261 research outputs found
Voltage quality monitoring, dips classification and responsibility sharing
No full textCEER and EURELECTRIC cooperation in the field
of quality of electricity supply, involving joint meetings and the
participation at the relevant CENELEC Technical Committee,
contributed to the results attained in the recent publication of the
EN 50160:2010 edition that includes a new voltage dips
classification table allowing harmonisation at European level on
voltage dips data collection. The generalisation of voltage quality
monitoring data publication all over Europe will allow the
definition of responsibility sharing between the different involved
stakeholders and the evolution of voltage quality regulation
applied at national level. Examples from Sweden and Italy are
briefly presented in this paper
An unsupervised learning schema for seeking patterns in rms voltage variations at the sub-10-minute time scale
No full textThis paper proposes an unsupervised learning schema for seeking the patterns in rms voltage variations at the time scale between 1 s and 10 min, a rarely considered time scale in studies but could be relevant for incorrect operation of end-user equipment. The proposed framework employs a Kernel Principal Component Analysis (KPCA) followed by a k-means clustering. The schema is applied on 10-min time series with a 1-s time resolution obtained from 44 different periods of a location south of Sweden. Then, ten patterns are obtained by reconstructing the 10-min time series from each cluster center. The results of the proposed schema show a good separation of cluster centers. Moreover, some statistical power-quality indices are applied to the whole dataset, showing voltage variation between (0.5–3) V over a 10-min window. Obtaining the most suitable indices and applying them to the ten obtained cluster centers and their belonging time series shows that the existing statistical indices may not be enough to show a complete picture of the sub-10 min actual variations. This outcome shows the necessity of extracting 10-min patterns through our proposed schema besides the existing statistics to quantify the voltage variations, levels, and patterns together. Findings of this paper are: Not forgetting the sub-10-min time scale; The necessity of employing both statistics and the proposed schema; Extraction of ten typical patterns; The need for the statistics and patterns that are justified as changes in equipment connected to the grid; and compressing a huge amount of data from power-quality monitoring. The proposed schema is applied to a much less understood phenomena/disturbance type so that this work will result in general knowledge beyond the specific case study
Understanding power quality problems voltage sags and interruptions
No full text"Power quality problems have increasingly become a substantial concern over the last decade, but surprisingly few analytical techniques have been developed to overcome these disturbances in system-equipment interactions. Now in this comprehensive book, power engineers and students can find the theoretical background necessary for understanding how to analyze, predict, and mitigate the two most severe power disturbances: voltage sags and interruptions. This is the first book to offer in-depth analysis of voltage sags and interruptions and to show how to apply mathematical techniques for practical solutions to these disturbances. From UNDERSTANDING AND SOLVING POWER QUALITY PROBLEMS you will gain important insights into . Various types of power quality phenomena and power quality standards. Current methods for power system reliability evaluation. Origins of voltage sags and interruptions. Essential analysis of voltage sags for characterization and prediction of equipment behavior and stochastic prediction. Mitigation methods against voltage sags and interruptions" An Instructor Support FTP site is available from the Wiley editorial department: ftp://ftp.ieee.org/uploads/press/bollen Sponsored by: IEEE Power Electronics Society, IEEE Industry Applications Society, IEEE Power Engineering Societ
A new joint sliding-window ESPRIT and DFT scheme for waveform distortion assessment in power systems
No full textThis paper proposes a novel scheme that jointly employs a sliding-window ESPRIT and DFT for estimating harmonic and interharmonic components in power system disturbance data. In the proposed scheme, separate stages are utilized to estimate the voltage fundamental component, harmonics and interharmonics. This includes the estimation of the fundamental component from lowpass filtered data using a sliding-window ESPRIT, of harmonics from a sliding-window DFT with a synchronized window, and of interharmonics from the residuals by applying the sliding-window ESPRIT. Main advantages of the approach include high resolution and accuracy in parameter estimation and significantly reduced computational cost. Experiments and comparisons are made on both synthetic and measurement data. Results have shown the effectiveness and efficiency of the proposed scheme
Voltage-sag source detection: Developing supervised methods and proposing a new unsupervised learning
No full textRecognition and analysis of voltage sags (dips) allow network operators to predict and prevent problems in real-life applications. Clearing the voltage sag source by direction detection methods is the most effective way to solve and improve the voltage sags and their related problems. However, the existing analytical methods use single or two input features as phasor-based (PB) or instantaneous-based (IB) values. Hence, their limited maximum accuracy is given at 93% and 84% when using PB features for noiseless and high-level noise signals, respectively. To increase the detection accuracy, the main contributions of this research by proposing machine learning (ML) methods include: (a) Developing nine supervised methods including support vector machine (SVM)-based, tree-based, others, and an ensemble learning of said methods, and providing a comparative analysis (b) Employing a set of PB, IB, and both PB and IB input features as noiseless and noisy; (c) Finding the best developed supervised methods by highest possible accuracy under subsets said in (b); (d) Proposing a new unsupervised method fed by both PB and IB features using a sparse principal component analysis (SPCA) applied to a k-means clustering with an internal SPCA approach. The proposed unsupervised schema does not use the upstream/downstream labels in developed supervised methods. Extensive simulations of voltage sags due to fault and transformer energizing on a Brazilian regional network show that regardless of the sag sources, input feature subset, and noise levels, the random forest (RF) models yield the best performance so that noiseless-RF (99.84%) using both PB and IB features is the most effective one. The proposed unsupervised method outcomes an overall accuracy of 99.17%-noiseless and about 90% for high-level noises. This performance is higher than analytical methods, very close to SVM-based supervised methods, and uses no predefined labels. Moreover, the results of Slovenian field measurements confirm the effectiveness of the best-developed supervised methods and the proposed unsupervised learning
Seeking patterns in rms voltage variations at the sub-10-minute scale from multiple locations via unsupervised learning and patterns' post-processing
No full textThis paper addresses the issue of seeking sub-10-min patterns in fast rms voltage variations from time-limited measurement data at multiple locations worldwide. This is a rarely considered time scale in studies that could be important for the incorrect operation of end-user equipment. Moreover, measurements from multiple locations could be significant from the view of seeking pattern methods. To learn more about this time scale, we propose an unsupervised learning method that employs a Kernel Principal Component Analysis (KPCA) with a Cosine kernel to extract principal features from 10-min time series of voltage variations with a 1-s resolution followed by a k-means clustering to group the features. The scheme is applied to measurements from 57 low-voltage locations in 19 countries from 2009 to 2018. Fifteen initial clusters/patterns are then extracted and converted to ten new (general) patterns using a clusters' merging strategy with highly similar patterns employed in a new post-processing approach useful for multiple locations. Utilizing data from multiple locations in multiple countries ensures a level of generality of the patterns. It also allows comparing the locations. Next to the ten general patterns, some typical patterns are extracted separately for every location. A statistical indices analysis confirms that a complete picture of sub-10-min oscillations needs both statistical indices (quantifying level and variations) and the proposed framework (quantifying patterns). The extracted patterns could be used as a reference for testing/putting requirements on the grid-connected equipment and quantifying the grid's hosting capacity for different types of new distributed generations connected to the grid. The framework is scalable and computationally cheap, making it appropriate for seeking typical patterns in the big data domain. Applying the framework to the much less understood phenomenon will result in providing general knowledge in the field of power quality
Integration of distributed generation in the power system
No full text"The integration of new sources of energy like wind power, solar-power, small-scale generation, or combined heat and power in the power grid is something that impacts a lot of stakeholders: network companies (both distribution and transmission), the owners and operators of the DG units, other end-users of the power grid (including normal consumers like you and me) and not in the least policy makers and regulators. There is a lot of misunderstanding about the impact of DG on the power grid, with one side (including mainly some but certainly not all, network companies) claiming that the lights will go out soon, whereas the other side (including some DG operators and large parks of the general public) claiming that there is nothing to worry about and that it's all a conspiracy of the large production companies that want to protect their own interests and keep the electricity price high. The authors are of the strong opinion that this is NOT the way one should approach such an important subject as the integration of new, more environmentally friendly, sources of energy in the power grid. With this book the authors aim to bring some clarity to the debate allowing all stakeholders together to move to a solution. This book will introduce systematic and transparent methods for quantifying the impact of DG on the power grid"--"Gives a power-system viewpoint contrary to the energy viewpoint presented in many other books on DG Emphasis on systematic and transparent calculation methods allowing a quantification of the amount of DG that can be integrated at a certain location of the grid or in the grid as a whole Provides an overview of the different energy sources, with emphasis on windpower, solar power and CHP (Combined Heat & Power) Provides a general overview of the different methods available to allow more DG to be connected to the grid, ranging from classical methods like building more lines, to advanced methods like power-electronics control, and even future methods like smartgrids and microgrids"-
Harmonic aspects of wind power integration
No full textThis paper discusses a number of ways in which wind power installations can impact the harmonic levels in the power system. Wind turbines are an additional source of harmonic emission, especially when it concerns “non-characteristic harmonics.” Parallel resonances can amplify the emission from individual turbines. A mathematical model is developed to quantify this amplification. Series resonances can result in high currents, driven by the background voltage distortion in the transmission grid, flowing into the wind park. Weakening of the transmission grid will increase lower order harmonics but reduce higher order harmonics.</p
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