Power system security enhancement by HVDC links using a closed-loop emergency control

In recent years, guaranteeing that large-scale interconnected systems operate safely, stably and economically has become a major and emergency issue. A number of high profile blackouts caused by cascading outages have focused attention on this issue. Embedded HVDC (High Voltage Direct Current) links within a larger AC power system are known to act as a “firewall” against cascading disturbances and therefore, can effectively contribute in preventing blackouts. A good example is the 2003 blackout in USA and Canada, where the Québec grid was not affected due to its HVDC interconnection. In the literature, many works have studied the impact of HVDC on the power system stability, but very few examples exist in the area of its impact on the system security. This paper presents a control strategy for HVDC systems to increase their contribution to system security. A real-time closed-loop control scheme is used to modulate the DC power of HVDC links to alleviate AC system overloads and improve system security. Simulations carried out on a simplified model of the Hydro-Québec network show that the proposed method works well and can greatly improve system security during emergency situations.Peer reviewedFinal Accepted Versio

HVDC links between North Africa and Europe: Impacts and benefits on the dynamic performance of the European system

This document is the Accepted Manuscript version of the following article: Mokhtar Benasla, Tayeb Allaoui, Mostefa Brahami, Mouloud Denai, and Vijay K. Sood, ‘HVDC links between North Africa and Europe: Impacts and benefits on the dynamic performance of the European system’, Renewable and Sustainable Energy Reviews, November 2017. Under embargo. Embargo end date: 20 November 2018. The published version is available online at doi: DOI: https://doi.org/10.1016/j.rser.2017.10.075. Published by Elsevier Ltd. This manuscript version is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.In the last decade, there have been several initiatives for the deployment of cross-Mediterranean HVDC (High Voltage Direct Current) links to enable the transmission of electrical power from renewable energy sources between North Africa and Europe. These initiatives were mainly driven by the potential economic, environmental and technical benefits of these HVDC interconnections. In previous studies on these projects, some technical aspects of critical importance have not been addressed or studied in sufficient detail. One of these key aspects relates to the impact and possible benefit of these HVDC links on the dynamic performance of the European system which is the major focus of this paper. Several issues relating to the dynamic performance of the system are addressed here. Based on the experience gained from existing AC/DC projects around the world, this paper shows that the HVDC links between North Africa and Europe can greatly improve the dynamic performance of the European system especially in the southern regions. In addition, some challenges on the operation and control of these HVDC links are highlighted and solutions to overcome these challenges are proposed. This review paper, therefore, serves as a preliminary study for further detailed investigation of specific impacts or benefits of these interconnections on the overall performance of the European system.Peer reviewe

Combining and comparing different machine learning algorithms to improve dissolved gas analysis interpretation

Since the discovery of dissolved gas analysis (DGA), it is considered as a leading technique for the diagnosis of liquid insulated power equipment. However, accurate analysis results can only be achieved if the measured gases closely reflect the actual equipment condition to enable an appropriate interpretation of these gases. In general, conventional techniques such as the ratio method, key gases, and Duval triangle combined or not with artificial intelligence techniques such as machine-learning algorithms are used for DGA interpretation. Here, four well-known machine-learning algorithms are compared in terms of DGA fault classification – Bayes network, multilayer perceptron, k-nearest neighbour, and J48 decision tree. Moreover, the effect of applying ensemble methods such as boosting through the Adaboost algorithm and bootstrap aggregation (bagging) is analysed, and the performances of these algorithms are evaluated. The data for developing classification models was transformed into three forms, other than the raw data. The obtained results clearly presented the efficiency and stability of some algorithms such as the J48 tree and Bayes networks for DGA fault classification, in particular, when the data is appropriately pre-processed. Moreover, the performance of these algorithms was found to consistently improve by integrating the concepts of multiple models or ensemble methods

Influence of Oil Quality on the Interpretation of Dissolved Gas Analysis Data

Dissolved gas analysis (DGA) is being used for many years as a diagnostic tool for the condition monitoring of power transformers. However, field experiences reveal that the interpretation of these dissolved gas data is not an easy task due to the complexity of the gassing phenomenon. This is due to a number of factors. One of those contributing factors namely, the impact of oil quality is investigated in this contribution. The measurement of the dissolved and undissolved (non-gaseous decomposition) aging by-products respectively assessed by UV / VIS spectrophotometry and turbidity allowed assessing the oil’s quality and its relationships with gas generation under electrical or thermal faults. The obtained results indicated that the degraded oils release more CO along with some small amounts of combustible gases under thermal stress. The volume of gases in the case of an electrical fault is almost the same regardless the oil’s condition

Transformer oil quality assessment using random forest with feature engineering

Machine learning is widely used as a panacea in many engineering applications including the condition assessment of power transformers. Most statistics attribute the main cause of transformer failure to insulation degradation. Thus, a new, simple, and effective machine-learning approach was proposed to monitor the condition of transformer oils based on some aging indicators. The proposed approach was used to compare the performance of two machine-learning classifiers: J48 decision tree and random forest. The service-aged transformer oils were classified into four groups: the oils that can be maintained in service, the oils that should be reconditioned or filtered, the oils that should be reclaimed, and the oils that must be discarded. From the two algorithms, random forest exhibited a better performance and high accuracy with only a small amount of data. Good performance was achieved through not only the application of the proposed algorithm but also the approach of data preprocessing. Before feeding the classification model, the available data were transformed using the simple k-means method. Subsequently, the obtained data were filtered through correlation-based feature selection (CFsSubset). The resulting features were again retransformed by conducting the principal component analysis and were passed through the CFsSubset filter. The transformation and filtration of the data improved the classification performance of the adopted algorithms, especially random forest. Another advantage of the proposed method is the decrease in the number of the datasets required for the condition assessment of transformer oils, which is valuable for transformer condition monitoring

Condition monitoring of in-service oil-filled transformers: Case studies and experience

Transformers are one of the most strategic components in balancing the voltage levels and hence a high priority is given to their performance [1]. It is established that, insulation technology plays a critical role in judging the performance and service life in oil filled apparatus [2]. Performance of the insulation system depends mainly on the deterioration behavior of insulation oil and paper. The mechanisms that are responsible for premature aging of oil/paper insulation are almost the same in all the oil filled apparatus. Yet, there will be a significant difference in the intensity of the aging mechanisms in different apparatus. This intensity is attributable to rating, design, and duration of operation for different machines. The detailed discussions on these mechanisms are presented in the subsequent sections of this paper. However, aging of service insulants is unavoidable and is to be maintained at a lower rate or arrested to the greatest possible extent, such that, catastrophic failures and unscheduled outages may be mitigated [3]. Normally, utilities follow scheduled condition monitoring activities to avoid the consequences of premature aging. Hence, knowledge on these in-service condition monitoring activities will be helpful in understanding the exact deterioration rate of the insulation system. Real time in-service experience of several transformer fleets that belong to United Kingdom utilities are reported in [4]. An early degradation of insulation is noticed through increase in acidity and furan concentration in oil for several transformers in the fleet. Authors investigated this early degradation in different perspectives including loading conditions, manufacturers, and oil chemistry changes. It is inferred that changes in oil chemistry is an important attribute for early degradation and hence utilities are advised to adopt different asset management strategies for affected and unaffected transformers in a fleet. Recently, failure rate data of service aged transformer fleets of an Australian utility were analyzed to establish the relationship between aging and different failure types [5]. It is noticed that degradation of the insulation system will be rapid after 20 years of service aging thus entailing the frequent condition monitoring activities after 20 years to identify premature aging failures

Comparative experimental analysis of ozone generation between surface and volume DBD generators

International audienceOzone generation by dielectric barrier discharge (DBD) is widely used in industry. Several DBD configurations can be used to generate ozone, which are classified in two main types depending on the geometry of the electrodes: volume DBD and surface DBD models. The aim of this work is to conduct a comparative experimental analysis between surface DBD and volume DBD ozone generators having a cylindrical geometry. This comparative investigation is carried out not only in terms of ozone concentration and energy efficiency, but also in terms of cooling performance. Two experimental setups were used in this work, one for measuring the ozone concentration (in mg/L) and the ozone rate production (in g/h) and a second for cooling experiments. The obtained results show that although the volume DBD model is the most frequently used in industry, the surface DBD reactor gives better results in terms of ozone rate production and cooling