2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)最新文献_第6页

Field Investigation of Solar Photovoltaic Modules Digression Against Manufacture's Claim and Application of Machine Learning Model in Life Prediction: A Case Study 太阳能光伏组件的现场调查:对制造商索赔的偏离及机器学习模型在寿命预测中的应用:一个案例研究

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862482

K. Sameer, K. Haritha, N. Ramchander, B. Reddy, K. Rayudu, K. R. Reddy

{"title":"Field Investigation of Solar Photovoltaic Modules Digression Against Manufacture's Claim and Application of Machine Learning Model in Life Prediction: A Case Study","authors":"K. Sameer, K. Haritha, N. Ramchander, B. Reddy, K. Rayudu, K. R. Reddy","doi":"10.1109/ICICCSP53532.2022.9862482","DOIUrl":"https://doi.org/10.1109/ICICCSP53532.2022.9862482","url":null,"abstract":"Renewable energy is being produced through various resources, mostly natural and abundantly available, such as wind, solar, and geothermal. Solar PV technology is a novice alternate renewable energy system which is becoming popular during 21st century. In Solar Photovoltaic (SPV) power systems, the major component are polycrystalline PV modules which have a shelf-life of around 25 years, as claimed by most of the PV module producers. Most of the installations started 10 years ago and there is a need to investigate the ageing upshot or digression of PV modules. To this end, a seven-year-old large-scale PV plant is considered for case study. Field experiments are conducted to know the power output of these modules and the manufactures claim of 25 years life with indicated digression is validated with the field values. Also, machine learning technique is used to derive an empirical relation for the power output of age old PV modules. Finally, conclusions are drawn with respect to ageing upshot and life predictions of PV Modules.","PeriodicalId":326163,"journal":{"name":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134136459","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

Prediction of Electric Energy Consumption for Demand Response using Deep Learning 基于深度学习的需求响应电能消耗预测

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862353

Radharani Panigrahi, N. Patne, Sumanth Pemmada, Ashwini D. Manchalwar

{"title":"Prediction of Electric Energy Consumption for Demand Response using Deep Learning","authors":"Radharani Panigrahi, N. Patne, Sumanth Pemmada, Ashwini D. Manchalwar","doi":"10.1109/ICICCSP53532.2022.9862353","DOIUrl":"https://doi.org/10.1109/ICICCSP53532.2022.9862353","url":null,"abstract":"This paper emphasizes the capability of Deep Learning (DL) models to conquer the Demand Response (DR) inherent when predicting the Electric Energy Consumption (EEC) of an office building. The prediction of EEC plays a key role in DR programs in a smart grid environment. In this study, historical energy consumption and ambient temperature data of three different climatic days (summer, winter, and cloudy days) of an office building located in Portugal at 10 seconds intervals are taken. A DL technique-based Deep Neural Network model is proposed for the prediction of future EEC. In this paper predictability of EEC of the whole office building has been analyzed. This study describes an evince DL application for commercial energy consumption prediction at 10 seconds intervals and performed precursory success. Moreover, two conventional Machine Learning (ML) models i.e., Support Vector Regressor (SVR) and Random Forest (RF) are developed and analyzed. Furthermore, the proposed DL model is compared with SVR and RF in terms of performance evaluation parameters such as Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). All the models are developed and executed on TensorFlow deep learning platform. The proposed model defeats SVR by 91.65%and RF by 87.38% on a summer day, similarly defeats SVR by 93.85% and RF by 91.68% on a winter day and defeats SVR by 95.63% and RF by 92.67% on a cloudy day in terms of MSE.","PeriodicalId":326163,"journal":{"name":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130314212","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

Dynamic stability improvement of a micro grid system by optimized PSS controller 优化PSS控制器对微网系统动态稳定性的改善

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862341

Narayan Nahak, R. Singh, S. Parida, Samarjeet Satapathy, P. Nayak

引用次数: 1

An Efficient Collision Avoidance Scheme for Healthcare Application 医疗保健应用的有效避碰方案

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862491

P. Dash, M. Panda

引用次数: 0

Performance Analysis of a DFIG Based Wind Turbine with BESS System for Voltage and Frequency Stability during Grid Fault 电网故障时基于DFIG的BESS风电机组电压频率稳定性分析

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862447

Ankur Adhikary, Niloy Goswami, Kaushik Barua, Ratul Dey, Arindam Barman, M. A. Shawon

{"title":"Performance Analysis of a DFIG Based Wind Turbine with BESS System for Voltage and Frequency Stability during Grid Fault","authors":"Ankur Adhikary, Niloy Goswami, Kaushik Barua, Ratul Dey, Arindam Barman, M. A. Shawon","doi":"10.1109/ICICCSP53532.2022.9862447","DOIUrl":"https://doi.org/10.1109/ICICCSP53532.2022.9862447","url":null,"abstract":"The ease of Doubly-Fed Induction Generator (DFIG) based wind turbines is largely deployed due to their variable speed feature and hence influencing system dynamics. However, owing to grid faults, the output power fluctuation in the DFIG wind turbine system brings a major concern to power system stability. In this paper, the grid voltage and frequency stability of the wind power system investigates different cases such as DFIG and the approach of a Battery Energy Storage System (BESS). Designing of a wind turbine model including Rotor Side Controller (RSC) and Grid Side Controller (GSC) and connected to the grid. An equivalent BESS is introduced in the power system model and connected to the grid through a three-phase inverter. The BESS system is designed to stabilize the frequency at a constant value with controlled active power also; voltage is controlled by reactive power. To design the wind turbine only active power is considered in this specific work. Therefore, the system performance has improved after including BESS. The performance analysis is observed by simulation work through “PSCAD/EMTDC” professional software, which is the most realistic and well-organized software, especially for power system analysis.","PeriodicalId":326163,"journal":{"name":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115227292","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

Intelligent Control of a Two-phase Interleaved Boost Converter-interfaced Fuel Cell Electric Vehicle 两相交错升压转换器接口燃料电池汽车的智能控制

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862469

Ashish Laddha, Satyanarayana Neeli

引用次数: 0

Harmonic and TIF Analysis in Distribution System with Integration of PV and Wind Systems 光伏与风电并网配电系统谐波与TIF分析

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862320

Arnab Ari, Aashish Kumar Bohre

{"title":"Harmonic and TIF Analysis in Distribution System with Integration of PV and Wind Systems","authors":"Arnab Ari, Aashish Kumar Bohre","doi":"10.1109/ICICCSP53532.2022.9862320","DOIUrl":"https://doi.org/10.1109/ICICCSP53532.2022.9862320","url":null,"abstract":"Renewable energy resources have an inherent drawback: they are intermittent. Due to this, individual renewable resources in standalone scenarios are not reliable and thus cannot be utilized for practical large-scale applications. A hybrid renewable energy system solves this issue by integrating multiple renewable, non-renewable and storage systems. Since multiple sources are used, distributed generation is possible in the case of HRES. This will not only help to satisfy the demand but also reduce losses and improve voltage profile besides reducing carbon footprint. This paper studies the effect of PV and WTG on the load flow and harmonics in a gird connected radial distribution system. Modeling of the system is discussed with four different cases for comparison. Harmonic analysis is performed to obtain the THD and TIF parameters to understand the voltage and current distortions and their effect on communication systems. It is found that the PV and Wind power generation system provides the best voltage profile. The bus voltage distortions are decreased and the branch currents are relatively distorted.","PeriodicalId":326163,"journal":{"name":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115007741","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

A Review of Architecture and Control Strategies of Hybrid AC/DC Microgrid 交直流混合微电网结构与控制策略综述

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862386

Pragya, Ritula Thakur

引用次数: 1

Firefly Algorithm based STATCOM Controller for Enhancement of Power System Dynamic Stability 基于萤火虫算法的STATCOM控制器增强电力系统动态稳定性

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862405

Subir Datta, S. Deb, Robert Singh, Rahul Roy, Akibul Islam, S. Adhikari

引用次数: 0

Detection of Ventricular Fibrillation by combining Signal Processing and Machine Learning approach 结合信号处理和机器学习方法检测心室颤动

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862477

Soumik Kundu, Subhankit Prusti, S. Patnaik

{"title":"Detection of Ventricular Fibrillation by combining Signal Processing and Machine Learning approach","authors":"Soumik Kundu, Subhankit Prusti, S. Patnaik","doi":"10.1109/ICICCSP53532.2022.9862477","DOIUrl":"https://doi.org/10.1109/ICICCSP53532.2022.9862477","url":null,"abstract":"Ventricular Fibrillation is a potentially fatal cardiac disorder that occurs when electrical impulses in the ventricles are disrupted, causing the heart to quiver instead of pump. In order to preserve lives during this form of arrhythmia, a strong current impulse is passed. Electrocardiograms (ECGs) record the electrical activity of the human heart, and specialists with years of experience may interpret the ECG signal to determine the heart's condition. Since it is a life-threatening disease, its earlier detection and prevention can help survive a patient's life. The fundamental idea behind tackling this challenge was to create an algorithm that could identify trends from continuous ECG readings from various individuals and identify arrhythmias early on. An efficient data was built for classification utilizing a Random Forest classifier algorithm employing signal processing tools such as Empirical Mode Decomposition (EMD) and Discrete Fourier Transform (DFT) for feature extraction. The pre-processed data when fed into the proposed machine learning method results in an accuracy of 96.58% and two classes were classified correctly with equal confidence (Specificity = 94.26% and Sensitivity = 98.97%). Furthermore, the results are compared with various other machine learning classification algorithms like Logistic Regression, Decision Tree classifier, Extra tree classifier where the accuracy was 86.49%, 91.77%, 95.84% respectively. The results obtained after experimental validation of proposed Random Forest classifier algorithm against the other machine learning achieves highest accuracy with optimal specificity and sensitivity.","PeriodicalId":326163,"journal":{"name":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125321406","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