Low-cost microprocessor-based alternating current voltage controller using genetic algorithms and neural network

Kaitwanidvilai, Somyot; Piyarungsan, Pairoj

doi:10.1049/iet-pel.2009.0035

Cited by 12 publications

(7 citation statements)

References 1 publication

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“…with the adaptive laws in (26) and (27). Then, there exists a choice of gains k 1 , k 2 , k 3 such that (i) all the states and signals of the closed-loop system are bounded and (ii) the asymptotic bound on the regulation error can be made arbitrarily small.…”

Section: Controller Design and Stability Analysismentioning

confidence: 99%

“…More recently, both neural network systems and fuzzy logic systems [18][19][20] have been successfully applied to universally approximate the mathematical models of dynamic systems. Several neural network-based and fuzzy-based control schemes have been developed to treat the robust control of uncertain non-linear power systems with various stable performances [21][22][23][24][25][26][27][28][29]. Farahani [21] proposed an intelligent wavelet neural network-based control of SVC in power system to enhance transient stability.…”

Section: Introductionmentioning

confidence: 99%

“…Wang and Truong [25] proposed the stability‐improvement results of a synchronous generator (SG)‐based one‐machine infinite‐bus system with permanent‐magnet SG (PMSG)‐based and doubly fed induction generator (DFIG)‐based offshore wind farms using an SVC. Kaitwanidvilai and Piyarungsan [26] proposed a low‐cost high‐performance microprocessor‐based pulse width modulation AC voltage controller using a novel harmonic reduction technique which was integrated with a genetic algorithm and an artificial neural network algorithm. A fuzzy‐based technique for modelling both static and dynamic responses of the voltage in proton exchange membrane fuel cell energy power generation systems was presented by Ramos‐Paja et al [27].…”

Section: Introductionmentioning

confidence: 99%

“…Then, from the adaptive laws in (26) and (27) that are the standard projection algorithm defined in [18,20] we can conclude that forQ…”

mentioning

confidence: 99%

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Robust neural network‐based control of static var compensator

Chang¹

2014

IET Power Electronics

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This study addresses the problem of designing robust stabilisation control for a large class of uncertain single-machine infinite-bus electrical power systems with static var compensator (SVC). This class of systems may be perturbed by plant uncertainties, unmodelled perturbations and external disturbances. An adaptive neural network-based dynamic feedback controller is developed such that all the states and signals of the closed-loop system are bounded and the stabilisation error can be made as small as possible. As the small perturbations in the input weighting gains are neglected, an H ∞ control performance can be guaranteed. The adaptive neural network approximation systems are designed to learn the behaviours of the unknown functions, and in turn a modified procedure is proposed such that the number of the neural network basis functions can be significantly reduced. Consequently, the intelligent robust control scheme developed here possesses the properties of computational simplicity and easy implementation from the viewpoint of practical applications. The developed robust control scheme not only can handle a large class of uncertain SVC-driven power systems, but also achieve the aim of enhancing the stability performance. Finally, simulations are provided to demonstrate the effectiveness and performance of the proposed control algorithm.

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Section: Controller Design and Stability Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Then, from the adaptive laws in (26) and (27) that are the standard projection algorithm defined in [18,20] we can conclude that forQ…”

mentioning

confidence: 99%

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Robust neural network‐based control of static var compensator

Chang¹

2014

IET Power Electronics

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“…Numerous algorithms and techniques have been proposed for the derivation of optimal switching angles of PWM patterns. Methods like genetic algorithms [9], [10] neural networks [11], particle swarm optimization [12], bee colony optimization [13] and their combinations [14] have been studied. A model of the proposed regulator is usually specified and the selected algorithm runs for a given set of output voltages, deriving the corresponding optimal switching angles with the purpose to achieve a given objective, usually the minimization of total harmonic distortion (THD) of the output current.…”

Section: Introductionmentioning

confidence: 99%

Voltage control of single-phase induction motors using asymmetrical PWM and fuzzy logic

Zigirkas

Kalomiros

2016

2016 5th International Conference on Modern Circuits and Systems Technologies (MOCAST)

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A microcontroller-based AC voltage regulator for single-phase induction motors, using asymmetrical pulse width modulation (PWM) and fuzzy logic is proposed. The optimal switching angles are computed in real-time, using a nine-rule fuzzy system, with the objective to correct the phase difference between the output voltage and current. The voltage controller achieves an almost linear response and the motor speed is smoothly regulated within the full dynamic range of the control input. The proposed fuzzy switching regulator is verified both in simulation and with a circuit prototype. The performance of the asymmetrical PWM fuzzy controller is compared against other methods.

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