Optimization of Fuzzy Logic Controller for Supervisory Power System Stabilizers

This paper presents a powerful supervisory power system stabilizer (PSS) using an adaptive fuzzy logic controller driven by an adaptive fuzzy set (AFS). The system under study consists of two synchronous generators, each fitted with a PSS, which are connected via double transmission lines. Different types of PSS-controller techniques are considered. The proposed genetic adaptive fuzzy logic controller (GAFLC)-PSS, using 25 rules, is compared with a static fuzzy logic controller (SFLC) driven by a fixed fuzzy set (FFS) which has 49 rules. Both fuzzy logic controller (FLC) algorithms utilize the speed error and its rate of change as an input vector. The adaptive FLC algorithm uses a genetic algorithmto tune the parameters of the fuzzy set of each PSS. The FLC’s are simulated and tested when the system is subjected to different disturbances under a wide range of operating points. The proposed GAFLC using AFS reduced the computational time of the FLC, where the number of rules is reduced from 49 to 25 rules. In addition, the proposed adaptive FLC driven by a genetic algorithm also reduced the complexity of the fuzzy model, while achieving a good dynamic response of the system under study.

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Power System Stabilizer Driven by an Adaptive Fuzzy Set for Better Dynamic Performance

Acta Polytechnica ◽

10.14311/810 ◽

2006 ◽

Vol 46 (2) ◽

Author(s):

H. F. Soliman ◽

A.-F. Attia ◽

M. Hellal ◽

M. A. L. Badr

Keyword(s):

Power System ◽

Fuzzy Set ◽

Fuzzy Logic Controller ◽

Dynamic Performance ◽

Reference Value ◽

Rate Of Change ◽

Power System Stabilizer ◽

Computational Time ◽

Adaptive Fuzzy ◽

System Stabilizer

This paper presents a novel application of a fuzzy logic controller (FLC) driven by an adaptive fuzzy set (AFS) for a power system stabilizer (PSS).The proposed FLC, driven by AFS, is compared with a classical FLC, driven by a fixed fuzzy set (FFS). Both FLC algorithms use the speed error and its rate of change as input vectors. A single generator equipped with FLC-PSS and connected to an infinite bus bar through double transmission lines is considered. Both FLCs, using AFS and FFS, are simulated and tested when the system is subjected to different step changes in the reference value. The simulation results of the proposed FLC, using the adaptive fuzzy set, give a better dynamic response of the overall system by improving the damping coefficient and decreasing the rise time and settling time compared with classical FLC using FFS. The proposed FLC using AFS also reduces the computational time of the FLC as the number of rules is reduced.

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Optimal Power Flow Using Adaptive Fuzzy Logic Controllers

Mathematical Problems in Engineering ◽

10.1155/2013/975170 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7 ◽

Cited By ~ 5

Author(s):

Abdullah M. Abusorrah

Keyword(s):

Fuzzy Logic ◽

Fuzzy Set ◽

Power Flow ◽

Optimal Power Flow ◽

Fuzzy Logic Controller ◽

Test System ◽

System Stability ◽

Main Function ◽

Adaptive Fuzzy ◽

Adaptive Fuzzy Logic

This paper presents an approach for optimum reactive power dispatch through the power network with flexible AC transmission systems (FACTSs) devices, using adaptive fuzzy logic controller (AFLC) driven by adaptive fuzzy sets (AFSs). The membership functions of AFLC are optimized based on 2nd-order fuzzy set specifications. The operation of FACTS devices (particularly, static VAR compensator (SVC)) and the setting of their control parameters (QSVC) are optimized dynamically based on the proposed AFLC to enhance the power system stability in addition to their main function of power flow control. The proposed AFLC is compared with a static fuzzy logic controller (SFLC), driven by a fixed fuzzy set (FFS). Simulation studies were carried out and validated on the standard IEEE 30-bus test system.

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Robust Longitudinal Aircraft- Control Based on an Adaptive Fuzzy-Logic Algorithm

Sultan Qaboos University Journal for Science [SQUJS] ◽

10.24200/squjs.vol7iss1pp187-198 ◽

2002 ◽

Vol 7 (1) ◽

pp. 187

Author(s):

Abdel- Latif Elshafei

Keyword(s):

Fuzzy Logic ◽

Feedback Linearization ◽

Fuzzy Logic Controller ◽

Control Law ◽

Adaptive Fuzzy ◽

Highly Nonlinear ◽

Short Period ◽

Wide Range ◽

Modeling Errors ◽

Adaptive Fuzzy Logic

To study the aircraft response to a fast pull-up manoeuvre, a short period approximation of the longitudinal model is considered. The model is highly nonlinear and includes parametric uncertainties. To cope with a wide range of command signals, a robust adaptive fuzzy logic controller is proposed. The proposed controller adopts a dynamic inversion approach. Since feedback linearization is practically imperfect, robustifying and adaptive components are included in the control law to compensate for modeling errors and achieve acceptable tracking errors. Two fuzzy systems are implemented. The first system models the nominal values of the system’s nonlinearity. The second system is an adaptive one that compensates for modeling errors. The derivation of the control law based on a dynamic game approach is given in detail. Stability of the closed-loop control system is also verified. Simulation results based on an F16-model illustrate a successful tracking performance of the proposed controller.

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