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Real-Time Compressive Sensing Based Control Strategy for a Multi-Area Power System

With the fast expansion and increasing complexity of power system to meet the ever growing load demand, more information needs to be transmitted for real-time monitoring, control and management purpose. The timely and accurate transmission of a huge quantity of data poses great challenge to the communication network. This paper proposes a novel real-time compressive sensing based strategy for the load frequency control of a multi-area interconnected power system. According to the proposed strategy, the measured data in each control area is compressed before being transmitted through the communication network, and then recovered accurately by the discrete central controller. The proposed strategy can significantly reduce the size of transmitted data and improve the reliability of the communication network by introducing model predictive control method. Simulation results demonstrate the effectiveness and applicability of the proposed compressive sensing based control strategy.
- Zhejiang Ocean University China (People's Republic of)
- Sun Yat-sen University China (People's Republic of)
- Harbin Institute of Technology China (People's Republic of)
- Sun Yat-sen University China (People's Republic of)
- Zhejiang Ocean University China (People's Republic of)
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).17 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
