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Content Provider | IET Digital Library |
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Author | Bukhari, Syed Basit Ali Kim, Chul Hwan Mehmood, Khawaja Khalid Haider, Raza Zaman, Muhammad Saeed Uz |
Abstract | Microgrids experience significantly different fault currents in different operating scenarios, which make microgrid protection challenging. Existing intelligent protection schemes rely on the extraction of appropriate fault features using statistical parameters. The selection of these features is difficult in a microgrid because of its various operating scenarios. This study develops a convolutional neural network-based intelligent fault protection strategy (CNNBIPS) for microgrids that inherently integrates the feature extraction and classification process. The proposed strategy is directly applicable to three-phase (TP) current signals; thus, it does not require any separate feature extractor. In the proposed CNNBIPS, TP current signals sampled by the protective relays are used as an input to three different CNNs. The CNNs apply convolution and pooling operations to extract the features from the input signals. Then, fully connected layers of the CNNs employ the features to develop fault-type, phase, and location information. To analyse the efficacy of the proposed design, we execute exhaustive simulations on a standard microgrid test system. The results confirm the effectiveness of the proposed strategy in terms of detection accuracy, security, and dependability. Moreover, comparisons with previous methods show that the proposed approach outperforms the existing microgrid protection schemes. |
Starting Page | 1177 |
Ending Page | 1185 |
Page Count | 9 |
ISSN | 17518687 |
Volume Number | 14 |
e-ISSN | 17518695 |
Issue Number | Issue 7, Apr (2020) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-gtd/14/7 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2018.7049 |
Journal | IET Generation, Transmission & Distribution |
Publisher Date | 2020-01-15 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Appropriate Fault Features CNNBIPS Control of Electric Power System Convolution Convolutional Neural Network-based Intelligent Fault Protection Strategy Different CNNs Different Fault Currents Different Operating Scenarios Distributed Power Generation Distribution Network Existing Microgrid Protection Scheme Fault Current Fault Diagnosis Fault-type Feature Extraction Input Signal Intelligent Protection Scheme Microgrid Protection Challenging Microgrid System Neural Computing Technique Neural Nets Pooling Operation Power Distribution Fault Power Distribution Protection Power Engineering Computing Power Generation Control Power Generation Protection Power System Protection Protective Relay Separate Feature Extractor Standard Microgrid Test System Statistical Parameter Three-phase Current Signal TP Current Signal |
Content Type | Text |
Resource Type | Article |
Subject | Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering |
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