| 1. |
- Bagheri, Elahe, et al.
(författare)
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A Novel Model for Emotion Detection from Facial Muscles Activity
- 2020
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Ingår i: Advances in Intelligent Systems and Computing. - Cham : Springer International Publishing. - 2194-5365 .- 2194-5357. ; 1093 AISC, s. 237-249
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Konferensbidrag (refereegranskat)abstract
- Considering human’s emotion in different applications and systems has received substantial attention over the last three decades. The traditional approach for emotion detection is to first extract different features and then apply a classifier, like SVM, to find the true class. However, recently proposed Deep Learning based models outperform traditional machine learning approaches without requirement of a separate feature extraction phase. This paper proposes a novel deep learning based facial emotion detection model, which uses facial muscles activities as raw input to recognize the type of the expressed emotion in the real time. To this end, we first use OpenFace to extract the activation values of the facial muscles, which are then presented to a Stacked Auto Encoder (SAE) as feature set. Afterward, the SAE returns the best combination of muscles in describing a particular emotion, these extracted features at the end are applied to a Softmax layer in order to fulfill multi classification task. The proposed model has been applied to the CK+, MMI and RADVESS datasets and achieved respectively average accuracies of 95.63%, 95.58%, and 84.91% for emotion type detection in six classes, which outperforms state-of-the-art algorithms.
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| 2. |
- Bagheri, Azam, et al.
(författare)
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A Framework Based on Machine Learning for Analytics of Voltage Quality Disturbances
- 2022
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Ingår i: Energies. - : MDPI. - 1996-1073. ; 15:4
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a machine-learning-based framework for voltage quality analytics, where the space phasor model (SPM) of the three-phase voltages before, during, and after the event is applied as input data. The framework proceeds along with three main steps: (a) event extraction, (b) event characterization, and (c) additional information extraction. During the first step, it utilizes a Gaussian-based anomaly detection (GAD) technique to extract the event data from the recording. Principal component analysis (PCA) is adopted during the second step, where it is shown that the principal components correspond to the semi-minor and semi-major axis of the ellipse formed by the SPM. During the third step, these characteristics are interpreted to extract additional information about the underlying cause of the event. The performance of the framework was verified through experiments conducted on datasets containing synthetic and measured power quality events. The results show that the combination of semi-major axis, semi-minor axis, and direction of the major axis forms a sufficient base to characterize, classify, and eventually extract additional information from recorded event data.
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| 3. |
- Bagheri, Azam, et al.
(författare)
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A Robust Transform-Domain Deep Convolutional Network for Voltage Dip Classification
- 2018
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Ingår i: IEEE Transactions on Power Delivery. - : Institute of Electrical and Electronics Engineers (IEEE). - 0885-8977 .- 1937-4208. ; 33:6, s. 2794-2802
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 4. |
- Bagheri, Azam, et al.
(författare)
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Additional information from voltage dips
- 2016
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Ingår i: 17th International Conference on Harmonics and Quality of Power. - Piscataway, NJ. - 9781509037926 ; , s. 326-332
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Konferensbidrag (refereegranskat)abstract
- 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
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| 5. |
- Bagheri, Azam
(författare)
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Artificial Intelligence-Based Characterization and Classification Methods for Power Quality Data Analytics
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 6. |
- Bagheri, Azam, et al.
(författare)
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Big data from smart grids
- 2018
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Ingår i: 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017. - New York : Institute of Electrical and Electronics Engineers (IEEE). - 9781538619537
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 7. |
- Bagheri, Azam, et al.
(författare)
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Characterizing three-phase unbalanced dips through the ellipse parameters of the space phasor model
- 2018
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Ingår i: 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017. - New York : Institute of Electrical and Electronics Engineers (IEEE). - 9781538619537
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 8. |
- Bagheri, Azam, et al.
(författare)
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Developments in voltage dip research and its applications, 2005-2015
- 2016
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Ingår i: 17th International Conference on Harmonics and Quality of Power. - Piscataway, NJ. - 9781509037926 ; , s. 48-54
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 9. |
- Bagheri, Azam, 1982, et al.
(författare)
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Estimation of frequency-dependent impedances in power grids by deep lstm autoencoder and random forest
- 2021
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Ingår i: Energies. - : MDPI AG. - 1996-1073 .- 1996-1073. ; 14:13
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a deep-learning-based method for frequency-dependent grid impedance estimation. Through measurement of voltages and currents at a specific system bus, the estimate of the grid impedance was obtained by first extracting the sequences of the time-dependent features for the measured data using a long short-term memory autoencoder (LSTM-AE) followed by a random forest (RF) regression method to find the nonlinear map function between extracted features and the corresponding grid impedance for a wide range of frequencies. The method was trained via simulation by using time-series measurements (i.e., voltage and current) for different system parameters and verified through several case studies. The obtained results show that: (1) extracting the time-dependent features of the voltage/current data improves the performance of the RF regression method; (2) the RF regression method is robust and allows grid impedance estimation within 1.5 grid cycles; (3) the proposed method can effectively estimate the grid impedance both in steady state and in case of large transients like electrical faults.
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| 10. |
- Bagheri, Azam
(författare)
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Extracting Information from Voltage-Dip Monitoring
- 2016
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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