Voltage stability analysis using conventional methods

In recent years, the Kenyan Power Network has witnessed large growths in load demand. Although the increased load demand has somewhat been matched with an increase in transmission and generation capacity, the rate of expansion has not been matched with the rate of increase in load demand due to economic, environmental, and geographical constraints. This has led to the system being prone to instability since it is being operated under stressed conditions. In the recent past, several studies have been carried out on voltage stability analysis and improvement using various conventional methods. However, conventional methods have various limitations in their utilization for voltage stability analysis. One solution to overcome these limitations is to employ a combination of one or more methods so as to get more information and greater degree of accuracy in voltage stability studies. In this paper, a methodology is proposed involving the combination of QV modal analysis, sensitivity analysis (VQ) and power-voltage curves in assessing the static voltage stability analysis taking a case study of the Kenyan Power Network. V-Q sensitivity analysis and QV modal analysis have been used to identify the load regions most susceptible to voltage instability and the corresponding weak buses in the network for various V-Q responses. Reactive power loss sensitivities for branches in the network have been used to determine the critical (weak) lines in the network. Loading margins (LM) and voltage stability margins (VSM) have then been used to determine the proximity to voltage collapse of the voltage weak buses identified by QV modal analysis. The effect of tripping one the critical lines on the voltage weak buses is also investigated. The current high voltage power network under the average peak loading conditions during the year 2019 is considered for the study. The paper also reviews existing voltage stability analysis methods and their limitations.

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Voltage Stability Analysis of Radial Distribution Systems with Realistic Loads

i-manager’s Journal on Electrical Engineering ◽

10.26634/jee.3.1.996 ◽

2009 ◽

Vol 3 (1) ◽

pp. 11-19

Author(s):

P.V. Prasad ◽

◽

S. Sivanagaraju ◽

B. Usha ◽

◽

...

Keyword(s):

Stability Analysis ◽

Radial Distribution ◽

Distribution Systems ◽

Voltage Stability

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Identification of Efficient Sampling Techniques for Probabilistic Voltage Stability Analysis of Renewable-Rich Power Systems

Energies ◽

10.3390/en14082328 ◽

2021 ◽

Vol 14 (8) ◽

pp. 2328

Author(s):

Mohammed Alzubaidi ◽

Kazi N. Hasan ◽

Lasantha Meegahapola ◽

Mir Toufikur Rahman

Keyword(s):

Monte Carlo ◽

Stability Analysis ◽

Power Systems ◽

Large Scale ◽

Voltage Stability ◽

Sampling Technique ◽

Coefficient Of Determination ◽

Sampling Techniques ◽

Quasi Monte Carlo ◽

Efficient Sampling

This paper presents a comparative analysis of six sampling techniques to identify an efficient and accurate sampling technique to be applied to probabilistic voltage stability assessment in large-scale power systems. In this study, six different sampling techniques are investigated and compared to each other in terms of their accuracy and efficiency, including Monte Carlo (MC), three versions of Quasi-Monte Carlo (QMC), i.e., Sobol, Halton, and Latin Hypercube, Markov Chain MC (MCMC), and importance sampling (IS) technique, to evaluate their suitability for application with probabilistic voltage stability analysis in large-scale uncertain power systems. The coefficient of determination (R2) and root mean square error (RMSE) are calculated to measure the accuracy and the efficiency of the sampling techniques compared to each other. All the six sampling techniques provide more than 99% accuracy by producing a large number of wind speed random samples (8760 samples). In terms of efficiency, on the other hand, the three versions of QMC are the most efficient sampling techniques, providing more than 96% accuracy with only a small number of generated samples (150 samples) compared to other techniques.

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