A neural network controlled optimal pulse-width modulated STATCOM

Mohaddes, M.; Gole, Aniruddha M.; McLaren, P.G.

doi:10.1109/61.754092

Cited by 31 publications

(7 citation statements)

References 6 publications

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“…This layer generates d-axis current of PWM based rectifier. In order to update input and output parameters by using analog teaching method with back propagation algorithm, the squared error (E) which minimizes tracking error (e) is determined as follows [23][24]:…”

Section: Neuro-fuzzy Controller Designmentioning

confidence: 99%

Power Quality Improvement Using Hybrid Passive Filter Configuration for Wind Energy Systems

Keçecioğlu¹,

Açıkgöz²,

Yıldız³

et al. 2017

Journal of Electrical Engineering and Technology

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-Wind energy conversion systems (WECS) which consist of wind turbines with permanent magnet synchronous generator (PMSG) and full-power converters have become widespread in the field of renewable power systems. Generally, conventional diode bridge rectifiers have used to obtain a constant DC bus voltage from output of PMSG based wind generator. In recent years, together advanced power electronics technology, Pulse Width Modulation (PWM) rectifiers have used in WECS. PWM rectifiers are used in many applications thanks to their characteristics such as high power factor and low harmonic distortion. In general, L, LC and LCL-type filter configurations are used in these rectifiers. These filter configurations are not exactly compensate current and voltage harmonics. This study proposes a hybrid passive filter configuration for PWM rectifiers instead of existing filters. The performance of hybrid passive filter was tested via MATLAB/Simulink environment under various operational conditions and was compared with LCL filter structure. In addition, neuro-fuzzy controller (NFC) was preferred to increase the performance of PWM rectifier in DC bus voltage control against disturbances because of its robust and nonlinear structure. The study demonstrates that the hybrid passive filter configuration proposed in this study successfully compensates current and voltage harmonics, and improves total harmonic distortion and true power factor.

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Section: Neuro-fuzzy Controller Designmentioning

confidence: 99%

Power Quality Improvement Using Hybrid Passive Filter Configuration for Wind Energy Systems

Keçecioğlu¹,

Açıkgöz²,

Yıldız³

et al. 2017

Journal of Electrical Engineering and Technology

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“…FUZZY RBF NEURAL NETWORK CONTROLLER DESIGN Radial Basis Function (RBF) network has better network performance, researchs have proved that the RBF network can approximate any accuracy with non-linear curve, and has a universal approximation capability, and it can solove local optimization problems of the BP network fundamentally [11]. The topology structure compactly, structural parameters can carry separated learning into effect, and the convergence speed is fast.…”

Section: Mathematical Modelmentioning

confidence: 99%

Rearch on RBF Neural Network Fuzzy Control for STATC0M

Chen

2009

2009 International Conference on Intelligent Human-Machine Systems and Cybernetics

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The basic principle for static synchronous compensator is analyzed in this paper, a new mode controller for STATCOM is proposed, which is based on RBF neural network and fuzzy control theory. The controller can improve the power angle stability for the power system and the local voltage characteristics. The power system transient simulation results on a single-machine infinite-bus system which is include STATCOM shown that the RBF neural network fuzzy controller can improve stability and dynamic response characteristics for the power system more effectively in contrast to conventional STATCOM controllers. The availability in this control method has been verified with digital simulation.

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“…Recently alternate methods [11] of implementing these switching patterns have been developed without using real time solution of nonlinear harmonic elimination equation; an ANN is trained offline to output the switching angles for wanted output voltage. The ANN to be used for the generation of the optimal switching angles has a single input neuron fed by the modulation index, one hidden layer and s outputs where each output represents a switching angle [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Transient Stability Improvement Using Neuro-Fuzzy Controller Design For STATCOM

Dhal

Rajan

2012

IOSRJEEE

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This paper presents a transient stability improvement using neural-fuzzy controller design for STATCOM with static synchronous time critical error and better damping system oscillations after a short circuit fault. This article on a STATCOM Control for transient stability improvement has proposed a system to meet with the addition of Lyapunov stability criterion to the ability and conditions as well. The performance is analyzed using digital simulation with SMIB.

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A neural network controlled optimal pulse-width modulated STATCOM

Cited by 31 publications

References 6 publications

Power Quality Improvement Using Hybrid Passive Filter Configuration for Wind Energy Systems

Power Quality Improvement Using Hybrid Passive Filter Configuration for Wind Energy Systems

Rearch on RBF Neural Network Fuzzy Control for STATC0M

Transient Stability Improvement Using Neuro-Fuzzy Controller Design For STATCOM

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