Pub Date : 2022-04-22DOI: 10.1109/CEEPE55110.2022.9783386
Weixiang Huang, Yangjun Zhou, Bin Zhang, Li Yu, Zhicheng Guo
Edge terminals will play a role in the smart distribution grid of the future, so it needs to process a large amount of user electricity consumption data, at the same time to meet the real-time requirements. The paper studies the multi-objective optimization task resource offloading algorithm for edge terminal server, such as computing power, service time, bandwidth, memory and so on. Firstly, the offloading model of edge computing resources for multi-objective optimization is proposed. On this basis, an improved genetic offloading algorithm is proposed to reduce the probability of population algorithm falling into local optimum by self-adaptation coefficient and factors of age and longevity. Meanwhile, the probability of finding the optimum solution is improved. Finally, the effectiveness of the proposed algorithm is verified through the Matlab/simulink simulation results.
{"title":"Improved Genetic Offloading Algorithm Based on Edge Terminal Server of Power Distribution Station Area","authors":"Weixiang Huang, Yangjun Zhou, Bin Zhang, Li Yu, Zhicheng Guo","doi":"10.1109/CEEPE55110.2022.9783386","DOIUrl":"https://doi.org/10.1109/CEEPE55110.2022.9783386","url":null,"abstract":"Edge terminals will play a role in the smart distribution grid of the future, so it needs to process a large amount of user electricity consumption data, at the same time to meet the real-time requirements. The paper studies the multi-objective optimization task resource offloading algorithm for edge terminal server, such as computing power, service time, bandwidth, memory and so on. Firstly, the offloading model of edge computing resources for multi-objective optimization is proposed. On this basis, an improved genetic offloading algorithm is proposed to reduce the probability of population algorithm falling into local optimum by self-adaptation coefficient and factors of age and longevity. Meanwhile, the probability of finding the optimum solution is improved. Finally, the effectiveness of the proposed algorithm is verified through the Matlab/simulink simulation results.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131618937","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 : 2022-04-22DOI: 10.1109/CEEPE55110.2022.9783280
Xiaoguang Du, Chao Zhang
In order to obtain the detailed energy consumption information of household internal equipment, master the composition of household energy consumption, in order to optimize the user’s power consumption behavior and reduce the household energy consumption, this paper proposes a non-invasive load monitoring optimization method based on blind source separation. This paper proposed using blind source separation algorithm to realize non-invasive load decomposition, analyzed the load independence, with negative entropy maximization as the criterion of the loads are independent of each other, to the load of the separated waveform total full load operation. The rationality of this method is verified by a simulation example of the Reference Energy Disaggregation Data (REDD) set.
{"title":"The Blind Source Separation-based Non-intrusive Load Monitoring Method","authors":"Xiaoguang Du, Chao Zhang","doi":"10.1109/CEEPE55110.2022.9783280","DOIUrl":"https://doi.org/10.1109/CEEPE55110.2022.9783280","url":null,"abstract":"In order to obtain the detailed energy consumption information of household internal equipment, master the composition of household energy consumption, in order to optimize the user’s power consumption behavior and reduce the household energy consumption, this paper proposes a non-invasive load monitoring optimization method based on blind source separation. This paper proposed using blind source separation algorithm to realize non-invasive load decomposition, analyzed the load independence, with negative entropy maximization as the criterion of the loads are independent of each other, to the load of the separated waveform total full load operation. The rationality of this method is verified by a simulation example of the Reference Energy Disaggregation Data (REDD) set.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131772224","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 : 2022-04-22DOI: 10.1109/CEEPE55110.2022.9783357
D. Hai, Tao Zhu, Shangqi Duan, Wei Huang, Wenyu Li
An increasing number of distributed generators are integrated to distribution systems, which has a huge impact on the network power flow and leads to severe voltage fluctuation problems. Volt/VAR control is regarded as an effective approach to improve voltage quality and reduce power loss. Deep reinforcement learning is a data-driven approach to effectively solve decision-making problems. The application of deep reinforcement learning in volt/VAR control circumvent the accurate knowledge of network information, and is endowed with less computational burden. This paper provides a general review of the application of deep reinforcement learning in volt/VAR control in terms of basic notations, Markov decision process formulations, and control framework. Future directions are also discussed.
{"title":"Deep Reinforcement Learning for Volt/VAR Control in Distribution Systems: A Review","authors":"D. Hai, Tao Zhu, Shangqi Duan, Wei Huang, Wenyu Li","doi":"10.1109/CEEPE55110.2022.9783357","DOIUrl":"https://doi.org/10.1109/CEEPE55110.2022.9783357","url":null,"abstract":"An increasing number of distributed generators are integrated to distribution systems, which has a huge impact on the network power flow and leads to severe voltage fluctuation problems. Volt/VAR control is regarded as an effective approach to improve voltage quality and reduce power loss. Deep reinforcement learning is a data-driven approach to effectively solve decision-making problems. The application of deep reinforcement learning in volt/VAR control circumvent the accurate knowledge of network information, and is endowed with less computational burden. This paper provides a general review of the application of deep reinforcement learning in volt/VAR control in terms of basic notations, Markov decision process formulations, and control framework. Future directions are also discussed.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"35 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132791941","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 : 2022-04-22DOI: 10.1109/CEEPE55110.2022.9783255
Dandan Han, L. Zhang, Ying Zheng
To effectively diagnose the defects of oil impregnated paper insulation bushing by using the result of dissolved gas analysis, the gas spectrum is constructed by using the relative content of dissolved gases in oil. The equivalent time length Teq and equivalent frequency band width Feq are extracted as the characteristic parameters of gas spectrum, and are used to cluster and separate the gas spectrum of different defects. Based on that, the time-frequency eigenvalue boundary for identifying different bushing defects is constructed, and the bushing state diagnosis method based on the time-frequency characteristics of spectrum is proposed according to the distribution results of the Teq - Feq graph. Finally, the diagnosis effect of time-frequency spectrum method and Duval triangle method is compared and analyzed, and the effectiveness of spectrum time-frequency feature in bushing defect diagnosis is verified by disassembly visual inspection. The results show that the time-frequency characteristics of spectrum can effectively diagnose the state of bushing, and its diagnostic accuracy is due to Duval triangle method, and it can make up for the defects of Duval triangle method in misjudging or difficult to judge the normal state of bushing, which can be used as an important basis for the state diagnosis of oil impregnated paper insulated bushing.
{"title":"Application of Time Frequency Characteristics of Gas Spectrogram for State Diagnosis of Bushing","authors":"Dandan Han, L. Zhang, Ying Zheng","doi":"10.1109/CEEPE55110.2022.9783255","DOIUrl":"https://doi.org/10.1109/CEEPE55110.2022.9783255","url":null,"abstract":"To effectively diagnose the defects of oil impregnated paper insulation bushing by using the result of dissolved gas analysis, the gas spectrum is constructed by using the relative content of dissolved gases in oil. The equivalent time length Teq and equivalent frequency band width Feq are extracted as the characteristic parameters of gas spectrum, and are used to cluster and separate the gas spectrum of different defects. Based on that, the time-frequency eigenvalue boundary for identifying different bushing defects is constructed, and the bushing state diagnosis method based on the time-frequency characteristics of spectrum is proposed according to the distribution results of the Teq - Feq graph. Finally, the diagnosis effect of time-frequency spectrum method and Duval triangle method is compared and analyzed, and the effectiveness of spectrum time-frequency feature in bushing defect diagnosis is verified by disassembly visual inspection. The results show that the time-frequency characteristics of spectrum can effectively diagnose the state of bushing, and its diagnostic accuracy is due to Duval triangle method, and it can make up for the defects of Duval triangle method in misjudging or difficult to judge the normal state of bushing, which can be used as an important basis for the state diagnosis of oil impregnated paper insulated bushing.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131082820","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 : 2022-04-22DOI: 10.1109/CEEPE55110.2022.9783332
Yingming Tian, F. Gao, Yi Chai
This article focuses on the failure prediction and health management of drum filter actuating device in nuclear power plant. Firstly, by the analysis of drum filter actuating device’s key feature, mechanical structure and main failure form, it found that equipment maintenance trend was transitioning from shutdown maintenance or periodic inspection to real-time online condition monitoring and fault analysis. Secondly, based on the actual situation of the fault concentrate on the medium/high speed first stage reducer, especially the bearing fault, this paper mainly introduced the application cases of drum filter failure prediction and health management, the validity and accuracy of diagnostic conclusions were verified by the equipment maintenance. This case provides practical guidance to implement predictive maintenance and condition maintenance, which provides strong guarantee for the stable operation and contributes to the goal of maximizing the efficiency of the plant.
{"title":"Failure Prediction and Health Management of Drum Filter Actuating Device in Nuclear Power Plant","authors":"Yingming Tian, F. Gao, Yi Chai","doi":"10.1109/CEEPE55110.2022.9783332","DOIUrl":"https://doi.org/10.1109/CEEPE55110.2022.9783332","url":null,"abstract":"This article focuses on the failure prediction and health management of drum filter actuating device in nuclear power plant. Firstly, by the analysis of drum filter actuating device’s key feature, mechanical structure and main failure form, it found that equipment maintenance trend was transitioning from shutdown maintenance or periodic inspection to real-time online condition monitoring and fault analysis. Secondly, based on the actual situation of the fault concentrate on the medium/high speed first stage reducer, especially the bearing fault, this paper mainly introduced the application cases of drum filter failure prediction and health management, the validity and accuracy of diagnostic conclusions were verified by the equipment maintenance. This case provides practical guidance to implement predictive maintenance and condition maintenance, which provides strong guarantee for the stable operation and contributes to the goal of maximizing the efficiency of the plant.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131102512","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}
This paper proposed a practical traveling wave (TW)-based fault location methodology in a cost-effective way on high-voltage transmission lines. In order to cope with the difficulty of identifying the faulty reflected wavefront caused by TW attenuation and noise interference, a TW wavefront detection method using progressive kurtosis is developed to capture the precise arrival time of the reflected wave, which can significantly eliminate the influence of false wave head calibration existing in conventional methods. The correlation coefficient is used to compare the polarities of the initial and the second reflected TWs, so as to identify the source of the second reflected wave, and thus identifying the faulty segment. Finally, the precise fault distance can be calculated through the appropriate TW fault location formula. The proposed method is validated through several actual fault records obtained in ±800 kV Kunbei substation.
{"title":"A Novel Fault Location Method for Transmission Lines Based on Progressive Kurtosis in ±800 kV Kunbei Converter Station","authors":"Shuai Yan, Jiajiang Xu, Xiang Ju, Zhenyu Wu, Haoran Fan, Pulin Cao","doi":"10.1109/CEEPE55110.2022.9783294","DOIUrl":"https://doi.org/10.1109/CEEPE55110.2022.9783294","url":null,"abstract":"This paper proposed a practical traveling wave (TW)-based fault location methodology in a cost-effective way on high-voltage transmission lines. In order to cope with the difficulty of identifying the faulty reflected wavefront caused by TW attenuation and noise interference, a TW wavefront detection method using progressive kurtosis is developed to capture the precise arrival time of the reflected wave, which can significantly eliminate the influence of false wave head calibration existing in conventional methods. The correlation coefficient is used to compare the polarities of the initial and the second reflected TWs, so as to identify the source of the second reflected wave, and thus identifying the faulty segment. Finally, the precise fault distance can be calculated through the appropriate TW fault location formula. The proposed method is validated through several actual fault records obtained in ±800 kV Kunbei substation.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131154425","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 : 2022-04-22DOI: 10.1109/CEEPE55110.2022.9783308
Dabo Zhang, Zhuwei Chu, Huaixin Guo, Boxin Wang
Aiming at the bottleneck problem of the lack of equipment fuzzy failure rate that can reflect its own health state in the calculation process of fuzzy probability hybrid reliability evaluation of power system, a fuzzy failure rate model of power transformers based on multi-source condition monitoring information is proposed. This method first establishes the transformer condition monitoring index system considering fuzzy condition monitoring information, expresses the condition monitoring data through the fuzzy normal distribution membership function, and uses the fuzzy synthetic evaluation method to evaluate the health state of the transformer; the concept of evaluation set is extended, the state evaluation result data is processed by means of decomposition, superposition, mapping and fitting. Finally, the transformer condition failure rate expressed by membership function is obtained. The calculation example of a power grid in a certain area shows the calculation process of the fuzzy failure rate of the transformer, and the results conform to the statistical law of reliability data, which can reflect the current state of the equipment. The fuzzy failure rate can be used to calculate the fuzzy probability hybrid reliability index of the power system, and can provide decision support for further equipment maintenance.
{"title":"Fuzzy Failure Rate Model of Power Transformer Based on Condition Monitoring","authors":"Dabo Zhang, Zhuwei Chu, Huaixin Guo, Boxin Wang","doi":"10.1109/CEEPE55110.2022.9783308","DOIUrl":"https://doi.org/10.1109/CEEPE55110.2022.9783308","url":null,"abstract":"Aiming at the bottleneck problem of the lack of equipment fuzzy failure rate that can reflect its own health state in the calculation process of fuzzy probability hybrid reliability evaluation of power system, a fuzzy failure rate model of power transformers based on multi-source condition monitoring information is proposed. This method first establishes the transformer condition monitoring index system considering fuzzy condition monitoring information, expresses the condition monitoring data through the fuzzy normal distribution membership function, and uses the fuzzy synthetic evaluation method to evaluate the health state of the transformer; the concept of evaluation set is extended, the state evaluation result data is processed by means of decomposition, superposition, mapping and fitting. Finally, the transformer condition failure rate expressed by membership function is obtained. The calculation example of a power grid in a certain area shows the calculation process of the fuzzy failure rate of the transformer, and the results conform to the statistical law of reliability data, which can reflect the current state of the equipment. The fuzzy failure rate can be used to calculate the fuzzy probability hybrid reliability index of the power system, and can provide decision support for further equipment maintenance.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133681898","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 : 2022-04-22DOI: 10.1109/CEEPE55110.2022.9783239
Ji Yu, Xianggen Yin, Jiaxuan Hu, J. Lai
Modular Multilevel Converter (MMC) is widely used in high-power systems such as HVDC transmission. Improving the fault ride-through capability of MMC is very important for a power system. Aiming at the problem of unbalanced leg power under asymmetric grid conditions, an improved leg balancing control strategy based on zero-sequence voltage and dc circulating current injection of MMC is proposed. When the grid voltage is asymmetric, the proposed leg balancing control strategy can eliminate the impact of ac grid voltage asymmetry on leg power, and ensure the safe and stable operation of MMC. Finally, the effectiveness of the proposed control strategy is verified by simulation.
{"title":"Leg-balancing Control of Modular Multilevel Converter under Asymmetric Condition","authors":"Ji Yu, Xianggen Yin, Jiaxuan Hu, J. Lai","doi":"10.1109/CEEPE55110.2022.9783239","DOIUrl":"https://doi.org/10.1109/CEEPE55110.2022.9783239","url":null,"abstract":"Modular Multilevel Converter (MMC) is widely used in high-power systems such as HVDC transmission. Improving the fault ride-through capability of MMC is very important for a power system. Aiming at the problem of unbalanced leg power under asymmetric grid conditions, an improved leg balancing control strategy based on zero-sequence voltage and dc circulating current injection of MMC is proposed. When the grid voltage is asymmetric, the proposed leg balancing control strategy can eliminate the impact of ac grid voltage asymmetry on leg power, and ensure the safe and stable operation of MMC. Finally, the effectiveness of the proposed control strategy is verified by simulation.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133109224","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 : 2022-04-22DOI: 10.1109/CEEPE55110.2022.9783343
Zhonghua Zhao, Li Zhu, Yiping Wang, Qunwu Huang, Yong Sun
Enough heat may be obtained by collecting the heat from the PV/T (photovoltaic thermal system), and the degradation of PV cell efficiency caused by overheating can be successfully avoided. Air cooling and liquid cooling are two common solar PV/T cooling solutions. The air-cooled PV/T system has numerous variables, and the heat transfer mathematical model is quite complicated. The BP (Back Propagation) neural network method can be used to create a simulation prediction model, which can be used to simulate and predict the thermal and electrical performance of a solar PV/T system. The simulation is based on data from a single-day experiment conducted during the heating season, and it records the BP neural network's six types of temperature difference prediction and electric power prediction under various input parameters (groups 2-group 4-group 6). The appropriate level of training has been attained. The electrical and thermal efficiency of PV/T systems can be calculated using these BP neural networks. The fitting degree of the anticipated value and the actual value of the BP neural network model fulfils the requirements when compared to the experimental data gathered over two days and the prediction results of the BP neural network model. On the two projected days, the prediction accuracy R2 of electric power value were 0.97009 and 0.95538, respectively. The temperature difference numerical fitting degree is somewhat worse than the electric power value. The R2 values were 0.90114 and 0.93547, respectively, for forecast accuracy. This research will assist in further predicting and analyzing the application benefit of PV/T systems in different regions and climate conditions， and will provide valuable information for PV/T system application in building integration.
{"title":"Study on Influence of Input Parameters by Back Propagation Neural Network on Performance Prediction of Air-type Photovoltaic Thermal System","authors":"Zhonghua Zhao, Li Zhu, Yiping Wang, Qunwu Huang, Yong Sun","doi":"10.1109/CEEPE55110.2022.9783343","DOIUrl":"https://doi.org/10.1109/CEEPE55110.2022.9783343","url":null,"abstract":"Enough heat may be obtained by collecting the heat from the PV/T (photovoltaic thermal system), and the degradation of PV cell efficiency caused by overheating can be successfully avoided. Air cooling and liquid cooling are two common solar PV/T cooling solutions. The air-cooled PV/T system has numerous variables, and the heat transfer mathematical model is quite complicated. The BP (Back Propagation) neural network method can be used to create a simulation prediction model, which can be used to simulate and predict the thermal and electrical performance of a solar PV/T system. The simulation is based on data from a single-day experiment conducted during the heating season, and it records the BP neural network's six types of temperature difference prediction and electric power prediction under various input parameters (groups 2-group 4-group 6). The appropriate level of training has been attained. The electrical and thermal efficiency of PV/T systems can be calculated using these BP neural networks. The fitting degree of the anticipated value and the actual value of the BP neural network model fulfils the requirements when compared to the experimental data gathered over two days and the prediction results of the BP neural network model. On the two projected days, the prediction accuracy R2 of electric power value were 0.97009 and 0.95538, respectively. The temperature difference numerical fitting degree is somewhat worse than the electric power value. The R2 values were 0.90114 and 0.93547, respectively, for forecast accuracy. This research will assist in further predicting and analyzing the application benefit of PV/T systems in different regions and climate conditions， and will provide valuable information for PV/T system application in building integration.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126584946","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 continuous increase of distributed photovoltaic penetration in low-voltage power distribution network, the problem of voltage overlimit caused by insufficient photovoltaic accommodation ability becomes more and more prominent. The construction of Power Distribution Internet of Things (PD-IoT) provides a new way for voltage optimization control and photovoltaic consumption. Combined with the radial structure of low-voltage power distribution network, the voltage regulation ability of distributed resources is analyzed by using the chain rule, and the comprehensive voltage sensitivity is proposed to describe the voltage regulation ability of distributed resources. The voltage adjustment strategy considering the cost is designed, and the point-by-point regulation method is adopted to control the voltage overlimit rapidly, so as to achieve a balance between more photovoltaic power generation, smaller line loss and larger user benefits. The control method designed is suitable for multi-node voltage overlimit.
{"title":"Research on Voltage Control Strategy Based on PD-IoT with High Penetration of Photovoltaic Power Supply","authors":"Shuaitao Bai, Peng Wang, Shen Guo, C. Tan, Jichuan Zhang, Mingyu Zhang","doi":"10.1109/CEEPE55110.2022.9783324","DOIUrl":"https://doi.org/10.1109/CEEPE55110.2022.9783324","url":null,"abstract":"With the continuous increase of distributed photovoltaic penetration in low-voltage power distribution network, the problem of voltage overlimit caused by insufficient photovoltaic accommodation ability becomes more and more prominent. The construction of Power Distribution Internet of Things (PD-IoT) provides a new way for voltage optimization control and photovoltaic consumption. Combined with the radial structure of low-voltage power distribution network, the voltage regulation ability of distributed resources is analyzed by using the chain rule, and the comprehensive voltage sensitivity is proposed to describe the voltage regulation ability of distributed resources. The voltage adjustment strategy considering the cost is designed, and the point-by-point regulation method is adopted to control the voltage overlimit rapidly, so as to achieve a balance between more photovoltaic power generation, smaller line loss and larger user benefits. The control method designed is suitable for multi-node voltage overlimit.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131263410","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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