Optimal Power Flow Using Adaptive Fuzzy Logic Controllers

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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Optimization of Fuzzy Logic Controller for Supervisory Power System Stabilizers

Acta Polytechnica ◽

10.14311/1518 ◽

2012 ◽

Vol 52 (2) ◽

Author(s):

Y. A. Al-Turki ◽

A.-F. Attia ◽

H. F. Soliman

Keyword(s):

Fuzzy Logic ◽

Power System ◽

Fuzzy Set ◽

Fuzzy Logic Controller ◽

Fuzzy Model ◽

Rate Of Change ◽

Computational Time ◽

Adaptive Fuzzy ◽

Wide Range ◽

Adaptive Fuzzy Logic

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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Robust Adaptive Fuzzy Logic Controllers for Intelligent Universal Transformers in ADA

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.403-408.5038 ◽

2011 ◽

Vol 403-408 ◽

pp. 5038-5044

Author(s):

Maryam Sadeghi ◽

Majid Gholami

Keyword(s):

Fuzzy Logic ◽

High Speed ◽

Fuzzy Logic Controller ◽

Voltage Regulation ◽

System Stability ◽

Distribution Automation ◽

Adaptive Fuzzy ◽

Adaptive Fuzzy Logic ◽

Robust Adaptive ◽

Output Stages

Intelligent Universal Transformer (IUT) will comprise in Advanced Distribution Automation (ADA) with a new invention in control and management in future. It evolves with a high speed traditional transformer in addition to power electronic base construction will eventuate to oil elimination, dimensional size and weight reduction. Adaptive Fuzzy Logic Control (AFLC) is an adaptive progressed method with the high system performance capability being raised even on the uncertainty conditions. It enhances system stability, improves flexibility and releases designers from precise mathematical model utilization. Expert designer Knowledge is a critical requirement for conventional fuzzy logic controller (FLC), in contrast the AFLC rules and parameters are generated by adaptive model and human knowledge will downright initialize the first parameters values. In this approach four layers IUT topology is considered for developing the end user service options as 48V DC, reliable power as 240V AC 400HZ and three phase power option. AFLC schemes are proposed for employing current and voltage controllers in input output stages. Real time voltage regulation, automatic sag correction, Harmonic Filtering, energy storage option and dynamic system monitoring are the resulting benefits of using IUT four layers topology. AFLC methodology, leading the system robustness in any cases of grid and load disturbances.

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