Deep learning-based transient stability assessment framework for large-scale modern power system

When severe disturbance occurs in power system, lack of efficacious information about transient stability state is a key challenge for power network operator. Especially for the system operating in boundary of security constraints due to economic reasons, it becomes more prominent. With the extensive deployment of phasor measurement units (PMUs), abundant historical data of power system have been collected and the data driven method based on machine learning model has become important tool to solve transient stability assessment (TSA) problem. However, the implementation of data-driven TSA method is difficult due to the characteristics of huge feature number and variable network topology of large-scale modern power system. In order to address this issue, a novel deep learning-based online TSA framework is proposed in this article. Firstly, parallel convolution algorithms are employed to address redundant input features. secondly, a novel machine learning model, deep forest is employed to train a TSA model. Some improvements are implemented for better assessment performance: 1) The internal feature transmission of deep forest is adjusted for higher assessment accuracy. 2) A fast update scheme is proposed based on active learning technique and graded strategy. 3) The cost-based method is employed to address class-imbalance training data. The test results on three power systems show that the proposed TSA framework has advantages in prediction accuracy, training speed, and update time. It is suitable for application of large-scale modern power system.

Automated summary

In this article, a novel deep learning-based online transient stability assessment (TSA) framework is proposed to solve the challenge of lack of efficacious information in power systems. By employing parallel convolution algorithms and a deep forest machine learning model, the framework addresses the issues of large feature number and variable network topology in large-scale modern power systems, demonstrating advantages in prediction accuracy, training speed, and update time.