kth.sePublications
Change search
Refine search result
1 - 8 of 8
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Cabus, Jose Eduardo Urrea
    et al.
    Karadeniz Tech Univ, Dept Elect & Elect Engn, Trabzon, Turkey..
    Cui, Yue
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Bertling, Lina
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    An Anomaly Detection Approach Based on Autoencoders for Condition Monitoring of Wind Turbines2022In: 2022 17th international conference on probabilistic methods applied to power systems (PMAPS), Institute of Electrical and Electronics Engineers (IEEE) , 2022Conference paper (Refereed)
    Abstract [en]

    This research presents an anomaly detection approach based on autoencoders for wind turbine condition monitoring. The overall goal is to develop a methodology for assessing wind turbine health in order to enable preventative maintenance programs. First, SCADA signals are used as data input in the approach. The approach then examines the differences between the estimated values by the autoencoder models and the measured signals from the SCADA system. Next, the Kernel Density Estimation is used to determine the distribution of the expected output's error. Finally, to efficiently extract anomalous activity in the data, a novel dynamic thresholding approach is used. The outcome reveals that the method is capable of detecting potential abnormalities prior to the onset of a breakdown. It also verifies that the approach can alert operators to potential changes within wind turbines even when the alarm records show no alerts.

  • 2.
    Cui, Yue
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Bertling Tjernberg, Lina
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Fault Diagnostics of Power Transformers Using Autoencoders and Gated Recurrent Units with Applications for Sensor Failures2022In: 2022 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)., Institute of Electrical and Electronics Engineers (IEEE), 2022Conference paper (Refereed)
    Abstract [en]

    With the industrial internet of things and SG technology developing, more and more operation data could be accessible, and condition-based maintenance shows promise for electrical equipment. This paper aims to develop a data-driven fault diagnosis utilizing operation data for high voltage equipment condition monitoring. To understand the asset management of power transformers, an interview is conducted as the expertise input for the study. The paper uses deep learning in an unsupervised way to model normal behaviors and identify underlying operational risks. The autoencoders are used to compress the raw data and extract the key features and the gated recurrent unit to model the dependencies between normal behaviors of power transformers. Finally, the method employs control charts to generate the alarm to indicate the underlying anomalies. The paper uses an online dataset to test the applications for sensor failures. The results show that the method can identify the operational risks before sensor failures.

  • 3.
    Cui, Yue
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering.
    A Fault Detection Framework Using Recurrent Neural Networks for Condition Monitoring of Wind Turbines2021Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The global energy system is experiencing a transition to a sustainable system with ambitious targets for increased use of renewable energy. One key trend for this transition has been the large introduction of wind power and integration into the electricity grid. In order to succeed in this transition, there is a need to develop efficient tools to support the handling of the assets. Asset management is a coordinated activity for the organization to get value from an asset. As the main part of asset management, maintenance includes all the technical and corresponding administrative actions to keep or restore the asset to the desired state in which it can perform its required functions. Traditional maintenance is usually based on scheduled monitoring and physical inspections. However, with new access to data and information about condition-based maintenance shows to be an efficient solution for asset management. This thesis explores data-driven solutions for electrical equipment to generate alerts towards potential operation risks, which targets digital, efficient, and cost-effective asset management. Specifically, the thesis investigates wind turbines.

    This thesis proposes a fault detection framework for cost-effective preventive maintenance of wind turbines by using condition monitoring systems. The thesis utilizes the data from supervisory control and data acquisition systems as the main input. For log events, each event is mapped to corresponding components based on the Reliawind taxonomy. For operation data, recurrent neural networks are applied to model normal behaviors, which can learn the long-time temporal dependencies between various time series. Based on the estimation results, a two-stage threshold method is proposed as post-processing to determine operation conditions. The method evaluates the shift values deviating from the estimated behaviors and their duration time to attenuate minor fluctuations. A two-level condition monitoring system is constructed to apply the proposed fault detection framework, which targets to detect possible faults of components and conduct performance analysis of turbines. The fault detection framework is tested with the experience data from onshore wind farms. The results demonstrate that the framework can detect operational risks and reduce false alarms.

    Download full text (pdf)
    fulltext
    Download full text (pdf)
    errata
  • 4.
    Cui, Yue
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering.
    Bangalore, Pramod
    Greenbyte.
    Bertling, Lina
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering.
    A fault detection framework using RNNs for condition monitoring of wind turbines2021In: Wind Energy, ISSN 1095-4244, E-ISSN 1099-1824Article in journal (Refereed)
    Abstract [en]

    This paper proposes a fault detection framework for the condition monitoring of wind turbines. The framework models and analyzes the data in supervisory control and data acquisition systems. For log information, each event is mapped to an assembly based on the IEA Reliawind taxonomy. For operation data, recurrent neural networks are applied to model normal behaviors, which can learn the long-time temporal dependencies between various time series. Based on the estimation results, a two-stage threshold method is proposed to determine the current operation status. The method evaluates the shift values deviating from the estimated behaviors and their duration time to attenuate the effect of minor fluctuations. The generated results from the framework can help to understand when the turbine deviates from normal operations. The framework is validated with the data from an onshore wind park. The numerical results show that the framework can detect operational risks and reduce false alarms.

  • 5.
    Huang, Qiuyi
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering.
    Cui, Yue
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering.
    Bertling, Lina
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering.
    Bangalore, Pramod
    Greenbyte AB, Gothenburg, Sweden.
    Wind Turbine Health Assessment Framework Based on Power Analysis Using Machine Learning Method2019In: Proceedings of 2019 IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2019, Bucharest, Romania: Institute of Electrical and Electronics Engineers (IEEE) , 2019Conference paper (Refereed)
    Abstract [en]

    This paper proposes a performance assessment framework to estimate operation status of wind turbines. The overall objective is to propose a method for health assessment to support preventive maintenance strategies for wind turbines. The framework uses the data in the supervisory control and data acquisition systems as input. The framework consists of three main stages: power curve prediction, sliding window method analysis and performance assessment. At the first stage, k-means and density-based clustering are applied to eliminate noisy measurements. Then both parametric and non-parametric methods are applied to estimate the ideal power curve, which is used as a reference value to assess the actual one. At the second stage, the sliding window method is used to calculate the deviation between actual power data and ideal values, which indicates the real time performance of wind turbines. At the third stage, different performance zones are defined to assess health conditions. The proposed approach has been applied with the experience data of six onshore wind turbines from a single wind farm. The results indicate that the introduced framework can monitor the operation conditions and evaluate the performance of wind turbines.

  • 6.
    Cui, Yue
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering.
    Bangalore, Pramod
    Greenbyte AB, Gothenburg, Sweden.
    Bertling Tjernberg, Lina
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering.
    An anomaly detection approach based on machine learning and scada data for condition monitoring of wind turbines2018In: 2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2018Conference paper (Refereed)
    Abstract [en]

    This paper presents an anomaly detection approach using machine learning to achieve condition monitoring for wind turbines. The approach applies the information in supervisory control and data acquisition systems as data input. First, machine learning is used to estimate the temperature signals of the gearbox component. Then the approach analyzes the deviations between the estimated values and the measurements of the signals. Finally, the information of alarm logs is integrated with the previous analysis to determine the operation states of wind turbines. The proposed approach has been tested with the data experience of a 2MW wind turbine in Sweden. The result demonstrates that the approach can detect possible anomalies before the failure occurrence. It also certifies that the approach can remind operators of the possible changes inside wind turbines even when the alarm logs do not report any alarms.

  • 7.
    Cui, Yue
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering.
    Bangalore, Pramod
    Greenbyte AB, Gothenburg, Sweden..
    Bertling Tjernberg, Lina
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering.
    An Anomaly Detection Approach Using Wavelet Transform and Artificial Neural Networks for Condition Monitoring of Wind Turbines' Gearboxes2018In: 2018 POWER SYSTEMS COMPUTATION CONFERENCE (PSCC), Institute of Electrical and Electronics Engineers (IEEE) , 2018Conference paper (Refereed)
    Abstract [en]

    This paper presents an anomaly detection approach using artificial neural networks and the wavelet transform for the condition monitoring of wind turbines. The method aims to attain early anomaly detection and to prevent possible false alarms under healthy operations. In the approach, nonlinear autoregressive neural networks are used to estimate the temperature signals of the gearbox. The Mahalanobis distances are then calculated to measure the deviations between the current states and healthy operations. Next, the wavelet transform is applied to remove noisy signals in the distance values. Finally, the operation information is considered together with the refined distance values to detect potential anomalies. The proposed approach has been tested with the real data of three 2 MW wind turbines in Sweden. The results show that the approach can detect possible anomalies before failure events occur and avoid reporting alarms under healthy operations.

  • 8.
    Yuan, Zhao
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Hesamzadeh, Mohammad Reza
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Cui, Yue
    KTH, School of Electrical Engineering (EES).
    Bertling Tjernberg, Lina
    KTH, School of Electrical Engineering (EES), Electromagnetic Engineering.
    Applying High Performance Computing to Probabilistic Convex Optimal Power Flow2016In: 2016 INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS (PMAPS), IEEE, 2016Conference paper (Refereed)
    Abstract [en]

    The issue of applying high performance computing (HPC) techniques to computation-intensive probabilistic optimal power flow has not been well discussed in literature. In this paper, the probabilistic convex AC OPF based on second order cone programming (P-SOCPF) is formulated. The application of P-SOCPF is demonstrated by accounting uncertainties of loads. To estimate the distributions of nodal prices calculated from PSOCPF, two point estimation method (2PEM) is deployed. By comparing with Monte Carlo (MC) method, the accuracy of 2PEM is proved numerically. The computation efficiency of 2PEM outperforms MC significantly. In the context of large scale estimation, we propose to apply high performance computing (HPC) to P-SOCPF. The HPC accelerated P-SOCPF is implemented in GAMS grid computing environment. A flexible parallel management algorithm is designed to assign execution threads to different CPUs and then collect completed solutions. Numerical results from IEEE 118-bus and modified 1354pegase case network demonstrate that grid computing is effective means to speed up large scale P-SOCPF computation. The speed up of P-SOCPF computation is approximately equal to the number of cores in the computation node.

1 - 8 of 8
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf