Real‐time stability assessment in smart cyber‐physical grids: a deep learning approach

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.

[1]  Rui Zhang,et al.  Post-disturbance transient stability assessment of power systems by a self-adaptive intelligent system , 2015 .

[2]  Hadis Karimipour,et al.  Robust Massively Parallel Dynamic State Estimation of Power Systems Against Cyber-Attack , 2018, IEEE Access.

[3]  Rong Yan,et al.  Fast Transient Stability Batch Assessment Using Cascaded Convolutional Neural Networks , 2019, IEEE Transactions on Power Systems.

[4]  David J. Hill,et al.  Intelligent Time-Adaptive Transient Stability Assessment System , 2016, IEEE Transactions on Power Systems.

[5]  Mohammad Reza Aghamohammadi,et al.  DT based intelligent predictor for out of step condition of generator by using PMU data , 2018 .

[6]  Qunying Liu,et al.  XGBoost-Based Algorithm Interpretation and Application on Post-Fault Transient Stability Status Prediction of Power System , 2019, IEEE Access.

[7]  Ali Dehghantanha,et al.  DRTHIS: Deep ransomware threat hunting and intelligence system at the fog layer , 2019, Future Gener. Comput. Syst..

[8]  Venkata Dinavahi,et al.  Extended Kalman filter-based parallel dynamic state estimation , 2015, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[9]  Henry Leung,et al.  A Deep and Scalable Unsupervised Machine Learning System for Cyber-Attack Detection in Large-Scale Smart Grids , 2019, IEEE Access.

[10]  M.A. El-Sharkawi,et al.  Support vector machines for transient stability analysis of large-scale power systems , 2004, IEEE Transactions on Power Systems.

[11]  Geza Joos,et al.  On the Accuracy Versus Transparency Trade-Off of Data-Mining Models for Fast-Response PMU-Based Catastrophe Predictors , 2012, IEEE Transactions on Smart Grid.

[12]  Gurunath Gurrala,et al.  An Online Power System Stability Monitoring System Using Convolutional Neural Networks , 2019, IEEE Transactions on Power Systems.

[13]  Juri Jatskevich,et al.  A Multi-Decomposition Approach for Accelerated Time-Domain Simulation of Transient Stability Problems , 2015, IEEE Transactions on Power Systems.

[14]  Xinyu Yang,et al.  Toward a Gaussian-Mixture Model-Based Detection Scheme Against Data Integrity Attacks in the Smart Grid , 2017, IEEE Internet Things J..

[15]  Arturo S. Bretas,et al.  A Distributed Control Approach for Enhancing Smart Grid Transient Stability and Resilience , 2017, IEEE Transactions on Smart Grid.

[16]  Ujjaval J. Patel,et al.  Distance Relaying with Power Swing Detection based on Voltage and Reactive Power Sensitivity , 2015 .

[17]  Sayyed Mohammad Hashemi,et al.  Current-Based Out-of-Step Detection Method to Enhance Line Differential Protection , 2019, IEEE Transactions on Power Delivery.

[18]  Ratna Babu Chinnam,et al.  mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification , 2011, Inf. Sci..

[19]  Ali Dehghantanha,et al.  A deep Recurrent Neural Network based approach for Internet of Things malware threat hunting , 2018, Future Gener. Comput. Syst..

[20]  Ming Yang,et al.  Model-Free MLE Estimation for Online Rotor Angle Stability Assessment With PMU Data , 2017, IEEE Transactions on Power Systems.

[21]  Xinyu Yang,et al.  Toward Data Integrity Attacks Against Optimal Power Flow in Smart Grid , 2017, IEEE Internet of Things Journal.

[22]  Bereket Tanju,et al.  A Machine Learning Decision-Support System Improves the Internet of Things’ Smart Meter Operations , 2017, IEEE Internet of Things Journal.

[23]  Mohammad Reza Aghamohammadi,et al.  Intelligent Out of Step Predictor for Inter Area Oscillations Using Speed-Acceleration Criterion as a Time Matching for Controlled Islanding , 2018, IEEE Transactions on Smart Grid.