Compressive Sensing-Based Optimal Reactive Power Control of a Multi-Area Power System

被引:10
作者
Khan, Irfan [1 ,2 ]
Xu, Yinliang [3 ,4 ]
Kari, Soummya [1 ]
Sun, Hongbin [3 ,4 ]
机构
[1] Carnegie Mellon Univ, Elect & Comp Engn Dept, Pittsburgh, PA 15213 USA
[2] Sun Yat Sen Univ, Sch Elect & Informat Technol, Guangzhou 510275, Guangdong, Peoples R China
[3] Tsinghua Berkeley Shenzhen Inst, Shenzhen 518055, Peoples R China
[4] Tsinghua Univ, Dept Elect Engn, State Key Lab Power Syst, Beijing 100084, Peoples R China
基金
中国国家自然科学基金;
关键词
Optimal reactive power control; compressive sensing; orthogonal matching pursuit algorithm; power loss; voltage deviation; SIGNAL RECOVERY; SMART GRIDS; VOLTAGE; OPTIMIZATION; COMMUNICATION; ALGORITHM; DISPATCH;
D O I
10.1109/ACCESS.2017.2752178
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
To deal with the increasing load demand and environmental effects of conventional power devices, power system has become an enlarged complex network with the integration of distributed generators. Real-time control of power system now needs a massive amount of information to be transmitted between each local device and the central controller. The transmission of a huge quantity of information data poses great challenge to the communication network. To deal with this issue in reactive power control, this paper proposes a novel real-time compressive sensing-based optimal reactive power control of a multi-area interconnected power system. The objective is to minimize the power loss, voltage deviation, and reactive power generation cost simultaneously. According to the proposed scheme, 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. Orthogonal matching pursuit algorithm is adopted to recover the compressed data, owing to its fast convergence speed. Simulation results demonstrate the effectiveness of the proposed compressive sensing-based approach by significantly reducing the data size of the transmitted data.
引用
收藏
页码:23576 / 23588
页数:13
相关论文
共 46 条
[11]   Application of Compressive Sampling in Synchrophasor Data Communication in WAMS [J].
Das, Sarasij ;
Sidhu, Tarlochan Singh .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2014, 10 (01) :450-460
[12]   Decentralized Reactive Power Control for Advanced Distribution Automation Systems [J].
Elkhatib, Mohamed E. ;
El Shatshat, Ramadan ;
Salama, Magdy M. A. .
IEEE TRANSACTIONS ON SMART GRID, 2012, 3 (03) :1482-1490
[13]   Scalable Synchrophasors Communication Network Design and Implementation for Real-Time Distributed Generation Grid [J].
Gharavi, Hamid ;
Hu, Bin .
IEEE TRANSACTIONS ON SMART GRID, 2015, 6 (05) :2539-2550
[14]   Compressed sensing for networked data [J].
Haupt, Jarvis ;
Bajwa, Waheed U. ;
Rabbat, Michael ;
Nowak, Robert .
IEEE SIGNAL PROCESSING MAGAZINE, 2008, 25 (02) :92-101
[15]   Decentralized Reactive Power Compensation Using Nash Bargaining Solution [J].
Hung Khanh Nguyen ;
Mohsenian-Rad, Hamed ;
Khodaei, Amin ;
Han, Zhu .
IEEE TRANSACTIONS ON SMART GRID, 2017, 8 (04) :1679-1688
[16]   Spatio-Temporal Kronecker Compressive Sensing for Traffic Matrix Recovery [J].
Jiang, Dingde ;
Nie, Laisen ;
Lv, Zhihan ;
Song, Houbing .
IEEE ACCESS, 2016, 4 :3046-3053
[17]  
Jumpasri Nattawat., 2014, Electrical Engineering Congress (iEECON), 2014 International, P1
[18]   Performance comparison of centralized versus distributed error recovery for reliable multicast [J].
Lacher, MS ;
Nonnenmacher, J ;
Biersack, EW .
IEEE-ACM TRANSACTIONS ON NETWORKING, 2000, 8 (02) :224-238
[19]   Sequential Compressed Sensing With Progressive Signal Reconstruction in Wireless Sensor Networks [J].
Leinonen, Markus ;
Codreanu, Marian ;
Juntti, Markku .
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2015, 14 (03) :1622-1635
[20]   Realizing Unified Microgrid Voltage Profile and Loss Minimization: A Cooperative Distributed Optimization and Control Approach [J].
Maknouninejad, Ali ;
Qu, Zhihua .
IEEE TRANSACTIONS ON SMART GRID, 2014, 5 (04) :1621-1630