Towards effective clustering techniques for the analysis of electric power grids

Hogan, Emilie; Cotilla‐Sanchez, Eduardo; Halappanavar, Mahantesh; Wang, Shaobu; Mackey, Patrick; Hines, Paul; Huang, Zhenyu

doi:10.1145/2536780.2536785

Cited by 10 publications

(7 citation statements)

References 13 publications

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“…The coherency-based approach is most popular for the clustering analysis of electric networks. Many related results have been reported in the literature (see [3,16,17,26,33,39,48] and the references therein). The generator coherency is introduced as the tendency of generators when the disturbances are affecting the system.…”

Section: Introductionmentioning

confidence: 73%

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Clustering approach to model order reduction of power networks with distributed controllers

Cheng

Scherpen

2018

Adv Comput Math

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This paper considers the network structure preserving model reduction of power networks with distributed controllers. The studied system and controller are modeled as second-order and first-order ordinary differential equations, which are coupled to a closed-loop model for analyzing the dissimilarities of the power units. By transfer functions, we characterize the behavior of each node (generator or load) in the power network and define a novel notion of dissimilarity between two nodes by the H 2-norm of the transfer function deviation. Then, the reduction methodology is developed based on separately clustering the generators and loads according to their behavior dissimilarities. The characteristic matrix of the resulting clustering is adopted for the Galerkin projection to derive explicit reduced-order power models and controllers. Finally, we illustrate the proposed method by the IEEE 30-bus system example.

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Section: Introductionmentioning

confidence: 73%

“…A contradiction proof is provided. Assume that two distinct symmetric matrices P 1 and P 2 are both the solutions of (25) and (26). From (25), we have…”

Section: Lemmamentioning

confidence: 99%

Clustering approach to model order reduction of power networks with distributed controllers

Cheng

Scherpen

2018

Adv Comput Math

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“…Recalling the workflow given in Figure 1 that identifies each model reduction method, this section describes their basic details. For full descriptions of these methods, refer to other detailed papers [14,29].…”

Section: Identifying Representative Generatorsmentioning

confidence: 99%

“…Once this matrix has been created, an -nearest-neighbor graph is formed by connecting each generator (vertex) to its closest generators. Alternate distances and graph constructions also can be used (see [14] for more details).…”

Section: Graph Clusteringmentioning

confidence: 99%

Comparative study of clustering techniques for real‐time dynamic model reduction

Purvine

Cotilla‐Sanchez

Halappanavar

et al. 2017

Statistical Analysis

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Dynamic model reduction in power systems is necessary for improving computational efficiency. Traditional model reduction using linearized models or offline analysis is not adequate to capture dynamic behaviors of the power system, especially with the new mix of intermittent generation and intelligent consumption, making the power system more dynamic and nonlinear. Real‐time dynamic model reduction has emerged to fill this important need. This paper explores using clustering techniques to analyze real‐time phasor measurements to identify groups of generators with similar behavior, as well as a representative generator from each group for dynamic model reduction. Two clustering techniques—graph clustering and k‐means—are considered. These techniques are compared with a previously developed dynamic model reduction approach using singular value decomposition. Two sample power grid datasets are used to test these different model reduction techniques. Based on the algorithms' relative performance, recommendations are provided for practical use.

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“…• Improved convergence: In the context of large-scale power system dynamic simulation, the existing relaxation-based decomposition methods have been criticized for the sensitivity to partitioning schemes and lack of control over convergence especially when the couplings between subproblems are strong. Most of the literature addresses this issue by solely relying on the utilization of effective/customized graph partitioning techniques [16], [23] [28][29]. However, the feasibility and effectiveness of this approach when applied to a large-scale problem is in doubt.…”

Section: Introductionmentioning

confidence: 99%

A Relaxation-Based Network Decomposition Algorithm for Parallel Transient Stability Simulation with Improved Convergence

Shi

Sullivan

Mazzola

et al. 2018

IEEE Trans. Parallel Distrib. Syst.

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Transient stability simulation of a large-scale and interconnected electric power system involves solving a large set of differential algebraic equations (DAEs) at every simulation time-step. With the ever-growing size and complexity of power grids, dynamic simulation becomes more time-consuming and computationally difficult using conventional sequential simulation techniques. To cope with this challenge, this paper aims to develop a fully distributed approach intended for implementation on High Performance Computer (HPC) clusters. A novel, relaxation-based domain decomposition algorithm known as Parallel-General-Norton with Multiple-port Equivalent (PGNME) is proposed as the core technique of a two-stage decomposition approach to divide the overall dynamic simulation problem into a set of subproblems that can be solved concurrently to exploit parallelism and scalability. While the convergence property has traditionally been a concern for relaxation-based decomposition, an estimation mechanism based on multiple-port network equivalent is adopted as the preconditioner to enhance the convergence of the proposed algorithm. The proposed algorithm is illustrated using rigorous mathematics and validated both in terms of speed-up and capability. Moreover, a complexity analysis is performed to support the observation that PGNME scales well when the size of the subproblems are sufficiently large.

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Towards effective clustering techniques for the analysis of electric power grids

Cited by 10 publications

References 13 publications

Clustering approach to model order reduction of power networks with distributed controllers

Clustering approach to model order reduction of power networks with distributed controllers

Comparative study of clustering techniques for real‐time dynamic model reduction

A Relaxation-Based Network Decomposition Algorithm for Parallel Transient Stability Simulation with Improved Convergence

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