Emilie Hogan, E. C. Sanchez, M. Halappanavar, Shaobu Wang, Patrick Mackey, P. Hines, Zhenyu Huang
Clustering is an important data analysis technique with numerous applications in the analysis of electric power grids. Standard clustering techniques are oblivious to the rich structural and dynamic information available for power grids. Therefore, by exploiting the inherent topological and electrical structure in the power grid data, we propose new methods for clustering with applications to model reduction, locational marginal pricing, phasor measurement unit (PMU or synchrophasor) placement, and power system protection. We focus our attention on model reduction for analysis based on time-series information from synchrophasor measurement devices, and spectral techniques for clustering. By comparing different clustering techniques on two instances of realistic power grids we show that the solutions are related and therefore one could leverage that relationship for a computational advantage. Thus, by contrasting different clustering techniques we make a case for exploiting structure inherent in the data with implications for several domains including power systems.
{"title":"Towards effective clustering techniques for the analysis of electric power grids","authors":"Emilie Hogan, E. C. Sanchez, M. Halappanavar, Shaobu Wang, Patrick Mackey, P. Hines, Zhenyu Huang","doi":"10.1145/2536780.2536785","DOIUrl":"https://doi.org/10.1145/2536780.2536785","url":null,"abstract":"Clustering is an important data analysis technique with numerous applications in the analysis of electric power grids. Standard clustering techniques are oblivious to the rich structural and dynamic information available for power grids. Therefore, by exploiting the inherent topological and electrical structure in the power grid data, we propose new methods for clustering with applications to model reduction, locational marginal pricing, phasor measurement unit (PMU or synchrophasor) placement, and power system protection. We focus our attention on model reduction for analysis based on time-series information from synchrophasor measurement devices, and spectral techniques for clustering. By comparing different clustering techniques on two instances of realistic power grids we show that the solutions are related and therefore one could leverage that relationship for a computational advantage. Thus, by contrasting different clustering techniques we make a case for exploiting structure inherent in the data with implications for several domains including power systems.","PeriodicalId":153844,"journal":{"name":"HiPCNA-PG '13","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123254314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The computational bottleneck for large nonlinear AC power flow problems using Newton's method is the solution of the linear system at each iteration. We present a parallel linear solution scheme using the Krylov subspace-based iterative solver GMRES preconditioned with overlapping restricted additive Schwarz method (RASM) that shows promising speedup for this linear system solution. This paper evaluates the performance of RASM with different amounts of overlap and presents its scalability and convergence behavior for three large power flow problems consisting of 22,996, 51,741, and 91,984 buses respectively.
{"title":"Evaluation of overlapping restricted additive schwarz preconditioning for parallel solution of very large power flow problems","authors":"S. Abhyankar, Barry F. Smith, E. Constantinescu","doi":"10.1145/2536780.2536784","DOIUrl":"https://doi.org/10.1145/2536780.2536784","url":null,"abstract":"The computational bottleneck for large nonlinear AC power flow problems using Newton's method is the solution of the linear system at each iteration. We present a parallel linear solution scheme using the Krylov subspace-based iterative solver GMRES preconditioned with overlapping restricted additive Schwarz method (RASM) that shows promising speedup for this linear system solution. This paper evaluates the performance of RASM with different amounts of overlap and presents its scalability and convergence behavior for three large power flow problems consisting of 22,996, 51,741, and 91,984 buses respectively.","PeriodicalId":153844,"journal":{"name":"HiPCNA-PG '13","volume":"386 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123390432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Large scale integration of stochastic energy resources in power systems requires probabilistic analysis approaches for comprehensive system analysis. The large-varying grid condition on the aging and stressed power system infrastructures also requires merging of offline security analyses into online operation. Meanwhile in computing, the recent rapid hardware performance growth comes from the more and more complicated architecture. Fully utilizing the computing power for specific applications becomes very difficult. Given the challenges and opportunities in both the power system and the computing fields, this paper presents the unique commodity high performance computing system solutions to the following fundamental tools for power system probabilistic and security analysis: 1) a high performance Monte Carlo simulation (MCS) based distribution probabilistic load flow solver for real-time distribution feeder probabilistic solutions. 2) A high performance MCS based transmission probabilistic load flow solver for transmission grid probabilistic analysis. 3) A SIMD accelerated AC contingency calculation solver based on Woodbury matrix identity on multi-core CPUs. By aggressive algorithm level and computer architecture level performance optimizations including optimized data structures, optimization for superscalar out-of-order execution, SIMDization, and multi-core scheduling, our software fully utilizes the modern commodity computing systems, makes the critical and computational intensive power system probabilistic and security analysis problems solvable in real-time on commodity computing systems.
{"title":"Power system probabilistic and security analysis on commodity high performance computing systems","authors":"Tao Cui, F. Franchetti","doi":"10.1145/2536780.2536781","DOIUrl":"https://doi.org/10.1145/2536780.2536781","url":null,"abstract":"Large scale integration of stochastic energy resources in power systems requires probabilistic analysis approaches for comprehensive system analysis. The large-varying grid condition on the aging and stressed power system infrastructures also requires merging of offline security analyses into online operation. Meanwhile in computing, the recent rapid hardware performance growth comes from the more and more complicated architecture. Fully utilizing the computing power for specific applications becomes very difficult. Given the challenges and opportunities in both the power system and the computing fields, this paper presents the unique commodity high performance computing system solutions to the following fundamental tools for power system probabilistic and security analysis: 1) a high performance Monte Carlo simulation (MCS) based distribution probabilistic load flow solver for real-time distribution feeder probabilistic solutions. 2) A high performance MCS based transmission probabilistic load flow solver for transmission grid probabilistic analysis. 3) A SIMD accelerated AC contingency calculation solver based on Woodbury matrix identity on multi-core CPUs. By aggressive algorithm level and computer architecture level performance optimizations including optimized data structures, optimization for superscalar out-of-order execution, SIMDization, and multi-core scheduling, our software fully utilizes the modern commodity computing systems, makes the critical and computational intensive power system probabilistic and security analysis problems solvable in real-time on commodity computing systems.","PeriodicalId":153844,"journal":{"name":"HiPCNA-PG '13","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122804073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we present an approach to large-scale data analysis, Divide and Recombine (D&R), and describe a hardware and software implementation that supports this approach. We then illustrate the use of D&R on large-scale power systems sensor data to perform initial exploration, discover multiple data integrity issues, build and validate algorithms to filter bad data, and construct statistical event detection algorithms. This paper also reports on experiences using a non-traditional Hadoop distributed computing setup on top of a HPC computing cluster.
{"title":"Large-scale exploratory analysis, cleaning, and modeling for event detection in real-world power systems data","authors":"R. Hafen, Tara D. Gibson, K. K. Dam, T. Critchlow","doi":"10.1145/2536780.2536783","DOIUrl":"https://doi.org/10.1145/2536780.2536783","url":null,"abstract":"In this paper, we present an approach to large-scale data analysis, Divide and Recombine (D&R), and describe a hardware and software implementation that supports this approach. We then illustrate the use of D&R on large-scale power systems sensor data to perform initial exploration, discover multiple data integrity issues, build and validate algorithms to filter bad data, and construct statistical event detection algorithms. This paper also reports on experiences using a non-traditional Hadoop distributed computing setup on top of a HPC computing cluster.","PeriodicalId":153844,"journal":{"name":"HiPCNA-PG '13","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117087104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Palmer, W. Perkins, Kevin A. Glass, Yousu Chen, Shuangshuang Jin, D. Callahan
This paper describes the GridPACK#8482; framework, which is designed to help power grid engineers develop modeling software capable of running on high performance computers. The framework contains modules for setting up distributed power grid networks, assigning buses and branches with arbitrary behaviors to the network, creating distributed matrices and vectors, using parallel linear and non-linear solvers to solve algebraic equations, and mapping functionality to create matrices and vectors based on properties of the network. In addition, the framework contains additional functionality to support IO and to manage errors. The goal of GridPACK#8482; is to provide developers with a comprehensive set of modules that can substantially reduce the complexity of writing software for parallel computers while still providing efficient and scalable software solutions.
{"title":"The GridPACK#8482; toolkit for developing power grid simulations on high performance computing platforms","authors":"B. Palmer, W. Perkins, Kevin A. Glass, Yousu Chen, Shuangshuang Jin, D. Callahan","doi":"10.1145/2536780.2536782","DOIUrl":"https://doi.org/10.1145/2536780.2536782","url":null,"abstract":"This paper describes the GridPACK#8482; framework, which is designed to help power grid engineers develop modeling software capable of running on high performance computers. The framework contains modules for setting up distributed power grid networks, assigning buses and branches with arbitrary behaviors to the network, creating distributed matrices and vectors, using parallel linear and non-linear solvers to solve algebraic equations, and mapping functionality to create matrices and vectors based on properties of the network. In addition, the framework contains additional functionality to support IO and to manage errors. The goal of GridPACK#8482; is to provide developers with a comprehensive set of modules that can substantially reduce the complexity of writing software for parallel computers while still providing efficient and scalable software solutions.","PeriodicalId":153844,"journal":{"name":"HiPCNA-PG '13","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130210291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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