. The main sources of uncertainties in the power system are the uncertainties in operation conditions, power system parameters, and measurement devices. Uncertainties do not allow the stabilizer to offer its full capabilities, and hence the efforts of the stabilizer may not satisfy the expectations. In order to evaluate the performance of the proposed controller, 2 case studies under different disturbances are investigated. The obtained results show that the proposed stabilizer can suitably provide appropriate damping in oscillatory modes of the system. KEYWORDSHybrid Big Bang-Big Crunch algorithm, interval type-2 fuzzy-PID, low-frequency oscillation, power system stabilizer, uncertainty | INTRODUCTIONThe stability of power system plays an important role in the power system studies area. The frequency and voltage level should be kept at a suitable range, while disturbances like changing in loading condition, generation loss, or the loss of tie lines are occurred. Low-frequency oscillations (LFO) are in the range of 0.1 and 3.0 Hz that can be divided into local and inter-area oscillations. 1 If these oscillations continue, the ability of tie lines for transmitting electrical energy will be reduced, and the power system may experience instability. These oscillations usually occur during or after a system disturbance. Therefore, it is essential to use control methods in order to eliminate these LFO. In order to enhance the stability of the power system, a power system stabilizer (PSS) has been installed in the exciter system of the generator. 2 The power system stabilization can be established centralized or decentralized. A centralized controller has an almost perfect performance in damping oscillation phenomena if it is intelligently tuned which is often a hard task. In addition, such a controller needs safe communication channels to transmit data. However, the delay in the communication system is an inevitable issue. This time delay may cause deterioration on oscillation damping and even result to an unstable power system. 3 One way to solve this problem is the use of predictive control. In this method, the model identification

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Reaching phase free adaptive fuzzy synergetic power system stabilizer

Cited by 45 publications

References 15 publications

Power System Stabilizer Application for Load Frequency Control in Hydro-Electric Power Plant

Power System Stabilizer Application for Load Frequency Control in Hydro-Electric Power Plant

New Adaptive Neural Network for Uncertain Nonlinear System with Disturbance

An enhanced and auto-tuned power system stabilizer based on optimized interval type-2 fuzzy PID scheme

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