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robust reactive Power Scheduling of Distribution Networks Based on Modified Bootstrap Technique

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 154-166页

作者： Wenlong Liao Shouxiang Wang Birgitte Bak-Jensen jayakrishnan radhakrishna Pillai Zhe Yang the AAU Energy Aalborg UniversityAalborg 9220Denmark the Key Laboratory of Smart Grid Ministry of EducationSchool of Electrical and Information EngineeringTianjin UniversityTianjin 300072China

The uncertainties of the power load,wind power,and photovoltaic power lead to errors between point prediction values and real values,which challenges the safe operation of distribution *** this paper,a robust reactive power scheduling(rrPS)model based on a modified bootstrap technique is proposed to consider the uncertainties of power loads and renewable energy ***,a deterministic reactive power scheduling(DrPS)model and an rrPS model are ***,a modified bootstrap technique is proposed to estimate prediction errors of power loads and renewable energy sources without artificially assuming the probability density function of prediction *** represent all possible scenarios,point prediction values and prediction errors are combined to construct two worst-case scenarios in the rrPS ***,the rrPS model is solved to find a scheduling scheme,which ensures the security of distribution networks for all possible scenarios in *** results show that the worst-case scenarios constructed by the modified bootstrap technique outperform popular ***,the rrPS model based on the modified bootstrap technique balances economics and security well.

关键词： Distribution network worst-case scenario robust programming prediction error bootstrap technique

Gas selectivity of SILAr grown CdS nano-bulk junction

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Journal of Semiconductors 2018年第3期39卷 8-13页

作者： r.jayakrishnan Varun G Nair Akhil M Anand Meera Venugopal Photovoltaic research Centre Department of Physics Christian College

Nano-particles of cadmium sulphide were deposited on cleaned copper substrate by an automated se- quential ionic layer adsorption reaction （SILAr） system. The grown nano-bulk junction exhibits Schottky diode behavior. The response of the nano-bulk junction was investigated under oxygen and hydrogen atmospheric condi- tions. The gas response ratio was found to be 198% for Oxygen and 34% for Hydrogen at room temperature. An increase in the operating temperature of the nano-bulk junction resulted in a decrease in their gas response ratio. A logarithmic dependence on the oxygen partial pressure to the junction response was observed, indicating a Temkin isothermal behavior. Work function measurements using a Kelvin probe demonstrate that the exposure to an oxy- gen atmosphere fails to effectively separate the charges due to the built-in electric field at the interface. Based on the benefits like simple structure, ease of fabrication and response ratio the studied device is a promising candidate for gas detection applications.

关键词： Cu/CdS nano-bulk junction hydrogen work function gas response ratio

Estimating impacts of emission specific characteristics on vehicle operation for quantifying air pollutant emissions and energy use

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Journal of Traffic and Transportation Engineering(English Edition) 2017年第3期4卷 215-229页

作者： K. S. Nesamani Jean-Daniel Saphores Michael G. McNally r. jayakrishnan Institute of Transportation Studies University of California Irvine CA 92697 USA Department of Civil and Environmental Engineering University of California Irvine CA 92697 USA

This paper proposes and illustrates a methodology to predict the fraction of time motor vehicles spend in different operating conditions from readily observable variables called emission specific characteristics （ESC）. ESC describe salient characteristics of vehicles, roadway geometry, the roadside environment, traffic, and driving style （aggressive, normal, and calm）. The information generated by our methodology can then be entered in vehicular emission models that rely on vehicle specific power, i.e., comprehensive modal emissions model （CMEM）, international vehicle emissions （IVE）, or motor vehicle emission simulator （MOVES） to compute energy consumption and vehicular emissions for various air pollutants. After generating second-by-second vehicle trajectories from a calibrated micro-simulation model, the authors estimated structural equation models to examine the influence of link ESC on vehicle operation. Authors＇ results show that 67% of the link speed variance is explained by ESC. Overall, the roadway geometry exerts a greater influence on link speed than traffic characteristics, the roadside environment, and driving style. Moreover, the speed limit has the strongest influence on vehicle operation, followed by facility type and driving style. Better understanding the impact on vehicle operation of ESC could help metropolitan planning organizations （MPOs） and regional transportation au- thorities predict vehicle operations and reduce the environmental footprint of motor vehicles.

关键词： Vehicle operation Emission specific characteristics Structural equation modeling Micro-simulation

Assessment of Convective Activity Using Stability Indices as Inferred from radiosonde and MODIS Data

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Atmospheric and Climate Sciences 2014年第1期4卷 122-130页

作者： P. r. jayakrishnan C. A. Babu Department of Atmospheric Sciences Cochin University of Science and Technology Cochin India

The combined use of both radiosonde data and three-dimensional satellite derived data over ocean and land is useful for a better understanding of atmospheric thermodynamics. Here, an attempt is made to study the thermodynamic structure of convective atmosphere during pre-monsoon season over southwest peninsular India utilizing satellite derived data and radiosonde data. The stability indices were computed for the selected stations over southwest peninsular India viz: Thiruvananthapuram and Cochin, using the radiosonde data for five premonsoon seasons. The stability indices studied for the region are Showalter Index (SI), K Index (KI), Lifted Index (LI), Total Totals Index (TTI), Humidity Index (HI), Deep Convective Index (DCI) and thermodynamic parameters such as Convective Available Potential Energy (CAPE) and Convective Inhibition Energy (CINE). The traditional Showalter Index has been modified to incorporate the thermodynamics over tropical region. MODIS data over South Peninsular India are also used for the study. When there is a convective system over south peninsular India, the value of LI over the region is less than ?4. On the other hand, the region where LI is more than 2 is comparatively stable without any convection. Similarly, when KI values are in the range 35 to 40, there is a possibility for convection. The threshold value for TTI is found to be between 50 and 55. Further, we found that prior to convection, dry bulb temperature at 1000, 850, 700 and 500 hPa is minimum and the dew point temperature is a maximum, which leads to increase in relative humidity. The total column water vapor is maximum in the convective region and minimum in the stable region. The threshold values for the different stability indices are found to agree with that reported in literature.

关键词： Atmospheric Thermodynamics Stability Indices Convection

Study of the Oceanic Heat Budget Components over the Arabian Sea during the Formation and Evolution of Super Cyclone, Gonu

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Atmospheric and Climate Sciences 2013年第3期3卷 282-290页

作者： P. r. jayakrishnan C. A. Babu Department of Atmospheric Sciences Cochin University of Science and Technology Cochin India

Oceans play a vital role in the global climate system. They absorb the incoming solar energy and redistribute the energy through horizontal and vertical transports. In this context it is important to investigate the variation of heat budget components during the formation of a low-pressure system. In 2007, the monsoon onset was on 28th May. A well-marked low-pressure area was formed in the eastern Arabian Sea after the onset and it further developed into a cyclone. We have analysed the heat budget components during different stages of the cyclone. The data used for the computation of heat budget components is Objectively Analyzed air-sea flux data obtained from WHOI (Woods Hole Oceanographic Institution) project. Its horizontal resolution is 1° × 1°. Over the low-pressure area, the latent heat flux was 180 Wm﹣2. It increased to a maximum value of 210 Wm﹣2 on 1st June 2007, on which the system was intensified into a cyclone (Gonu) with latent heat flux values ranging from 200 to 250 Wm﹣2. It sharply decreased after the passage of cyclone. The high value of latent heat flux is attributed to the latent heat release due to the cyclone by the formation of clouds. Long wave radiation flux is decreased sharply from 100 Wm﹣2 to 30 Wm﹣2 when the low-pressure system intensified into a cyclone. The decrease in long wave radiation flux is due to the presence of clouds. Net heat flux also decreases sharply to ﹣200 Wm﹣2 on 1st June 2007. After the passage, the flux value increased to normal value (150 Wm﹣2) within one day. A sharp increase in the sensible heat flux value (20 Wm﹣2) is observed on 1st June 2007 and it decreased thereafter. Short wave radiation flux decreased from 300 Wm﹣2 to 90 Wm﹣2 during the intensification on 1st June 2007. Over this region, short wave radiation flux sharply increased to higher value soon after the passage of the cyclone.

关键词： Oceanic Heat Budget Cyclone Arabian Sea

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

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

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

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