Voltage stability analysis for load center with distributed generation

2016 ◽  
Author(s):  
Shichao Qu ◽  
Zhenshu Wang ◽  
Qi Zhang ◽  
Jie Zuo ◽  
Yu Sun ◽  
...  
Keyword(s):  
Stability Analysis ◽  
Distributed Generation ◽  
Voltage Stability ◽  
Load Center

Voltage Stability Analysis of Radial Distribution Systems with Realistic Loads

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

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

Energies ◽  
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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