2020 IEEE Sustainable Power and Energy Conference (iSPEC)最新文献

{"title":"Study on Fault Deduction and Operation Mode Optimization of VSC HVDC Power Grid","authors":"Guangming Xin, Huan Xie, Xiaoran Wei, Rui Chen, Shuxian Yi","doi":"10.1109/iSPEC50848.2020.9351285","DOIUrl":"https://doi.org/10.1109/iSPEC50848.2020.9351285","url":null,"abstract":"The operation characteristics and mode of VSC HVDC power network are studied. Based on the analysis of VSC HVDC power grid fault characteristics and protection strategy, the optimization and deduction of VSC HVDC power grid operation mode is studied, and the optimization scheme of power grid operation mode after fault based on objective function and priority is proposed. The analytic coding rules of the operation mode are formulated and the software for optimizing the operation mode is developed.","PeriodicalId":403879,"journal":{"name":"2020 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131636966","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}

{"title":"A Self-Constructed CNN Classifier for Keyhole Detection and Location","authors":"Junjie Ye, Bingtuan Gao, Hao Chen, Weilun Xu, Linlin Zhong, Chuande Liu","doi":"10.1109/iSPEC50848.2020.9351106","DOIUrl":"https://doi.org/10.1109/iSPEC50848.2020.9351106","url":null,"abstract":"With the development of the demand for robots to have the ability of opening and unlocking, a real-time detection algorithm for keyhole is proposed in this paper, which combines a fast circle detection based on circle edge feature and a keyhole classifier using self-constructed CNN. The algorithm needs three steps: CNN construction, circle detection and keyhole classification of local circle image. Firstly, collecting keyhole images as positive samples, collecting non-keyhole images and random images as negative samples. Then, constructing a CNN classifier to solve the classification problems and training it based on the samples collected before. Consequently, detecting the real-time collected image whether it has circles and where they are. Furthermore, extract images might have circle area and using the CNN keyhole classifier trained before to determine whether each of them is a keyhole image or not. The experimental results show that using the same training and testing samples, the accuracy of self-constructed CNN classifier(>98 %) is about 1 % lower than that the CNN classifier using AlexNet migration learning network (>99 %), but its time use is about 95% less than the AlexNet and its net size is about 99.5% less than the AlexNet.","PeriodicalId":403879,"journal":{"name":"2020 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130870000","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}

{"title":"Research on Typical Power Supply Mode of DC Distribution and Consumption System","authors":"Wei Zhang, Yuanhong Liu, Yanyan Cui, Mingxin Zhao, Tao Wei, Qing Chen","doi":"10.1109/iSPEC50848.2020.9350972","DOIUrl":"https://doi.org/10.1109/iSPEC50848.2020.9350972","url":null,"abstract":"With the increasing of charging facilities, 5G base station, DC household appliances and other new DC load and photovoltaic DC source, the research of DC distribution network with safety, reliability and efficiency has tremendous market potential and application value. This paper puts forward the grid structure of medium voltage DC distribution network and the bus structure of low voltage DC power system, analyzes the characteristics and application of different converter connection forms, and puts forward the typical grounding mode of pseudo bipolar and true bipolar DC distribution system composed of two-level converter and MMC converter. Finally, the typical application scenarios of DC load concentration area were taken as an example to construct its typical power supply mode from voltage level, grid structure, wiring mode and grounding mode.","PeriodicalId":403879,"journal":{"name":"2020 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130875202","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}

{"title":"Robust Optimal Power Flow Model of Electricity-Gas Integrated Energy System Considering the Uncertainty of Roof Photovoltaic: *Note: Sub-titles are not captured in Xplore and should not be used","authors":"Zhengfeng Qin, Xiaoqing Bai","doi":"10.1109/iSPEC50848.2020.9350978","DOIUrl":"https://doi.org/10.1109/iSPEC50848.2020.9350978","url":null,"abstract":"With more and more distributed roof photovoltaic generators connected to the grid and their complementarity with gas turbines, which providing many possibilities for the coordinated operation of electricity-gas integrated energy system (IES). In this paper, aiming at minimize the total generation costs, a robust optimal power flow (ROPF) model was proposed for the electricity-gas IES connecting roof photovoltaic (PV) generators in the distribution network. Then, the proposed ROPF model is transformed into the mixed-integer second-order conic programming (MISOCP) model, which can be solved by Gurobi. The numerical simulations are carried on IG-30 system and IG-118 system to verify the applicability, safety and economy of the proposed ROPF model.","PeriodicalId":403879,"journal":{"name":"2020 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131008769","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}

{"title":"Single-phase grounding fault line selection method for distribution network with same bus loop based on cluster","authors":"Rui Mei, Suya Qin, Jing Xu, Tinglong Guan, Shuting Zhao, Chao Yuan","doi":"10.1109/iSPEC50848.2020.9351016","DOIUrl":"https://doi.org/10.1109/iSPEC50848.2020.9351016","url":null,"abstract":"This paper proposes a fault line selection method using zero-sequence current waveform clustering for distribution network with same bus loop which has a high reliability for single-phase grounding faults. The purpose is to improve the adaptability of fault detection method for different line structure and fault conditions. Firstly, a single-phase grounding fault composite sequence (module) network for distribution network with same bus loop is established. It is used to analyze the zero-sequence current waveform difference between fault line/fault loop and healthy line during fault transient time. Furthermore, a fault line selection method using zero-sequence current waveform clustering is proposed. This method can be applied to low and high impedance grounding faults on a single outgoing line, the loop with the same bus, and the bus line. It has a strong applicability and improves the reliability of single-phase grounding fault line selection for resonant grounding systems. Simulation data verify the correctness of the method.","PeriodicalId":403879,"journal":{"name":"2020 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131083563","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}

{"title":"A New Method to Evaluate and Predict the Load Capacity of Transformer in Energy Internet with Data Mining Algorithm","authors":"Yingjie Li, Zhining Lv, Ning Pang, Yi Luo, Gen Zhao, Jun Hu","doi":"10.1109/iSPEC50848.2020.9351289","DOIUrl":"https://doi.org/10.1109/iSPEC50848.2020.9351289","url":null,"abstract":"Based on realistic transformer dataset, this paper comes up with a method to predict the top oil temperature (TOT) of a main transformer based on the historic TOT, ambient temperature (AT), transformer load (TL) and present AT, TL. Technically, TOT is predicted by striking a balance between univariate time series prediction and multivariate prediction, more specifically, between considering time series features such as trend, seasonality and considering relationship among TOT, AT and TL. From the results, the proposed scheme significantly outperforms the tradition time series model and support vector regression.","PeriodicalId":403879,"journal":{"name":"2020 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"26 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131150344","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}

{"title":"Reserve-energy joint optimal scheduling based on Conditional Value-at-Risk for Islanded Microgrids","authors":"Siliang Liu, Zehuai Liu, Qinhao Li, Wenyang Deng, Lai Zhou","doi":"10.1109/iSPEC50848.2020.9351024","DOIUrl":"https://doi.org/10.1109/iSPEC50848.2020.9351024","url":null,"abstract":"The uncertainty of renewable energy sources (RESs) has adverse effect to the islanded microgrids (IMGs) operation. The traditional method by using spinning reserve as supporting reserve capacity is neither economic nor environmental. The reasonable allocation of the reserve capacity is necessary. Thus, a reserve-energy joint optimal scheduling model is proposed for the economic and reliable operation of IMGs. Firstly, a multi-type reserve capacity system for IMGs considering the different characteristics of generation-load-storage is proposed. Then, the conditional value-at-risk (CVaR) method is introduced to assess the operation risk loss produced by reserve shortage. In order to improve the solving efficiency of the reserve-energy joint optimal scheduling model, Latin hypercube sampling (LHS) considering correlation is adopted. Finally, the effectiveness of the proposed method and model is verified by numerical simulation results.","PeriodicalId":403879,"journal":{"name":"2020 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130751214","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}

{"title":"Distribution power system state estimation based on Gaussian mixture model-Neural network","authors":"Jun Yang, Ruiping Tian, Shaofei Hu, Bing-jie Fan, Bingying Peng, Xinyu Qiu","doi":"10.1109/iSPEC50848.2020.9350955","DOIUrl":"https://doi.org/10.1109/iSPEC50848.2020.9350955","url":null,"abstract":"The main problem in the state estimation of distribution power system is that there are many nodes but few measuring points such that they are unobservable. With the construction and development of distribution network, most measuring devices of distribution power system have covered all nodes, but uploading their measured values to the power dispatching center in real time will take up a lot of communication resources, and once the data cannot be uploaded due to network congestion and other problems, the state estimation will be impossible to calculate. In this paper, the load Gaussian mixture model is established, and the load model under different scenarios is constructed. Obtain the load data from the smart meter, train the load model, and upload the model parameters to the power dispatching center where the neural network is trained with data such as node injection power generated by each node load model. Finally, the trained neural network is used to calculate the voltage and amplitude of each node. When some measure data is missing, the measure data generated by the compound model of the node stored in the power dispatching center is used as pseudo-measure for state estimation. The smart meter will update the training model regularly according to the change of node load, which helps to improve the robustness of the system. Compared with the traditional method of using power prediction as a pseudo-measurement, this method has the advantages of fast computing speed, high computing accuracy, small consumption of communication resources and strong robustness.","PeriodicalId":403879,"journal":{"name":"2020 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133051360","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}

{"title":"An Optimized Strategy of Switching Crowbar Improve LVRT of DFIG Based on RTDS","authors":"Junfei Han, Weiqing Jia, Yuqiang Wang, Lingzhi Zhou, Hongbin Hu, Yongfeng Ren","doi":"10.1109/iSPEC50848.2020.9351162","DOIUrl":"https://doi.org/10.1109/iSPEC50848.2020.9351162","url":null,"abstract":"The mathematical models of 2MW doubly-fed induction generator (DFIG) wind power generation system under steady and transient state are built in this paper. On the basis theory of control strategy and crowbar circuit parameter optimization, a complete model of DFIG low voltage ride through (LVRT) system is developed by real time digital simulator (RTDS). There is improvement in traditional crowbar protection method during quantitative simulation of grid voltage sag. An improved LVRT strategy of DFIG wind turbine by changing time of switching crowbar circuit is proposed in this paper. Contrastive study between traditional and improved LVRT method is provided to verify the performance of doubly-fed wind power system. Results show that the proposed method can realize uninterrupted operation of DFIG and meet the requirements of LVRT under grid voltage sag.","PeriodicalId":403879,"journal":{"name":"2020 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"39 15","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133086948","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}

{"title":"Research and application of predictive control based on deep learning modeling","authors":"Fengfeng Yin, Quan Li, Ye Su, Jiandong Sun","doi":"10.1109/iSPEC50848.2020.9351141","DOIUrl":"https://doi.org/10.1109/iSPEC50848.2020.9351141","url":null,"abstract":"The industrial process is usually a lag inertial system. Predictive control is an effective control algorithm for this kind of system, but a more accurate object model is needed. In this paper, DMC predictive control algorithm is used, which does not need the specific form of the model, only needs the step excitation response data of the model. In this paper, the deep learning algorithm is applied to the modeling of industrial process system. After obtaining the more accurate step excitation response data, the predictive control can be carried out, and the ideal control quality can be obtained. First, the input and output data of the closed-loop system are obtained by adding pseudo-random sequence of appropriate period and amplitude into the control instruction of the closed-loop system. The first-order inertia and delay object are used to fit the characteristics of the object, and the first-order inertia time constant T is obtained by using genetic optimization algorithm. Secondly, a third-order inertial link and DNN deep learning network are embedded in the discrete structure of the third-order inertial model to build the intelligent model structure; In order to ensure that the third-order inertial link is close to the inertia time of the object, the inertia time constant of each link is set to t / 3, the input and output data are sent to the intelligent model for training, and the dnn1 model of the object can be obtained; After adding delay $tau$ to dnn1 model, the genetic algorithm is used to fit the characteristics of the object, and the delay time $tau$ is obtained; According to the input and output data, the DNN model with delay $tau$ is trained for the second time to obtain a more accurate identification model dnn2. Thirdly, step excitation is applied to dnn2 model to obtain excitation response data, which is put into predictive controller to obtain excellent control quality. Finally, the first-order object model identified by the least square method is put into the predictive controller, and the control effect is compared with that of this paper. This method has great practical significance for the design and application of predictive control based on deep learning modeling.","PeriodicalId":403879,"journal":{"name":"2020 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133509018","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}