Pub Date : 2024-07-16DOI: 10.13052/dgaej2156-3306.39310
Gang Liu, Huiming Zhang
The wind energy conversion system (WECS) has a complex structure, and its state space model is highly nonlinear. Due to the random uncertainty of wind speed, it poses a huge challenge to achieve optimal control tasks and ensure the safe and stable operation of the system. Therefore, this article proposes a stochastic model predictive control strategy based on Polynomial Chaotic Expansion (PCE), which achieves the control tasks of MPPT and constant power regions in wind energy conversion systems. Firstly, a simple algorithm is proposed to obtain a set of basis functions that are suitable for the stochastic variable wind speed. Then, the obtained basis functions are used to propagate the uncertainty of the original uncertain differential equation of the wind energy conversion system through polynomial chaotic expansion. Combining the operating region and constraint conditions of the wind energy conversion system, the original stochastic uncertainty problem is transformed into a deterministic convex optimization problem. Using NREL 5MW wind turbine as the research object for simulation, the task of capturing maximum wind energy in MPPT area and tracking rated power points in constant power area was achieved. The experimental results show that the proposed control method can effectively improve the wind energy capture capability and achieve accurate tracking of output power to rated power.
{"title":"Stochastic Model Predictive Control Based on Polynomial Chaos Expansion With Application to Wind Energy Conversion Systems","authors":"Gang Liu, Huiming Zhang","doi":"10.13052/dgaej2156-3306.39310","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.39310","url":null,"abstract":"The wind energy conversion system (WECS) has a complex structure, and its state space model is highly nonlinear. Due to the random uncertainty of wind speed, it poses a huge challenge to achieve optimal control tasks and ensure the safe and stable operation of the system. Therefore, this article proposes a stochastic model predictive control strategy based on Polynomial Chaotic Expansion (PCE), which achieves the control tasks of MPPT and constant power regions in wind energy conversion systems. Firstly, a simple algorithm is proposed to obtain a set of basis functions that are suitable for the stochastic variable wind speed. Then, the obtained basis functions are used to propagate the uncertainty of the original uncertain differential equation of the wind energy conversion system through polynomial chaotic expansion. Combining the operating region and constraint conditions of the wind energy conversion system, the original stochastic uncertainty problem is transformed into a deterministic convex optimization problem. Using NREL 5MW wind turbine as the research object for simulation, the task of capturing maximum wind energy in MPPT area and tracking rated power points in constant power area was achieved. The experimental results show that the proposed control method can effectively improve the wind energy capture capability and achieve accurate tracking of output power to rated power.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141832249","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 : 2024-07-16DOI: 10.13052/dgaej2156-3306.3933
Jie Huang, Rong Nie, Zhenyu Zhao, Yan Wang
Energy storage plays a vital role in enhancing the resilience of the power grid. Utilizing typical capacity and power energy storage application scenarios, coupled with industry research data and technical analysis of energy storage, this study calculates the cost of energy storage per kilowatt-hour and the associated mileage cost. The findings indicate that the current cost per kilowatt-hour of electrochemical energy storage ranges from approximately 0.6 to 0.9 yuan/(kW⋅h), revealing a considerable gap between the target cost for widespread application and the range of 0.3 to 0.4 yuan/(kW⋅h). Therefore, the development of energy storage technologies (EST) should prioritize achieving “low cost, long life, high safety, and easy recycling,” taking into account a comprehensive assessment of system manufacturing, system lifespan, system safety, and recycling. This paper delves into the changing trend of battery costs and their impact on kilowatt-hours, presenting strategic suggestions to reduce the kilowatt-hour cost of ESP stations. The research underscores that a continuous reduction in battery costs will contribute to enhancing the economic benefits of ESP stations and provide robust support for the future development of the energy storage industry.
{"title":"KWH Cost Analysis of Energy Storage Power Station Based on Changing Trend of Battery Cost","authors":"Jie Huang, Rong Nie, Zhenyu Zhao, Yan Wang","doi":"10.13052/dgaej2156-3306.3933","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3933","url":null,"abstract":"Energy storage plays a vital role in enhancing the resilience of the power grid. Utilizing typical capacity and power energy storage application scenarios, coupled with industry research data and technical analysis of energy storage, this study calculates the cost of energy storage per kilowatt-hour and the associated mileage cost. The findings indicate that the current cost per kilowatt-hour of electrochemical energy storage ranges from approximately 0.6 to 0.9 yuan/(kW⋅h), revealing a considerable gap between the target cost for widespread application and the range of 0.3 to 0.4 yuan/(kW⋅h). Therefore, the development of energy storage technologies (EST) should prioritize achieving “low cost, long life, high safety, and easy recycling,” taking into account a comprehensive assessment of system manufacturing, system lifespan, system safety, and recycling. This paper delves into the changing trend of battery costs and their impact on kilowatt-hours, presenting strategic suggestions to reduce the kilowatt-hour cost of ESP stations. The research underscores that a continuous reduction in battery costs will contribute to enhancing the economic benefits of ESP stations and provide robust support for the future development of the energy storage industry.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141831662","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 : 2024-07-16DOI: 10.13052/dgaej2156-3306.3936
Zhiwei Xu, Kexian Xiang, Bin Wang, Xianguo Li
This research proposes a combined approach for predicting photovoltaic power by integrating variational modal decomposition (VMD), an improved gray wolf optimization algorithm (IGWO), and long- and short-term memory neural network (LSTM) techniques. The model takes into account the impact of varying environmental factors on photovoltaic power and aims to enhance prediction accuracy. Firstly, the four environmental factors constraining the PV output power are decomposed into eigenfunctions (IMFs) through variational modal decomposition; then the improved gray wolf optimization algorithm is used to optimize the long and short-term memory neural network; finally, the dimensionality-reduced dataset is inputted into the LSTM neural network, and the dynamic temporal modeling and comparative analysis on the multivariate feature sequences are carried out. The results show that the VMD-LSTM model optimized by the improved Gray Wolf algorithm predicts better than the comparison models LSTM, VMD-LSTM and VMD-GWO-LSTM, and achieves the accurate prediction of time-volt power in the external environmental changes.
{"title":"Study on PV Power Prediction Based on VMD-IGWO-LSTM","authors":"Zhiwei Xu, Kexian Xiang, Bin Wang, Xianguo Li","doi":"10.13052/dgaej2156-3306.3936","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3936","url":null,"abstract":"This research proposes a combined approach for predicting photovoltaic power by integrating variational modal decomposition (VMD), an improved gray wolf optimization algorithm (IGWO), and long- and short-term memory neural network (LSTM) techniques. The model takes into account the impact of varying environmental factors on photovoltaic power and aims to enhance prediction accuracy. Firstly, the four environmental factors constraining the PV output power are decomposed into eigenfunctions (IMFs) through variational modal decomposition; then the improved gray wolf optimization algorithm is used to optimize the long and short-term memory neural network; finally, the dimensionality-reduced dataset is inputted into the LSTM neural network, and the dynamic temporal modeling and comparative analysis on the multivariate feature sequences are carried out. The results show that the VMD-LSTM model optimized by the improved Gray Wolf algorithm predicts better than the comparison models LSTM, VMD-LSTM and VMD-GWO-LSTM, and achieves the accurate prediction of time-volt power in the external environmental changes.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":" 0","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141831682","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}
With the rapid development of renewable energy, the new energy power system is facing the challenge of large-scale grid connection and consumption of renewable energy. In order to achieve efficient utilization and stable power supply of renewable energy, this study proposes a renewable energy consumption mechanism based on optimization methods. By establishing a power and electricity balance model, consider the relationship between different types of renewable energy generation and electricity demand. Various optimization strategies have been proposed for energy consumption issues in different scenarios, including power generation scheduling, energy storage optimization, and flexible load management. Validate the effectiveness of the proposed mechanism in terms of electricity balance and metering optimization through a model. The experimental results indicate that this mechanism can effectively enhance the renewable energy consumption capacity of the new energy power system, reduce energy waste, promote energy cleanliness and sustainable development, and has certain theoretical and practical significance for promoting the sustainable development of the new energy power system and responding to energy transformation.
{"title":"Research on Electricity Balance and Measurement Optimization of New Energy Power System Considering Renewable Energy Consumption Mechanism","authors":"Zhihao Guo, Yongzhi Cai, Sheng Huang, Zeming Jiang, KeFei Guan","doi":"10.13052/dgaej2156-3306.3935","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3935","url":null,"abstract":"With the rapid development of renewable energy, the new energy power system is facing the challenge of large-scale grid connection and consumption of renewable energy. In order to achieve efficient utilization and stable power supply of renewable energy, this study proposes a renewable energy consumption mechanism based on optimization methods. By establishing a power and electricity balance model, consider the relationship between different types of renewable energy generation and electricity demand. Various optimization strategies have been proposed for energy consumption issues in different scenarios, including power generation scheduling, energy storage optimization, and flexible load management. Validate the effectiveness of the proposed mechanism in terms of electricity balance and metering optimization through a model. The experimental results indicate that this mechanism can effectively enhance the renewable energy consumption capacity of the new energy power system, reduce energy waste, promote energy cleanliness and sustainable development, and has certain theoretical and practical significance for promoting the sustainable development of the new energy power system and responding to energy transformation.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141832083","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 : 2024-07-16DOI: 10.13052/dgaej2156-3306.3938
Indradeo Pratap Bharti, N. Singh, Om Hari Gupta, Asheesh Kumar Singh, Vijay K. Sood
Incorporation of environmentally friendly energy sources (RESs) into the electricity grid has many benefits, including economic, technological, and environmental. However, excessive renewable energy sources (RES) in the power grid provide technical problems, including equipment protection, DG operation, and islanding detection. One of the most serious challenges is the islanding phenomenon. Islanding can cause several problems, such as frequency instability and voltage fluctuations resulting in damage to electrical equipment or threatening utility workers who may be working/accessing the equipment. This research proposes an efficient islanding detection algorithm to lessen the impact of such threats. This novel passive islanding detection scheme is based on superimposed positive sequence impedance (SPSI). For calculating the superimposed positive sequence impedance (SPSI), the voltage and current signals are obtained from targeted DG points. The scheme’s performance is tested on multiple bus systems across islanding and non-islanding conditions using a MATLAB/Simulink environment. It is shown that even in the presence of noise, the algorithm can determine an islanding decision with high accuracy and a short detection time of 84 ms. In comparison to other algorithms, it operates at zero power mismatch (ZPM) and does not affect power quality.
{"title":"Superimposed Positive Sequence Impedance for Detecting Unintentional Islanding in Microgrid","authors":"Indradeo Pratap Bharti, N. Singh, Om Hari Gupta, Asheesh Kumar Singh, Vijay K. Sood","doi":"10.13052/dgaej2156-3306.3938","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3938","url":null,"abstract":"Incorporation of environmentally friendly energy sources (RESs) into the electricity grid has many benefits, including economic, technological, and environmental. However, excessive renewable energy sources (RES) in the power grid provide technical problems, including equipment protection, DG operation, and islanding detection. One of the most serious challenges is the islanding phenomenon. Islanding can cause several problems, such as frequency instability and voltage fluctuations resulting in damage to electrical equipment or threatening utility workers who may be working/accessing the equipment. This research proposes an efficient islanding detection algorithm to lessen the impact of such threats. This novel passive islanding detection scheme is based on superimposed positive sequence impedance (SPSI). For calculating the superimposed positive sequence impedance (SPSI), the voltage and current signals are obtained from targeted DG points. The scheme’s performance is tested on multiple bus systems across islanding and non-islanding conditions using a MATLAB/Simulink environment. It is shown that even in the presence of noise, the algorithm can determine an islanding decision with high accuracy and a short detection time of 84 ms. In comparison to other algorithms, it operates at zero power mismatch (ZPM) and does not affect power quality.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":" 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141831853","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 : 2024-07-16DOI: 10.13052/dgaej2156-3306.39311
Xinshan Wang
Solar thermal power generation shares technical characteristics with traditional thermal power generation. This enables rapid adjustment of turbine generator output to meet the demands of the power grid load for frequency modulation. However, fluctuations in light intensity lead to variations in interconnected power system parameters, posing challenges for load frequency control (LFC). In this study, we propose a Robust Distributed Model Predictive Control (RDMPC) method. This method achieves system trajectory tracking by solving the nominal system optimization problem. It also flexibly adjusts the weights of different Tube models to determine the optimal control law using the standard Tube online combination with various gain values. Additionally, we incorporate the states of adjacent areas into the feedback control law to achieve effective coordination between these areas. Using MATLAB/Simulink, we simulated the power system in two areas. Compared to standard Tube DMPC, our proposed algorithm effectively mitigates the impact of light intensity, enhances adjustment speed, reduces frequency fluctuation, and demonstrates superior control effectiveness.
{"title":"Load Frequency Control Strategy of Interconnected Power System Based on Tube DMPC","authors":"Xinshan Wang","doi":"10.13052/dgaej2156-3306.39311","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.39311","url":null,"abstract":"Solar thermal power generation shares technical characteristics with traditional thermal power generation. This enables rapid adjustment of turbine generator output to meet the demands of the power grid load for frequency modulation. However, fluctuations in light intensity lead to variations in interconnected power system parameters, posing challenges for load frequency control (LFC). In this study, we propose a Robust Distributed Model Predictive Control (RDMPC) method. This method achieves system trajectory tracking by solving the nominal system optimization problem. It also flexibly adjusts the weights of different Tube models to determine the optimal control law using the standard Tube online combination with various gain values. Additionally, we incorporate the states of adjacent areas into the feedback control law to achieve effective coordination between these areas. Using MATLAB/Simulink, we simulated the power system in two areas. Compared to standard Tube DMPC, our proposed algorithm effectively mitigates the impact of light intensity, enhances adjustment speed, reduces frequency fluctuation, and demonstrates superior control effectiveness.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":" 31","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141831623","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 : 2024-07-16DOI: 10.13052/dgaej2156-3306.3934
Ying Sun, Jiajia Huang, Fusheng Wei, Yanzhe Fu, LiangZhao He
The continuous development of smart cities has put forward higher requirements for the supply of power systems. In response to the constraints in the environmental performance and measurement of smart city power supply, this paper proposes a research model for smart city power supply environmental performance and measurement based on non-radial network DEA based on the characteristics of DEA model and distance function. This model can combine different stages of power supply to conduct more reasonable statistics and analysis of efficiency in different regions. In addition, correlation coefficients were analyzed for the impact of efficiency factors on the phase ratio in the production and sales stages of the power supply system. The research results indicate that there is a positive correlation between the output value and power generation of electricity sales and the efficiency of the electricity sales stage, with correlation coefficients of 0.57 and 0.092, respectively; The length of newly added lines, capacity of new equipment, and line loss rate are all negatively correlated with their efficiency, with correlation coefficients of −0.42, −0.12, and −0.46, respectively. Based on the above analysis, this study provides more theoretical support for the study of environmental performance and measurement of smart city power supply.
智慧城市的不断发展对电力系统的供给提出了更高的要求。针对智慧城市供电环境绩效与衡量中存在的制约因素，本文根据 DEA 模型和距离函数的特点，提出了一种基于非径向网络 DEA 的智慧城市供电环境绩效与衡量研究模型。该模型可以结合供电的不同阶段，对不同区域的供电效率进行更合理的统计和分析。此外，还分析了供电系统生产和销售阶段效率因素对相位比影响的相关系数。研究结果表明，售电产值和发电量与售电阶段效率呈正相关关系，相关系数分别为 0.57 和 0.092；新增线路长度、新增设备容量、线损率均与其效率呈负相关关系，相关系数分别为-0.42、-0.12 和-0.46。基于以上分析，本研究为智慧城市供电环境绩效研究与测量提供了更多理论支持。
{"title":"Research on Environmental Performance and Measurement of Smart City Power Supply Based on Non Radial Network DEA","authors":"Ying Sun, Jiajia Huang, Fusheng Wei, Yanzhe Fu, LiangZhao He","doi":"10.13052/dgaej2156-3306.3934","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3934","url":null,"abstract":"The continuous development of smart cities has put forward higher requirements for the supply of power systems. In response to the constraints in the environmental performance and measurement of smart city power supply, this paper proposes a research model for smart city power supply environmental performance and measurement based on non-radial network DEA based on the characteristics of DEA model and distance function. This model can combine different stages of power supply to conduct more reasonable statistics and analysis of efficiency in different regions. In addition, correlation coefficients were analyzed for the impact of efficiency factors on the phase ratio in the production and sales stages of the power supply system. The research results indicate that there is a positive correlation between the output value and power generation of electricity sales and the efficiency of the electricity sales stage, with correlation coefficients of 0.57 and 0.092, respectively; The length of newly added lines, capacity of new equipment, and line loss rate are all negatively correlated with their efficiency, with correlation coefficients of −0.42, −0.12, and −0.46, respectively. Based on the above analysis, this study provides more theoretical support for the study of environmental performance and measurement of smart city power supply.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141831703","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 : 2024-07-16DOI: 10.13052/dgaej2156-3306.3937
Yan Wang, Jiawei Xu, Xiaowen Chen, Ying Huang
A data mining based power grid user behavior analysis system has been designed to address the issues of insufficient stability and accuracy in existing power grid user behavior analysis systems. Design the overall structure of the power grid user behavior analysis system; In terms of system hardware design, select a core controller, build and install a server as the foundation for system information transmission and logical operation; Based on ZigBee wireless communication technology, a ZigBee wireless communication protocol stack and communication expansion board were designed; In terms of system software design, Python is used to crawl user behavior data in the system data collection layer, and Python language is used to maintain the crawling program; Use the K-means algorithm to perform secondary mining and clustering on power grid user behavior data, obtain the analysis results of power grid user behavior, and transmit them to the system visualization display layer. The weight and Rand coefficient of data analysis were used as indicators to test the application effect of the method in this paper. The experimental results showed that the system can stably and accurately analyze the behavior of power grid users, and has good application effect. This research achievement has important reference significance for the research in the field of power grid user behavior analysis in the world scientific community.
{"title":"Analysis of Power Grid User Behavior Based on Data Mining Algorithms – System Design and Implementation","authors":"Yan Wang, Jiawei Xu, Xiaowen Chen, Ying Huang","doi":"10.13052/dgaej2156-3306.3937","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3937","url":null,"abstract":"A data mining based power grid user behavior analysis system has been designed to address the issues of insufficient stability and accuracy in existing power grid user behavior analysis systems. Design the overall structure of the power grid user behavior analysis system; In terms of system hardware design, select a core controller, build and install a server as the foundation for system information transmission and logical operation; Based on ZigBee wireless communication technology, a ZigBee wireless communication protocol stack and communication expansion board were designed; In terms of system software design, Python is used to crawl user behavior data in the system data collection layer, and Python language is used to maintain the crawling program; Use the K-means algorithm to perform secondary mining and clustering on power grid user behavior data, obtain the analysis results of power grid user behavior, and transmit them to the system visualization display layer. The weight and Rand coefficient of data analysis were used as indicators to test the application effect of the method in this paper. The experimental results showed that the system can stably and accurately analyze the behavior of power grid users, and has good application effect. This research achievement has important reference significance for the research in the field of power grid user behavior analysis in the world scientific community.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":" 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141831263","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 : 2024-07-16DOI: 10.13052/dgaej2156-3306.3939
K. Karishma, A. Sivaprasad, Nithin Raj
The need for multi-input DC-DC converters is highly demanded in the context of the integration of different energy sources. When integrating several energy sources, such as batteries, solar PV arrays, fuel cells, etc., multi-input DC-DC converters preferred over multiple single-input DC-DC converters. The vast majority of MISO converters in use today only have one mode of operation, which is conventional operation using all sources. Very few MISO topologies have been presented with fault-tolerant capability. In this work, a non-isolated two-input single-output (MISO) boost converter is investigated for low-voltage DC (LVDC) applications with fault tolerant capability. The presented converter works in three modes, such as DC sources of equal values, DC sources of unequal values, and one of the DC sources that is faulty or out of order. The converter is simulated in a MATLAB/Simulink environment and experimentally validated using a scaled prototype. The outcomes demonstrate that the converter can integrate systems with two DC energy sources, accommodates DC sources of equal values, DC sources of unequal values, and works even when one of the DC sources is faulty or out of order. This work points out that the presented MISO converter would be an apt solution for integrating varying input voltage sources with fault-tolerant capability.
{"title":"Two-Input Single-Output Boost Converter with Fault Tolerant Operation","authors":"K. Karishma, A. Sivaprasad, Nithin Raj","doi":"10.13052/dgaej2156-3306.3939","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3939","url":null,"abstract":"The need for multi-input DC-DC converters is highly demanded in the context of the integration of different energy sources. When integrating several energy sources, such as batteries, solar PV arrays, fuel cells, etc., multi-input DC-DC converters preferred over multiple single-input DC-DC converters. The vast majority of MISO converters in use today only have one mode of operation, which is conventional operation using all sources. Very few MISO topologies have been presented with fault-tolerant capability. In this work, a non-isolated two-input single-output (MISO) boost converter is investigated for low-voltage DC (LVDC) applications with fault tolerant capability. The presented converter works in three modes, such as DC sources of equal values, DC sources of unequal values, and one of the DC sources that is faulty or out of order. The converter is simulated in a MATLAB/Simulink environment and experimentally validated using a scaled prototype. The outcomes demonstrate that the converter can integrate systems with two DC energy sources, accommodates DC sources of equal values, DC sources of unequal values, and works even when one of the DC sources is faulty or out of order. This work points out that the presented MISO converter would be an apt solution for integrating varying input voltage sources with fault-tolerant capability.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141831816","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 : 2024-07-16DOI: 10.13052/dgaej2156-3306.3932
Weidong Hu, Zhao Bo, Chen Jie
With the advancement of digital transformation in distribution substations, a large number of smart devices are being integrated into substations. Addressing the challenges of automatic topology recognition and the issue of unstable recognition accuracy in distribution substations has become crucial. This paper proposes a substation topology recognition method based on an improved matrix approach and the Minimum Conditional Probability of Packet Loss Theorem. The improved matrix approach is utilized to calculate the topological signals, enabling automatic bottom-up topology recognition within the substation. The application of the Minimum Conditional Probability of Packet Loss Theorem in processing topological data significantly enhances the accuracy of substation topology recognition, reducing the impact of external factors on recognition accuracy. Experimental validation demonstrates that the proposed method is highly feasible and exhibits fault tolerance, indicating practical engineering applications.
{"title":"Research on Distribution Substation Topology Identification Methods","authors":"Weidong Hu, Zhao Bo, Chen Jie","doi":"10.13052/dgaej2156-3306.3932","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3932","url":null,"abstract":"With the advancement of digital transformation in distribution substations, a large number of smart devices are being integrated into substations. Addressing the challenges of automatic topology recognition and the issue of unstable recognition accuracy in distribution substations has become crucial. This paper proposes a substation topology recognition method based on an improved matrix approach and the Minimum Conditional Probability of Packet Loss Theorem. The improved matrix approach is utilized to calculate the topological signals, enabling automatic bottom-up topology recognition within the substation. The application of the Minimum Conditional Probability of Packet Loss Theorem in processing topological data significantly enhances the accuracy of substation topology recognition, reducing the impact of external factors on recognition accuracy. Experimental validation demonstrates that the proposed method is highly feasible and exhibits fault tolerance, indicating practical engineering applications.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":" 47","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141832176","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：