Intelligent mitigation of blackout in real-time microgrids: Neural Network Approach

In this paper, a novel application of Artificial Neural Networks (ANN) is deployed to prevent blackout in a microgrid after N-1-1 contingency condition. In fact, microgrids are vulnerable to disturbances and abnormal conditions due to their inherent small inertia. Therefore, stability of microgrids after a disturbance turns into a challenge in power systems. The key contribution of this paper is to utilize the artificial intelligence concept to prevent cascading failure practically at early stages and to make microgrids more reliable and robust by intelligent and adaptive re-dispatch of power. The proposed ANN control approach is tested on an experimental testbed microgrid. Experimental results verify the robustness, accuracy, and effectiveness of the ANN method for preventing cascading failure in addition to providing voltage and frequency stability after initiation of a disturbance.

[1]  Kai Sun,et al.  An Interaction Model for Simulation and Mitigation of Cascading Failures , 2014, IEEE Transactions on Power Systems.

[2]  Faruk Kazi,et al.  Support-Vector-Machine-Based Proactive Cascade Prediction in Smart Grid Using Probabilistic Framework , 2015, IEEE Transactions on Industrial Electronics.

[3]  Rudra Prakash Maheshwari,et al.  Improved Fault Classification in Series Compensated Transmission Line: Comparative Evaluation of Chebyshev Neural Network Training Algorithms , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[4]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[5]  Gang Wang,et al.  An Improved OPA Model and Blackout Risk Assessment , 2009, IEEE Transactions on Power Systems.

[6]  Fabrice Locment,et al.  Intelligent DC microgrid with smart grid communications: Control strategy consideration and design , 2013, PES 2013.

[7]  Georgios Theodoridis,et al.  Intelligent Energy Systems: Introducing Power–ICT Interdependency in Modeling and Control Design , 2015, IEEE Transactions on Industrial Electronics.

[8]  Kashem M. Muttaqi,et al.  A Decentralized Multiagent-Based Voltage Control for Catastrophic Disturbances in a Power System , 2015 .

[9]  Martin T. Hagan,et al.  Neural network design , 1995 .

[10]  Sukumar Mishra,et al.  Neural-network-based adaptive UPFC for improving transient stability performance of power system , 2006, IEEE Transactions on Neural Networks.

[11]  R. Sunitha,et al.  Online Static Security Assessment Module Using Artificial Neural Networks , 2013, IEEE Transactions on Power Systems.

[12]  Yasunori Mitani,et al.  Intelligent Frequency Control in an AC Microgrid: Online PSO-Based Fuzzy Tuning Approach , 2012, IEEE Transactions on Smart Grid.

[13]  Ia An N Do.  A LOADING-DEPENDENT MODEL OF PROBABILISTIC CASCADING FAILURE , 2005 .

[14]  Wenxin Liu,et al.  Novel Multiagent Based Load Restoration Algorithm for Microgrids , 2011, IEEE Transactions on Smart Grid.

[15]  R. Belkacemi,et al.  Experimental Transient Stability Analysis of MicroGrid systems: Lessons learned , 2015, 2015 IEEE Power & Energy Society General Meeting.

[16]  Yi Zhang,et al.  Mitigating Blackouts via Smart Relays: A Machine Learning Approach , 2011, Proceedings of the IEEE.

[17]  Jun Yan,et al.  Cascading Failure Analysis With DC Power Flow Model and Transient Stability Analysis , 2015, IEEE Transactions on Power Systems.

[18]  Savu Crivat Savulescu.  Real-time stability in power systems : techniques for early detection of the risk of blackout , 2014 .

[19]  Faruk Kazi,et al.  Neural Network Based Early Warning System for an Emerging Blackout in Smart Grid Power Networks , 2014, ISI.

[20]  Nasir Ghani,et al.  Stochastic Analysis of Cascading-Failure Dynamics in Power Grids , 2014, IEEE Transactions on Power Systems.

[21]  Ramon Zamora,et al.  Real-Time Implementation of Intelligent Reconfiguration Algorithm for Microgrid , 2014 .

[22]  Cheng-I Chen,et al.  A Neural-Network-Based Data-Driven Nonlinear Model on Time- and Frequency-Domain Voltage–Current Characterization for Power-Quality Study , 2015, IEEE Transactions on Power Delivery.

[23]  Savu C. Savulescu,et al.  Real-time stability in power systems , 2014 .

[24]  Ian Dobson,et al.  Branching Process Models for the Exponentially Increasing Portions of Cascading Failure Blackouts , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[25]  R. Belkacemi,et al.  Experimental implementation of Multi-Agent System Algorithm to prevent Cascading Failure after N-1-1 contingency in smart grid systems , 2015, 2015 IEEE Power & Energy Society General Meeting.

[26]  R. Belkacemi,et al.  Multi-Agent System algorithm for preventing cascading failures in smart grid systems , 2014, 2014 North American Power Symposium (NAPS).

[27]  Alfredo Vaccaro,et al.  A Self-Organizing Architecture for Decentralized Smart Microgrids Synchronization, Control, and Monitoring , 2015, IEEE Transactions on Industrial Informatics.