A power-system protection device built using Internet-of-Things (IoT) technologies in an intelligent environment. IoT supports electrical and physical parameters monitoring. One of the characteristics that must be checked is electricity usage from electronic gadgets. It is a complex problem to design energy-efficient IoT methods. IoT gets more complicated because of its vast size, and current wireless sensor network approaches cannot be used directly to IoT. Information gathering on the area is monitored by intelligent cellular terminals, intelligent security tools, and other multi-source sensing equipment. That is the foundation for the combined analysis and evaluation of security risk extensive data by cloud computing and edge computing. The IoT-based Power safety tools management (IoT-PSTM) system has been developed to integrate it into intelligent settings, such as smart homes or smart cities, to safeguard electrical equipment. It is meant to increase power security by quickly disconnecting in failure events such as leaking current. The system allows for real-time monitoring and alerting of events using a sophisticated data-concentration architecture communication interface. The goal is to progress and merge several technologies technically and integrate them into a personal safety system to increase security, preserve their availability, eliminate mistakes, and reduce the time required for scheduled or ad hoc interventions. Real-time data transmission, instant data processing from diverse sources, local intelligence in low-power embedded systems, interaction with many on-site users, sophisticated user interfaces, portability, and wearability are the main difficulties for the research project. This article offers a comprehensive explanation of the design and execution of the proposed system and the test findings. The results denote the higher performance of the suggested IoT-PSTM system with IoT module and enhanced performance of 94.7%.
{"title":"Research on Supervision System of Power Safety Tools and Equipment Based on Internet of Things Technology","authors":"Ping He, Zheng Zhu, Xu-yan Wang, Can Zhang, Wei Yuan, Junhua Hao","doi":"10.13052/dgaej2156-3306.3847","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3847","url":null,"abstract":"A power-system protection device built using Internet-of-Things (IoT) technologies in an intelligent environment. IoT supports electrical and physical parameters monitoring. One of the characteristics that must be checked is electricity usage from electronic gadgets. It is a complex problem to design energy-efficient IoT methods. IoT gets more complicated because of its vast size, and current wireless sensor network approaches cannot be used directly to IoT. Information gathering on the area is monitored by intelligent cellular terminals, intelligent security tools, and other multi-source sensing equipment. That is the foundation for the combined analysis and evaluation of security risk extensive data by cloud computing and edge computing. The IoT-based Power safety tools management (IoT-PSTM) system has been developed to integrate it into intelligent settings, such as smart homes or smart cities, to safeguard electrical equipment. It is meant to increase power security by quickly disconnecting in failure events such as leaking current. The system allows for real-time monitoring and alerting of events using a sophisticated data-concentration architecture communication interface. The goal is to progress and merge several technologies technically and integrate them into a personal safety system to increase security, preserve their availability, eliminate mistakes, and reduce the time required for scheduled or ad hoc interventions. Real-time data transmission, instant data processing from diverse sources, local intelligence in low-power embedded systems, interaction with many on-site users, sophisticated user interfaces, portability, and wearability are the main difficulties for the research project. This article offers a comprehensive explanation of the design and execution of the proposed system and the test findings. The results denote the higher performance of the suggested IoT-PSTM system with IoT module and enhanced performance of 94.7%.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90575807","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}
Pub Date : 2023-05-18DOI: 10.13052/dgaej2156-3306.3844
B. Sujatha, A. Usha, R. Geetha, E. Poornima
This research work is concentrated on swarm-based intelligence Particle Swarm Optimization algorithm for combined assignment of D-STATCOM device and Distributed Generation source in a radial distribution structure. This work intends to diminish total real power loss, total cost and voltage magnitude profile enhancement for different circumstances. Generally Constant Power load design analysis is carried out for a distribution scheme. However, it is observed that load models remarkably impact the optimum sizing and positioning of DG source and D-STATCOM device. In this paper, work has been carried out for constant power load, polynomial load, and load growth model under various load factor conditions from light load factor (0.6) to heavy load factor (1.6) for power system planning. The sizing and positioning of D-STATOM device and DG source are considered based on loss sensitivity factor computation and PSO algorithmic rule. The planned scheme is investigated on IEEE 69 node and IEEE 33 node radial distribution structures. Further, the simulated results obtained by this algorithm is compared with other available techniques.
{"title":"Impact of Various Load Models for Combined Assignment of DG Source and D-STATCOM Device in the Radial Distribution System","authors":"B. Sujatha, A. Usha, R. Geetha, E. Poornima","doi":"10.13052/dgaej2156-3306.3844","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3844","url":null,"abstract":"This research work is concentrated on swarm-based intelligence Particle Swarm Optimization algorithm for combined assignment of D-STATCOM device and Distributed Generation source in a radial distribution structure. This work intends to diminish total real power loss, total cost and voltage magnitude profile enhancement for different circumstances. Generally Constant Power load design analysis is carried out for a distribution scheme. However, it is observed that load models remarkably impact the optimum sizing and positioning of DG source and D-STATCOM device. In this paper, work has been carried out for constant power load, polynomial load, and load growth model under various load factor conditions from light load factor (0.6) to heavy load factor (1.6) for power system planning. The sizing and positioning of D-STATOM device and DG source are considered based on loss sensitivity factor computation and PSO algorithmic rule. The planned scheme is investigated on IEEE 69 node and IEEE 33 node radial distribution structures. Further, the simulated results obtained by this algorithm is compared with other available techniques.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"80 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72709954","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}
Pub Date : 2023-05-18DOI: 10.13052/dgaej2156-3306.3843
Priyadarshini Balasubramanyam, Vijay K. Sood
A grid-connected or islanded microgrid made up of distributed energy sources (DERs), requires a power management/dispatch system to control the power dispatch and meet the load demand in the system. At the tertiary control level in a typical microgrid, an optimal scheduling mechanism is used to manage the power generated from the local DERs, energy drawn from the grid and energy consumption by the load. This paper proposes a novel hybrid optimization technique for day-ahead scheduling in a smart-grid. A Hybrid Feedback PSO-MCS algorithm is implemented using swarm intelligence and cuckoo search to enhance the performance and obtain a cost-effective solution for a microgrid prosumer. A comparison has been made of the Hybrid Feedback PSO-MCS (HFPSOMCS) algorithm with PSO and modified CS (MCS) algorithm. The best performing algorithm among the three is executed in MATLAB/Simulink and Python IDE platforms to compare the execution time.
{"title":"A Novel Hybrid Swarm Intelligence and Cuckoo Search Based Microgrid EMS for Optimal Energy Scheduling","authors":"Priyadarshini Balasubramanyam, Vijay K. Sood","doi":"10.13052/dgaej2156-3306.3843","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3843","url":null,"abstract":"A grid-connected or islanded microgrid made up of distributed energy sources (DERs), requires a power management/dispatch system to control the power dispatch and meet the load demand in the system. At the tertiary control level in a typical microgrid, an optimal scheduling mechanism is used to manage the power generated from the local DERs, energy drawn from the grid and energy consumption by the load. This paper proposes a novel hybrid optimization technique for day-ahead scheduling in a smart-grid. A Hybrid Feedback PSO-MCS algorithm is implemented using swarm intelligence and cuckoo search to enhance the performance and obtain a cost-effective solution for a microgrid prosumer. A comparison has been made of the Hybrid Feedback PSO-MCS (HFPSOMCS) algorithm with PSO and modified CS (MCS) algorithm. The best performing algorithm among the three is executed in MATLAB/Simulink and Python IDE platforms to compare the execution time.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82879880","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}
Pub Date : 2023-05-18DOI: 10.13052/dgaej2156-3306.3842
Anuj Gupta, Sharad Sharma, Sumit Saroha
An accurate and efficient forecasting of solar energy is necessary for managing the electricity generation and distribution in today’s electricity supply system. However, due to its random character in its time series, accurate forecasting of solar irradiation is a difficult task; but it is important for grid management, scheduling and its balancing. To fully utilize the solar energy in order to balance the generation and consumption, this paper proposed an ensemble approach using CEEMDAN-BiLSTM combination to forecast short term solar irradiation. In this, Complete Ensemble Empirical Mode Decomposition with adaptive noise (CEEMDAN) extract the inherent characteristics of time series data by decomposing it into low and high frequency Intrinsic Mode Functions (IMF’s) and Bidirectional Long Short Term Memory (BiLSTM) used as a forecasting tool to forecast the solar Global Horizontal Irradiance (GHI). Furthermore, using extensive experimental analysis, the research minimizes the number of IMF’s by integrating the CEEMDAN decomposed component (IMF1–IMF14) in order to increase the prediction accuracy. Then, for each IMF subseries, the trained standalone BiLSTM network are assigned to carry out the forecasting. In last stage, the forecasted results of each BiLSTM network are aggregate to compile final results. Two year data (2012–13) of Delhi, India from National Solar Radiation Database (NSRDB) has been used for training while one year data (2014) used for testing purpose for the same location. The proposed model performance is measured in terms of root mean square error (RMSE), mean absolute percentage error (MAPE), Correlation coefficient (R22) and forecast skill (FS). For the comparative analysis of proposed model, several others models: persistence model, unidirectional deep learning models: long short term memory (LSTM), gated recurrent unit (GRU), BiLSTM and two CEEMDAN based BiLSTM models are developed. The proposed model achieved lowest annual average RMSE (18.86 W/m22, 22.24 W/m22, 26.25 W/m22) and MAPE (2.19%, 4.81%, 6.77%) among the other developed models for 1-hr, 2-hr and 3-hr ahead solar GHI forecasting respectively. The maximum correlation coefficient (R22) obtained by the proposed model is 96.4 for 1-hr ahead respectively; on the other hand, forecast skill (%) of 89% with reference to benchmark model. Various test such as: Diebold Mariano Hypothesis test (DMH) and directional change in forecasting (DC) are used to analyze the sensitivity with reference to the difference in forecasted and observed value.
在当今的电力供应系统中，准确、高效的太阳能预测是管理发电和分配的必要条件。然而，由于其时间序列的随机性，准确预报太阳辐射是一项困难的任务;但它对网格管理、调度及其平衡具有重要意义。为了充分利用太阳能，实现产用平衡，本文提出了利用CEEMDAN-BiLSTM组合进行短期太阳辐照预报的集合方法。其中，CEEMDAN (Complete Ensemble Empirical Mode Decomposition with adaptive noise)将时间序列数据分解为低频和高频固有模态函数(IMF’s)和双向长短期记忆(BiLSTM)，提取时间序列数据的固有特征，作为预测太阳全球水平辐照度(GHI)的预测工具。此外，通过大量的实验分析，本研究通过整合CEEMDAN分解分量(IMF1-IMF14)来最小化IMF的数量，以提高预测精度。然后，对于每个IMF子序列，分配训练好的独立BiLSTM网络进行预测。最后，对各BiLSTM网络的预测结果进行汇总，得到最终结果。来自印度国家太阳辐射数据库(NSRDB)的两年数据(2012-13)用于培训，而一年数据(2014)用于同一地点的测试目的。采用均方根误差(RMSE)、平均绝对百分比误差(MAPE)、相关系数(R22)和预测技能(FS)来衡量模型的性能。为了对所提出的模型进行比较分析，本文还开发了其他几个模型:持久模型、单向深度学习模型:长短期记忆(LSTM)、门控循环单元(GRU)、BiLSTM和两个基于CEEMDAN的BiLSTM模型。该模式在提前1、2、3小时预测太阳GHI的年平均RMSE (18.86 W/m22, 22.24 W/m22, 26.25 W/m22)和MAPE(2.19%， 4.81%， 6.77%)均低于其他模式。该模型提前1小时得到的最大相关系数(R22)分别为96.4;另一方面，参考基准模型的预测技巧(%)为89%。参考预测值与实测值的差异，采用Diebold Mariano Hypothesis test (DMH)、directional change in forecasting (DC)等检验分析敏感性。
{"title":"A New Hybrid Short Term Solar Irradiation Forecasting Method Based on CEEMDAN Decomposition Approach and BiLSTM Deep Learning Network with Grid Search Algorithm","authors":"Anuj Gupta, Sharad Sharma, Sumit Saroha","doi":"10.13052/dgaej2156-3306.3842","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3842","url":null,"abstract":"An accurate and efficient forecasting of solar energy is necessary for managing the electricity generation and distribution in today’s electricity supply system. However, due to its random character in its time series, accurate forecasting of solar irradiation is a difficult task; but it is important for grid management, scheduling and its balancing. To fully utilize the solar energy in order to balance the generation and consumption, this paper proposed an ensemble approach using CEEMDAN-BiLSTM combination to forecast short term solar irradiation. In this, Complete Ensemble Empirical Mode Decomposition with adaptive noise (CEEMDAN) extract the inherent characteristics of time series data by decomposing it into low and high frequency Intrinsic Mode Functions (IMF’s) and Bidirectional Long Short Term Memory (BiLSTM) used as a forecasting tool to forecast the solar Global Horizontal Irradiance (GHI). Furthermore, using extensive experimental analysis, the research minimizes the number of IMF’s by integrating the CEEMDAN decomposed component (IMF1–IMF14) in order to increase the prediction accuracy. Then, for each IMF subseries, the trained standalone BiLSTM network are assigned to carry out the forecasting. In last stage, the forecasted results of each BiLSTM network are aggregate to compile final results. Two year data (2012–13) of Delhi, India from National Solar Radiation Database (NSRDB) has been used for training while one year data (2014) used for testing purpose for the same location. The proposed model performance is measured in terms of root mean square error (RMSE), mean absolute percentage error (MAPE), Correlation coefficient (R22) and forecast skill (FS). For the comparative analysis of proposed model, several others models: persistence model, unidirectional deep learning models: long short term memory (LSTM), gated recurrent unit (GRU), BiLSTM and two CEEMDAN based BiLSTM models are developed. The proposed model achieved lowest annual average RMSE (18.86 W/m22, 22.24 W/m22, 26.25 W/m22) and MAPE (2.19%, 4.81%, 6.77%) among the other developed models for 1-hr, 2-hr and 3-hr ahead solar GHI forecasting respectively. The maximum correlation coefficient (R22) obtained by the proposed model is 96.4 for 1-hr ahead respectively; on the other hand, forecast skill (%) of 89% with reference to benchmark model. Various test such as: Diebold Mariano Hypothesis test (DMH) and directional change in forecasting (DC) are used to analyze the sensitivity with reference to the difference in forecasted and observed value.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82270538","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}
Brushless Direct Current (BLDC) motors are advantageous because of their higher efficiency, higher speed operations and higher power density. Industrial applications demand BLDC motors free from torque ripple. The torque ripple is due to the unequal commutation period between the energised phase and unenergized phase current. It is a perilous problem in sensorless BLDC drive as it leads to speed oscillations, acoustic noise, serious faults, and vibration in machines. The torque ripple can be reduced either by improving motor design parameter or by improving the motor control strategy. This paper proposes a Proportional Integral (PI) controller-based control scheme for a cuk converter driven sensorless BLDC motor to reduce the torque ripple. The proposed scheme invokes Zero Crossing Point (ZCP) detection with back emf sensing approach. The presence of inductor reduces the ripple in the input and output currents. The performance of the strategy is verified using MATLAB R2018a Simulink for different operating conditions of a BLDC drive and the results prove that the recommended scheme decreases the torque ripple compared to the conventional scheme.
{"title":"Torque Ripple Minimization Technique of Position Sensorless BLDC Motor for Variable Speed Drives","authors":"Karthika Mahalingam, Nisha Kandencheri Chellaiah Ramji","doi":"10.13052/dgaej2156-3306.3848","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3848","url":null,"abstract":"Brushless Direct Current (BLDC) motors are advantageous because of their higher efficiency, higher speed operations and higher power density. Industrial applications demand BLDC motors free from torque ripple. The torque ripple is due to the unequal commutation period between the energised phase and unenergized phase current. It is a perilous problem in sensorless BLDC drive as it leads to speed oscillations, acoustic noise, serious faults, and vibration in machines. The torque ripple can be reduced either by improving motor design parameter or by improving the motor control strategy. This paper proposes a Proportional Integral (PI) controller-based control scheme for a cuk converter driven sensorless BLDC motor to reduce the torque ripple. The proposed scheme invokes Zero Crossing Point (ZCP) detection with back emf sensing approach. The presence of inductor reduces the ripple in the input and output currents. The performance of the strategy is verified using MATLAB R2018a Simulink for different operating conditions of a BLDC drive and the results prove that the recommended scheme decreases the torque ripple compared to the conventional scheme.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73963202","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}
Pub Date : 2023-05-18DOI: 10.13052/dgaej2156-3306.3849
Yingjun He, Shenzhang Li, Hexiong Chen, Xiu Liu, Lin Wang, Shaolong Li
In the context of sustainable development, the research on the optimization method of charging station layout based on the Internet of things can effectively shorten the distance between the charging demand point and the charging station candidate point. Based on the perception of the charging status of the electric station and the transmission layer of the RFID, the charging system is designed to collect and store the relevant information from the charging system of the electric station in real time according to the charging status of the electric station and the transmission layer of the RFID. Based on the above information, taking the minimum distance from the user to the charging station, the expected waiting time and the construction cost as the objective function, all demand points are allocated to the corresponding charging station, charging can be provided to users only by building a charging station at the candidate point, and users at all demand points can only enjoy charging services at a specific charging station as the constraint. The optimization model of charging station layout is constructed and solved by genetic algorithm to obtain the best charging station layout. The experimental results show that the layout scale of electric vehicle charging stations based on this method has the advantages of global optimization, strongest adaptability and good economic benefits, and the increase in the number of charging stations can effectively improve user satisfaction.
{"title":"Optimization Method of Charging Station Layout Based on Internet of Things Under the Background of Sustainable Development","authors":"Yingjun He, Shenzhang Li, Hexiong Chen, Xiu Liu, Lin Wang, Shaolong Li","doi":"10.13052/dgaej2156-3306.3849","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3849","url":null,"abstract":"In the context of sustainable development, the research on the optimization method of charging station layout based on the Internet of things can effectively shorten the distance between the charging demand point and the charging station candidate point. Based on the perception of the charging status of the electric station and the transmission layer of the RFID, the charging system is designed to collect and store the relevant information from the charging system of the electric station in real time according to the charging status of the electric station and the transmission layer of the RFID. Based on the above information, taking the minimum distance from the user to the charging station, the expected waiting time and the construction cost as the objective function, all demand points are allocated to the corresponding charging station, charging can be provided to users only by building a charging station at the candidate point, and users at all demand points can only enjoy charging services at a specific charging station as the constraint. The optimization model of charging station layout is constructed and solved by genetic algorithm to obtain the best charging station layout. The experimental results show that the layout scale of electric vehicle charging stations based on this method has the advantages of global optimization, strongest adaptability and good economic benefits, and the increase in the number of charging stations can effectively improve user satisfaction.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82084931","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}
Pub Date : 2023-05-18DOI: 10.13052/dgaej2156-3306.38410
B. Reddy, V. Reddy, M. Kumar
Renewable energy sources (RES) are inherently stochastic, require the deployment of an energy storage device to round off variations in power. A hybrid system consisting solar PV and PEMFC for grid-connected applications is proposed and analysed. For grid-tied applications, a radial basis function network (RBFN) type maximum power point tracking (MPPT) approach for PEM (Proton Exchange Membrane) fuel cells and a fuzzy logic controller (FLC) type MPPT approach for Photovoltaic system respectively is developed and analysed. In addition, a high step-up hybrid boost converter (HSHBC) for fuel cells has been designed, which provides a higher voltage gain than a conventional Boost converter. Developing a fuzzy logic controller for PV system at different solar irradiation levels and a RBFN based MPPT technique for PEM Fuel Cell with different temperatures respectively to get the maximum power. The developed system is simulated using the Simulink/MATLAB platform to analyse it.
{"title":"Design and Analysis of DC-DC Converters with Artificial Intelligence Based MPPT Approaches for Grid Tied Hybrid PV-PEMFC System","authors":"B. Reddy, V. Reddy, M. Kumar","doi":"10.13052/dgaej2156-3306.38410","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.38410","url":null,"abstract":"Renewable energy sources (RES) are inherently stochastic, require the deployment of an energy storage device to round off variations in power. A hybrid system consisting solar PV and PEMFC for grid-connected applications is proposed and analysed. For grid-tied applications, a radial basis function network (RBFN) type maximum power point tracking (MPPT) approach for PEM (Proton Exchange Membrane) fuel cells and a fuzzy logic controller (FLC) type MPPT approach for Photovoltaic system respectively is developed and analysed. In addition, a high step-up hybrid boost converter (HSHBC) for fuel cells has been designed, which provides a higher voltage gain than a conventional Boost converter. Developing a fuzzy logic controller for PV system at different solar irradiation levels and a RBFN based MPPT technique for PEM Fuel Cell with different temperatures respectively to get the maximum power. The developed system is simulated using the Simulink/MATLAB platform to analyse it.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80196728","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}
Pub Date : 2023-05-18DOI: 10.13052/dgaej2156-3306.3841
Ying Li, Yu-Liang Lin
During the heating period, the solid thermal storage electric boiler is added to use the waste electricity for local heating, and the corresponding energy storage optimization model of wind solar hybrid power generation system is constructed with the maximum waste electricity contotalityption and the minimum power purchase cost as the objective function, and the contotalityption and waste electricity constraint, system power balance constraint, electric boiler power constraint, heat storage constraint, and regulation times constraint as constraint conditions. Improved moth algorithm is constructed to solve the optimization model, and a Pareto solution set with both economy and reliability is obtained. Optimal compromise solution is screened out from the Pareto solution set, and optimal configuration capacity of the thermal storage electric boiler and annual amount of electricity discarded in the system can be obtained. Thus, the power rejection in the system can be reduced, promoting widespread use of independent renewable energy power generation systems.
{"title":"Energy Storage Optimization of Wind Solar Hybrid Power Generation System Based on Improved Grasshopper Algorithm","authors":"Ying Li, Yu-Liang Lin","doi":"10.13052/dgaej2156-3306.3841","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3841","url":null,"abstract":"During the heating period, the solid thermal storage electric boiler is added to use the waste electricity for local heating, and the corresponding energy storage optimization model of wind solar hybrid power generation system is constructed with the maximum waste electricity contotalityption and the minimum power purchase cost as the objective function, and the contotalityption and waste electricity constraint, system power balance constraint, electric boiler power constraint, heat storage constraint, and regulation times constraint as constraint conditions. Improved moth algorithm is constructed to solve the optimization model, and a Pareto solution set with both economy and reliability is obtained. Optimal compromise solution is screened out from the Pareto solution set, and optimal configuration capacity of the thermal storage electric boiler and annual amount of electricity discarded in the system can be obtained. Thus, the power rejection in the system can be reduced, promoting widespread use of independent renewable energy power generation systems.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81692810","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}
Pub Date : 2023-05-18DOI: 10.13052/dgaej2156-3306.3845
Sonia Dhiman, A. Dahiya
The rising number of intermittent wind energy-based generation systems in power systems affects the grid system’s stability and reliability. These wind generators reduce the inertia of the system, thus making the system sensitive to grid disturbances. Due to their unique features, the Doubly Fed Induction Generators (DFIG) wind generators are being connected at a large scale. The Energy Storage Device (ESD) offers a viable solution to the integration issues caused by variable-natured renewable energy sources. In this work, the Static Compensator (STATCOM) is attached to the Superconducting Magnetic Energy Storage (SMES) technology to strengthen the wind farm integrated grid system for better performance. The SMES is interlinked with the grid system via a power electronic interface (PEI) and chopper for the energy exchange. This work examines the functioning of the proposed STATCOM as PEI and three-level chopper control circuit based on fuzzy logic for the SMES system. The fuzzy logic based SMES with STATCOM (STAT-SMES) is proposed for a DFIG-based integrated system under different fault conditions. This coupled controller can compensate for both real and reactive powers, improve voltage stability, and can damp power oscillations at a fast rate. The results have been compared without any controller, with STATCOM only, and with the proposed, fuzzy based SMES coupled to STATCOM using MATLAB. The simulation outcomes prove that coupling SMES to STATCOM is effective in handling wind farm integration issues in a better way than STATCOM.
{"title":"Stability Improvement of Wind Farm by Utilising SMES and STATCOM Coupled System","authors":"Sonia Dhiman, A. Dahiya","doi":"10.13052/dgaej2156-3306.3845","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3845","url":null,"abstract":"The rising number of intermittent wind energy-based generation systems in power systems affects the grid system’s stability and reliability. These wind generators reduce the inertia of the system, thus making the system sensitive to grid disturbances. Due to their unique features, the Doubly Fed Induction Generators (DFIG) wind generators are being connected at a large scale. The Energy Storage Device (ESD) offers a viable solution to the integration issues caused by variable-natured renewable energy sources. In this work, the Static Compensator (STATCOM) is attached to the Superconducting Magnetic Energy Storage (SMES) technology to strengthen the wind farm integrated grid system for better performance. The SMES is interlinked with the grid system via a power electronic interface (PEI) and chopper for the energy exchange. This work examines the functioning of the proposed STATCOM as PEI and three-level chopper control circuit based on fuzzy logic for the SMES system. The fuzzy logic based SMES with STATCOM (STAT-SMES) is proposed for a DFIG-based integrated system under different fault conditions. This coupled controller can compensate for both real and reactive powers, improve voltage stability, and can damp power oscillations at a fast rate. The results have been compared without any controller, with STATCOM only, and with the proposed, fuzzy based SMES coupled to STATCOM using MATLAB. The simulation outcomes prove that coupling SMES to STATCOM is effective in handling wind farm integration issues in a better way than STATCOM.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"2009 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82586543","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}
Pub Date : 2023-03-03DOI: 10.13052/dgaej2156-3306.3835
G. Kumar, K. Palanisamy
This paper proposes a ramp-rate control (RRC) for mitigation of solar PV fluctuations with a hybrid energy storage system (HESS). The highly fluctuating primary energy source causes photovoltaic (PV) generators to suffer from variable output capacity. Such variations can lead to instability in power systems and problems with power quality due to large PV penetration. The role of energy storage devices (ESSs) as a fluctuation compensator is suggested to minimize these issues using RRC. Distributed Generation Systems (DGs) have become a key challenge as the disruption of DG from the grid during faults results in severe difficulties such as power outages and voltage flickers. Low voltage ride through (LVRT) is a promising method for supplying reactive power under low voltage conditions. The proposed method will enable dynamic control of integrated battery storage (BS) to mitigate power fluctuations during the day while simultaneously charging or discharging the integrated super-capacitor (SC) storage to control sudden variations in a BS to a certain magnitude. A system for exchanging energy between the BS and the SC storage provides uninterrupted control of the rapid fluctuations of the passing cloud. The storage capacity savings are evaluated by using the RRC for the smoothing impact of geographical deflection on PV power production. Simulations conducted with real operational PV power output data taken every 1 s from the power plant during one year confirm the validity of the model. The OP-5700 HIL test-bench is used for the real-time results.
{"title":"Ramp-Rate Control for Mitigation of Solar PV Fluctuations with Hybrid Energy Storage System","authors":"G. Kumar, K. Palanisamy","doi":"10.13052/dgaej2156-3306.3835","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3835","url":null,"abstract":"This paper proposes a ramp-rate control (RRC) for mitigation of solar PV fluctuations with a hybrid energy storage system (HESS). The highly fluctuating primary energy source causes photovoltaic (PV) generators to suffer from variable output capacity. Such variations can lead to instability in power systems and problems with power quality due to large PV penetration. The role of energy storage devices (ESSs) as a fluctuation compensator is suggested to minimize these issues using RRC. Distributed Generation Systems (DGs) have become a key challenge as the disruption of DG from the grid during faults results in severe difficulties such as power outages and voltage flickers. Low voltage ride through (LVRT) is a promising method for supplying reactive power under low voltage conditions. The proposed method will enable dynamic control of integrated battery storage (BS) to mitigate power fluctuations during the day while simultaneously charging or discharging the integrated super-capacitor (SC) storage to control sudden variations in a BS to a certain magnitude. A system for exchanging energy between the BS and the SC storage provides uninterrupted control of the rapid fluctuations of the passing cloud. The storage capacity savings are evaluated by using the RRC for the smoothing impact of geographical deflection on PV power production. Simulations conducted with real operational PV power output data taken every 1 s from the power plant during one year confirm the validity of the model. The OP-5700 HIL test-bench is used for the real-time results.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74085327","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}
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