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Bad Data Detection Using Linear WLS and Sampled Values in Digital Substations
KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.ORCID iD: 0000-0002-6330-3055
KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.ORCID iD: 0000-0003-3014-5609
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2018 (English)In: IEEE Transactions on Power Delivery, ISSN 0885-8977, E-ISSN 1937-4208, Vol. 33, no 1, p. 150-157, article id 7867789Article in journal (Refereed) Published
Abstract [en]

Smart Grids employ intelligent control applications that require high quality data: fast, secure, and error free. Several researchers have focused on providing techniques for low latency and secured data links for these applications. Bad data detection is however generally provided only at the central level due to limitations in legacy technologies employed in many substations. With the introduction of IEC61850 data sharing within the substation becomes more flexible and transparent allowing more sophisticated management of data quality. Hence, this paper proposes a substation level bad data detection algorithm to facilitate also these types of requirements from applications. The algorithm is based on automatically detecting the substation topology by parsing standard substation description files and online state of circuit breakers and disconnectors. By applying linear weighted least square based state estimation algorithm, bad data from failing current transformers (CT) can be detected. By conducting the verification of different types of bad data, the results show the output of bad data detection algorithm provides higher accuracy than output from both measurement and protective CT in both static and faulty situations.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. Vol. 33, no 1, p. 150-157, article id 7867789
Keywords [en]
bad data detection, digital substation, Linear WLS, process bus, sampled values
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-222247DOI: 10.1109/TPWRD.2017.2669110ISI: 000423091100016Scopus ID: 2-s2.0-85041035552OAI: oai:DiVA.org:kth-222247DiVA, id: diva2:1180312
Note

QC 20180205

Available from: 2018-02-05 Created: 2018-02-05 Last updated: 2019-09-18Bibliographically approved
In thesis
1. Quality Assurance of Time Critical Data Using Adaptive Data Delivery Mechanisms in Smart Grids
Open this publication in new window or tab >>Quality Assurance of Time Critical Data Using Adaptive Data Delivery Mechanisms in Smart Grids
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Smart grids are proposed to integrate renewable energy and to improve the efficiency of power system operation. The challenges of integrating renewable energy are the inherent fluctuations of generation which are difficult to predict which may lead to challenges to the safe operation of power systems. Improving the efficiency of power systems means they should be operated closer to their limits. To solve these challenges, advanced protection and control applications, have been proposed that increase the accuracy of reactions and reduce the response time to events and faults. However, not all the data required for these applications can be delivered by the conventional Supervisory Control And Data Acquisition (SCADA) systems. As an example, synchrophasor data are intended to be delivered via a Wide Area Monitoring and Control (WAMC) system, which can be seen as a parallel data delivery infrastructure to SCADA. As deployment of intelligent substation secondary devices such as Intelligent Electronic Devices (IEDs), Merging Units (MUs), and Phasor Measurement Units (PMUs), data volume and data resolution in the power systems is increasing. To flexibly share data in the power system, different data delivery architectures have been proposed. Flexible data sharing brings benefits of information resiliency from different data sources. But it also raises the requirements on cyber-security for protection of the smart grid applications. Such needs are in turn being gradually addressed by new cyber-security mechanisms.

In this thesis, the main quality attributes for time critical smart grid applications being; data accuracy, information resiliency, communication performance, and cyber-security and their interrelation are studied. Most previous research has been focused on assurance on one of these quality attributes, while in practical implementation the attributes are clearly related and interdependent. There is consequently a lack of study of the interactions between these quality attributes.

This thesis focuses on the interactions of three pairs of these four time critical data quality attributes. The hypothesis of the interaction of each pair has been formulated as research question which is answered in different sections of the thesis. The results of this thesis show that information resiliency can increase the data accuracy and enhance the communication performance assurance to the smart grid application. This is in the thesis shown by two of the contributions presented in this thesis, being the implementation and validation of an adaptive data source selection mechanism to realize at substation level and wide area system level separately. In addition, since cyber-security mechanisms can affect the communication performance, specifically latency, and a trade off between security and performance may be needed. A third contribution in the thesis is a framework incorporating these two time critical quality attributes consisting of  an adaptive cyber-security scheme which contributes to the incorporation of performance requirements.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2018. p. 69
Series
TRITA-EECS-AVL ; 2019:16
Keywords
time critical data quality, adaptive mechanisms, quality assurance, Smart Grid, information resiliency, data accuracy, end-to-end latency, cyber-security, substation automation, power system communication
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering; Industrial Information and Control Systems
Identifiers
urn:nbn:se:kth:diva-243862 (URN)978-91-7873-105-3 (ISBN)
Public defence
2019-03-13, F3, Lindstedtsvägen 26, Stockholm, 22:41 (English)
Opponent
Supervisors
Note

QC 20190208

Available from: 2019-02-08 Created: 2019-02-07 Last updated: 2019-02-08Bibliographically approved

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Yiming, WuHohn, FabianNordström, Lars

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