{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/distributed-robust-power-system-state","title":"Distributed Robust Power System State Estimation","arxiv_id":"1204.0991","date":"2012-04-04","proceeding":null,"authors":["Vassilis Kekatos","Georgios B. Giannakis"],"abstract":"Deregulation of energy markets, penetration of renewables, advanced metering\ncapabilities, and the urge for situational awareness, all call for system-wide\npower system state estimation (PSSE). Implementing a centralized estimator\nthough is practically infeasible due to the complexity scale of an\ninterconnection, the communication bottleneck in real-time monitoring, regional\ndisclosure policies, and reliability issues. In this context, distributed PSSE\nmethods are treated here under a unified and systematic framework. A novel\nalgorithm is developed based on the alternating direction method of\nmultipliers. It leverages existing PSSE solvers, respects privacy policies,\nexhibits low communication load, and its convergence to the centralized\nestimates is guaranteed even in the absence of local observability. Beyond the\nconventional least-squares based PSSE, the decentralized framework accommodates\na robust state estimator. By exploiting interesting links to the compressive\nsampling advances, the latter jointly estimates the state and identifies\ncorrupted measurements. The novel algorithms are numerically evaluated using\nthe IEEE 14-, 118-bus, and a 4,200-bus benchmarks. Simulations demonstrate that\nthe attainable accuracy can be reached within a few inter-area exchanges, while\nlargest residual tests are outperformed.","url_abs":"http://arxiv.org/abs/1204.0991v2","url_pdf":"http://arxiv.org/pdf/1204.0991v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"distributed-robust-power-system-state","repo_url":"https://github.com/joaofcmota/DistributedOptimizationWithLocalDomains","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"state-estimation","task_name":"State Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1204.0991","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}