Application of artificial neural networks in voltage stability assessment

El-Keib, A.A.; Ma, Xingwang

doi:10.1109/59.476054

Cited by 123 publications

(37 citation statements)

References 17 publications

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“…Many techniques have been proposed in the literature for evaluating and predicting voltage stability. Among these techniques are P-V curves (Pal, 1992;Suzuki et al, 1992), Q-V curves (D'Aquila et al, 1993;Schluter, 1998;Taylor, 1994), modal analysis (Gao et al, 1992;Morrison et al, 1993;Mansour et al, 1994), minimum singular value (Löf et al, 1993;Liu et al, 1998), sensitivity analysis (Begovic and Phadke, 1992;Fltabo and Dommel 1990), energy function (Overbye and DeMacro, 1995;DeMacro and Overby, 1990), reactive power optimization (Tamura et al, 1982), artificial neural networks (Jesasuray, 1990;El-Keib and Ma, 1995) and neuro-fuzzy networks (Liu and Su, 1998). Large numbers of research papers on power systems voltage stability have been published recently by the IEEE Voltage Stability Working Group (Ajjarapu and Lee, 1998).…”

Section: ‫ﺍﻟﻌﺒﺭﻱ‬ ‫ﺯﺍﻫﺭ‬ ‫ﻭ‬ ‫ﺍﻟﻬﻨﺎﺌﻲ،‬ ‫ﻋﺎﻤﺭ‬ ، ‫ﺍﻟﻌﻠﻭﻱ‬ ‫ﺼﺎﻟﺢ‬ ، ‫ﺍmentioning

confidence: 99%

Voltage Stability Analysis of Muscat Power System During Summer Weather Conditions

Ellithy

Gastli

Al-Alawi

et al. 2000

SQU Journal for Science

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‫ﺍﻟﺼﻴﻑ‬ ‫ﻁﻘﺱ‬ ‫ﺤﺎﻻﺕ‬ ‫ﺘﺤﺕ‬ ‫ﺒﻤﺴﻘﻁ‬ ‫ﺍﻟﻘﻭﻯ‬ ‫ﺒﺸﺒﻜﺔ‬ ‫ﺍﻟﻜﻬﺭﺒﺎﺌﻲ‬ ‫ﺍﻟﺠﻬﺩ‬ ‫ﺍﺴﺘﻘﺭﺍﺭ‬ ‫ﺘﺤﻠﻴل‬ ‫ﺍﻟﻌﺒﺭﻱ‬ ‫ﺯﺍﻫﺭ‬ ‫ﻭ‬ ‫ﺍﻟﻬﻨﺎﺌﻲ،‬ ‫ﻋﺎﻤﺭ‬ ، ‫ﺍﻟﻌﻠﻭﻱ‬ ‫ﺼﺎﻟﺢ‬ ، ‫ﺍﻟﻘﺼﻁﻠﻲ‬ ‫ﻋﺎﺩل‬ABSTRACT: A systematic and general formulation of a Propagation Simulation Program (PSP) is developed for the coherent field of microwave and millimeter wave carrier signals traversing intermediate layered precipitation media taking into account the random behavior of particle size, orientation, shape and concentration distributions. Based on a rigorous solution of the volumetric multiple-scattering integral equations, the formalism offers the capability of treating the potential transmission impairments on satelliteearth links and radar remote sensing generated by composite atmospheric layers of precipitation in conjunction with the finite polarization isolation of dual-polarized transmitting and receiving antennas. A multi-layered formulation is employed which encompasses an ensemble of discrete particles comprising an arbitrary mixture of ice crystals, melting snow and raindrops that may exist simultaneously along satellite-earth communication paths.

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Section: ‫ﺍﻟﻌﺒﺭﻱ‬ ‫ﺯﺍﻫﺭ‬ ‫ﻭ‬ ‫ﺍﻟﻬﻨﺎﺌﻲ،‬ ‫ﻋﺎﻤﺭ‬ ، ‫ﺍﻟﻌﻠﻭﻱ‬ ‫ﺼﺎﻟﺢ‬ ، ‫ﺍmentioning

confidence: 99%

Voltage Stability Analysis of Muscat Power System During Summer Weather Conditions

Ellithy

Gastli

Al-Alawi

et al. 2000

SQU Journal for Science

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“…Most of the earlier studies based on conventional approach primarily depend on load flow simulation over a specific time period of observation and are faced with several limitations such as; complexity in modeling, unavailability of real time database, simulation of contingencies and more so. Hence, most of the present day research is inclined to get an edge over the same by supplementing the analysis with soft computing tools such as fuzzy logic and neural networks [11][12][13][14][15][16]. Being partially motivated by this, the authors of this paper have tried to implement neural network tools for identification of critical conditions in electric power system through a comparative study and performance analysis of the proposed network with the basic objective of evolving an optimal network structure and learning criteria.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Critical Conditions in Electric Power Systems by Feed Forward and Layer Recurrent Neural Networks

Panda¹,

Sahoo²,

Satpathy³

et al. 2014

ijeei

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Abstract:In this paper, critical conditions in electric power systems are monitored by applying various neural networks. In order to accomplish the stated goal, the authors tried several combinations of Feed Forward Neural Network and Layer Recurrent Neural Networks by imparting appropriate training schemes through supervised learning in order to formulate a comparative analysis on their performance. Once, training goes successful, the neural network learns how to deal with a set of newly presented data through validation and testing mechanism so as to evolve the best network structure and learning criteria. The proposed methodology has been tested on the standard IEEE 30-bus test system with the support of MATLAB based neural network toolbox. The results presented in this paper signify that the multi-layered feed forward neural network with Levenberg-Marquardt back propagation algorithm gives best training performance of all possible cases considered in this paper, thus validating the proposed methodology.

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“…Repo [11] presented the use of neural network for voltage stability by calculation of margins by the point of collapse method. El-Keib [12] investigated the application of ANN in voltage stability assessment. Based on the energy methods, a direct mapping relation between system loading condition and the voltage stability margins is set up via the ANN.…”

Section: Introductionmentioning

confidence: 99%

Computational Intelligence and Voltage Stability Analysis for Mitigation of Blackout

Shaikh¹,

Din²,

Asghar³

2011

IJCA

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Mitigation of power system blackouts are still going on all over the world. Millions of people are affected each year due to power system blackouts. Integration the knowledge of computational intelligence with roots of blackout can lead us to triumph over these tremendous losses. Voltage stability study is an essential aspect as it avoids power system blackouts. This paper presents a computational intelligence based evaluation for a very important aspect, voltage stability of an electric power system. Large amounts of data, fuzziness of that data, and the endless variations of system configurations are all factors contributing to the complexity of power system analysis and diagnosis. This complexity has necessitated the need for computational intelligent tools to aid system engineers. Artificial Neural Network and Fuzzy Logic have emerged as amongst most suitable tools for power system applications. The combination of two intelligent disciplines into one system i.e. neuro-fuzzy technique for power system analysis has proven even more effective. KeywordsComputational intelligence, power system blackout, neural network.

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Application of artificial neural networks in voltage stability assessment

Cited by 123 publications

References 17 publications

Voltage Stability Analysis of Muscat Power System During Summer Weather Conditions

Voltage Stability Analysis of Muscat Power System During Summer Weather Conditions

Analysis of Critical Conditions in Electric Power Systems by Feed Forward and Layer Recurrent Neural Networks

Computational Intelligence and Voltage Stability Analysis for Mitigation of Blackout

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