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.

[1]  David G. Dorrell,et al.  Multi-Objective Model-Predictive Control for High-Power Converters , 2013, IEEE Transactions on Energy Conversion.

[2]  Lida Xu,et al.  Compressed Sensing Signal and Data Acquisition in Wireless Sensor Networks and Internet of Things , 2013, IEEE Transactions on Industrial Informatics.

[3]  Catherine Rosenberg,et al.  Compressed Data Aggregation: Energy-Efficient and High-Fidelity Data Collection , 2013, IEEE/ACM Transactions on Networking.

[4]  Le-Ren Chang-Chien,et al.  Incorporating Demand Response With Spinning Reserve to Realize an Adaptive Frequency Restoration Plan for System Contingencies , 2012, IEEE Transactions on Smart Grid.

[5]  Namrata Vaswani,et al.  Time Invariant Error Bounds for Modified-CS-Based Sparse Signal Sequence Recovery , 2015, IEEE Trans. Inf. Theory.

[6]  Jinyu Wen,et al.  Wide-Area Damping Controller for Power System Interarea Oscillations: A Networked Predictive Control Approach , 2015, IEEE Transactions on Control Systems Technology.

[7]  Manfred Morari,et al.  Model predictive control: Theory and practice - A survey , 1989, Autom..

[8]  Daniel E. Quevedo,et al.  Sparse Representations for Packetized Predictive Networked Control , 2013, ArXiv.

[9]  Zhigang Zeng,et al.  Event-Triggering Load Frequency Control for Multiarea Power Systems With Communication Delays , 2016, IEEE Transactions on Industrial Electronics.

[10]  François Bouffard,et al.  Decentralized Demand-Side Contribution to Primary Frequency Control , 2011, IEEE Transactions on Power Systems.

[11]  Frank L. Lewis,et al.  Distributed Cooperative Control of DC Microgrids , 2015, IEEE Transactions on Power Electronics.

[12]  Jinfang Zhang,et al.  Fault localization in electrical power systems: A pattern recognition approach , 2011 .

[13]  Chika O. Nwankpa,et al.  An Exact Method for Computing Delay Margin for Stability of Load Frequency Control Systems With Constant Communication Delays , 2016, IEEE Transactions on Power Systems.

[14]  H. Shayeghi,et al.  Load frequency control strategies: A state-of-the-art survey for the researcher , 2009 .

[15]  Thomas Leibfried,et al.  A Cooperative Multi-Area Optimization With Renewable Generation and Storage Devices , 2015, IEEE Transactions on Power Systems.

[16]  Ibraheem,et al.  Recent philosophies of automatic generation control strategies in power systems , 2005, IEEE Transactions on Power Systems.

[17]  H. K. Bizaki,et al.  Digital Frequency Determination of Real Waveforms Based on Multiple Sensors With Low Sampling Rates , 2012, IEEE Sensors Journal.

[18]  Q. H. Wu,et al.  Delay-Dependent Stability for Load Frequency Control With Constant and Time-Varying Delays , 2009, IEEE Transactions on Power Systems.

[19]  Anjan Bose,et al.  Smart Transmission Grid Applications and Their Supporting Infrastructure , 2010, IEEE Transactions on Smart Grid.

[20]  Min Wu,et al.  Delay-Dependent Robust Load Frequency Control for Time Delay Power Systems , 2013, IEEE Transactions on Power Systems.

[21]  Yang Mi,et al.  Decentralized Sliding Mode Load Frequency Control for Multi-Area Power Systems , 2013, IEEE Transactions on Power Systems.

[22]  Kevin Tomsovic,et al.  Designing the Next Generation of Real-Time Control, Communication, and Computations for Large Power Systems , 2005, Proceedings of the IEEE.

[23]  Hashem Nehrir,et al.  Introducing Dynamic Demand Response in the LFC Model , 2014, IEEE Transactions on Power Systems.

[24]  Michele Zorzi,et al.  Sensing, Compression, and Recovery for WSNs: Sparse Signal Modeling and Monitoring Framework , 2012, IEEE Transactions on Wireless Communications.

[25]  Juan C. Vasquez,et al.  Distributed Secondary Control for Islanded Microgrids—A Novel Approach , 2014, IEEE Transactions on Power Electronics.

[26]  Ufuk Topcu,et al.  Optimal Load Control via Frequency Measurement and Neighborhood Area Communication , 2013, IEEE Transactions on Power Systems.

[27]  Marian Codreanu,et al.  Sequential Compressed Sensing With Progressive Signal Reconstruction in Wireless Sensor Networks , 2015, IEEE Transactions on Wireless Communications.

[28]  Luca Benini,et al.  Compressive Sensing Optimization for Signal Ensembles in WSNs , 2014, IEEE Transactions on Industrial Informatics.

[29]  Jun Sun,et al.  Efficient Measurement Generation and Pervasive Sparsity for Compressive Data Gathering , 2010, IEEE Transactions on Wireless Communications.

[30]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.