Design of an Adaptive Neurofuzzy Inference Control System for the Unified Power-Flow Controller

Farrag, Mohamed Emad; Putrus, Ghanim

doi:10.1109/tpwrd.2011.2171061

Cited by 36 publications

(13 citation statements)

References 17 publications

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“…Two online trained fuzzy NN controllers for the UPFC are proposed to improve power system dynamic control performance [106]. An adaptive neurofuzzy inference control system for the UPFC independently controls the real and reactive power flow over a wide range of possible operating points and extreme conditions [136]. A decision tree-induced fuzzy rule-based relaying scheme is proposed that provides robust protection to the transmission line including UPFC and wind farm [166].…”

Section: Hybrid Systemsmentioning

confidence: 99%

Unified power flow controllers in smart power systems: models, methods, and future research

Georgilakis

Hatziargyriou

2019

IET Smart Grid

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Power flow control has become increasingly important in recent years in the area of smart power systems that have to integrate increased shares of variable renewable energy sources. The unified power flow controller (UPFC) provides in realtime, simultaneously or selectively, active and reactive power flow control as well as voltage control in smart power systems. Several models and methods have been suggested for the control, analysis, operation, and planning of UPFCs in smart power systems. This study introduces a review of the state-of-the-art models and methods of UPFCs in smart power systems, analysing and classifying current and future research trends in this field.

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Section: Hybrid Systemsmentioning

confidence: 99%

Unified power flow controllers in smart power systems: models, methods, and future research

Georgilakis

Hatziargyriou

2019

IET Smart Grid

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“…The critic network and proposed FLNRFNN, the gradient based on the chain rule of the error term can be represented as Equations (33)- (36) for an online supervised training algorithm. The formulae for adjusting the w ab of the critic network and the weight w FLNRFNN of FLNRFNN [23,24].…”

Section: The Training Process Of Flnrfnn and Critic Networkmentioning

confidence: 99%

Improvement of Transient Stability in a Hybrid Power Multi-System Using a Designed NIDC (Novel Intelligent Damping Controller)

Huang

2017

Energies

105

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This paper endeavors to apply a novel intelligent damping controller (NIDC) for the static synchronous compensator (STATCOM) to reduce the power fluctuations, voltage support and damping in a hybrid power multi-system. In this paper, we discuss the integration of an offshore wind farm (OWF) and a seashore wave power farm (SWPF) via a high-voltage, alternating current (HVAC) electric power transmission line that connects the STATCOM and the 12-bus hybrid power multi-system. The hybrid multi-system consists of a battery energy storage system (BESS) and a micro-turbine generation (MTG). The proposed NIDC consists of a designed proportional-integral-derivative (PID) linear controller, an adaptive critic network and a proposed functional link-based novel recurrent fuzzy neural network (FLNRFNN). Test results show that the proposed controller can achieve better damping characteristics and effectively stabilize the network under unstable conditions. Keywords: static synchronous compensator (STATCOM); offshore wind farm (OWF); seashore wave power farm (SWPF); functional link-based novel recurrent fuzzy neural network (FLNRFNN); novel intelligent damping controller (NIDC)

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“…Control system based on fast repetitive control of DVR was applied in [ 13 ]. Various control approaches such as the PI, fuzzy logic, neural network controller, optimal predictive, sliding mode, and adaptive neurofuzzy inference system are in use [ 14 – 21 ]. Adaptive method based on Hebb learning algorithm for controlling DVR was introduced in [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Multiobjective Control of DVR to Enhance Power Quality of Sensitive Load

Pandu¹,

Kamaraj²

2015

The Scientific World Journal

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The Dynamic Voltage Restorer (DVR) is one of the fast, flexible, and cost effective solutions available in compensating the voltage-related power quality problems in power distribution systems. In this paper is discussed how power quality enhancement of sensitive load is achieved by applying three versions of Autonomous Group Particle Swarm Optimization like AGPSO1, AGPSO2, and AGPSO3 for tuning the Proportional-Integral DVR controller under balanced and nonlinear load conditions. A novel multiobjective function is formulated to express the control performance of the system, which is quantified using three power quality indices such as Total Harmonic Distortion (THD), voltage sag index, and RMS voltage variation. The obtained results are compared with the Proportional-Integral (PI) controller tuned by Ziegler-Nichols (ZN) method and also by Simple Particle Swarm Optimization based PI controlled DVR. The proposed methodology has improved the performance in terms of the considered power quality indices and the simulation has been carried out in MATLAB/Simulink environment.

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Design of an Adaptive Neurofuzzy Inference Control System for the Unified Power-Flow Controller

Cited by 36 publications

References 17 publications

Unified power flow controllers in smart power systems: models, methods, and future research

Unified power flow controllers in smart power systems: models, methods, and future research

Improvement of Transient Stability in a Hybrid Power Multi-System Using a Designed NIDC (Novel Intelligent Damping Controller)

A Novel Multiobjective Control of DVR to Enhance Power Quality of Sensitive Load

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