Cascade Failure Prediction for Transmission Lines at Risk in Power Systems Based on Machine Learning Techniques

• Main Authors: Yun-Chung Yu, 于允中
• Other Authors: Yuen-Chung Lee
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/ya38m3

碩士 === 國立臺灣大學 === 生物產業機電工程學研究所 === 107 === With the development of technology, electricity has become indispensable in human life, and power systems have become one of the most important infrastructures in the world. Because of safety measures of transmission lines, a simple fault often creates cascading failures. Cascading failures may lead to wide-area power outages, major safety hazards and economic losses. Therefore, the control of cascading failures and the prevention of power outages have become the main issues for researchers. In recent years, due to the advancement of artificial intelligence, machine learning methods have been used in various studies, such as studies on the estimation of line overload, system vulnerability assessment, and risk line prediction. Such methods are able to yield accurate analysis results. They are highly reliable since they are based on simulation or historical data. Therefore, this study proposes a two-stage model that combines a recurrent neural network and a binary supervised classifier to predict a potential line that faces a cascading failure, causing the whole power transmission system to crash. The proposed model uses the historical data of trapped lines as the input in the first stage to predict a line to trip at the next time step, and then determines if the predicted line will cause system instability and power outage in the second stage. Each stage of the model can employ different algorithms depending on what network is analyzed. In this study, the data set used in the model is the set of cascading failure data of the IEEE 39-bus system generated by an RTDS simulator. A total of 2,000 data points are divided into training data, validation data and test data. The training data are used to adjust the weight and bias of the model. The validation data are used to adjust the network architecture and select the best parameters. The test data are used to test the final performance of the model. Each type of data contains multiple feature information, so principal component analysis (PCA) is employed for dimension reduction. Then the K-means clustering algorithm is used to effectively select useful feature inputs. In the two-stage model, three popular recurrent neural network algorithms, including the recurrent neural network (RNN), long short-term memory (LSTM) and gate recurrent unit (GRU), are applied in the first stage, while three commonly used supervised classification algorithms, including the decision tree (DT), random forest (RF) and support vector machine (SVM), are applied in the second stage. The results show that the classification accuracy of each algorithm in the first and second stage reaches approximately 99% and 98%, respectively. According to multiple performance indexes, the combination of RNN as the algorithm in the first stage and RF as the algorithm in the second stage is able to yield 97% of the best classification results. In other words, the two-stage model combining RNN and RF is the most appropriate model for identifying potential risk lines for IEEE 39 buses systems. In addition, an additional experiment is conducted to ensure that the model is robust enough to tolerate noise data and error data. In this experiment,100 error data are added to the training data set, and the data are used to retrain the model. The results show that the classification accuracy of the model in the first stage maintains at 99%, while the accuracy of the model in the second stage where an RF is selected as a classifier maintains at 98% accuracy. Therefore, the proposed two-stage model is proven to be able not only to effectively identifying potential risk lines that might lead to cascading failures, but also to have sufficient anti-noise capability to implement various applications in the future.