A Novel Image Classification Approach via MobileNet Model for Security Assessment of Power System

Abstract

The computational complexities of conventional techniques used for security assessment of power system are addressed by proposing a maiden deep learning-based technique capable of boosting security calculations, increasing decision-making efficiency, and reducing hazards for real time operation. This approach uses MobileNet Convolutional Neural Network (MCNN) to evaluate security with reduced model size and computational burden as compared to traditional CNN architectures by leveraging special type of convolution technique. MCNN's automatic feature extraction and strong generalization allow the operator to determine whether the current operating state of power system is secured or not. All of the system’s critical operational scenarios are developed via load flow studies for generation of data while accounting for small signal stability with $\ell -1$ security and converted into RGB images of varied input dimensions for training and testing of the MCNN. Once trained, the MCNN is well-suited for real-time applications like assessing whether current state of the system is secured or not including unforeseen occurrences like topological changes in a computation-free manner. The comparative performance of the proposed method with customized CNN on three typical IEEE test system demonstrates its superior performance in terms of better accuracy and computational efficiency.

Keywords:

• MobileNet
• security assessment
• image generation
• deep learning
• convolutional neural network

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Mahesh Pal Singh

Mahesh Pal Singh is currently pursuing his Ph.D. in Electrical Engineering at National Institute of Technology Silchar, Assam, India. He received M.E. in High Voltage & Power Systems from Jabalpur (Government) Engineering College, Jabalpur, India, in 2015, and B.Tech. in Electrical & Electronics Engineering from Sam Higginbottom Institute of Agriculture, Technology & Sciences, Allahabad, India, in 2012. His research focuses on machine learning applications in power system security assessment, renewables scenario generation, and load scenario forecasting.

Nidul Sinha

Nidul Sinha was born in Tripura, India, in 1962. He is currently working as a professor in the Department of Electrical Engineering, National Institute of Technology, Silchar, Assam, India. He received his Ph.D. degree in electrical engineering from Jadavpur University, Kolkata. His Ph.D. thesis was on the application of intelligent techniques in the optimal operation of a power system. He received the M.Tech. degree in power apparatus and systems from IIT Delhi, New Delhi, in 1989, and the B.E. degree in electrical engineering from Calcutta University, Kolkata, India, in 1984. He has been engaged in active research in different areas like automatic generation control, optimal operation of the power system under conventional and non-conventional environments, control of renewable energy sources and micro-grid, image denoising, and video motion estimation, EEG-based emotion detection, and silent speech reading. He has more than 90 national and international publications in diverse fields. He has completed four sponsored Research and Development Projects. He has also been a reviewer of several international journals, such as IEEE, IET, Elsevier, Taylor and Francis, and Springer.