Data Reduction for Dynamic Stability Classification in Power System

Abstract

A large oscillation of power system caused by faults must be quickly treated so that the opportunity driving power system into re-stability state can be easier. Necessity is to select a compact data-set that is the representative of all data-sets with the aim of reducing computing costs, reducing computer memory, and improving classification accuracy. The K-means (KM) algorithm is the most commonly used clustering algorithm because it can be easily implemented and is the most effective one in terms of the execution time on large data size. The key problem of KM is that it is sensitive to initial center and may converge to a local optimization. In this paper, we proposed the use of Hybrid K-means (HKM) data clustering algorithm that can avoid being trapped in a local optimal solution. The HKM was applied with the aim of reducing data space. We also proposed a process data clustering applied to classify the problem of dynamic stability in power system. The K-Nearest Neighbor (K-NN) was chosen as the classifier. The K-NN participated in the evaluating classification accuracy stage. The study was done on IEEE 39-bus. The results showed that the proposed algorithm achieved effective data size reduction and high accuracy classification.

KEYWORDS:

• Data clustering
• Feature selection
• Power system stability
• Prediction/recognition/classification

ACKNOWLEDGEMENTS

The authors would like to thank the support from Renewable Energy and Power System Lab C201 of HCMC University of Technology and Education.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Ngoc Au Nguyen

Ngoc Au Nguyen received his MSc degree in electrical engineering from HCMUT, Vietnam, in 2003. Currently, he is a lecturer in the Faculty Electrical and Electronics Engineering, HCMUTE. His research interests are load shedding and dynamic stability power system prediction.

Corresponding author. E-mail: [email protected]

Trong Nghia Le

Trong Nghia Le received his MSc degree in electrical engineering from HCMUTE, Vietnam, in 2012. Currently, he is a lecturer in the Faculty Electrical and Electronics Engineering, HCMUTE. His research interests are load shedding and power systems stability.

E-mail: [email protected]

Huy Anh Quyen

Huy Anh Quyen received PhD degree in power system from MPIE, Russia, in 1993. Currently, he is a professor, lecturer in the Faculty Electrical and Electronics Engineering, HCMUTE. His research interests are modeling power systems, pattern recognition in dynamic stability of power systems, and artificial intelligence.

E-mail: [email protected]

Thi Thanh Binh Phan

Thi Thanh Binh Phan received PhD degree in electrical engineering from Kiev Polytechnique University, Ukraine in 1995. Currently, she is a professor, lecturer in the Faculty Electrical and Electronics Engineering, HCMUT. Her research interests are power systems stability, power systems operation and control, load forecasting, and data mining.

E-mail: [email protected]