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Data-driven Missing Data Imputation for Wind Farms Using Context Encoder

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Journal of Modern Power Systems and Clean Energy 2022年第4期10卷 964-976页

作者： Wenlong Liao birgitte bak.jensen Jayakrishnan Radhakrishna Pillai Dechang Yang Yusen Wang Department of Energy Technology Aalborg UniversityAalborgDenmark College of Information and Electrical Engineering China Agricultural UniversityBeijingChina School of Electrical Engineering and Computer Science KTH Royal Institute of TechnologyStockholmSweden

High-quality datasets are of paramount importance for the operation and planning of wind ***,the datasets collected by the supervisory control and data acquisition(SCADA)system may contain missing data due to various factors such as sensor failure and communication *** this paper,a data-driven approach is proposed to fill the missing data of wind farms based on a context encoder(CE),which consists of an encoder,a decoder,and a *** deep convolutional neural networks,the proposed method is able to automatically explore the complex nonlinear characteristics of the datasets that are difficult to be modeled *** proposed method can not only fully use the surrounding context information by the reconstructed loss,but also make filling data look real by the adversarial *** addition,the correlation among multiple missing attributes is taken into account by adjusting the format of input *** simulation results show that CE performs better than traditional methods for the attributes of wind farms with hallmark characteristics such as large peaks,large valleys,and fast ***,the CE shows stronger generalization ability than traditional methods such as auto-encoder,K-means,k-nearest neighbor,back propagation neural network,cubic interpolation,and conditional generative adversarial network for different missing data scales.

关键词： Data-driven missing data imputation wind farm deep learning context encoder

A Review of Graph Neural Networks and Their Applications in Power Systems

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Journal of Modern Power Systems and Clean Energy 2022年第2期10卷 345-360页

作者： Wenlong Liao birgitte bak.jensen Jayakrishnan Radhakrishna Pillai Yuelong Wang Yusen Wang the Department of Energy Technology Aalborg UniversityAalborgDenmark the State Grid Tianjin Chengxi Electric Power Supply Branch TianjinChina the School of Electrical Engineering and Computer Science KTH Royal Institute of TechnologyStockholmSweden

Deep neural networks have revolutionized many machine learning tasks in power systems,ranging from pattern recognition to signal *** data in these tasks are typically represented in Euclidean ***,there is an increasing number of applications in power systems,where data are collected from non-Euclidean domains and represented as graph-structured data with high-dimensional features and interdependency among *** complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean ***,many publications generalizing deep neural networks for graph-structured data in power systems have *** this paper,a comprehensive overview of graph neural networks(GNNs)in power systems is ***,several classical paradigms of GNN structures,e.g.,graph convolutional networks,are *** applications in power systems such as fault scenario application,time-series prediction,power flow calculation,and data generation are reviewed in ***,main issues and some research trends about the applications of GNNs in power systems are discussed.

关键词： Machine learning power system deep neural network graph neural network artificial intelligence

Ultra-short-term Interval Prediction of Wind Power Based on Graph Neural Network and Improved Bootstrap Technique

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Journal of Modern Power Systems and Clean Energy 2023年第4期11卷 1100-1114页

作者： Wenlong Liao Shouxiang Wang birgitte bak.jensen Jayakrishnan Radhakrishna Pillai Zhe Yang Kuangpu Liu AAU Energy Aalborg UniversityAalborg 9220Denmark Key Laboratory of Smart Grid of Ministry of Education School of Electrical and Information EngineeringTianjin UniversityTianjin 300072China

Reliable and accurate ultra-short-term prediction of wind power is vital for the operation and optimization of power ***,the volatility and intermittence of wind power pose uncertainties to traditional point prediction,resulting in an increased risk of power system *** represent the uncertainty of wind power,this paper proposes a new method for ultra-short-term interval prediction of wind power based on a graph neural network(GNN)and an improved Bootstrap ***,adjacent wind farms and local meteorological factors are modeled as the new form of a graph from the graph-theoretic ***,the graph convolutional network(GCN)and bi-directional long short-term memory(Bi-LSTM)are proposed to capture spatiotemporal features between nodes in the *** obtain highquality prediction intervals(PIs),an improved Bootstrap technique is designed to increase coverage percentage and narrow PIs *** simulations demonstrate that the proposed method can capture the spatiotemporal correlations from the graph,and the prediction results outperform popular baselines on two real-world datasets,which implies a high potential for practical applications in power systems.

关键词： Wind power graph neural network(GNN) bidirectional long short-term memory(Bi-LSTM) prediction interval Bootstrap technique

Scenario Generations for Renewable Energy Sources and Loads Based on Implicit Maximum Likelihood Estimations

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Journal of Modern Power Systems and Clean Energy 2022年第6期10卷 1563-1575页

作者： Wenlong Liao birgitte bak.jensen Jayakrishnan Radhakrishna Pillai Zhe Yang Yusen Wang Kuangpu Liu AAU Energy Aalborg UniversityAalborgDenmark School of Electrical Engineering and Computer Science KTH Royal Institute of TechnologyStockholmSweden

Scenario generations for renewable energy sources and loads play an important role in the stable operation and risk assessment of integrated energy *** paper proposes a deep generative network based method to model time-series curves,e.g.,power generation curves and load curves,of renewable energy sources and loads based on implicit maximum likelihood estimations(IMLEs),which can generate realistic scenarios with similar patterns as real *** training the model,any number of new scenarios can be obtained by simply inputting Gaussian noises into the data generator of *** proposed approach does not require any model assumptions or prior knowledge of the form in the likelihood function being made during the training process,which leads to stronger applicability than explicit density model based *** extensive experiments show that the IMLEs accurately capture the complex shapes,frequency-domain characteristics,probability distributions,and correlations of renewable energy sources and ***,the proposed approach can be easily generalized to scenario generation tasks of various renewable energy sources and loads by fine-tuning parameters and structures.

关键词： Renewable energy source scenario generation implicit maximum likelihood estimation(IMLE) deep learning generative network

Prognostics for Lithium-ion batteries for electric Vertical Take-off andLanding aircraft using data-driven machine learning

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能源与人工智能(英文) 2023年第2期12卷 145-162页

作者： Mihaela Mitici birgitte Hennink Marilena Pavel Jianning Dong Faculty of Science Utrecht UniversityHeidelberglaan 83584 CSUtrechtThe Netherlands Faculty of Aerospace Engineering Delft University of TechnologyKluyverweg 1HS 2926DelftThe Netherlands Electrical Engineering Mathematics and Computer Science FacultyTU Delft2628 CDDelftThe Netherlands

The health management of batteries is a key enabler for the adoption of Electric Vertical Take-off and Landingvehicles (eVTOLs). Currently, few studies consider the health management of eVTOL batteries. One distinctcharacteristic of batteries for eVTOLs is that the discharge rates are significantly larger during take-off andlanding, compared with the battery discharge rates needed for automotives. Such discharge protocols areexpected to impact the long-run health of batteries. This paper proposes a data-driven machine learningframework to estimate the state-of-health and remaining-useful-lifetime of eVTOL batteries under varying flightconditions and taking into account the entire flight profile of the eVTOLs. Three main features are consideredfor the assessment of the health of the batteries: charge, discharge and temperature. The importance of thesefeatures is also quantified. Considering battery charging before flight, a selection of missions for state-ofhealth and remaining-useful-lifetime prediction is performed. The results show that indeed, discharge-relatedfeatures have the highest importance when predicting battery state-of-health and *** several machine learning algorithms, it is shown that the battery state-of-health and remaining-useful-lifeare well estimated using Random Forest regression and Extreme Gradient Boosting, respectively.

关键词： Electric Vertical Take-off and Landing vehicles Lithium-ion battery State-of-health Machine learning Remaining-useful-life

Protection of Low Voltage CIGRE Distribution Network

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Smart Grid and Renewable Energy 2013年第9期4卷 489-500页

作者： Ghullam Mustafa Bhutto birgitte bak.jensen Claus Leth bak Jayakrishnan R. Pillai Department of Energy Technology Aalborg University Aalborg Denmark Department of Energy Technology Aalborg University Aalborg Denmark Department of Electrical power Quaid-E-Awam University of Sciences & Technology Nawabshah Pakistan

High quality electricity services are the prime objectives in the modern power systems around the world. One of the main players to achieve this is the protection of the system which needs to be fast, reliable and cost effective. The study about the protection of the Low Voltage (LV) CIGRE distribution grid and networks like this has been proposed in this paper. The main objective of this paper is to develop protection against short circuit faults which might appear anywhere in the network. The protection of the power networks that comprises of renewable energy generation units is complicated because of the bidirectional flow of the current and is a challenge for the protection engineers. The selection of the protection devices in this paper is made to protect the network against faults in grid connected and island mode of operation. Ultra-fast fuses are proposed in order to protect the inverters used for Photovoltaic (PV) and battery applications. The disconnection of the PV solar panels when in island mode is made by proposing switch disconnecting devices. ABB is currently using these kinds of disconnection devices for the purpose of protecting solar panels against over voltages in the case of islanding. The over speed protection of the existing Wind Turbine Generator (WTG) in the CIGRE network in case of grid loss is also proposed in this paper.

关键词： Current Based Protection Voltage Based Protection Over Speed Protection Fuse Under Voltage Relay Circuit Breaker Switch Disconnector

Mitigation of Unbalanced Voltage Sags and Voltage Unbalance in CIGRE Low Voltage Distribution Network

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Energy and Power Engineering 2013年第9期5卷 551-559页

作者： Ghullam Mustafa Bhutto birgitte bak.jensen Pukar Mahat Carlo Cecati Department of Energy Technology Aalborg University Aalborg Denmark Department of Energy Technology Aalborg University Aalborg Denmark Department of Electrical Power Quaid-E-Awam University of Sciences & Technology Nawabshah Pakistan Department of Information Engineering Computer Sciences and Mathematics Via G. Gronchi L’Aquila Italy

Any problem with voltage in a power network is undesirable as it aggravates the quality of the power. Power electronic devices such as Voltage Source Converter (VSC) based Static Synchronous Compensator (STATCOM) etc. can be used to mitigate the voltage problems in the distribution system. The voltage problems dealt with in this paper are to show how to mitigate unbalanced voltage sags and voltage unbalance in the CIGRE Low Voltage (LV) test network and networks like this. The voltage unbalances, for the tested cases in the CIGRE LV test network are mainly due to single phase loads and due to unbalanced faults. The compensation of unbalanced voltage sags and voltage unbalance in the CIGRE distribution network is done by using the four STATCOM compensators already existing in the test grid. The simulations are carried out in DIgSILENT power factory software version 15.0.

关键词： Power Quality Unbalanced Voltage Sags Mitigation of Voltage Unbalance Voltage Unbalance Factor (VUF) Distributed Static Compensator (STATCOM)

Jure Piskur (1960—2014)

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Journal of Genetics and Genomics 2015年第5期42卷 275-277页

作者： birgitte Munch-Petersen Zoran Gojkovic Wolfgang Knecht Department of Science Systems and Models Roskilde University The Department of Applied Yeast Research Carlsberg Laboratory Department of Biology Molecular Cell Biology & Lund Protein Production Platform Lund University

Following a short struggle against an aggressive cancer, Prof. Jure Piskur （Fig. 1 ）, a professor of molecular genetics at Lund University （LU）, Sweden, and the University of Nova Gorica （UNG）, Slovenia, died in his home in Lund on 18 May 2014. He leaves behind his lifelong partner and wife, Judita Gartner, and their sons, Jan and Jure.

关键词： Jure Piskur ICAP

Non-Viral Deoxyribonucleoside Kinases——Diversity and Practical Use

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Journal of Genetics and Genomics 2015年第5期42卷 235-248页

作者： Louise Slot Christiansen birgitte Munch-Petersen Wolfgang Knecht Department of Biology Lund University Department of Science Systems and ModelsRoskilde University Lund Protein Production Platform Lund University

Deoxyribonucleoside kinases （dNKs） phosphorylate deoxyribonucleosides to their corresponding monophosphate compounds, dNks also phosphorylate deoxyribonucleoside analogues that are used in the treatment of cancer or viral infections. The study of the mammalian dNKs has therefore always been of great medical interest. However, during the last 20 years, research on dNKs has gone into non- mammalian organisms. In this review, we focus on non-viral dNKs, in particular their diversity and their practical applications. The diversity of this enzyme family in different organisms has proven to be valuable in studying the evolution of enzymes. Some of these newly discovered enzymes have been useful in numerous practical applications in medicine and biotechnology, and have contributed to our understanding of the structural basis of nucleoside and nucleoside analogue activation.

关键词： Nucleosides Nucleotides Nucleoside salvage pathway Evolution Suicide gene therapy Deoxynucleosides Deoxynucleotides Deoxy-nucleoside kinases