Communication-Failure-Resilient Distributed Frequency Control in Smart Grids: Part I: Architecture and Distributed Algorithms

The problem of secure load frequency control of smart grids is investigated in this paper. The networked data transmission within the smart grid is corrupted by stochastic deception attacks. First, a unified Load frequency control model is constructed to account for both network-induced effects and deception attacks. Second, with the Lyapunov functional method, a piecewise delay analysis is conducted to study the stability of the established model, which is of less conservativeness. Third, based on the stability analysis, a controller design method is provided in terms of linear matrix inequalities. Finally, a case study is carried out to demonstrate the derived results.

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Distributed Algorithms for Demand Management and Grid Stability in Smart Grids

Communication and Networking in Smart Grids ◽

10.1201/b11897-4 ◽

2012 ◽

pp. 19-55 ◽

Cited By ~ 1

Author(s):

Monica Navarro ◽

Lorenza Giupponi ◽

Christian Ibars ◽

David Gregoratti ◽

Javier Matamoros

Keyword(s):

Distributed Algorithms ◽

Smart Grids ◽

Demand Management

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Real-Time Smart Grids Control for Preventing Cascading Failures and Blackout using Neural Networks: Experimental Approach for N-1-1 Contingency

International Journal of Emerging Electric Power Systems ◽

10.1515/ijeeps-2016-0039 ◽

2016 ◽

Vol 17 (6) ◽

pp. 703-716 ◽

Cited By ~ 2

Author(s):

Sina Zarrabian ◽

Rabie Belkacemi ◽

Adeniyi A. Babalola

Keyword(s):

Neural Networks ◽

Real Time ◽

Smart Grids ◽

Intelligent Systems ◽

Large Scale ◽

Frequency Control ◽

Test System ◽

Selection Strategy ◽

Control Approach ◽

Contingency Condition

Abstract In this paper, a novel intelligent control is proposed based on Artificial Neural Networks (ANN) to mitigate cascading failure (CF) and prevent blackout in smart grid systems after N-1-1 contingency condition in real-time. The fundamental contribution of this research is to deploy the machine learning concept for preventing blackout at early stages of its occurrence and to make smart grids more resilient, reliable, and robust. The proposed method provides the best action selection strategy for adaptive adjustment of generators’ output power through frequency control. This method is able to relieve congestion of transmission lines and prevent consecutive transmission line outage after N-1-1 contingency condition. The proposed ANN-based control approach is tested on an experimental 100 kW test system developed by the authors to test intelligent systems. Additionally, the proposed approach is validated on the large-scale IEEE 118-bus power system by simulation studies. Experimental results show that the ANN approach is very promising and provides accurate and robust control by preventing blackout. The technique is compared to a heuristic multi-agent system (MAS) approach based on communication interchanges. The ANN approach showed more accurate and robust response than the MAS algorithm.

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