Visible to the public Online Analysis of Voltage Security in a Microgrid Using Convolutional Neural Networks

TitleOnline Analysis of Voltage Security in a Microgrid Using Convolutional Neural Networks
Publication TypeConference Paper
Year of Publication2017
AuthorsWang, Y., Pulgar-Painemal, H., Sun, K.
Conference Name2017 IEEE Power Energy Society General Meeting
ISBN Number978-1-5386-2212-4
KeywordsArtificial neural networks, back-propagation neural network, classification problem, convolutional neural networks, Decision trees, distributed power generation, Impedance, learning (artificial intelligence), machine learning, Metrics, microgrid, Microgrids, modern power systems, modified IEEE 14-bus system, neural nets, Neurons, online analysis, online method, policy-based governance, power engineering computing, power grids, power system security, power system stability, pubcrawl, Reactive power, resilience, Resiliency, security, Stability criteria, Support vector machines, traditional voltage stability problem, voltage instability problems, voltage security
Abstract

Although connecting a microgrid to modern power systems can alleviate issues arising from a large penetration of distributed generation, it can also cause severe voltage instability problems. This paper presents an online method to analyze voltage security in a microgrid using convolutional neural networks. To transform the traditional voltage stability problem into a classification problem, three steps are considered: 1) creating data sets using offline simulation results; 2) training the model with dimensional reduction and convolutional neural networks; 3) testing the online data set and evaluating performance. A case study in the modified IEEE 14-bus system shows the accuracy of the proposed analysis method increases by 6% compared to back-propagation neural network and has better performance than decision tree and support vector machine. The proposed algorithm has great potential in future applications.

URLhttp://ieeexplore.ieee.org/document/8274200/
DOI10.1109/PESGM.2017.8274200
Citation Keywang_online_2017