Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems最新文献

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Implement Using KY Converter for Hybrid Renewable Energy Applications: Design, Analysis, and Implementation 使用KY转换器实现混合可再生能源应用:设计、分析和实现

Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems Pub Date : 2020-04-01 DOI: 10.5772/intechopen.90755

P. Murugan, Jothi Swaroopan Nesa Mony

{"title":"Implement Using KY Converter for Hybrid Renewable Energy Applications: Design, Analysis, and Implementation","authors":"P. Murugan, Jothi Swaroopan Nesa Mony","doi":"10.5772/intechopen.90755","DOIUrl":"https://doi.org/10.5772/intechopen.90755","url":null,"abstract":"This chapter mainly focuses on meeting the energy demand and methodologies of renewable energy. Nowadays, researchers are mainly focusing on renewable energy from the sun, wind, biomass, etc. due to energy crises and the lack of non-renewable energy. The potential for solar energy is high and this demand can best be met with hybrid systems, which can provide an uninterruptible power supply. This chapter looks at the performance metrics of hybrid energy as well as the methodologies and various control techniques connected with power management. The chapter also defines the photovoltaic (PV)-based, novel, dual KY boost converter. Dual PV sources act as input for the dual KY boost converter to generate as much energy as possible from the dual PV system, using the inverter module to produce single-phase alternating current output. A dual KY boost converter can provide higher maximum power, a faster response, and smaller voltage ripple. KY boost converters are designed to generate stable output values according to various conditions because of various control techniques and the maximum power point tracking control algorithm.","PeriodicalId":260050,"journal":{"name":"Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129949994","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

Static and Dynamic Photovoltaic Cell/Module Parameters Identification 静态和动态光伏电池/组件参数识别

Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems Pub Date : 2020-04-01 DOI: 10.5772/intechopen.89449

S. Blaifi, B. Taghezouit

{"title":"Static and Dynamic Photovoltaic Cell/Module Parameters Identification","authors":"S. Blaifi, B. Taghezouit","doi":"10.5772/intechopen.89449","DOIUrl":"https://doi.org/10.5772/intechopen.89449","url":null,"abstract":"The accurate parameters extraction is an important step to obtain a robust PV outputs forecasting for static or dynamic modes. For these aims, several approaches have been proposed for photovoltaic (PV) cell modeling including electrical circuit-based model, empirical models, and non-parametrical models. Moreover, numerous parameter extraction methods have been introduced in the literature depending on the proposed model and the operating mode. These methods can be classified into two main approaches including automatic numerical and analytical approaches. These approaches are commonly applied in the static mode, whereas they can be employed for dynamic parameters extraction. In this chapter, as a first stage, the static parameters extraction for both single and double diodes models is exposed wherein Genetic Algorithm and outdoor measurements are considered for fixed irradiation and temperature. In the second stage, a dynamic parameters extraction is carried out using Levenberg-Marquardt algorithm, where 1 day profile outdoor measurement is considered. After that, the robustness of the proposed approaches is evaluated and the parameters obtained by the static method and that given by the dynamic technique are compared. The test is carried out using 3 days with different weather conditions profiles. The obtained results show that the parameters extraction by dynamic techniques gives satisfactory performances in terms of agreement with the real data.","PeriodicalId":260050,"journal":{"name":"Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127186170","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

Forecasting of Photovoltaic Solar Power Production Using LSTM Approach 基于LSTM方法的光伏太阳能发电预测

Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems Pub Date : 2020-04-01 DOI: 10.5772/intechopen.91248

F. Harrou, F. Kadri, Ying Sun

{"title":"Forecasting of Photovoltaic Solar Power Production Using LSTM Approach","authors":"F. Harrou, F. Kadri, Ying Sun","doi":"10.5772/intechopen.91248","DOIUrl":"https://doi.org/10.5772/intechopen.91248","url":null,"abstract":"Solar-based energy is becoming one of the most promising sources for producing power for residential, commercial, and industrial applications. Energy production based on solar photovoltaic (PV) systems has gained much attention from researchers and practitioners recently due to its desirable characteristics. However, the main difficulty in solar energy production is the volatility intermittent of photovoltaic system power generation, which is mainly due to weather conditions. For the large-scale solar farms, the power imbalance of the photovoltaic system may cause a significant loss in their economical profit. Accurate forecasting of the power output of PV systems in a short term is of great importance for daily/hourly efficient management of power grid production, delivery, and storage, as well as for decision-making on the energy market. The aim of this chapter is to provide reliable short-term forecasting of power generation of PV solar systems. Specifically, this chapter presents a long short-term memory (LSTM)-based deep learning approach for forecasting power generation of a PV system. This is motivated by the desirable features of LSTM to describe dependencies in time series data. The performance of the algorithm is evaluated using data from a 9 MWp grid-connected plant. Results show promising power forecasting results of LSTM.","PeriodicalId":260050,"journal":{"name":"Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121846075","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: 37