2022 International Conference on Power, Energy and Innovations (ICPEI)最新文献_第4页

Life Assessment of Transmission Line Using Historical Health Index 基于历史健康指数的输电线路寿命评估

2022 International Conference on Power, Energy and Innovations (ICPEI) Pub Date : 2022-10-19 DOI: 10.1109/ICPEI55293.2022.9987206

C. Suwanasri, Ittiphong Yongyee, T. Suwanasri

引用次数: 0

Weekly Power Generation Forecasting using Deep Learning Techniques: Case Study of a 1.5 MWp Floating PV Power Plant 利用深度学习技术进行每周发电量预测:以1.5 MWp浮动光伏电站为例

2022 International Conference on Power, Energy and Innovations (ICPEI) Pub Date : 2022-10-19 DOI: 10.1109/ICPEI55293.2022.9986628

Nonthawat Khotsirwong, T. Boonraksa, Promphak Boonraksa, T. Fangsuwannarak, Asada Boonsrirat, B. Marungsri

{"title":"Weekly Power Generation Forecasting using Deep Learning Techniques: Case Study of a 1.5 MWp Floating PV Power Plant","authors":"Nonthawat Khotsirwong, T. Boonraksa, Promphak Boonraksa, T. Fangsuwannarak, Asada Boonsrirat, B. Marungsri","doi":"10.1109/ICPEI55293.2022.9986628","DOIUrl":"https://doi.org/10.1109/ICPEI55293.2022.9986628","url":null,"abstract":"Power generation forecasting is essential for power system planning. Deep learning (DL) is an artificial intelligence technology applied to power generation forecasting. This paper aims to compare the effectiveness of various deep learning techniques to forecast the weekly power generation of a 1.5 MWp floating PV power plant at Suranaree university of technology's hospital, Nakhon Ratchasima, Thailand. This study uses eleven deep learning techniques to examine forecasting effectiveness, including CNN, LSTM, BiLSTM, GRU, BiGRU, CNN-LSTM, CNN-BiLSTM, CNN-GRU, CNN-BiGRU, BiLSTM-GRU, and BiLSTM-BiGRU. The actual three months measurement data trained each deep learning technique and then was used to forecast a weekly power generation. Three error indexes, MAE, MAPE, and RMSE, were used to confirm the effectiveness of each deep learning technique. The forecasting results showed that LSTM, CNN-BiLSTM, and CNN-BiGRU techniques gave the slightest inaccuracies in the forecasting results of the interesting deep learning techniques.","PeriodicalId":223538,"journal":{"name":"2022 International Conference on Power, Energy and Innovations (ICPEI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130740634","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: 1

The Harmonic Mitigation in the Smelting Industry Connected to Active Distribution Network in Phetchaburi Province Using Single-Tuned Passive Harmonic Filters Considering Load Demand 考虑负荷需求的单调谐无源谐波滤波器对Phetchaburi省有源配电网冶炼工业的谐波抑制

2022 International Conference on Power, Energy and Innovations (ICPEI) Pub Date : 2022-10-19 DOI: 10.1109/ICPEI55293.2022.9986542

Sermsak Kor.srisuwan, Natin Janjamraj, K. Bhumkittipich, S. Romphochai

{"title":"The Harmonic Mitigation in the Smelting Industry Connected to Active Distribution Network in Phetchaburi Province Using Single-Tuned Passive Harmonic Filters Considering Load Demand","authors":"Sermsak Kor.srisuwan, Natin Janjamraj, K. Bhumkittipich, S. Romphochai","doi":"10.1109/ICPEI55293.2022.9986542","DOIUrl":"https://doi.org/10.1109/ICPEI55293.2022.9986542","url":null,"abstract":"A large number of nonlinear loads have been implemented in modern power systems, such as electric vehicles, electronic ballasts, and equipment inverter circuits. Moreover, most industrial devices have adopted inverters, including mining, metal smelting, and textile industries. The mentioned nonlinear loads cause harmonics in the power system, resulting in greater power loss, fail protection, and a shorter lifespan. Therefore, this paper presents the harmonic mitigation in the smelting industry connected to the active distribution network in Phetchaburi province using single-tuned passive harmonic filters considering load demand. To determine the suitable single-tuned harmonic filters for reducing harmonics in various load ranges by using the data of the smelting industry, the actual load profile is measured and analyzed as the average value for the proposed method. The simulation results are carried out using DIgSILENT PowerFactory software to verify the proposed method performances, consisting of three scenarios, which are light, normal, and peak loads. The results show that the single-tuned passive harmonic filters using the proposed average load demand-based passive filter method can reduce the harmonic currents of 7th, 9th, and 11th for various load conditions and also improve the harmonic distortion of the power system and the total harmonic distortion of current, satisfying the PEA grid code requirement.","PeriodicalId":223538,"journal":{"name":"2022 International Conference on Power, Energy and Innovations (ICPEI)","volume":"18 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132898728","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: 1

Investigation of DBSCAN Data Clustering For MCA Cooperative Spectrum Sensing 面向MCA协同频谱感知的DBSCAN数据聚类研究

2022 International Conference on Power, Energy and Innovations (ICPEI) Pub Date : 2022-10-19 DOI: 10.1109/ICPEI55293.2022.9987052

Boonpipob Napasiripakorn, Wilaiporn Lee, K. Srisomboon

{"title":"Investigation of DBSCAN Data Clustering For MCA Cooperative Spectrum Sensing","authors":"Boonpipob Napasiripakorn, Wilaiporn Lee, K. Srisomboon","doi":"10.1109/ICPEI55293.2022.9987052","DOIUrl":"https://doi.org/10.1109/ICPEI55293.2022.9987052","url":null,"abstract":"Once the quality of secondary users are taken into the account of cooperative spectrum sensing, the modern cooperative data fusion rules show a noticeably improve the cooperative spectrum sensing performance as compared to the traditional cooperative spectrum sensing techniques. Among those modern techniques, MCA for cooperative spectrum sensing adopts a simple data clustering of a machine learning to detect the reliable SUs and takes only reliable sensing results into the account. However, the performance of MCA is still constrained by the framework of K-mean clustering which requires a prior knowledge about a certain cluster number. In practice, sensing results from all SUs are required to ensure the channel condition under whether the licensed user utilizes the licensed band or not where the desired condition about using all sensing results cannot be met by using K-mean clustering. Therefore, in this paper, we simulate the cooperative spectrum sensing performance of MCA with a modern data clustering algorithm, DBSCAN, and compare its performance to the traditional MCA. The simulation results show that the MCA with DBSCAN meets the desired performance of a cooperative spectrum sensing where it presents high detection performance and low false alarm.","PeriodicalId":223538,"journal":{"name":"2022 International Conference on Power, Energy and Innovations (ICPEI)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132512593","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: 1

Artificial Intelligence-Based Approach for Prediction of Power Transformer Health Index 基于人工智能的电力变压器健康指数预测方法

2022 International Conference on Power, Energy and Innovations (ICPEI) Pub Date : 2022-10-19 DOI: 10.1109/ICPEI55293.2022.9986545

Irfan Ahmad, M. Jamil, K. N. Paracha

引用次数: 1

Investigation of microstructure and mechanical properties of laser welded complex phase steel lap joints 激光焊接复杂相钢搭接接头的组织与力学性能研究

2022 International Conference on Power, Energy and Innovations (ICPEI) Pub Date : 2022-10-19 DOI: 10.1109/ICPEI55293.2022.9987027

M. Hietala, A. Hamada, M. Keskitalo

{"title":"Investigation of microstructure and mechanical properties of laser welded complex phase steel lap joints","authors":"M. Hietala, A. Hamada, M. Keskitalo","doi":"10.1109/ICPEI55293.2022.9987027","DOIUrl":"https://doi.org/10.1109/ICPEI55293.2022.9987027","url":null,"abstract":"In this study the microstructure and mechanical properties of laser welded complex phase steel lap joints were evaluated. Three different welding parameters were used in the experiments. The weld geometry and microstructure of the weld were studied. The joints were evaluated by hardness measurements, shear strength and fatigue strength tests. The geometry of the lap joints was affected by the welding parameters, and the width of the joint increased the lower the welding speed was. The weld metal (WM) microstructure with highest welding speed exhibits a fine columnar grain structure. As welding speed increased, the dendritic grain structure was coarser. With lowest welding speed, the microstructure changed to an equiaxed grain structure due to slower cooling. The WM with highest welding speed had the highest hardness with an average value 413 HV. The shear strengths of the joints were markedly influenced by the welding speed. With the lowest welding speed, the shear strength of the single weld was 222% higher than that of the highest welding speed. A joint with multiple welds achieved shear strength corresponding to the strength of the base material (1000 MPa). Fatigue limit of the lap joint with lowest welding speed was low < 20 MPa.","PeriodicalId":223538,"journal":{"name":"2022 International Conference on Power, Energy and Innovations (ICPEI)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129571157","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

The Cattle Rut Behavior Detection Base on AI Deep Neural Network 基于AI深度神经网络的牛发车行为检测

2022 International Conference on Power, Energy and Innovations (ICPEI) Pub Date : 2022-10-19 DOI: 10.1109/ICPEI55293.2022.9986577

Phatsarut Phaechaiyaphum, Poom Konghuayrob

引用次数: 0

Phase Shift-Based Fault Detection of Induction Motor Using IoT System 基于物联网系统的感应电机移相故障检测

2022 International Conference on Power, Energy and Innovations (ICPEI) Pub Date : 2022-10-19 DOI: 10.1109/ICPEI55293.2022.9986538

Sirawit Chaihang, Nattawat Chamnongwai, P. Chansri

引用次数: 0

Development of Floating Covered Objects from Recycled Materials and Natural Rubber for Covering Water 用再生材料和天然橡胶制作水上覆盖物的研究

2022 International Conference on Power, Energy and Innovations (ICPEI) Pub Date : 2022-10-19 DOI: 10.1109/ICPEI55293.2022.9987030

Akanut Chuenphirom, R. Yeetsorn, P. Uawongsuwan, A. Tohsan

引用次数: 0

Transmission System Expansion Planning Using Optimal Power Flow and Genetic Algorithm 基于最优潮流和遗传算法的输电系统扩容规划

2022 International Conference on Power, Energy and Innovations (ICPEI) Pub Date : 2022-10-19 DOI: 10.1109/ICPEI55293.2022.9987016

Weeratep Wiwechpaisankul, O. Rattananatthawon, S. Sirisumrannukul, Noppatee Sabpayakom

引用次数: 0