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Publications (10 of 178) Show all publications
Jürgensen, J. H., Nordström, L. & Hilber, P. (2019). Estimation of Individual Failure Rates for Power System Components Based on Risk Functions. IEEE Transactions on Power Delivery, 34(4), 1599-1607
Open this publication in new window or tab >>Estimation of Individual Failure Rates for Power System Components Based on Risk Functions
2019 (English)In: IEEE Transactions on Power Delivery, ISSN 0885-8977, E-ISSN 1937-4208, Vol. 34, no 4, p. 1599-1607Article in journal (Refereed) Published
Abstract [en]

The failure rate is essential in power system reliability assessment and thus far, it has been commonly assumed as constant. This is a basic approach that delivers reasonable results. However, this approach neglects the heterogeneity in component populations, which has a negative impact on the accuracy of the failure rate. This paper proposes a method based on risk functions, which describes the risk behavior of condition measurements over time, to compute individual failure rates within populations. The method is applied to a population of 12 power transformers on transmission level. The computed individual failure rates depict the impact of maintenance and that power transformers with long operation times have a higher failure rate. Moreover, this paper presents a procedure based on the proposed approach to forecast failure rates. Finally, the individual failure rates are calculated over a specified prediction horizon and depicted with a 95% confidence interval.

Keywords
failure analysis; maintenance engineering; power system reliability; power transformers; reliability theory; higher failure rate; power system components; risk functions; power system reliability assessment;power transformers;individual failure rates estimation;transmission level;Sociology;Statistics;Maintenance engineering;Power transformers;Reliability;Power system reliability;Asset management; condition monitoring; failure rate; failure rate modeling; power transformer diagnostics
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-255877 (URN)10.1109/TPWRD.2019.2913777 (DOI)000477724800042 ()2-s2.0-85069930869 (Scopus ID)
Note

QC 20190820

Available from: 2019-08-14 Created: 2019-08-14 Last updated: 2019-12-10Bibliographically approved
Paridari, K. & Nordström, L. (2019). Flexibility prediction, scheduling and control of aggregated TCLs. Electric power systems research
Open this publication in new window or tab >>Flexibility prediction, scheduling and control of aggregated TCLs
2019 (English)In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046Article in journal (Other academic) Published
Abstract [en]

There should be a constant balance between the demand and supply of electrical power. In Nordic countries, electricity markets have been formulated in such a way so as to facilitate this balance. These markets enable purchases, through bids, for buying and selling the energy (e.g., the day-ahead market) and the reserves (e.g., the frequency containment reserve for normal operation (FCR-N)). Demand response (DR) has received increased attention in recent years since it can efficiently support bidding in these markets. Aggregators, which act as mediators between end-users and the system operator, play an important role here. The aggregator contracts a large number of end-users for DR programs, and plans and controls their heterogeneous thermostatically controlled loads (TCLs), and offers their load flexibility to the markets. Taking into account the small market value of each contributing unit, the cost for the communication and control system enabling the DR service must be kept at a minimum. In this paper, we propose a framework which is adaptable to pre-existing and newly emerging TCLs, with no need for major re-design of the local control loops. We then design a strategy for the aggregator, to predict, schedule and control the aggregated flexibility of the contracted heterogeneous TCLs, in response to the DR signals and in the presence of end-users’ behavior uncertainties. In this strategy, we have applied a recurrent neural network (RNN) which learns the aggregated consumption of end-users and predict their aggregated load flexibility. The scheduling and control algorithms are then designed with the aim of participation in FCR-N market. We show that uncertainties in the prediction and scheduling are compensated in the control stage by activating back-up resources. A numerical study on 2000 number of detached houses has been conducted, which shows available 500 kW capacity for participation in the FCR-N market.

National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Energy Technology; Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-244849 (URN)10.1016/j.epsr.2019.106004 (DOI)2-s2.0-85071689991 (Scopus ID)
Note

QC 20190301

Available from: 2019-03-01 Created: 2019-03-01 Last updated: 2019-12-05Bibliographically approved
Nilsson, M., Söder, L., Olauson, J., Eriksson, R., Nordström, L. & Ericsson, G. N. (2018). A Machine Learning Method Creating Network Models Based on Measurements. In: 2018 POWER SYSTEMS COMPUTATION CONFERENCE (PSCC): . Paper presented at 2018 POWER SYSTEMS COMPUTATION CONFERENCE (PSCC). IEEE
Open this publication in new window or tab >>A Machine Learning Method Creating Network Models Based on Measurements
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2018 (English)In: 2018 POWER SYSTEMS COMPUTATION CONFERENCE (PSCC), IEEE , 2018Conference paper, Published paper (Refereed)
Abstract [en]

Network models are essential to perform power flow analyses. In this paper a supervised regression method creating simplified network models using measurements is presented. It is an iterative method creating a network model by minimizing the difference between measurements and obtained power flow using measured net-exchanges for each node. The method is tested in a case study for the Nordic Synchronous Area considering each bidding zone as a node. The simplified network model is created using a training set and is validated using various validation methods. The obtained reactances are not correct in absolute terms; however results indicate that the obtained power flows using the created network model are accurate enough for several different applications.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Machine learning, Nordic Power System, power flow analyses, simplified network model
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-238162 (URN)10.23919/PSCC.2018.8442822 (DOI)000447282400121 ()2-s2.0-85054003071 (Scopus ID)
Conference
2018 POWER SYSTEMS COMPUTATION CONFERENCE (PSCC)
Note

QC 20181107

Available from: 2018-11-07 Created: 2018-11-07 Last updated: 2018-11-07Bibliographically approved
Paridari, K., Azuatalam, D., Chapman, A. C., Verbič, G. & Nordström, L. (2018). A plug-and-play home energy management algorithm using optimization and machine learning techniques. In: 2018 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, CONTROL, AND COMPUTING TECHNOLOGIES FOR SMART GRIDS (SMARTGRIDCOMM): . Paper presented at EEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), OCT 29-31, 2018, Aalborg, DENMARK.
Open this publication in new window or tab >>A plug-and-play home energy management algorithm using optimization and machine learning techniques
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2018 (English)In: 2018 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, CONTROL, AND COMPUTING TECHNOLOGIES FOR SMART GRIDS (SMARTGRIDCOMM), 2018Conference paper, Published paper (Refereed)
Abstract [en]

A smart home is considered as an automated residential house that is provided with distributed energy resources and a home energy management system (HEMS). The distributed energy resources comprise PV solar panels and battery storage unit, in the smart homes in this study. In the literature, HEMSs apply optimization algorithms to efficiently plan and control the PV-storage, for the day ahead, to minimize daily electricity cost. This is a sequential stochastic decision making problem, which is computationally intensive. Thus, it is required to develop a computationally efficient approach. Here, we apply a recurrent neural network (RNN) to deal with the sequential decision-making problem. The RNN is trained offline, on the historical data of end-users’ demand, PV generation, time of use tariff and optimal state of charge of the battery storage. Here, optimal state of charge trace is generated by solving a mixed integer linear program, generated from the historical demand and PV traces and tariffs, with the aim of minimizing daily electricity cost. The trained RNN is called policy function approximation (PFA), and its output is filtered by a control policy, to derive efficient and feasible day-ahead state of charge. Furthermore, knowing that there are always new end-users installing PV-storage systems, that don’t have historical data of their own, we propose a computationally efficient and close-to-optimal plug-and-play planning and control algorithm for their HEMSs. Performance of the proposed algorithm is then evaluated in comparison with the optimal strategies, through numerical studies.

National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-240663 (URN)10.1109/SmartGridComm.2018.8587418 (DOI)000458801500004 ()978-1-5386-7954-8 (ISBN)
Conference
EEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), OCT 29-31, 2018, Aalborg, DENMARK
Note

QC 20180121

Available from: 2018-12-30 Created: 2018-12-30 Last updated: 2019-03-08Bibliographically approved
Menon, V. K., Variyar, S. V., Soman, K. P., Gopalakrishnan, E. A., Kottayil, S. K., Almas, M. S. & Nordström, L. (2018). A Spark (TM) Based Client for Synchrophasor Data Stream Processing. In: PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE AND UTILITY EXHIBITION ON GREEN ENERGY FOR SUSTAINABLE DEVELOPMENT (ICUE 2018): . Paper presented at International Conference and Utility Exhibition on Green Energy for Sustainable Development (ICUE),OCT 24-26, 2018, Phuket, THAILAND. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>A Spark (TM) Based Client for Synchrophasor Data Stream Processing
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2018 (English)In: PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE AND UTILITY EXHIBITION ON GREEN ENERGY FOR SUSTAINABLE DEVELOPMENT (ICUE 2018), Institute of Electrical and Electronics Engineers (IEEE), 2018Conference paper, Published paper (Refereed)
Abstract [en]

The SCADA based monitoring systems, having a very low sampling of one reading per 2-4 seconds is known to produce roughly 4.3 Tera Bytes (TiBs) of data annually. With synchrophasor technology, this will go up at least 100 times more as the rate of streaming is as high as 50/100 (60/120) Hz. Phasor data concentrators (PDCs) transmit byte streams encapsulating a comprehensive list of power system parameter including multiple phasor measurements, instantaneous frequency estimates, rate of change of frequency and several analog and digital quantities; this high volume and velocity of data makes it truly 'Big Data'. This helps in making the power grid a lot more observable, enabling real-time monitoring of crucial grid events such as voltage stability, grid stress and transient oscillations. Synchrophasor technology uses the IEEE C37.118.2-2011 (TM) Phasor Measurement Unit (PMU) /PDC communication protocol for data exchange which has no direct interface with any contemporary big data stream APIs or protocols. In this paper we propose a streaming interface in Apache Spark (TM), a popular big data platform, using Scala programming language, implementing a complete IEEE C37.118.2-2011 (TM) client inside a stream receiver so that we can effortlessly receive synchrophasor data directly to Spark (TM) applications for real-time processing and archiving.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Keywords
Apache Spark, Big Data, C37.118.2, PDC, PMU, Smart Grid, Streaming, Synchrophasor
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-249914 (URN)10.23919/ICUE-GESD.2018.8635650 (DOI)000462214700014 ()2-s2.0-85062358083 (Scopus ID)978-974-8257-99-0 (ISBN)
Conference
International Conference and Utility Exhibition on Green Energy for Sustainable Development (ICUE),OCT 24-26, 2018, Phuket, THAILAND
Note

QC 20190429

Available from: 2019-04-29 Created: 2019-04-29 Last updated: 2019-05-23Bibliographically approved
Wu, Y., Nordström, L., Wang, Y. & Hauser, C. (2018). Adaptive Cyber-Security Scheme Incorporating QoS Requirements for WAMC Applications. In: 2018 POWER SYSTEMS COMPUTATION CONFERENCE (PSCC): . Paper presented at 2018 POWER SYSTEMS COMPUTATION CONFERENCE (PSCC). IEEE
Open this publication in new window or tab >>Adaptive Cyber-Security Scheme Incorporating QoS Requirements for WAMC Applications
2018 (English)In: 2018 POWER SYSTEMS COMPUTATION CONFERENCE (PSCC), IEEE , 2018Conference paper, Published paper (Refereed)
Abstract [en]

Smart Grid integrates power systems and communication systems to support intelligent control and operation applications. The inputs of those applications are normally measured data collected anywhere in the system and they are transmitted over Wide Area Networks (WAN). Challenges associated with Quality of Service (QoS) and cyber-security in the delivery of these data need to be addressed. In most research work in Smart Grid, QoS and cyber-security are considered separately. However, there is tension between the two: in order to maintain a certain level of QoS security might have to be compromised. The relationship is not, however, deterministic as both QoS and the performance of cyber-security countermeasures may vary over time. To address such challenges, this paper proposes a novel adaptive cyber-security scheme. It quantifies experts opinions of available cyber-security algorithms into a metric called security coverage. Then security coverage is adaptively optimized, by switching cyber-security algorithms, depending on observed data link QoS performance. The scheme is validated with simulation studies on a typical Wide Area Monitoring Control (WAMC) application, power oscillation damping control. The proposed adaptive cyber-security scheme is generally applicable to Smart Grid or power system applications that face varying communication performance during operation.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Adaptive Scheme, Cyber-Security, Quality of Service, Stateful Data Delivery Service, WAMC
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-238165 (URN)10.23919/PSCC.2018.8450583 (DOI)000447282400194 ()2-s2.0-85054028740 (Scopus ID)
Conference
2018 POWER SYSTEMS COMPUTATION CONFERENCE (PSCC)
Note

QC 20181107

Available from: 2018-11-07 Created: 2018-11-07 Last updated: 2018-11-07Bibliographically approved
Yiming, W., Nordström, L., Wang, Y. & Hauser, C. (2018). Adaptive Cyber-Security Scheme Incorporating QoS Requirements for WAMC Applications. In: Power Systems Computation Conference (PSCC2018): . Paper presented at 20th Power Systems Computation Conference (PSCC2018), 11th-15th June 2018, Dublin, Ireland. IEEE conference proceedings
Open this publication in new window or tab >>Adaptive Cyber-Security Scheme Incorporating QoS Requirements for WAMC Applications
2018 (English)In: Power Systems Computation Conference (PSCC2018), IEEE conference proceedings, 2018Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE conference proceedings, 2018
Keywords
Adaptive Scheme, Cyber-Security, Quality of Service, Stateful Data Delivery Service, WAMC
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-230616 (URN)000447282400215 ()
Conference
20th Power Systems Computation Conference (PSCC2018), 11th-15th June 2018, Dublin, Ireland
Note

QC 2018-06-25

Available from: 2018-06-13 Created: 2018-06-13 Last updated: 2019-02-07Bibliographically approved
Paridari, K., Nordström, L. & Sandels, C. (2018). Aggregator strategy for planning demand response resources under uncertainty based on load flexibility modeling. In: 2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017: . Paper presented at 2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017, Dresden, Germany, 23 October 2017 through 26 October 2017 (pp. 338-343). Institute of Electrical and Electronics Engineers (IEEE), Article ID 8340694.
Open this publication in new window or tab >>Aggregator strategy for planning demand response resources under uncertainty based on load flexibility modeling
2018 (English)In: 2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 338-343, article id 8340694Conference paper, Published paper (Refereed)
Abstract [en]

Nowadays, end-users can participate in demand response (DR) programs, and even slight load reductions from many houses can add up to major load shifts in the power system. Aggregators, which act as mediators between end-users and distribution system operator (DSO), play an important role here. The aggregator contracts the end-users for DR programs, plans ahead for times when customers should shift their load, and controls the load shifts in the running time. In this paper, our main focus is on planning the end-users for load shifting. Here, we first define and formulate the flexibilities (e.g., Stamina, repetition, and capacity) related to the dynamic loads such as space heating systems (SHSs) in detached houses. Assuming some end-users being contracted for DR program, based on estimation of their house characteristics and load flexibilities, an algorithm is then proposed to plan the SHSs for load shifting. In this algorithm the states in which a flexible load can be planned, kept in backup, or unavailable are considered by the aggregator. Another algorithm has been proposed here to deal with the different sources of uncertainties (which cause some of the planned SHSs to become unavailable). Numerical results are presented at the end, which discuss performance of the proposed strategy in terms of load flexibilities, load shifts in response to DR signals, and sensitivity analysis. Here, how to estimate the houses characteristics is a difficult issue, and we approximate them based on available models in the literature.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
National Category
Engineering and Technology Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-226678 (URN)10.1109/SmartGridComm.2017.8340694 (DOI)2-s2.0-85051029519 (Scopus ID)9781538640555 (ISBN)
Conference
2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017, Dresden, Germany, 23 October 2017 through 26 October 2017
Note

QC 20180515

Available from: 2018-04-24 Created: 2018-04-24 Last updated: 2019-02-28Bibliographically approved
Yiming, W., Xiao, Y., Hohn, F., Nordström, L., Wang, J. & Zhao, W. (2018). Bad Data Detection Using Linear WLS and Sampled Values in Digital Substations. IEEE Transactions on Power Delivery, 33(1), 150-157, Article ID 7867789.
Open this publication in new window or tab >>Bad Data Detection Using Linear WLS and Sampled Values in Digital Substations
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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
Keywords
bad data detection, digital substation, Linear WLS, process bus, sampled values
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-222247 (URN)10.1109/TPWRD.2017.2669110 (DOI)000423091100016 ()2-s2.0-85041035552 (Scopus ID)
Note

QC 20180205

Available from: 2018-02-05 Created: 2018-02-05 Last updated: 2019-09-18Bibliographically approved
Rabuzin, T., Lavenius, J., Taylor, N. & Nordström, L. (2018). Bayesian Detection of Islanding Events Using Voltage Angle Measurements. In: 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2018: . Paper presented at 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2018; Aalborg; Denmark; 29 October 2018 through 31 October 2018. IEEE, Article ID 8587561.
Open this publication in new window or tab >>Bayesian Detection of Islanding Events Using Voltage Angle Measurements
2018 (English)In: 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2018, IEEE, 2018, article id 8587561Conference paper, Published paper (Refereed)
Abstract [en]

The growing presence of distributed generation in power systems increases the risk for the unintentional creation of electrical islands. It is important to apply reliable and quick is landing protection methods. At the same time, the deployment of phasor measurement units facilitates the usage of data-oriented techniques for the development of new wide-area protection applications, one of which is islanding protection. This paper presents a Bayesian approach to detecting an islanding event, which utilizes measurements of voltage angles at the system's buses. A model of mixtures of probabilistic principal component analysers has been fitted to the data using a variational inference algorithm and subsequently used for islanding detection. The proposed approach removes the need for setting parameters of the probabilistic model. The performance of the method is demonstrated on synthetic power system measurements.

Place, publisher, year, edition, pages
IEEE, 2018
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-245971 (URN)10.1109/SmartGridComm.2018.8587561 (DOI)000458801500076 ()2-s2.0-85061060271 (Scopus ID)978-1-5386-7954-8 (ISBN)
Conference
2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2018; Aalborg; Denmark; 29 October 2018 through 31 October 2018
Note

QC 20190314

Available from: 2019-03-14 Created: 2019-03-14 Last updated: 2019-05-10Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0003-3014-5609

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