Online Analysis of Voltage Security in a Microgrid Using Convolutional Neural Networks
Title | Online Analysis of Voltage Security in a Microgrid Using Convolutional Neural Networks |
Publication Type | Conference Paper |
Year of Publication | 2017 |
Authors | Wang, Y., Pulgar-Painemal, H., Sun, K. |
Conference Name | 2017 IEEE Power Energy Society General Meeting |
ISBN Number | 978-1-5386-2212-4 |
Keywords | Artificial 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. |
URL | http://ieeexplore.ieee.org/document/8274200/ |
DOI | 10.1109/PESGM.2017.8274200 |
Citation Key | wang_online_2017 |
- resilience
- online method
- policy-based governance
- power engineering computing
- power grids
- power system security
- power system stability
- pubcrawl
- Reactive power
- online analysis
- Resiliency
- security
- Stability criteria
- Support vector machines
- traditional voltage stability problem
- voltage instability problems
- voltage security
- Artificial Neural Networks
- Neurons
- neural nets
- modified IEEE 14-bus system
- modern power systems
- Microgrids
- microgrid
- Metrics
- machine learning
- learning (artificial intelligence)
- Impedance
- distributed power generation
- Decision trees
- convolutional neural networks
- classification problem
- back-propagation neural network