Two Separate Continually Online-Trained Neurocontrollers for a Unified Power Flow Controller

Venayagamoorthy, G.K.; Kalyani, R.P.

doi:10.1109/tia.2005.851571

Cited by 29 publications

(11 citation statements)

References 15 publications

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“…As known widely, the nonlinear modeling of the time varying dynamics of an electric power system is still a challenge using classical techniques but computational intelligence paradigms such as neural networks have been shown to successful model the nonlinear dynamics offline or online. Multilayer perceptrons, radial basis functions (RBFs), simultaneous recurrent neural networks (SRNs) and echo state networks have all been reported very effective models for the design of nonlinear, adaptive and/or optimal controllers such as for generator excitation systems, turbines and FACTS devices [6][7][8]. Most of the identification/modeling techniques are based on supervised learning techniques as illustrated in Fig.…”

Section: CI Methods For Smart Gridsmentioning

confidence: 99%

Potentials and promises of computational intelligence for smart grids

Venayagamoorthy

2009

2009 IEEE Power &Amp; Energy Society General Meeting

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The electric power grid is a complex adaptive system under semi-autonomous distributed control. It is spatially and temporally complex, non-convex, nonlinear and non-stationary with a lot of uncertainties. The integration of renewable energy such as wind farms, and plug-in hybrid and electric vehicles further adds complexity and challenges to the various controllers at all levels of the power grid. A lot of efforts have gone into the development of a smart grid to align the interests of the electric utilities, consumers and environmentalists. Advanced computational methods are required for planning and optimization, fast control of power system elements, processing of field data and coordination across the grid. Distributed and coordinated intelligence at all levels and across levels of the electric power grid -generation, transmission and distribution is inevitable if a true smart grid is to be reality. Computational intelligence (CI) is the study of adaptive mechanisms to enable or facilitate intelligent behavior in complex, uncertain and changing environments. These adaptive mechanisms include artificial and bioinspired intelligence paradigms that exhibit an ability to learn or adapt to new situations, to generalize, abstract, discover and associate. The paradigms of CI mimic nature for solving complex problems. CI is successor of artificial intelligence and is the way of the future computing. This paper presents the potentials and promises of CI to realize an intelligent smart grid.Index Terms-adaptive control, adaptive critic designs, computational intelligence, FACTS, plug-in vehicles, renewable energy, wide area monitoring and control.

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Section: CI Methods For Smart Gridsmentioning

confidence: 99%

Potentials and promises of computational intelligence for smart grids

Venayagamoorthy

2009

2009 IEEE Power &Amp; Energy Society General Meeting

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“…Despite their popularity in applications to financial variables, ANNs have not been utilized very well in Nigerian financial market. Similarly, multi-layer feedforward Artificial Neural Networks using Backpropagation algorithms for training have been used in several literatures for example [11][12][13][14]. Since the Backpropagation algorithm has been successfully applied to train neural networks, this work aims to investigate the training performance of the some variants of the back propagation algorithm in training the proposed model for forecasting volatility.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Backpropagation Algorithms in Forecasting Volatility of Crude Oil Price in Nigeria

Suleiman

Gulumbe²,

Asare³

et al. 2016

SJAMS

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This paper explores the application of artificial neural network in volatility forecasting. A recurrent neural network has been integrated in to GARCH model to form the hybrid model called GARCH-Neural model. The emphasis of the research is to investigate the performance of the variants of Backpropagation algorithms in training the proposed GARCHneural model. In the first place, EGARCH (3, 3) was identified in this paper most preferred model describing crude oil price volatility in Nigeria. Similarly, Levenberg-Marquardt (LM) training algorithms were found to be fastest in convergence and also provide most accurate predictions of the volatility when to other training techniques.

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“…In recent years a number of investigations have been carried out on various capabilities of UPFC such as power flow control [5][6][7] and oscillation damping [8,9]. Moreover some neural network-based intelligent controllers [10,11] have been developed in the literature.…”

Section: Introductionmentioning

confidence: 99%

An Approach to Embed Power Flow Control Model of UPFC into PSASP Based on User Program Interface Function

Zhang¹,

Chen²,

Li³

et al. 2014

JCP

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The unified power flow controller (UPFC) is one of the most promising flexible AC transmission systems (FACTS) devices for the power flow control. The main objective of power flow control for UPFC is to improve the power flow distribution and increase the transmission capacity over the existing transmission corridor or line. This paper presents a practical engineering methodology of embedding the power flow control model of UPFC into the commercial software-power system analysis software package (PSASP) based on its user program interface (UPI) function. In the proposed methodology, the interface currents of UPFC series side and UPFC shunt side between the UPFC device and the network are used to control the transmission line power flow and UPFC bus voltage, respectively. In UPFC series side, the current of UPFC series branch is calculated from the target equation of the power flow in the controlled line. In UPFC shunt side, the decoupling control between the active power and reactive power is implemented and the reactive current of UPFC shunt side is used to control the bus voltage. In this study, simulations are performed on a typical two-area power system and a practical 496-bus power system and simulation results show that the proposed methodology can be efficiently applied to the engineering research and analysis of the real power grid with UPFC with good convergence and with only one control parameter needed to be prescribed.

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Two Separate Continually Online-Trained Neurocontrollers for a Unified Power Flow Controller

Cited by 29 publications

References 15 publications

Potentials and promises of computational intelligence for smart grids

Potentials and promises of computational intelligence for smart grids

Comparative Study of Backpropagation Algorithms in Forecasting Volatility of Crude Oil Price in Nigeria

An Approach to Embed Power Flow Control Model of UPFC into PSASP Based on User Program Interface Function

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