Artificial neural networks and their applications to power systems—a bibliographical survey

Vankayala, Vidya; Rao, N.D.

doi:10.1016/0378-7796(93)90081-o

Cited by 117 publications

(26 citation statements)

References 24 publications

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“…Xuan et al [11] and Vankayala et al [91] proposed an adaptive protection scheme based on an ANN that addresses the problems experienced by conventional distance protection for double-circuit lines with different voltage levels arising principally due to mutual under-the-different-fault conditions. The performance of this scheme is guaranteed only for single-phase-to-earth faults.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Trends in Adaptive Distance Protection of Multiterminal and Double-Circuit Lines

Bhalja

Maheshwari

2006

Electric Power Components and Systems

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With the changing technologies in the field of power system relaying, new protection concepts addressing new technologies are coming to the market. Therefore we need to keep track of international experiences and activities taking place in the field of modern protective systems. This paper gives a bibliographical survey, mathematical formulations, and general backgrounds of research and developments in the field of protection of multiterminal lines and double-circuit lines during the past 50 years, based on more than 100 published articles. Different techniques for parallel line protection and various methods for multiterminal lines are studied and an evaluation of them is undertaken with their advantages and disadvantages for identification of optimum technique as applied to specific applications. At the end, the philosophy of a new adaptive digital distance relaying scheme is also conceptualized. The collected literature has been divided into many sections so that new researchers do not face any difficulty in carrying out research in any specific area of the next generation's adaptive distance protection.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Trends in Adaptive Distance Protection of Multiterminal and Double-Circuit Lines

Bhalja

Maheshwari

2006

Electric Power Components and Systems

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“…Neural network models are providing new approaches to problem solving [Mori et al, 1992;Vankayala et al, 1993]. Arti cial neural networks can achieve high computational speed by employing a massive number of simple processing elements arranged in parallel with a high degree of connectivity between the elements.…”

Section: Introductionmentioning

confidence: 99%

Power System State Forecasting Using Artificial Neural Networks

Kumar¹,

Srivastava²

1999

Electric Machines & Power Systems

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This paper presents a new method for power system state forecasting using arti cial neural networks (ANN). The state forecasting problem has been solved in two steps: the ltering step and the forecasting step in an open loop con guration. Because under normal operating conditions the power system behaves in a quasi-static manner, a simpli ed model of the dynamic behavior of the power system states is considered. Two diOE erent ANN models have been used for these two steps of power system state forecasting problem. For the ltering step, a functional link network (FLN), and for the forecasting step, a time delay neural network (TDNN) have been used to simulate the dynamic behavior of the power system states. The proposed method has been tested on two IEEE test systems, and a practical Indian system and results have been compared with an extended Kalman lter (EKF) based technique [Leite da Silva et al., 1983].

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“…Vankayala et al [65] have presented a bibliographical survey of neural network and their applications to power systems. Neural network has been mainly used in following areas of power systems:…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Optimization Methods for Electric Power Systems: An Overview

Bansal¹

2005

International Journal of Emerging Electric Power Systems

128

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Power systems optimization problems are very difficult to solve because power systems are very large, complex, geographically widely distributed and are influenced by many unexpected events. It is therefore necessary to employ most efficient optimization methods to take full advantages in simplifying the formulation and implementation of the problem. This article presents an overview of important mathematical optimization and artificial intelligence (AI) techniques used in power optimization problems. Applications of hybrid AI techniques have also been discussed in this article.

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Artificial neural networks and their applications to power systems—a bibliographical survey

Cited by 117 publications

References 24 publications

Trends in Adaptive Distance Protection of Multiterminal and Double-Circuit Lines

Trends in Adaptive Distance Protection of Multiterminal and Double-Circuit Lines

Power System State Forecasting Using Artificial Neural Networks

Optimization Methods for Electric Power Systems: An Overview

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