{"title":"Operator-based Robust Nonlinear Vibration Control System for A Flexible Arm with Double-sided SMA","authors":"Guang Jin, M. Deng","doi":"10.1109/CPEEE54404.2022.9738679","DOIUrl":"https://doi.org/10.1109/CPEEE54404.2022.9738679","url":null,"abstract":"The nonlinear vibration control system for a flexible arm using shape memory alloy (SMA) is discussed in this paper. The dynamic model of the flexible arm with SMA actuator is described. For considering the effect of hysteresis nonlinearity, an operator-based nonlinear control scheme is introduced to satisfy stability of the nonlinear control system. Simultaneously, the tracking performance is considered by designed n-times feedback loops based on operator theory. And, the desired vibration control performance can be obtained by the designed nonlinear control scheme. Finally, numerical simulations are conducted to verify the effectiveness of the designed control system.","PeriodicalId":438265,"journal":{"name":"2022 12th International Conference on Power, Energy and Electrical Engineering (CPEEE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124278114","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}
Jahidul Islam, M. M. Rahman, Mohammad Hashibur Rahman Shanto, Arnob Kumar Pias, M. Tanseer Ali
{"title":"Wireless Power Transmission Circuit and System Design","authors":"Jahidul Islam, M. M. Rahman, Mohammad Hashibur Rahman Shanto, Arnob Kumar Pias, M. Tanseer Ali","doi":"10.1109/CPEEE54404.2022.9738666","DOIUrl":"https://doi.org/10.1109/CPEEE54404.2022.9738666","url":null,"abstract":"Wireless technology opens a wide range of research opportunities in all areas of electrical science, particularly in the electrical power sector. Nonetheless, it grows richer with each addition and change made by scientists and researchers. This entire project is based on Wireless Power Transmission systems, whose ultimate purpose is to transmit power in a uniform gain in a registered region. To accomplish this purpose, first, a schematic diagram was designed which has two parts. The first part is for transferring the power and the second part is for receiving the power. The power transferring part is modified as Antenna and the receiver part is Rectenna and both the antenna and rectenna were designed in CST software. A receiver circuit was designed for delivering the power which was designed in ADS software. The receiver circuit was constructed in three individual sections. They are antenna circuit, rectifier circuit, and buck converter. The resonance frequency of the system is 2.4GHz and after completing the analysis we get the output of (5.655 to 4.95) V DC in rectangular shaped patch antenna and (5.573 to 4.86) V DC in circular shaped patch antenna in (2 to 60) meter. In addition, we tried to figure out which antenna provides better output by differentiate the shape of the antenna. The project’s primary goal is to transmit power to a focused area and also determine the estimated cost, health concerns, and repercussions in terms of social, political, and technological factors.","PeriodicalId":438265,"journal":{"name":"2022 12th International Conference on Power, Energy and Electrical Engineering (CPEEE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115097787","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":"Study of Diesel Combining with Vaporizing Ethanol via a Carburetor for a Diesel-engine Generator","authors":"E. Sutheerasak, W. Pirompugd, S. Chuepeng","doi":"10.1109/CPEEE54404.2022.9738713","DOIUrl":"https://doi.org/10.1109/CPEEE54404.2022.9738713","url":null,"abstract":"Nowadays, diesel prices in Thailand are increasing steadily due to crude oil market volatility. To reduce the diesel consumption, the research aims to study the performance and emission characteristics of a diesel-engine generator fueled with vaporizing ethanol via a carburetor combining with diesel on a dual fuel mode. The vaporizing ethanol is changed by increasing main jet diameter from 0.8 to 1.0 mm combined with diesel injection on the dual fuel mode, as compared with only diesel mode at a constant speed of 3,000 rpm and different loads. Results shows that the addition of vaporizing ethanol by adding main jet diameter combining with diesel led to the reduction of engine performance. The exhaust emissions, especially nitric oxide and black smoke, were reduced with vaporizing ethanol increased, but there was the increase of carbon dioxide and carbon monoxide. The research found that the use of main jet diameter at 0.8 mm in vaporizing ethanol combining with diesel resulted the addition of diesel saving to 47%, and brake thermal efficiency was dropped only by 2%. Nitric oxide and black smoke emissions were decreased up to 12%, while carbon dioxide and carbon monoxide releases were only slightly increased.","PeriodicalId":438265,"journal":{"name":"2022 12th International Conference on Power, Energy and Electrical Engineering (CPEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124210104","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}
Ruben Lliuyacc-Blas, Svein Olav Nyberg, Muhandiram Arachchige Subodha Tharangi Ireshika, M. Kolhe, P. Kepplinger
{"title":"PV Hosting Capacity Estimation in Low Voltage Feeders Through Bayesian Statistical Inference","authors":"Ruben Lliuyacc-Blas, Svein Olav Nyberg, Muhandiram Arachchige Subodha Tharangi Ireshika, M. Kolhe, P. Kepplinger","doi":"10.1109/CPEEE54404.2022.9738661","DOIUrl":"https://doi.org/10.1109/CPEEE54404.2022.9738661","url":null,"abstract":"PV hosting capacity provides utilities the knowledge of the maximum amount of solar installations possible to accommodate in low voltage grids such that no operational problems arise. As the quantification of the hosting capacity requires data collection, grid modelling, and often time-consuming simulations, simplified estimations for large-scale applications are of interest. In this paper, Bayesian statistical inference is applied to estimate the hosting capacities of more than 5000 real feeders in Austria. The results show that the hosting capacity of 95% of the total feeders can be estimated with a mean error below 20% by only having knowledge of a random sample of 5%. Moreover, the hosting capacity estimation at a regional level shows a maximum error below 9%, also relying on a random sample of 5% of the total feeders. Furthermore, the approach proposed provides a methodology to assess new parameters aiming to improve the accuracy of the hosting capacity estimation at a feeder level.","PeriodicalId":438265,"journal":{"name":"2022 12th International Conference on Power, Energy and Electrical Engineering (CPEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130315589","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 Study on the Effectiveness of Detection of Unbalanced Datasets Based on Faster R-CNN","authors":"Delong Cai, Zhaoyun Zhang, Guanfeng He","doi":"10.1109/CPEEE54404.2022.9738687","DOIUrl":"https://doi.org/10.1109/CPEEE54404.2022.9738687","url":null,"abstract":"In the Faster R-CNN target detection task, to address the problem of how the unbalanced distribution of different kinds of datasets affects the recognition effect of the model, a research scheme is proposed to train the model using two different ratios of dataset combination, the two ratios are 1:1 and 1:2 respectively.In this paper, two kinds of objects in electrical equipment, anti-vibration hammer and insulator, are taken as the research objects, and each type of dataset After data processing, the data sets are combined according to the two different ratios set, and then put into the model for training to get the recognition effect. The experimental results show that at Epoch=40,000, the AP(the explanation is shown in TABLE I) of the trained model is much higher for the categories with a small proportion of data than for the categories with a large proportion of data when the ratio of the data sets of anti-vibration hammer and insulator is 1:2 or 2:1, and the AP of the model trained by unbalanced data distribution is unstable compared with the AP of the model trained by the ratio of the two categories of data sets of 1:1. In the actual detection task, it is easy to cause \"false detection\" or \"missed detection\". Based on the data unbalance problem, we propose data processing methods such as undersampling and oversampling of the dataset.","PeriodicalId":438265,"journal":{"name":"2022 12th International Conference on Power, Energy and Electrical Engineering (CPEEE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131382398","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}
Jizhong Chen, Tao Yan, Min Zhang, Jun Zhao, Shuili Yang, Xiao Chang, Kai Wang
{"title":"Identification Method of Energy Storage Operating Conditions based on Random Forest","authors":"Jizhong Chen, Tao Yan, Min Zhang, Jun Zhao, Shuili Yang, Xiao Chang, Kai Wang","doi":"10.1109/CPEEE54404.2022.9738721","DOIUrl":"https://doi.org/10.1109/CPEEE54404.2022.9738721","url":null,"abstract":"Accurate and effective identification of the operating characteristics and application modes of electrochemical energy storage systems (EESS) is of great significance for the safe and reliable operation of large-scale EESS in power generation, transmission and distribution. The paper proposes a method for identifying the operating conditions of EESS based on the random forest algorithm. Analyzing and characterizing the typical operating conditions, such as frequency regulation, PV-EESS combination, Wind-EESS output schedule and smoothing, and peak shaving, the method introduces the random forest algorithm to build the identification model of EESS operating conditions. The variable importance measures, training model and test analysis have been completed. The results show that the method has an identification accuracy of more than 95% for the five typical operating conditions, which can effectively identify the EESS operating conditions.","PeriodicalId":438265,"journal":{"name":"2022 12th International Conference on Power, Energy and Electrical Engineering (CPEEE)","volume":"783 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134569845","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":"Battery Degradation Modelling and Prediction with Combination of Machine Learning and Semi-empirical Methods","authors":"Yongjian Xue, Haitao Zhou, Yunqi Luo, James Lam","doi":"10.1109/CPEEE54404.2022.9738723","DOIUrl":"https://doi.org/10.1109/CPEEE54404.2022.9738723","url":null,"abstract":"Battery energy storage systems (BESS) are being widely deployed as part of the energy transition. Accurate battery degradation modelling and prediction play an important role in BESS investment and revenue, planning and sizing, operational monitoring, and warranty check-ups. Complex operational behaviors and system variability make the battery degradation modelling and prediction more challenging. In this paper, we propose a novel methodology of combining a machine learning model with semi-empirical equations to have a robust state of health (SOH) estimation. Together with cycle decomposition analysis, this model can be deployed for real-time degradation analysis of BESS.","PeriodicalId":438265,"journal":{"name":"2022 12th International Conference on Power, Energy and Electrical Engineering (CPEEE)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121458588","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}
Yangjun Zhou, Bin Zhang, Weixiang Huang, Liwen Qin, Li Yu
{"title":"Reliability Centered Weak Point Identification of Distribution Network","authors":"Yangjun Zhou, Bin Zhang, Weixiang Huang, Liwen Qin, Li Yu","doi":"10.1109/CPEEE54404.2022.9738659","DOIUrl":"https://doi.org/10.1109/CPEEE54404.2022.9738659","url":null,"abstract":"The existing methods for identifying weak points of reliability of distribution network components mainly rely on traditional analytical methods to establish reliability tracking models, but this method is only suitable for simple radiant distribution networks. When the scale of the distribution network expands, this method becomes very lengthy. In order to fill this gap, this paper proposes a Monte-Carlo-based method for identifying weak points of distribution network components. Firstly, considering the topological structure and distribution information of the distribution network, the Monte Carlo method is used to calculate the system reliability index of the distribution network. Then, through the component reliability tracking analysis, the influence factors of each component on the system reliability index are obtained, and the weak points of the system are determined by the size of the influence factors. Finally, a typical low-voltage distribution system with 10 load nodes is analyzed, and the weak components of the distribution network are found. The improvement measures to improve the reliability of the system are given. By comparing the improvement of the system reliability index before and after the weak point is modified, the validity of the weak point identification is verified.","PeriodicalId":438265,"journal":{"name":"2022 12th International Conference on Power, Energy and Electrical Engineering (CPEEE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126531377","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":"MEMS Electric Field Sensor with Biased Electrically Floating Cover to Measure Electric Field in Ionic Environments","authors":"Tao Chen, Sadna Işik, C. Shafai, L. Shafai","doi":"10.1109/CPEEE54404.2022.9738672","DOIUrl":"https://doi.org/10.1109/CPEEE54404.2022.9738672","url":null,"abstract":"Electric field measurement is widely applied in HVDC area. However, it is a challenge to measure dc electric field in ionic environment using a packaged sensor. When the sensor is packaged in an enclosure, the landing of charge particles from environment on the enclosure surface would give an off-setting electric field, and so cause measurement error. In this paper we propose a method to bias an electrically floating metal cover above the packaged sensor, to prevent ions landing on the package cover, while still being able to measure electric field external to the enclosure. Experimental results show that when the floating cover is pre-biased, ionic charge accumulation can be reduced and eliminated. The sensor sensitivity remains at 70 mV(kV/m)-1, with and without ionic charge present in the environment.","PeriodicalId":438265,"journal":{"name":"2022 12th International Conference on Power, Energy and Electrical Engineering (CPEEE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116829356","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":"Temperature Difference Monitoring Method for Metal Devices of Electrical Equipment by Visible Images and Machine Learning","authors":"Zhe Yuan, Qizheng Ye, Mengting Han, Xiaofei Nie","doi":"10.1109/CPEEE54404.2022.9738708","DOIUrl":"https://doi.org/10.1109/CPEEE54404.2022.9738708","url":null,"abstract":"In this paper, ordinary visible images were used to measure the temperature difference between two metal devices that were used in electrical equipment and transmission lines. The temperature-difference experiments of three kinds of metal devices used in 110kV transmission lines were conducted for collecting their visible images with varying temperature differences in a sunlight environment. The machine learning (ML) technique was applied for modeling the relationship functions between visible image features and temperature differences. The results showed that the corresponding temperature difference of metal devices in these images could be well predicted by trained ML models. Larger illuminance of sunlight helped to obtain higher prediction accuracy. This method could be used for monitoring the abnormal temperature rise of metal parts in electrical equipment by comparing the temperatures of two different phases. It would provide a new choice for intelligent temperature monitoring in power systems, helping save costs and improve inspection efficiency.","PeriodicalId":438265,"journal":{"name":"2022 12th International Conference on Power, Energy and Electrical Engineering (CPEEE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116834729","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}