Open AccessReal‐time stability assessment in smart cyber‐physical grids: a deep learning approach
Author(s) -
Darbandi Farzad,
Jafari Amirreza,
Karimipour Hadis,
Dehghantanha Ali,
Derakhshan Farnaz,
Raymond Choo KimKwang
Publication year - 2020
Publication title -
iet smart grid
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.612
H-Index - 11
ISSN - 2515-2947
DOI - 10.1049/iet-stg.2019.0191
Subject(s) - smart grid , computer science , cyber physical system , electric power system , stability (learning theory) , artificial neural network , redundancy (engineering) , transient (computer programming) , feed forward , backpropagation , real time computing , process (computing) , artificial intelligence , machine learning , power (physics) , control engineering , engineering , electrical engineering , operating system , physics , quantum mechanics
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.