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Power system steady-state voltage stability assessment based on support vector machine

TLDR
In this article, a method of model construction for the power system steady-state voltage stability assessment based on support vector machine (SVM) is presented, which takes full advantage of SVM's ability to solve the problem with high dimension, nonlinear and small sample.
Abstract
To prevent voltage collapse, it is necessary to evaluate the distance of the operation state to the voltage collapse point. This distance is calculated with power flow equations normally.The calculation speed of this technique is slow for power system with high dimension, so it is difficult to realize real-time voltage stability assessment. The application of a fast and reliable evaluation technique is very important to diminish the evaluation time. This paper presents a method of model construction for the power system steady-state voltage stability assessment based on support vector machine (SVM). This method takes full advantage of SVM's ability to solve the problem with high dimension, nonlinear and small sample. Hence better generalization ability is guaranteed, and the model works with the quicker assessment speed and the higher forecast precision. The WSCC 9-bus test system is employed to demonstrate the validity of the proposed approach.

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Voltage stability assessment of multi-machine power systems using energy function and neural netwoks techniques

TL;DR: In this paper, a generalized energy function for voltage stability assessment of multi-machine power systems is presented, which is used to rank the system buses according to their contributions to voltage collapse.
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Power quality disturbances classification Based on multi-class classification SVM

TL;DR: In this paper, the authors used the multiscale classification for support vector machine and combined with the good amplitude-frequency characteristic of fourier transform, the good time-frequency characteristics of wavelet transform and the excellent statistical learning ability of SVM to make the classification and recognition to the disturbances of power quality.
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On-line static voltage security risk assessment based on Markov chain model and SVM for wind integrated power system

TL;DR: An online assessment method to deal with static voltage security risk caused by wind power, where the relationship between security limitation and power flow status is trained offline through GA-SVM from continuation power flow (CPF) sample and static voltageSecurity risk is assessed online by calculating operation status probability and distance to security limitation.
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Using Artificial Neural Network Algorithm for Voltage Stability Improvement

TL;DR: The results obtain from the test clearly show that the trained neural network is capable of improving the voltage stability in power system with a high level of precision and speed.
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