Abstract:
With the rapid growth in power systems and extensive application of wide-area
measurements, the security of the power system becomes a crucial aspect. Monitoring
transient stability is required for reliable operation of the grid and instability prediction.
The major idea of this research is to implement a fast and robust online transient stability
assessment tool for the classification of the system operating states. Artificial Intelligence
(AI) has drawn the attention of the power industry and is now being used to build a robust
Transient Stability Assessment (TSA) system. Compared with traditional ANN
techniques, this research work proposes an advanced hybrid TSA monitoring system
which is a combination of a Convolutional Neural Network and Random Forest classifier.
A Convolutional Neural Network (CNN) utilizes images for detecting the transient
stability status and is hereby used as a feature extractor for the Random Forest (RF)
classifier. This research determines that the combination of these two models can
increase the efficiency of the system. A dataset is obtained by the Phasor Measurement
Unit (PMU) measurements through time-domain simulations. Owing to the huge size and
unapproachability of transmission systems, real-time testing can become problematic.
Therefore, the simulations are performed on the standard IEEE-14 bus system and the
IEEE-39 bus system as case studies to analyze the system for future practice in a lab and
real-time grid. The efficiency of the system is tested under various contingency scenarios
by implementing a number of faults in certain locations under varying loading conditions.
A doubly-fed induction generator wind turbine is also installed to examine the
intermittent effects on the power system stability. Moreover, a CNN-based fault
classification system is also proposed for the identification of a fault that occurred in the
system. The test results verified that the proposed hybrid model is accurate and more
robust as the RF classifier is proved to have higher precision and required less time as
compared to the CNN-based classifier. A combination of these techniques exhibited
excellent results.
