| 1. |
- 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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| 2. |
- Bagheri, Azam, et al.
(författare)
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Improved characterization of multi-stage voltage dips based on the space phasor model
- 2018
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Ingår i: Electric power systems research. - : Elsevier. - 0378-7796 .- 1873-2046. ; 154, s. 319-328
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 3. |
- Bollen, Math, et al.
(författare)
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Classification of Underlying Causes of Power Quality Disturbances: Deterministic versus Statistical Methods
- 2007
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Ingår i: Eurasip Journal on Applied Signal Processing. - : Springer Science and Business Media LLC. - 1110-8657 .- 1687-0433 .- 1687-6172 .- 1687-6180. ; , s. 17 pages (Article ID 79747)-
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents the two main types of classification methods for power quality disturbances based on underlying causes: deterministic classification, giving an expert system as an example, and statistical classification, with support vector machines as an example. An expert system is suitable when one has limited amount of data and sufficient power system expert knowledge, however its application requires a set of threshold values. Statistical methods are suitable when large amount of data is available for training. Two important issues to guarantee the effectiveness of a classifier, data segmentation and featureextraction, are discussed. Segmentation of a sequence of data recording is pre-processing to partition the datainto segments each representing a duration containing either an event or transition between two events. Extraction of features is applied to each segment individually. Some useful features and their effectiveness are then discussed. Some experimental results are included for demonstrating theeffectiveness of both systems. Finally, conclusions are given together with the discussion of some future research directions.
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| 4. |
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| 5. |
- Gu, Irene Yu-Hua, 1953, et al.
(författare)
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Signal Processing and Classification Tools for Intelligent Distributed Monitoring and Analysis of the Smart Grid
- 2011
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Ingår i: IEEE PES Innovative Smart Grid Technologies Conference Europe. 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies, ISGT Europe 2011, Manchester, 5 - 7 December 2011. - Piscataway, N.J : IEEE Communications Society. - 9781457714214
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Konferensbidrag (refereegranskat)abstract
- This paper proposes a novel framework for an intelligent monitoring system that supervises the performance of the future power system. The increased complexity of the power system could endanger the reliability, voltage quality, operational security or resilience of the power system. A distributed structure for such a monitoring system is described and some of the advanced signal processing techniques or tools that could be used in such a monitoring system are given. Several examples for seeking the spatial locations and finding the underlying causes of disturbances are included.
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| 6. |
- Le, Cuong, 1982, et al.
(författare)
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Analysis of Power Disturbances from Monitoring Multiple Levels and Locations in a Power System
- 2010
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Ingår i: The 14th IEEE Int'l Conf. on Harmonics and Quality of Power, 26-29 Sep. 2010, Bergamo, Italy. - : IEEE Communications Society. - 9781424472444 - 9781424472451
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Konferensbidrag (refereegranskat)abstract
- This paper proposes a new methodology for diagnosing the original source and underlying causes of power system disturbances, where voltage and current recordings from different locations of a power system are collected. In the proposed method, disturbances are first pre-classified based on the number of transition segments. The spatial zone of the source of disturbances is coarsely determined from voltage recordings only. Disturbances are then further analyzed and characterized by extracting information from both voltages and currents. Finally more accurate information about the location of the source of disturbances is obtained by different techniques depending on the type of disturbances. Several underlying causes are analyzed and classified by using the proposed features extracted from both voltage and current waveforms. Finally, the location of the source of disturbances is refined once the underlying causes are found. Case studies were performed on a large grid-connected wind farm with disturbances from several underlying causes, including: fault, unit tripping, transformer, capacitor, and cable energizing generated by PSCAD/EMTDC.
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| 7. |
- Axelberg, P.G.V., et al.
(författare)
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Support vector machine for classification of voltage disturbances
- 2007
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Ingår i: IEEE Transactions on Power Delivery. - : IEEE. - 0885-8977 .- 1937-4208. ; 22:3, s. 1297-1303
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Tidskriftsartikel (refereegranskat)abstract
- The support vector machine (SVM) is a powerful method for statistical classification of data used in a number of different applications. However, the usefulness of the method in a commercial available system is very much dependent on whether the SVM classifier can be pretrained from a factory since it is not realistic that the SVM classifier must be trained by the customers themselves before it can be used. This paper proposes a novel SVM classification system for voltage disturbances. The performance of the proposed SVM classifier is investigated when the voltage disturbance data used for training and testing originated from different sources. The data used in the experiments were obtained from both real disturbances recorded in two different power networks and from synthetic data. The experimental results shown high accuracy in classification with training data from one power network and unseen testing data from another. High accuracy was also achieved when the SVM classifier was trained on data from a real power network and test data originated from synthetic data. A lower accuracy resulted when the SVM classifier was trained on synthetic data and test data originated from the power network.
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| 8. |
- Axelberg, P., et al.
(författare)
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Trace of Flicker Sources by using the Quantity of Flicker power.
- 2008
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Ingår i: IEEE Transactions on Power Delivery. - : IEEE. - 0885-8977 .- 1937-4208. ; 23:1, s. 465-471
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Tidskriftsartikel (refereegranskat)abstract
- Industries that produce flicker are often placed close to each other and connected to the same power grid system. This implies that the measured flicker level at the point of common coupling (PCC) is a result of contribution from a number of different flicker sources. In a mitigation process it is essential to know which one of the flicker sources is the dominant one. We propose a method to determine the flicker propagations and trace the flicker sources by using flicker power measurements. Flicker power is considered as a quantity containing both sign and magnitude. The sign determines if a flicker source is placed downstream or upstream with respect to a given monitoring point and the magnitude is used to determine the propagation of flicker power throughout the power network and to trace the dominant flicker source. This paper covers the theoretical background of flicker power and describes a novel method for calculation of flicker power that can be implemented in a power network analyzer. Also conducted simulations and a field test based on the proposed method will be described in the paper.
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| 9. |
- 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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| 10. |
- Bagheri, Azam, et al.
(författare)
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Generative Adversarial Model-Guided Deep Active Learning for Voltage Dip Labelling
- 2019
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Ingår i: 2019 IEEE Milan PowerTech. - : IEEE.
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Konferensbidrag (refereegranskat)abstract
- In many real applications, the ground truths of class labels from voltage dip sequences used for training a voltage dip classification system are unknown, and require manual labelling by human experts. This paper proposes a novel deep active learning method for automatic labelling of voltage dip sequences used for the training process. We propose a novel deep active learning method, guided by a generative adversarial network (GAN), where the generator is formed by modelling data with a Gaussian mixture model and provides the estimated probability distribution function (pdf) where the query criterion of the deep active learning method is built upon. Furthermore, the discriminator is formed by a support vector machine (SVM). The proposed method has been tested on a voltage dip dataset (containing 916 dips) measured in a European country. The experiments have resulted in good performance (classification rate 83% and false alarm 3.2%), which have demonstrated the effectiveness of the proposed method.
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