2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)最新文献_第3页

Energy Trading in Prosumer based Smart Grid Integrated with Distributed Energy Resources 基于产消智能电网与分布式能源集成的能源交易

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862448

Krishna Mohan Boddapati, N. Patne, Ashwini D. Manchalwar

{"title":"Energy Trading in Prosumer based Smart Grid Integrated with Distributed Energy Resources","authors":"Krishna Mohan Boddapati, N. Patne, Ashwini D. Manchalwar","doi":"10.1109/ICICCSP53532.2022.9862448","DOIUrl":"https://doi.org/10.1109/ICICCSP53532.2022.9862448","url":null,"abstract":"Energy trading in a smart microgrid based Peer to Peer (P2P) microgrid requires a novel framework. Where in a microgrid each prosumer equipped with Distributed Energy Resources (DER) is encouraged to trade energy. Energy is traded with peers in the microgrid or sold to power grid to generate revenue whenever there is a surplus of energy. As there is an economic benefit, more prosumers are attracted to install DER. The peers in the microgrid are incentivized to buy locally produced energy which is available at a cheaper cost compared to buying from power grid. This results in reducing energy consumption from power grid which in turn results in reducing power losses in transmission lines and results in reduction of $CO_{2}$ emissions. Three market paradigms are considered in this paper, i.e. Bill sharing, Supply to Demand Ratio method and Mid-market rate method. Each of the pricing models are elaborated with local energy trading price and prosumers energy costs. In this case, these methods are studied concerning a smart microgrid equipped with DER, Solar Photovoltaic (SPV) system in this case, and the effectiveness of the pricing mechanisms and their respective benefits are established.","PeriodicalId":326163,"journal":{"name":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116294514","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}

引用次数: 0

Hybrid FA-GA Controller for Path Planning of Mobile Robot 移动机器人路径规划的混合FA-GA控制器

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862422

B. Patle, N. Pagar, D. Parhi, S. Sanap

引用次数: 0

Impact on Electrical Distribution Networks with The Integration of Shunt Capacitor Model Using Exhaustive Search Based Load Flow Algorithm 基于穷举搜索的并联电容器模型集成对配电网的影响

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862506

Joseph Sanam, Yenimireddy Venkata Rajeswari, E. U. Sri, K. Spandhana, Chandu Bhavana Laxmi, Nandhiraju Gayathri

引用次数: 0

Convolutional Neural Network Based Fault Detection for Transmission Line 基于卷积神经网络的输电线路故障检测

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862446

A. Bhuyan, B. Panigrahi, Kumaresh Pal, Subhendu Pati

引用次数: 3

Bright Sunshine Duration Index-Based Prediction of Solar PV Power Using ANN Approach 基于日照时数指数的人工神经网络预测太阳能光伏发电能力

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862452

D. V. S. K. Rao, B. Prusty, Hareesh Myneni

{"title":"Bright Sunshine Duration Index-Based Prediction of Solar PV Power Using ANN Approach","authors":"D. V. S. K. Rao, B. Prusty, Hareesh Myneni","doi":"10.1109/ICICCSP53532.2022.9862452","DOIUrl":"https://doi.org/10.1109/ICICCSP53532.2022.9862452","url":null,"abstract":"The grid integration of solar photovoltaic (PV) systems has recently grabbed considerable research attention. Simultaneously, the grid has been subjected to disturbances due to PV generations' variability, uncertainty, and intermittency; therefore, accurately estimating the weather-dependent PV power is imperative. The daily global solar radiation, temperature, and sunshine duration of a location can reflect its weather condition; hence, they are used to estimate PV power output using artificial neural network (ANN). A sunshine duration index, “k,” has been introduced to classify a location's weather condition. Accordingly, two weather conditions are considered based on “k,” and solar PV power estimation models are developed for both cases (Condition-I: 0<k<0.8 and condition-II: 0.8k1). The performance of the proposed ANN-based models is evaluated using error metrics, namely, mean absolute percentage error (MAPE) and relative root mean square error (RRMSE). The temperature of the considered location has resulted in a minimum error in estimating PV power output. The ANN model for 0.8k1 has resulted in a MAPE of 1.888 % with temperature as input. The ANN model for 0<k<0.8 has resulted in a MAPE of 10.599 % with temperature as input. Excellent performance is noticed with the developed forecasting models in estimating PV power. These models are helpful for feasibility studies of PV power plant installations and economic scheduling.","PeriodicalId":326163,"journal":{"name":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125451990","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}

引用次数: 1

Review on Battery Management System in EV 电动汽车电池管理系统研究进展

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862367

Spoorthi B, P. Pradeepa

引用次数: 6

Sag Rooting Out in Grid Connected Windfarm by Deploying Deep Learning 通过部署深度学习来消除电网连接风电场中的凹陷

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862520

R. Karpagam, T. A. Dheeven

引用次数: 0

Parameter Extraction of PV Module using CamWOA Technique 基于CamWOA技术的光伏组件参数提取

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862471

B. Prasad, Nutan Saha, G. Panda

引用次数: 0

Cancer Tumor Detection Using Genetic Mutated Data and Machine Learning Models 利用基因突变数据和机器学习模型进行癌症肿瘤检测

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862476

Aniruddha Mohanty, Alok R. Prusty, Ravindranath Cherukuri

{"title":"Cancer Tumor Detection Using Genetic Mutated Data and Machine Learning Models","authors":"Aniruddha Mohanty, Alok R. Prusty, Ravindranath Cherukuri","doi":"10.1109/ICICCSP53532.2022.9862476","DOIUrl":"https://doi.org/10.1109/ICICCSP53532.2022.9862476","url":null,"abstract":"Early detection of a disease is a crucial task because of unavailability of proper medical facilities. Cancer is one of the critical diseases that needs early detection for survival. A cancer tumor is caused due to thousands of genetic mutations. Understanding the genetic mutations of cancer tumor is a tedious and time-consuming task. A list of genetic variations is analysed manually by a molecular pathologist. The clinical strips of indication are of nine classes, but the classification is still unknown. The objective of this implementation is to suggest a multiclass classifier which classifies the genetic mutations with respect to the clinical signs. The clinical evidences are text-evidences of gene mutations and analysed by Natural Language Processing (NLP). Various machine learning concepts like Naive Bayes, Logistic Regression, Linear Support Vector Machine, Random Forest Classifier applied on the collected dataset which contain the evidence based on genetic mutations and other clinical evidences that pathology or specialists used to classify the gene mutations. The performances of the models are analysed to get the best results. The machine learning models are implemented and analyzed with the help of gene, variance and text features. Based on the variants of gene mutation, the risk of the cancer can be detected and the medications can be prescribed accordingly.","PeriodicalId":326163,"journal":{"name":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","volume":"59 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133390493","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}

引用次数: 0

Improving Indoor occupancy estimation using a hybrid CNN-LSTM approach 利用CNN-LSTM混合方法改进室内占用估计

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862328

E. Ramanujam, Arpit Sharma, J. Hussian, Thinagaran Perumal

{"title":"Improving Indoor occupancy estimation using a hybrid CNN-LSTM approach","authors":"E. Ramanujam, Arpit Sharma, J. Hussian, Thinagaran Perumal","doi":"10.1109/ICICCSP53532.2022.9862328","DOIUrl":"https://doi.org/10.1109/ICICCSP53532.2022.9862328","url":null,"abstract":"Indoor Air Quality (IAQ) monitoring has been a significant research domain in energy conservation. Many energy resources are required to maintain the IAQ using airconditioning or other ventilation systems. Currently, the research works highly optimize an on-demand driven energy usage depending on the occupant present inside the building. In the last decade, numerous research works have evolved for such an optimization by installing sensors and predicting occupants using machine learning techniques. This research fails to deploy non-intrusive sensors and appropriate machine learning algorithms to predict the occupancy count. Advancement in neural network techniques termed deep learning has made significant performance in recognition and cognitive tasks. Thus, this paper proposes a hybrid deep learning model that stacks the convolutional neural network (CNN) and long short term memory (LSTM) to improve the prediction rate of the occupancy count. Experimentation has been carried out in real-time multivariate sensor data for the occupancy estimation and evaluated the performance in terms of accuracy, RMSE, MAPE, and coefficients of determinants.","PeriodicalId":326163,"journal":{"name":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133444421","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}

引用次数: 2