{"title":"Global Lightning Phenomena and Time Series Model of Lightning Flash Radiance","authors":"Mehdi Hasan Rafi, M. Mostafa","doi":"10.1109/ICEPE56629.2022.10044878","DOIUrl":"https://doi.org/10.1109/ICEPE56629.2022.10044878","url":null,"abstract":"Lightning is a fundamental atmospheric phenomenon that significantly affects the Earth's climatology. Recently, Bangladesh has hosted a sensor station of the World-Wide Lightning Location Network (WWLLN). This is the first study of the data obtained from this sensor station that reveals some interesting features and shows a high connection with the flashes detected by the International Space Station (ISS) Lightning Imaging Sensor (LIS). We present global maps depicting the lightning distribution of WWLLN in relation to ISS-LIS across landmasses and oceanic regions. It confirms that global distribution has no resemblance to the usual three lightning chimneys. Our analysis reveals that about 60% of the total lightning of the globe occurs in the oceans and the rest 40% in the landmass, which is further distributed according to continent and ocean. Our study also reveals that the number of lightning strokes/km2 over Bangladesh's landmass and over the Bay of Bengal is significantly high. During Summer, the lightning maxima lie along 30°N and during Winter, the lightning maxima shifts towards the low latitude region by around 45°N. This analysis also reveals that lightning strokes occur most frequently in the morning and afternoon and least frequently at night. In an effort to switch data analysis from descriptive to predictive, ISS-LIS lightning flash radiance (J/m2/steradian/s) is modeled using time series analysis. After a detailed diagnostic test, ARIMA (2,1,2)x(0,1,1)12 is found to be the best-fitted model.","PeriodicalId":162510,"journal":{"name":"2022 International Conference on Energy and Power Engineering (ICEPE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124713546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Effect of Dataset Size and Hidden Layers on the Stability Classification of IEEE-14 Bus System Using Deep Neural Network","authors":"Md. Rayid Hasan Mojumder, N. K. Roy","doi":"10.1109/ICEPE56629.2022.10044902","DOIUrl":"https://doi.org/10.1109/ICEPE56629.2022.10044902","url":null,"abstract":"This research considers the dataset of the IEEE 14 bus system, generated from Modelica Dymola, to correctly classify the power system stability using deep neural networks and classical machine learning algorithms. The ground truth is set from the damping ratio metric of the system eigenvalues. The size of the dataset decreases the efficiency of the neural network slightly, but the efficiency of the classical machine learning algorithms drops drastically. Different architecture and activation functions are used for neural network design. Increasing the number of hidden layers increases prediction precision, however, increasing more than two hidden layers does not further improve the classification efficiency. This research will help in further research on the stability classification of power systems using damping ratio or eigenvalue as the base and using deep learning and machine learning algorithms for the prediction.","PeriodicalId":162510,"journal":{"name":"2022 International Conference on Energy and Power Engineering (ICEPE)","volume":"92 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128021970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"2022 International Conference on Energy and Power Engineering (ICEPE)","authors":"","doi":"10.1109/icepe45588.2019","DOIUrl":"https://doi.org/10.1109/icepe45588.2019","url":null,"abstract":"2022 International Conference on Energy and Power Engineering (ICEPE).","PeriodicalId":162510,"journal":{"name":"2022 International Conference on Energy and Power Engineering (ICEPE)","volume":"336 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124306755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}