Real-time stability assessment in smart cyber-physical grids: a deep learning approach
| Autor: | Farzad Darbandi, Amirreza Jafari, Hadis Karimipour, Ali Dehghantanha, Farnaz Derakhshan, Kim-Kwang Raymond Choo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
backpropagation
self-organising feature maps power system transient stability smart power grids power system security feedforward neural nets conjugate gradient methods power engineering computing cyber-physical systems smart cyber-physical grids deep learning approach physical communication layers cyber-physical system cps system monitoring information and communication technologies transient stability assessment effective tsa system operators cyber-attacks real-time stability condition predictor feedforward neural network conjugate gradient backpropagation algorithm fletcher–reeves updates kohonen learning algorithm minimum redundancy maximum relevancy algorithm ieee 39-bus test system real-time stability assessment Electrical engineering. Electronics. Nuclear engineering TK1-9971 |
| Zdroj: | IET Smart Grid (2020) |
| Druh dokumentu: | article |
| ISSN: | 2515-2947 |
| DOI: | 10.1049/iet-stg.2019.0191 |
| Popis: | The increasing coupling between the physical and communication layers in the cyber-physical system (CPS) brings up new challenges in system monitoring and control. Smart power grids with the integration of information and communication technologies are one of the most important types of CPS. Proper monitoring and control of the smart grid are highly dependent on the transient stability assessment (TSA). Effective TSA can provide system operators with insightful information on stability statuses and causes under various contingencies and cyber-attacks. In this study, a real-time stability condition predictor based on a feedforward neural network is proposed. The conjugate gradient backpropagation algorithm and Fletcher–Reeves updates are used for training, and the Kohonen learning algorithm is utilised to improve the learning process. By real-time assessment of the network features based on the minimum redundancy maximum relevancy algorithm, the proposed method can successfully predict transient stability and out of step conditions for the network and generators, respectively. Simulation results on the IEEE 39-bus test system indicate the superiority of the proposed method in terms of accuracy, precision, false positive rate, and true positive rate. |
| Databáze: | Directory of Open Access Journals |
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