Parallel relaxation-based joint dynamic state estimation of large-scale power systems

Massive amounts of data generated in large-scale grids poses a formidable challenge for real-time monitoring of power systems. Dynamic state estimation which is a prerequisite for normal operation of power systems involves the time-constrained solution of a large set of equations which requires significant computational resources. In this study, an efficient and accurate relaxation-based parallel processing technique is proposed in the presence of phasor measurement units. A combination of different types of parallelism is used on both single and multiple graphic processing units to accelerate large-scale joint dynamic state estimation simulation. The estimation results for both generator and network states verify that proper massive-thread parallel programming makes the entire implementation scalable and efficient with high accuracy.

[1]  V. Seshadri Sravan Kumar,et al.  State estimation in power systems using linear model infinity norm-based trust region approach , 2013 .

[2]  Antonio J. Conejo,et al.  Participation factor approach for phasor measurement unit placement in power system state estimation , 2012 .

[3]  A. Gomez-Exposito,et al.  Two-Level State Estimation With Local Measurement Pre-Processing , 2009, IEEE Transactions on Power Systems.

[4]  Liam Murphy,et al.  Parallel and distributed state estimation , 1995 .

[5]  Robert C. Green,et al.  Applications and Trends of High Performance Computing for Electric Power Systems: Focusing on Smart Grid , 2013, IEEE Transactions on Smart Grid.

[6]  Nikolaos M. Manousakis,et al.  State estimation and observability analysis for phasor measurement unit measured systems , 2012 .

[7]  Georgios B. Giannakis,et al.  Distributed Robust Power System State Estimation , 2012, IEEE Transactions on Power Systems.

[8]  George N Korres,et al.  A Distributed Multiarea State Estimation , 2011, IEEE Transactions on Power Systems.

[9]  Gevork B. Gharehpetian,et al.  Optimal Integration of Phasor Measurement Units in Power Systems Considering Conventional Measurements , 2013, IEEE Transactions on Smart Grid.

[10]  Zahir Tari,et al.  SCADASim—A Framework for Building SCADA Simulations , 2011, IEEE Transactions on Smart Grid.

[11]  Farrokh Aminifar,et al.  Power System Dynamic State Estimation With Synchronized Phasor Measurements , 2014, IEEE Transactions on Instrumentation and Measurement.

[12]  A. G. Expósito,et al.  Generalized observability analysis and measurement classification , 1997, Proceedings of the 20th International Conference on Power Industry Computer Applications.

[13]  Arindam Ghosh,et al.  Inclusion of PMU current phasor measurements in a power system state estimator , 2010 .

[14]  Venkata Dinavahi,et al.  Large-Scale Transient Stability Simulation of Electrical Power Systems on Parallel GPUs , 2012, IEEE Transactions on Parallel and Distributed Systems.

[15]  Ali Mohammad Ranjbar,et al.  Optimal PMU Placement by an Equivalent Linear Formulation for Exhaustive Search , 2012, IEEE Transactions on Smart Grid.

[16]  H. Poor,et al.  Fully Distributed State Estimation for Wide-Area Monitoring Systems , 2012, IEEE Transactions on Smart Grid.

[17]  Venkata Dinavahi,et al.  Parallel massive-thread electromagnetic transient simulation on GPU , 2015, 2015 IEEE Power & Energy Society General Meeting.

[18]  Venkata Dinavahi,et al.  SIMD-Based Large-Scale Transient Stability Simulation on the Graphics Processing Unit , 2010, IEEE Transactions on Power Systems.

[19]  Shaobu Wang,et al.  An Alternative Method for Power System Dynamic State Estimation Based on Unscented Transform , 2012, IEEE Transactions on Power Systems.

[20]  J. K. Mandal,et al.  Incorporating nonlinearities of measurement function in power system dynamic state estimation , 1995 .

[21]  Gustavo Valverde,et al.  Unscented kalman filter for power system dynamic state estimation , 2011 .

[22]  A. Monticelli,et al.  Reliable Bad Data Processing for Real-Time State Estimation , 1983, IEEE Power Engineering Review.

[23]  Greg Welch,et al.  A Two-Stage Kalman Filter Approach for Robust and Real-Time Power System State Estimation , 2014, IEEE Transactions on Sustainable Energy.

[24]  G.T. Heydt,et al.  Diakoptic State Estimation Using Phasor Measurement Units , 2008, IEEE Transactions on Power Systems.

[25]  A. Abur,et al.  Multi area state estimation using synchronized phasor measurements , 2005, IEEE Transactions on Power Systems.

[26]  Greg Welch,et al.  Dynamic State Estimation of a Synchronous Machine Using PMU Data: A Comparative Study , 2015, IEEE Transactions on Smart Grid.

[27]  V. Vittal,et al.  Slow coherency-based islanding , 2004, IEEE Transactions on Power Systems.

[28]  A. Conejo,et al.  State estimation via mathematical programming: a comparison of different estimation algorithms , 2012 .

[29]  Ali Abur,et al.  Parallel state estimation using multiprocessors , 1990 .

[30]  Shyh-Jier Huang,et al.  Application of a Robust Algorithm for Dynamic State Estimation of a Power System , 2002 .

[31]  Mariesa L. Crow,et al.  The parallel implementation of the waveform relaxation method for transient stability simulations , 1990 .