Data quality analysis in power system state estimators

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Project abstract

This project aims at assessing these aggregated uncertainties in the state estimation process by taking both measurement uncertainties and inaccuracies in the static input data into account. In a second step, the aim is to investigate how these new uncertainty measures can be exploited in the EMS, such as in power flow calculations. Next, we provide some technical details for the proposed project.

Summary of work

2015. Project started: initial problem formulation and development of outline for the Matlab functions started.

2015/2016. Our work is based on the sensitivity analysis of the estimated telemetry data with respect to the measurement data and the branch parameters with the main goal of locating relevant input components. In order to find relevant inputs, we analyse the normalized sensitivity matrix by sparse principal component analysis (PCA). The non-zero entries of the loading vectors related to the dominant principal components are considered to be the relevant inputs to the state estimator as they mainly contribute to the amplification of the estimated values. We prove that PCA shows an elementary structure of the sensitivity matrix: All non-zero entries of a loading vector corresponding to a positive singular value belong either to the telemetry data or to the branch data. We show that this property is also valid for PCA with different sparsity-promoting constraints on the loading vector. The proposed analysis method is demonstrated by a numerical study. These results were accepted for presentation at the 56th IEEE Conference on Decision and Control, held in Las Vegas in December 2016. An extended journal version is also in preparation.

Event log

2016-12: Presentation at 56th IEEE Conference on Decision and Control, held in Las Vegas, USA. (Presentation of project results, after completion of the project.)

2017-02: Presentation at the international conference on Future Electric Power Systems and the Energy Transition, Champéry, Switzerland. (Presentation of project results, after completion of the project.)

Project reference-group

Lars Nordström,  KTH
Magnus Johansson,  Svenska kraftnät
Andreas Westberg,  Svenska kraftnät

Publications by this researcher

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Error-dependent data scheduling in resource-aware multi-loop networked control systems
M. H. Mamduhi,   Adam Molin,   D. Tolić,   S. Hirche.
2017,   Automatica, vol. 81

Event-Triggered State Estimation : An Iterative Algorithm and Optimality Properties
Adam Molin,   Sandra Hirche.
2017,   IEEE Transactions on Automatic Control, vol. 62(11)

Safety Analysis for Controller Handover in Mobile Systems
Dirk van Dooren,   Sebastian Schiessl,   Adam Molin,   James Gross,   Karl Henrik Johansson.
2017,   IFAC 2017 World Congress, Toulouse, France The 20th World Congress of the International Federation of Automatic Control, 9-14 July 2017

Event-triggered Scheduling for Infrastructure-supported Collaborative Vehicle Control
Adam Molin,   H. Esen,   Karl H. Johansson.
2016,   IFAC-PapersOnLine, vol. 49(22)

A Study on the Sensitivity Matrix in Power System State Estimation by Using Sparse Principal Component Analysis
Adam Molin,   Henrik Sandberg,   Magnus Johansson.
2016,   55th IEEE Conference on Decision and Control, CDC 2016, ARIA Resort and Casino, Las Vegas, United States, 12 December 2016 through 14 December 2016

Innovations-based priority assignment for control over CAN-like networks
Adam Molin,   C. Ramesh,   H. Esen,   Karl Henrik Johansson.
2015,   54th IEEE Conference on Decision and Control (CDC)

Consistency-preserving event-triggered estimation in sensor networks
Adam Molin,   Henrik Sandberg,   Karl Henrik Johansson.
2015,   54th IEEE Conference on Decision and Control (CDC)

Publication list last updated from DiVA on 2024-08-22 22:58.

Page started: 2014-12-01
Last generated: 2024-08-22