Pub Date : 2021-07-18DOI: 10.1109/ICPSAsia52756.2021.9621499
Jinran Guo, Zhaohao Ding
Given that many countries have issued a series of policies to promote the development of electric vehicles (EVs), the amount of EVs is growing rapidly around the world. The charging behaviour of EVs is related to the transportation system, power grid and passenger travel. Considering the uncertainty of passenger travel choice, we establish a EV fleet charging management method in this paper to optimize the dispatch of EV fleet and minimize the total system cost. On the one hand, we design the EV routing mechanism and charging scheduling strategy based on the traffic condition and the limit of charging stations. On the other hand, the uncertainty of passenger travel choice resulted from the consumer preference is also proposed in this model, which greatly affect the operation condition of EVs. In addition, a numerical case study is utilized to verify the effectiveness of the proposed model.
{"title":"EV Fleet Charging Management Method Considering the Uncertainty of Passenger Travel Choice","authors":"Jinran Guo, Zhaohao Ding","doi":"10.1109/ICPSAsia52756.2021.9621499","DOIUrl":"https://doi.org/10.1109/ICPSAsia52756.2021.9621499","url":null,"abstract":"Given that many countries have issued a series of policies to promote the development of electric vehicles (EVs), the amount of EVs is growing rapidly around the world. The charging behaviour of EVs is related to the transportation system, power grid and passenger travel. Considering the uncertainty of passenger travel choice, we establish a EV fleet charging management method in this paper to optimize the dispatch of EV fleet and minimize the total system cost. On the one hand, we design the EV routing mechanism and charging scheduling strategy based on the traffic condition and the limit of charging stations. On the other hand, the uncertainty of passenger travel choice resulted from the consumer preference is also proposed in this model, which greatly affect the operation condition of EVs. In addition, a numerical case study is utilized to verify the effectiveness of the proposed model.","PeriodicalId":296085,"journal":{"name":"2021 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115510440","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}
Taking account of day-ahead dispatch of electricity-gas-heating regional integrated energy system, the active power flow model of distribution network, heating network transmission equation and natural gas network model with the consideration of dynamic characteristics of natural gas are first established. Then, a day-ahead dispatch model is established aiming at minimizing the system economic cost. Considering the privacy and opacity of information between electricity-gas-heating entities, based on improved alternating direction multiplier method, a distributed optimization scheduling method is proposed. The augmented Lagrange function of the whole system is decomposed into three sub-problems of electricity, natural gas and heating for iterative solution. Finally, case study verified the effectiveness and validity of the proposed model and optimization method.
{"title":"Distributed Optimal Dispatch of Regional Integrated Energy System Considering Electricity-Gas-Heating","authors":"Wenzhao Nie, Jing Wang, Yongkai Zhang, Zhipeng Qiu, Encheng Dong, Jian Chen","doi":"10.1109/ICPSAsia52756.2021.9621710","DOIUrl":"https://doi.org/10.1109/ICPSAsia52756.2021.9621710","url":null,"abstract":"Taking account of day-ahead dispatch of electricity-gas-heating regional integrated energy system, the active power flow model of distribution network, heating network transmission equation and natural gas network model with the consideration of dynamic characteristics of natural gas are first established. Then, a day-ahead dispatch model is established aiming at minimizing the system economic cost. Considering the privacy and opacity of information between electricity-gas-heating entities, based on improved alternating direction multiplier method, a distributed optimization scheduling method is proposed. The augmented Lagrange function of the whole system is decomposed into three sub-problems of electricity, natural gas and heating for iterative solution. Finally, case study verified the effectiveness and validity of the proposed model and optimization method.","PeriodicalId":296085,"journal":{"name":"2021 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)","volume":"08 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127216067","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 : 2021-07-18DOI: 10.1109/ICPSAsia52756.2021.9621732
Xueshen Zhao, Lin Zhu, Haolin Lu, Zhanfeng Deng, Jie Song, Li Guo
For medium-voltage DC (MVDC) distribution system, the MVDC bus voltage is prone to instability owing to the dynamic interaction among source, constant power load and cable. Based on the equivalent model of MVDC distribution system, the input and output impedance considering the cable are established in detail. In addition, the voltage closed-loop transfer function of the equivalent model is also established. Then, the instability mechanism of system oscillation caused by cable can be revealed intuitively. To address this oscillation issue, the input filter capacitor of constant power load is further increased, thus the oscillation instability of the system is suppressed effectively. Finally, PLECS based simulation verification have been provided.
{"title":"Oscillation Coupling Analysis of MVDC Distribution System Based on Impedance Measurement","authors":"Xueshen Zhao, Lin Zhu, Haolin Lu, Zhanfeng Deng, Jie Song, Li Guo","doi":"10.1109/ICPSAsia52756.2021.9621732","DOIUrl":"https://doi.org/10.1109/ICPSAsia52756.2021.9621732","url":null,"abstract":"For medium-voltage DC (MVDC) distribution system, the MVDC bus voltage is prone to instability owing to the dynamic interaction among source, constant power load and cable. Based on the equivalent model of MVDC distribution system, the input and output impedance considering the cable are established in detail. In addition, the voltage closed-loop transfer function of the equivalent model is also established. Then, the instability mechanism of system oscillation caused by cable can be revealed intuitively. To address this oscillation issue, the input filter capacitor of constant power load is further increased, thus the oscillation instability of the system is suppressed effectively. Finally, PLECS based simulation verification have been provided.","PeriodicalId":296085,"journal":{"name":"2021 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127544502","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 : 2021-07-18DOI: 10.1109/ICPSAsia52756.2021.9621554
Zhenhe Ma, Changgang Li, Hua Ye, Yutian Liu
The open-source simulation toolkit for electrical power systems (STEPS) was released in Github with power flow and dynamic simulation functions. However, voltage source converter based multi-terminal direct current (VSC-MTDC) was not supported in STEPS. In this paper, the steady-state model of VSC-MTDC is introduced with diverse control modes. Then, the AC/DC power flow algorithm with VSC-MTDC is designed with alternating iteration method. The controlled variables exceeding limits with different control modes are handled in the algorithm. Besides, a general data structure of VSC-MTDC is established based on PSS/E and realized in STEPS. Finally, Two cases with VSC-MTDC are tested to verify the feasibility and effectiveness of the designed algorithm.
{"title":"Extension of Power Flow Algorithm in STEPS for AC/DC Hybrid Power Systems with VSC-MTDC","authors":"Zhenhe Ma, Changgang Li, Hua Ye, Yutian Liu","doi":"10.1109/ICPSAsia52756.2021.9621554","DOIUrl":"https://doi.org/10.1109/ICPSAsia52756.2021.9621554","url":null,"abstract":"The open-source simulation toolkit for electrical power systems (STEPS) was released in Github with power flow and dynamic simulation functions. However, voltage source converter based multi-terminal direct current (VSC-MTDC) was not supported in STEPS. In this paper, the steady-state model of VSC-MTDC is introduced with diverse control modes. Then, the AC/DC power flow algorithm with VSC-MTDC is designed with alternating iteration method. The controlled variables exceeding limits with different control modes are handled in the algorithm. Besides, a general data structure of VSC-MTDC is established based on PSS/E and realized in STEPS. Finally, Two cases with VSC-MTDC are tested to verify the feasibility and effectiveness of the designed algorithm.","PeriodicalId":296085,"journal":{"name":"2021 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125388324","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 : 2021-07-18DOI: 10.1109/ICPSAsia52756.2021.9621428
Pengfei Zhao, Zhenyuan Zhang, Haoran Chen, Peng Wang
Various hybrid load forecasting models have been proposed in recent years, but they generally assign weights to individual forecasting models for optimal combinations and without taking full advantage of the strengths of each model. In this paper, a hybrid Deep Learning Gaussian Process (HDLGP) model for short-term deterministic and probabilistic load forecasting (DLF and PLF) is proposed. This model merges the predictive power of artificial neural networks (ANN) and the ability to handle uncertainty of Gaussian Process (GP) by a composite kernel. Firstly, we design a multi-layer perception (MLP) neural network to learn high fluctuating load data. Then a GP with a composite kernel is incorporated to capture the residuals based on MLP so that further boost accuracy of DLF, meanwhile performing high-quality probability density estimation. Our model guarantees both reliability and sharpness of the PLF. Verifying our proposed model based on the realistically available data, it indicates that our model outperforms the other list approaches both in DLF and PLF.
{"title":"Hybrid Deep Learning Gaussian Process for Deterministic and Probabilistic Load Forecasting","authors":"Pengfei Zhao, Zhenyuan Zhang, Haoran Chen, Peng Wang","doi":"10.1109/ICPSAsia52756.2021.9621428","DOIUrl":"https://doi.org/10.1109/ICPSAsia52756.2021.9621428","url":null,"abstract":"Various hybrid load forecasting models have been proposed in recent years, but they generally assign weights to individual forecasting models for optimal combinations and without taking full advantage of the strengths of each model. In this paper, a hybrid Deep Learning Gaussian Process (HDLGP) model for short-term deterministic and probabilistic load forecasting (DLF and PLF) is proposed. This model merges the predictive power of artificial neural networks (ANN) and the ability to handle uncertainty of Gaussian Process (GP) by a composite kernel. Firstly, we design a multi-layer perception (MLP) neural network to learn high fluctuating load data. Then a GP with a composite kernel is incorporated to capture the residuals based on MLP so that further boost accuracy of DLF, meanwhile performing high-quality probability density estimation. Our model guarantees both reliability and sharpness of the PLF. Verifying our proposed model based on the realistically available data, it indicates that our model outperforms the other list approaches both in DLF and PLF.","PeriodicalId":296085,"journal":{"name":"2021 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)","volume":"51 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126156574","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 : 2021-07-18DOI: 10.1109/ICPSAsia52756.2021.9621615
Wang Xinyi, Han Xueshan, Yang Ming, Yu Yixiao
Large-scale wind power integration makes the impact of wind power ramp events more impossible to ignore. Compared with single station prediction, cluster prediction can reflect the impact of power mutation on the power system more intuitively, and the prediction results are more conducive to the decision-making of dispatchers. Therefore, this paper proposed an imprecise probabilistic prediction method for wind farm cluster. Data dimensionality reduction was carried out through correlation analysis and principal component analysis to avoid problems such as excessive data dimension caused by too many input variables and the influence of calculation speed. The credal network (CN) was established to express the dependent relationship between wind farm cluster ramp events and evidence variables, and the conditional dependent relationship was statistically quantified by using the imprecise Dirichlet model (IDM). Finally, combined with meteorological information, the ramp events were classified and inferred in the form of probability intervals, and the prediction performance was evaluated by using evaluation indexes. In this paper, the validity of the method was verified by using the data of a wind farm cluster in Xinjiang.
{"title":"Interval Probability Estimation of Wind Farm Cluster Ramp Events Based on Credal Network","authors":"Wang Xinyi, Han Xueshan, Yang Ming, Yu Yixiao","doi":"10.1109/ICPSAsia52756.2021.9621615","DOIUrl":"https://doi.org/10.1109/ICPSAsia52756.2021.9621615","url":null,"abstract":"Large-scale wind power integration makes the impact of wind power ramp events more impossible to ignore. Compared with single station prediction, cluster prediction can reflect the impact of power mutation on the power system more intuitively, and the prediction results are more conducive to the decision-making of dispatchers. Therefore, this paper proposed an imprecise probabilistic prediction method for wind farm cluster. Data dimensionality reduction was carried out through correlation analysis and principal component analysis to avoid problems such as excessive data dimension caused by too many input variables and the influence of calculation speed. The credal network (CN) was established to express the dependent relationship between wind farm cluster ramp events and evidence variables, and the conditional dependent relationship was statistically quantified by using the imprecise Dirichlet model (IDM). Finally, combined with meteorological information, the ramp events were classified and inferred in the form of probability intervals, and the prediction performance was evaluated by using evaluation indexes. In this paper, the validity of the method was verified by using the data of a wind farm cluster in Xinjiang.","PeriodicalId":296085,"journal":{"name":"2021 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126002481","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 : 2021-07-18DOI: 10.1109/ICPSAsia52756.2021.9621574
Zhenwei Zhang, Chengfu Wang, Ruiqi Wang, Guanghua Guo, Shuai Chen, Yan Wang
Secure and economic operation of the integrated energy system (IES) is challenged by the high level of the uncertainty and fluctuation introduced by wind power sources. In this paper, a multi-time scale co-optimization scheduling scheme of IES is proposed, which considers tracking the wind power uncertainty to achieve accurate power balance and optimal economic operation of the whole system. First, a multi-time scale co-optimization model framework is established, and the electric power system, natural gas system and district heating system are coordinated to achieve more flexibility in each time scale. In the day-ahead stage, the optimal unit commitment is determined, furthermore, the operation scheme is adjusted on a rolling basis to track the random fluctuation of wind power in the intra-day stage. In the real-time stage, model predictive control (MPC) is used to achieve precise control, which takes the intra-day scheme as a reference to minimize operating deviations. Besides, the auto regressive moving average (ARMA) model and scenario method are employed to represent the wind power uncertainty by typical scenarios with corresponding probabilities. Finally, simulation results on an IEEE39-NGS20-DHS21 test system demonstrate the superiority of the proposed method in operational economy and wind power utilization, and also verify the effectiveness of the method to satisfy the uncertainty balancing.
{"title":"Multi-time Scale Co-optimization Scheduling of Integrated Energy System for Uncertainty Balancing","authors":"Zhenwei Zhang, Chengfu Wang, Ruiqi Wang, Guanghua Guo, Shuai Chen, Yan Wang","doi":"10.1109/ICPSAsia52756.2021.9621574","DOIUrl":"https://doi.org/10.1109/ICPSAsia52756.2021.9621574","url":null,"abstract":"Secure and economic operation of the integrated energy system (IES) is challenged by the high level of the uncertainty and fluctuation introduced by wind power sources. In this paper, a multi-time scale co-optimization scheduling scheme of IES is proposed, which considers tracking the wind power uncertainty to achieve accurate power balance and optimal economic operation of the whole system. First, a multi-time scale co-optimization model framework is established, and the electric power system, natural gas system and district heating system are coordinated to achieve more flexibility in each time scale. In the day-ahead stage, the optimal unit commitment is determined, furthermore, the operation scheme is adjusted on a rolling basis to track the random fluctuation of wind power in the intra-day stage. In the real-time stage, model predictive control (MPC) is used to achieve precise control, which takes the intra-day scheme as a reference to minimize operating deviations. Besides, the auto regressive moving average (ARMA) model and scenario method are employed to represent the wind power uncertainty by typical scenarios with corresponding probabilities. Finally, simulation results on an IEEE39-NGS20-DHS21 test system demonstrate the superiority of the proposed method in operational economy and wind power utilization, and also verify the effectiveness of the method to satisfy the uncertainty balancing.","PeriodicalId":296085,"journal":{"name":"2021 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)","volume":"164 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127300888","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 : 2021-07-18DOI: 10.1109/ICPSAsia52756.2021.9621385
Kejing Duan, Wei Wang, Wu Wei, Zhenya Ji
Nowadays, comprehensive utilization of multiple forms of energy to improve energy efficiency is popularly concerned. To this end, an isolated combined cooling, heating and power (CCHP) system is deeply explored in this paper. Aiming for further optimization of the system according to characteristics of island operation mode, two integrated energy system (IES) evaluation indexes are taken as the objective function. Furthermore, a time-varying weight factor is proposed to calculate multi-objective function and optimize the system scheduling. Case study results demonstrate that the potential benefits of the proposed optimization strategy in terms of operation economics, energy utilization, as well as the load balancing of isolated system.
{"title":"Optimal Operation of Isolated CCHP System Considering Time-Varying Weighting Factors","authors":"Kejing Duan, Wei Wang, Wu Wei, Zhenya Ji","doi":"10.1109/ICPSAsia52756.2021.9621385","DOIUrl":"https://doi.org/10.1109/ICPSAsia52756.2021.9621385","url":null,"abstract":"Nowadays, comprehensive utilization of multiple forms of energy to improve energy efficiency is popularly concerned. To this end, an isolated combined cooling, heating and power (CCHP) system is deeply explored in this paper. Aiming for further optimization of the system according to characteristics of island operation mode, two integrated energy system (IES) evaluation indexes are taken as the objective function. Furthermore, a time-varying weight factor is proposed to calculate multi-objective function and optimize the system scheduling. Case study results demonstrate that the potential benefits of the proposed optimization strategy in terms of operation economics, energy utilization, as well as the load balancing of isolated system.","PeriodicalId":296085,"journal":{"name":"2021 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123609170","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 : 2021-07-18DOI: 10.1109/ICPSAsia52756.2021.9621561
Jiaxin Zhao, M. Zeng, Xiaorui Qian, Zhipeng Zhong, Yongli Wang
Studying the cost-benefit of demand response is helpful for participants to understand their own input and benefit. The system dynamics method provides a more systematic, dynamic, and clear causal feedback solution for the cost-benefit analysis of demand response. Based on the analysis of the cost-benefit sources of demand response related entities, this paper applies the system dynamics method to the cost-benefit model of demand response, and verifies the rationality and effectiveness of the model through simulation examples, which provides theoretical reference value for the further development of demand response resources.
{"title":"Research on Cost and Benefit of Demand Response Related Entities Based on System Dynamics","authors":"Jiaxin Zhao, M. Zeng, Xiaorui Qian, Zhipeng Zhong, Yongli Wang","doi":"10.1109/ICPSAsia52756.2021.9621561","DOIUrl":"https://doi.org/10.1109/ICPSAsia52756.2021.9621561","url":null,"abstract":"Studying the cost-benefit of demand response is helpful for participants to understand their own input and benefit. The system dynamics method provides a more systematic, dynamic, and clear causal feedback solution for the cost-benefit analysis of demand response. Based on the analysis of the cost-benefit sources of demand response related entities, this paper applies the system dynamics method to the cost-benefit model of demand response, and verifies the rationality and effectiveness of the model through simulation examples, which provides theoretical reference value for the further development of demand response resources.","PeriodicalId":296085,"journal":{"name":"2021 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121567959","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 : 2021-07-18DOI: 10.1109/ICPSAsia52756.2021.9621758
Huayv Ji, Fan Xie, Wenxun Xiao, Bo Zhang
With the development of renewable energy and distributed generation, DC-AC converters are widely used in the power system. For the safety of the power system, the analysis of the stability in the inverter is vital. For this issue, the small-signal model of the inverter is established in this paper. Some non-ideal components are investigated, like response delay time and the parasitic inductance of the resistance. This paper determines that these factors do affect the stability of the DC-AC converter. The slow-scale instability is detected by the small-signal analysis. In the final, the experimental device is carried out to verify the correctness of the investigation.
{"title":"Stability Analysis of Single-Phase Full H-bridge DC-AC Converter with Considering Time Delay and Parasitic Inductance Based on Small-Signal Model","authors":"Huayv Ji, Fan Xie, Wenxun Xiao, Bo Zhang","doi":"10.1109/ICPSAsia52756.2021.9621758","DOIUrl":"https://doi.org/10.1109/ICPSAsia52756.2021.9621758","url":null,"abstract":"With the development of renewable energy and distributed generation, DC-AC converters are widely used in the power system. For the safety of the power system, the analysis of the stability in the inverter is vital. For this issue, the small-signal model of the inverter is established in this paper. Some non-ideal components are investigated, like response delay time and the parasitic inductance of the resistance. This paper determines that these factors do affect the stability of the DC-AC converter. The slow-scale instability is detected by the small-signal analysis. In the final, the experimental device is carried out to verify the correctness of the investigation.","PeriodicalId":296085,"journal":{"name":"2021 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121752222","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}