2023 13th International Conference on Power, Energy and Electrical Engineering (CPEEE)最新文献

Performance Analysis of Ejector-Based Solid Oxide Fuel Cell Anode Gas Circulation System 基于喷射器的固体氧化物燃料电池阳极气体循环系统性能分析

2023 13th International Conference on Power, Energy and Electrical Engineering (CPEEE) Pub Date : 2023-02-25 DOI: 10.1109/CPEEE56777.2023.10217359

Fanchen Kong, Dongdong Li, Hongxia Zhao, Guanmin Zhang

引用次数: 0

Structural Vibration Self-sensing Piezoelectric Device 结构振动自传感压电装置

2023 13th International Conference on Power, Energy and Electrical Engineering (CPEEE) Pub Date : 2023-02-25 DOI: 10.1109/CPEEE56777.2023.10217642

M. Bani-Hani, N. Nahas, M. Abouheaf, S. A. Kouritem, Yasmin Ayman

引用次数: 0

Estimating Substation Lightning Arrester Deterioration Guideline: A k-NN Algorithm with Leakage Current Measurement 变电站避雷器劣化准则估计:泄漏电流测量的k-NN算法

2023 13th International Conference on Power, Energy and Electrical Engineering (CPEEE) Pub Date : 2023-02-25 DOI: 10.1109/CPEEE56777.2023.10217781

Yujung Gil, Jihie Kim, Dongho Kim

引用次数: 0

PV and Battery Sizing and Cost Study for Small Remote Islands Considering Load Demand Peak Shift 考虑负荷需求峰移的偏远小岛光伏与电池尺寸及成本研究

2023 13th International Conference on Power, Energy and Electrical Engineering (CPEEE) Pub Date : 2023-02-25 DOI: 10.1109/CPEEE56777.2023.10217713

Clarence Kitalong, Y. Noro

引用次数: 0

Research of Auto Adjustment Method for Cross-Section Power of Power Grid Based on Reverse Equivalent Matching Method with AI Strategy 基于人工智能策略的反向等效匹配电网截面功率自动调节方法研究

2023 13th International Conference on Power, Energy and Electrical Engineering (CPEEE) Pub Date : 2023-02-25 DOI: 10.1109/CPEEE56777.2023.10217535

Lei Wang, G. Wang, Gang Ma, Qingguang Yu, Le Li, Xiaoyu Li, M. Guo, Shi Wang

{"title":"Research of Auto Adjustment Method for Cross-Section Power of Power Grid Based on Reverse Equivalent Matching Method with AI Strategy","authors":"Lei Wang, G. Wang, Gang Ma, Qingguang Yu, Le Li, Xiaoyu Li, M. Guo, Shi Wang","doi":"10.1109/CPEEE56777.2023.10217535","DOIUrl":"https://doi.org/10.1109/CPEEE56777.2023.10217535","url":null,"abstract":"At present, due to the heavy workload and high repeatability of power adjustment for key transmission sections of power grids, the computational speed is difficult to meet the requirements of online assisted decision-making. Based on the deep learning theory, a method is proposed to perform feature self-learning on the cross-section power adjustment data to realize automatic fast adjustment of the cross-section power of power grids. First, the reverse equal-quantity matching method is used to simulate the manual adjustment operation, and the massive data set required by deep learning is constructed. Then, under the constraints of unit sensitivity and adjustment amount, the effective set of units participating in the power adjustment in the power grid is screened. On this basis, an optimal regression model is built with the determination coefficient as the index to accurately predict the output value of the adjusting unit, thereby realizing the automatic fast adjustment of the cross-section power. Finally, the proposed method is validated by taking the inter-provincial cross-section power adjustment in a practical regional power grid in IEEE example. Simulation results show that the determination coefficient values of the optimal model and the success rate of the cross-section power adjustment are both relatively ideal, which greatly shortens the section power adjustment time, and the adjustment efficiency is not affected by the system operation mode and the difference between the actual cross-section power and the target power.","PeriodicalId":364883,"journal":{"name":"2023 13th International Conference on Power, Energy and Electrical Engineering (CPEEE)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122348177","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}

引用次数: 0

Evaluation of Off-Grid Solar Power Plant Implementation on Biofloc Fishery System 离网太阳能电站在生物絮团渔业系统中的应用评价

2023 13th International Conference on Power, Energy and Electrical Engineering (CPEEE) Pub Date : 2023-02-25 DOI: 10.1109/CPEEE56777.2023.10217414

Henri Setiawan, W. B. Pramono

引用次数: 0

Battery Energy Storage System Supporting Frequency Regulation of Power System with High Renewable Penetration 支持高可再生渗透率电力系统调频的电池储能系统

2023 13th International Conference on Power, Energy and Electrical Engineering (CPEEE) Pub Date : 2023-02-25 DOI: 10.1109/CPEEE56777.2023.10217425

Nomvelo Nzimande, Y. Noro

引用次数: 0

Interference from Unshielded HV Harnesses with Realistic Groundplane Models 非屏蔽高压线束的干扰与现实地平面模型

2023 13th International Conference on Power, Energy and Electrical Engineering (CPEEE) Pub Date : 2023-02-25 DOI: 10.1109/CPEEE56777.2023.10217755

Raik Orbay, Helin Zhou, Åke Lindbeck, G. Mademlis, Anders Bergqvist, Per Torstensson, Thomas Bernichon, T. Thiringer

{"title":"Interference from Unshielded HV Harnesses with Realistic Groundplane Models","authors":"Raik Orbay, Helin Zhou, Åke Lindbeck, G. Mademlis, Anders Bergqvist, Per Torstensson, Thomas Bernichon, T. Thiringer","doi":"10.1109/CPEEE56777.2023.10217755","DOIUrl":"https://doi.org/10.1109/CPEEE56777.2023.10217755","url":null,"abstract":"In a vehicle, unintended electromagnetic coupling among sensitive components such as antennas etc. needs to be suppressed to prevent the interference to cables. The cables for power and signal transmission are, therefore, only allowed to be packed in each other’s vicinity after proper shielding. As the shielding decreases the payload for a given vehicle design and increases its cost, it is very relevant to shed light on alternative packaging omitting shielding completely. Accordingly in this report, computations based on 3-dimensional transmission line matrix method will be launched to execute parameter sweeps comprising intercable distance, cable distance to groundplane, cable length, and signal cable termination impedance. A Vector Network Analyzer based experimental study to verify the computational approach is presented. Subsequently, crosstalk effect of realistic groundplane from a vehicle concept is also compared to a simple planar groundplane using 3D Transmission Line Matrix approach. It will be shown that the crosstalk will be kept below the limits as specified by the standards and internal OEM requirements with minimal intercable spacing, eliminating the cable shielding while reducing the environmental footprint.","PeriodicalId":364883,"journal":{"name":"2023 13th International Conference on Power, Energy and Electrical Engineering (CPEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129178111","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}

引用次数: 0

Early Identification of Battery End-of-Life Using Machine Learning 利用机器学习技术早期识别电池寿命

2023 13th International Conference on Power, Energy and Electrical Engineering (CPEEE) Pub Date : 2023-02-25 DOI: 10.1109/CPEEE56777.2023.10217699

Rudy Tjandra, Hao Jiang, Eryan Bin Zainudin, Muhammad Isa Bin Yasmin, C. B. Soh, Elsa Feng, D. Soh, S. Cao, Kuan Tak Tan

{"title":"Early Identification of Battery End-of-Life Using Machine Learning","authors":"Rudy Tjandra, Hao Jiang, Eryan Bin Zainudin, Muhammad Isa Bin Yasmin, C. B. Soh, Elsa Feng, D. Soh, S. Cao, Kuan Tak Tan","doi":"10.1109/CPEEE56777.2023.10217699","DOIUrl":"https://doi.org/10.1109/CPEEE56777.2023.10217699","url":null,"abstract":"This paper presents a methodology to predict near End-of-Life (EOL) of lithium-ion batteries. By predicting if a battery is expected to fail (unable to be discharged to 70% of its nominal capacity) within 30 cycles, timely replacement of battery can be carried out to minimize downtime. This methodology is validated using data from NASA Prognostics Center of Excellence. The battery charge cycling data is extracted and preprocessed as time series input features. The input features are based on mean and range of charge voltage, charge Ah capacity, charge time, and temperature. These features are used to train various machine learning techniques to perform multiclass classification on battery status. Battery status is categorized into three status, namely nonfailure status, near failure status (battery EOL is reached within 30 cycles), and failure status (battery EOL has already been reached). From various machine learning techniques studied, Fine kNN, ensemble bagged trees and ensemble boosted trees are three best techniques with more than 90% accuracy.","PeriodicalId":364883,"journal":{"name":"2023 13th International Conference on Power, Energy and Electrical Engineering (CPEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130568579","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}

引用次数: 0

Variable Selections in Machine Learning Based Efficiency Estimation of the Combined Cycle Power Plant 基于机器学习的联合循环电厂效率评估中的变量选择

2023 13th International Conference on Power, Energy and Electrical Engineering (CPEEE) Pub Date : 2023-02-25 DOI: 10.1109/CPEEE56777.2023.10217483

Vishakha Singh, Phisan Kaewprapha, Pradya Prempaneerach

引用次数: 0