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  • 1.
    Bagheri, Azam
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Artificial Intelligence-Based Characterization and Classification Methods for Power Quality Data Analytics2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    One of the important developments in the electric power system is the fast increasing amount of data. An example of such data is formed by the voltages and currents coming from power-quality measurements. Power quality disturbances like voltage dips, harmonics and voltage transient can have a serious negative impact on the performance of equipment exposed to such disturbances. Voltage dips, short duration reductions in voltage magnitude, are especially considered as important disturbances because they regularly lead to stoppages in industrial process installations and subsequently to high costs.

    The overall aim of this dissertation is the development of automatic analysis methods and other methods for extracting information from large amounts of power-quality data. This includes, methods to detect and extract event characteristics from recorded data and classify the events, for instance, based on their origins or their impact on equipment. The classification facilitates further analysis steps including reasoning and interpretation. Once the data corresponding to each class is available, a proper characterization method can be used to create more semantic data useful for information extraction. The resulting information can be used to improve the performance of the whole system, e.g., updating grid-codes, or immunity requirements of sensitive installations or processes.

    This dissertation proposes different methods to fulfil each one of the above-mentioned steps. It proposes particularly a space-phasor model (SPM) of the three phase-to-neutral voltages as basis for analytic methods. The SPM is especially suitable as it is a time-domain transform without loss of any information. Another important contribution of the work is that most of the developed methods have been applied to a large dataset of about 6000 real-world voltage dips measured in existing HV and MV power networks.

    The main contributions of this dissertation are as follows:

    A complete framework has been proposed for automatic voltage quality analysis based on the SPM. The SPM has been used before, but this is the first time it has been used in a framework covering a range of voltage quality disturbances. A Gaussian-based anomaly detection method is used to detect and extract voltage quality disturbances. A principal component analysis (PCA) algorithm is used for event characterization. The obtained single-event characteristics are used to extract additional information like origin, fault type and location. 

    Two deep learning-based voltage dip classifier has been developed. In both classifier a 2D convolutional neural network (2D-CNN) architecture has been employed to perform automatic feature extraction task. The soft-max activation function fulfills supervised classification method in first classifier. The second classifier uses a semi-supervised classification method based on generative-discriminative model pairs in active learning context.

    The same SPM was shown to enable the effective extraction of dip characteristics for multi-stage voltage dips. Applying the k-means clustering algorithm, the event is clustered into its individual stages. For each stage of the dip, a logistic regression algorithm is used to characterize that stage. The proposed method offers a new solution to the problem with transition segments that is one of the main challenges of existing methods for characterization of multi-stage dips.  

    It is also shown in the dissertation that the SPM is an effective method for voltage transient analysis. It is possible to extract corresponding sample data and get appropriate single-event characteristics.

    A systematic way has been developed and applied for comparing different sets of voltage dip characteristics. With this method, both measured and synthetic voltage dips are applied to generic models of sensitive loads. The best set of characteristics is the one most accurately reproducing the behavior of equipment when exposed to measured voltage dips.

    The dissertation further contains a number of practical applications of the before-mentioned theoretical contributions: a proposal to an international standard-setting group; energy storage for voltage-dip ride-through of microgrids; impact of different voltage dips on wind-power installations.

  • 2.
    Bagheri, Azam
    Luleå University of Technology, Department of Engineering Sciences and Mathematics.
    Extracting Information from Voltage-Dip Monitoring2016Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    A voltage dip is a short duration reduction in voltage magnitude due to a short duration increase in current magnitude. Causes of dips are, among others, electrical faults, large motor starting, transformer energizing and failure of power-electronic converters.

    Voltage dips are considered as a very important power quality issue because they lead to trip or malfunction of sensitive loads especially in industrial process installations and subsequently they lead to high costs.

    In this thesis the overall aim is extracting additional information from large voltage dip monitoring databases. An important step to this end is providing efficient characterization methods for voltage dips. Voltage dip characterization aids by describing voltage dip events (a set of voltage waveforms with high time resolution) as a limited number of values such that this set gives as much as possible information about the dip. This thesis contributes to the voltage dip characterization development through three different methods.

    The first method consists of a systematic way for comparison different sets of voltage dip characteristic. With this method, both real-measured and synthetic voltage dips are applied to generic models of sensitive loads. The best set of characteristics, for representing the voltage dip, is the one best enables the reproduction of the behaviour of equipment when exposed to real-measured voltage dips.

    The second method compares 12 different sets of characteristics for describing three-phase single-events.. The method determines the most efficient and feasible way that gives more realistic characteristics as well as comparable with existing standard methods. The proposed set of characteristics has been proposed for inclusion in international standard documents.

    The third method enables the extraction of dip characteristics based on machine learning approaches. It is applicable for characterization of multi-stage voltage dips in particular and for single-stage (normal) voltage dips as well. The proposed method uses the space-phasor model of three-phase voltages as an input data for k-means clustering algorithm. Then the calculated data are modeled as a general form of an ellipse by exploiting logistic regression algorithm. Finally the optimized obtained ellipse parameters are applied to calculate single-segment characteristics for each individual stage of a multi-stage voltage dip.

    Further, all proposed methods are implemented in an Matlab environment and validated by applying them to a large number of real-measured voltage dips in actual HV and MV power networks and some suitable synthetic voltage dips.

  • 3.
    Bagheri, Azam
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Bollen, Math
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Additional information from voltage dips2016In: 17th International Conference on Harmonics and Quality of Power, Piscataway, NJ, 2016, p. 326-332, article id 7783434Conference paper (Refereed)
    Abstract [en]

    This paper presents some methods to extract additional information from voltage dip recordings, beyond residual voltage and duration. Additionally it discusses some issues related to the massive amount of data obtained from modern measurements that, is referred to as Big Data. The paper proposes some Deep Learning based algorithms as good candidates to extract complex features from big data as a step towards additional information. The applications of the information include predicting individual equipment performance, fault type and location, protection operation, and overall load behavior. Individual equipment and overall load include production as well as consumption

  • 4.
    Bagheri, Azam
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Bollen, Math
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Characterizing three-phase unbalanced dips through the ellipse parameters of the space phasor model2018In: 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017: proceedings, New York: Institute of Electrical and Electronics Engineers (IEEE), 2018Conference paper (Refereed)
    Abstract [en]

    This paper verifies the potential of ellipse parameters as voltage dip characteristics. The space-phasor model (SPM) of three phase voltages is generally in form of an ellipse in the complex plane. Mathematical relations are derived between the single-event characteristics (Characteristic Voltage; PN factor and Dip Type), and the ellipse parameters (semi-major axis, Semi-minor axis and major axis direction). The relations are validated by applying them to several actual recorded voltage dips and synthetic voltage dips.

  • 5.
    Bagheri, Azam
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Bollen, Math
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Developments in voltage dip research and its applications, 2005-20152016In: 17th International Conference on Harmonics and Quality of Power, Piscataway, NJ, 2016, p. 48-54, article id 7783428Conference paper (Refereed)
    Abstract [en]

    This paper presents a review of literature on voltage dips, from several points of view, throughout the last decade. It also summarizes the results related to voltage dip mitigation in both AC and DC power systems whereas it shows the remaining challenges that requires further research on voltage dips.

  • 6.
    Bagheri, Azam
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Bollen, Math
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Generative Adversarial Model-Guided Deep Active Learning for Voltage Dip Labelling2019Conference paper (Refereed)
  • 7.
    Bagheri, Azam
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Bollen, Math
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Simple diagnostic technique of switch failure modes of VSI power converter2018In: 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017: proceedings, New York: Institute of Electrical and Electronics Engineers (IEEE), 2018Conference paper (Refereed)
    Abstract [en]

    This paper presents a diagnostic method to detect switch failure of PWM power converters. The proposed method uses space phasor model (SPM) of three voltages measured at terminal of the power converter, then it applies principal component analysis to detect and localize the failure mode. The SPM results in one unique rotated ellipse or semi ellipse for every failure mode of every faulty leg. The quadrants occupied by the ellipse or semi ellipse also determine the faulty switch location in the leg. The proposed method is validated through comprehensive simulations.

  • 8.
    Bagheri, Azam
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Bollen, Math
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Space Phasor Model Based Monitoring of Voltages in Three Phase Systems2018In: Proceedings of International Conference on Harmonics and Quality of Power, ICHQP, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2018Conference paper (Refereed)
    Abstract [en]

    This paper proposes a method for monitoring of voltages in three-phase systems using parameters of the ellipse, correspondent to the space phasor model of three-phase voltages. Three main parameters, semi-minor axis, semi-major axis and rotating angle of the ellipse are calculated as single-cycle characteristics. Once these characteristics exceed predefined threshold values, different voltage events are detected. Given whole event data the parameters of the corresponding ellipse are calculated as ‘single-event characteristics’. The proposed method is applied to different measured voltage waveforms. The simulation results confirm that the ellipse parameters are a good basis for both detecting and characterizing voltage events.

  • 9.
    Bagheri, Azam
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Bollen, Math
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    The impact of voltage dips to low-voltage-ride-through capacity of doubly fed induction generator based wind turbine2019Conference paper (Refereed)
  • 10.
    Bagheri, Azam
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Bollen, Math
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    The Novel Method for Voltage Transient Detection and Characterization2019Conference paper (Refereed)
  • 11.
    Bagheri, Azam
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Bollen, Math
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Gu, Irene Y.H.
    Signal Processing group, Chalmers University of Technology.
    Big data from smart grids2018In: 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017: proceedings, New York: Institute of Electrical and Electronics Engineers (IEEE), 2018Conference paper (Refereed)
    Abstract [en]

    This paper gives a general introduction to “Big Data” in general and to Big Data in smart grids in particular. Large amounts of data (Big Data) contains a lots of information, however developing the analytics to extract such information is a big challenge due to some of the particular characteristics of Big Data. This paper investigates some existing analytic algorithms, especially deep learning algorithms, as tools for handling Big Data. The paper also explains the potential for deep learning application in smart grids.

  • 12.
    Bagheri, Azam
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Bollen, Math
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Gu, Irene Y.H.
    Chalmers University of Technology.
    Improved characterization of multi-stage voltage dips based on the space phasor model2018In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 154, p. 319-328Article in journal (Refereed)
    Abstract [en]

    This paper proposes a method for characterizing voltage dips based on the space phasor model of the three phase-to-neutral voltages, instead of the individual voltages. This has several advantages. Using a K-means clustering algorithm, a multi-stage dip is separated into its individual event segments directly instead of first detecting the transition segments. The logistic regression algorithm fits the best single-segment characteristics to every individual segment, instead of extreme values being used for this, as in earlier methods. The method is validated by applying it to synthetic and measured dips. It can be generalized for application to both single- and multi-stage dips.

  • 13.
    Bagheri, Azam
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Gu, Irene Y.H.
    Department of Electrical Engineering, Chalmers University of Technology.
    Bollen, Math
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Balouji, Ebrahim
    Department of Electrical Engineering, Chalmers University of Technology.
    A Robust Transform-Domain Deep Convolutional Network for Voltage Dip Classification2018In: IEEE Transactions on Power Delivery, ISSN 0885-8977, E-ISSN 1937-4208, Vol. 33, no 6, p. 2794-2802Article in journal (Refereed)
    Abstract [en]

    This paper proposes a novel method for voltage dip classification using deep convolutional neural networks. The main contributions of this paper include: (a) to propose a new effective deep convolutional neural network architecture for automatically learning voltage dip features, rather than extracting hand-crafted features; (b) to employ the deep learning in an effective two-dimensional transform domain, under space-phasor model (SPM), for efficient learning of dip features; (c) to characterize voltage dips by two-dimensional SPM-based deep learning, which leads to voltage dip features independent of the duration and sampling frequency of dip recordings; (d) to develop robust automatically-extracted features that are insensitive to training and test datasets measured from different countries/regions.

    Experiments were conducted on datasets containing about 6000 measured voltage dips spread over seven classes measured from several different countries. Results have shown good performance of the proposed method: average classification rate is about 97% and false alarm rate is about 0.50%. The test results from the proposed method are compared with the results from two existing dip classification methods. The proposed method is shown to out-perform these existing methods.

  • 14.
    Balouji, Ebrahim
    et al.
    Department of Electrical Engineering, Chalmers University of Technology.
    Gu, Irene Y.H.
    Department of Electrical Engineering, Chalmers University of Technology.
    Bollen, Math
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Bagheri, Azam
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Nazari, Mahmood
    Department of Electrical Engineering, Chalmers University of Technology.
    A LSTM-based deep learning method with application to voltage dip classification2018In: Proceedings of International Conference on Harmonics and Quality of Power, ICHQP, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2018Conference paper (Refereed)
    Abstract [en]

    In this paper, a deep learning (DL)-based method for automatic feature extraction and classification of voltage dips is proposed. The method consists of a dedicated architecture of Long Short-Term Memory (LSTM), which is a special type of Recurrent Neural Networks (RNNs). A total of 5982 three-phase one-cycle voltage dip RMS sequences, measured from several countries, has been used in our experiments. Our results have shown that the proposed method is able to classify the voltage dips from learned features in LSTM, with 93.40% classification accuracy on the test data set. The developed architecture is shown to be novel for feature learning and classification of voltage dips. Different from the conventional machine learning methods, the proposed method is able to learn dip features without requiring transition-event segmentation, selecting thresholds, and using expert rules or human expert knowledge, when a large amount of measurement data is available. This opens a new possibility of exploiting deep learning technology for power quality data analytics and classification.

  • 15.
    Schwanz, Daphne
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Bagheri, Azam
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Bollen, Math
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Larsson, Anders
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Active harmonic filters: control techniques review2016In: 17th International Conference on Harmonics and Quality of Power, Piscataway, NJ, 2016, p. 36-41, article id 7783423Conference paper (Refereed)
    Abstract [en]

    The use of active filters for harmonic mitigation compensation is increasing together with the improvement of their control techniques. Depending on the situation, the use of one technique instead of the other makes the difference of achieving a better harmonic mitigation or not. In this paper, a review of control techniques related to harmonic filters for harmonic mitigation is presented, together with the advantages and disadvantages. From the literature review it was observed that new techniques are being used and classical ones are being improved.

  • 16.
    Wang, Ying
    et al.
    College of Electrical Engineering and Information Technology, Sichuan University.
    Bagheri, Azam
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Bollen, Math
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Xiao, Xian-Xong
    College of Electrical Engineering and Information Technology, Sichuan University.
    Single-Event Characteristics for Voltage Dips in Three-Phase Systems2017In: IEEE Transactions on Power Delivery, ISSN 0885-8977, E-ISSN 1937-4208, Vol. 32, no 2, p. 832-840, article id 7496837Article in journal (Refereed)
    Abstract [en]

    This paper compares different methods for voltage-dip characterization. Those methods are based on earlier proposed algorithms for extracting three-phase characteristics (dip type, characteristic voltage, and so-called “PN factor”). The difference between the 12 methods being studied in this paper is in the way in which the time variation of those characteristics is treated to result in single-event characteristics. The methods are applied to 259 measured voltage dips and the performance of the different methods is compared. It is found that small differences in method can result in big difference in results. From the comparison, two methods are selected and recommended for inclusion in international standards.

  • 17.
    Wang, Ying
    et al.
    College of Electrical Engineering and Information Technology, Sichuan University.
    Bollen, Math
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Bagheri, Azam
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Xiao, Xian-Xong
    College of Electrical Engineering and Information Technology, Sichuan University.
    Olofsson, Magnus
    Elforsk, Energiforsk AB, Stockholm.
    A quantitative comparison approach for different voltage dip characterization methods2016In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 133, p. 182-190Article in journal (Refereed)
    Abstract [en]

    This paper presents a systematic approach to compare different methods for characterizing voltage dips in a quantitative way. A prediction error is calculated between measured and synthetic dips (reproduced from single-event characteristics for the measured dips) with respect to the way they impact the performance of a generic device. The proposed approach is illustrated by comparing seven different characterization methods and their ability to predict the minimum dc-bus voltage of a three-phase adjustable-speed drive. A generic model of such a drive is used for this. Based in this comparison it is concluded that characterization method for dips in three-phase systems should include unbalance and phase-angle jump.

1 - 17 of 17
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