Artificial Neural Network Based Power System Stability Analysis
In this paper, an Artificial Neural Network (ANN) approach for the analysis of a power system stability has been proposed and proved to be effective. Here the main consideration is the power system voltage stability i.e. static voltage stability. With ins
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Artificial Neural Network Based Power System Stability Analysis S. Kumari Lalitha and Y. Chittemma
Abstract In this paper, an Artificial Neural Network (ANN) approach for the analysis of a power system stability has been proposed and proved to be effective. Here the main consideration is the power system voltage stability i.e. static voltage stability. With instance of 9-Bus [3] power system, also worked on IEEE-57 Bus [4] system and it is verified that the method is effective for power system voltage stability assessment.[3, 4, 8] The implementation of these structures is shown through Mat lab and by the use of ANN approach [5, 6] and the above two methods are compared for the test system. The network would be a useful tool to assess power system voltage stability quickly.
Keywords ANN Power system voltage stability method Load flow BP neural network
VCPI Newton–Raphson
65.1 Introduction Power System Voltage Stability [8] is the ability of a power system to maintain acceptable voltages at all buses in the system under normal conditions and after being subjected to disturbance. Power system Voltage stability assessment is to determine whether the power system is voltage stable or not [7]. Power system is
S. K. Lalitha (&) Y. Chittemma Department of Electrical and Electronics Engineering, G.M.R. Institute Of Technology, Rajam, Andhra Pradesh, India e-mail: [email protected] Y. Chittemma e-mail: [email protected]
V. V. Das (ed.), Proceedings of the Third International Conference on Trends in Information, Telecommunication and Computing, Lecture Notes in Electrical Engineering 150, DOI: 10.1007/978-1-4614-3363-7_65, Ó Springer Science+Business Media New York 2013
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S. K. Lalitha and Y. Chittemma
Fig. 65.1 9-Bus system
inherently complex, non-linear, uncertain and so on. As a result, it is difficult to use conventional techniques and mathematical models to describe power system voltage stability assessment. For this, the voltage collapse proximity indicator (VCPI) [2] is used. This paper presents a Back Propagation Neural Network approach [5, 6] for the assessment of power system voltage stability with the VCPI [2] as assessment index. The static model is based on load flow calculation. With instances of 9-Bus, IEEE-57 Bus power system, it is verified that the method is effective to voltage stability assessment [3, 4] on power system.
65.2 The Voltage Collapse Proximity Indicator Voltage collapse proximity indicators [2] are considered as measures to estimate whether the voltage of a system collapses or not. It varies in the range between 0 and 1 with L \ 1 for stable state and L = 1 for voltage collapse state. The VCPI for a node j can be calculated by Sj V Sj 0j Lj ¼ 1 þ ¼ 2 ¼ 2 V2j Vj Yjj Vj Yjj Where Sj is the transformed power, Yjj is the transformed admittance ¼ 1=Zjj ; and Vj is the consumer node voltage. It is difficult to calculate the index L for each load bus directly by the mathematical analysis. As a result, this paper presents a BP neural network to estimate the
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