Pub Date : 2024-10-04DOI: 10.23919/SAIEE.2024.10705976
{"title":"Editors and reviewers","authors":"","doi":"10.23919/SAIEE.2024.10705976","DOIUrl":"https://doi.org/10.23919/SAIEE.2024.10705976","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"115 4","pages":"112-112"},"PeriodicalIF":1.0,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705976","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-06DOI: 10.23919/SAIEE.2024.10551303
Ronewa Mabodi;Jahvaid Hammujuddy
This research presents the implementation and evaluation of machine learning models to predict solar irradiance (W/m 2 ). The objective is to provide valuable insights for making informed decisions regarding solar system design and financing. A thorough exploratory data analysis was conducted on the Southern African Universities Radiometric Network (SAURAN) data collected at the University of Pretoria’s station to gain insights into the patterns of solar irradiance over the past 10 years. Python’s functions and libraries are utilized extensively for conducting exploratory data analysis, model implementation, model testing, forecasting, and data visualization. Random Forest (RF), k-Nearest Neighbors (KNN), Feedforward Neural Network (FFNN), Support Vector Regression (SVR), and eXtreme Gradient Boosting models (XGBoost) are implemented and evaluated. The KNN model was found to be superior achieving a relative Root Mean Squared Error (RMSE), relative Mean Absolute Error (MAE), and R-Squared (R 2 ) of 5.77%, 4.51% and 0.89 respectively on testing data. The variable importance analysis revealed that temperature (X!) exerted the greatest influence on predicting solar irradiance, accounting for 44% of the predictive power. The KNN model is suitable to inform solar systems design and financing decisions. Directions for future studies are identified and suggestions for areas of exploration are provided to contribute to the advancement of solar irradiance predictions.
{"title":"Solar Irradiance Forecasting for Informed Solar Systems Design and Financing Decisions","authors":"Ronewa Mabodi;Jahvaid Hammujuddy","doi":"10.23919/SAIEE.2024.10551303","DOIUrl":"https://doi.org/10.23919/SAIEE.2024.10551303","url":null,"abstract":"This research presents the implementation and evaluation of machine learning models to predict solar irradiance (W/m\u0000<sup>2</sup>\u0000). The objective is to provide valuable insights for making informed decisions regarding solar system design and financing. A thorough exploratory data analysis was conducted on the Southern African Universities Radiometric Network (SAURAN) data collected at the University of Pretoria’s station to gain insights into the patterns of solar irradiance over the past 10 years. Python’s functions and libraries are utilized extensively for conducting exploratory data analysis, model implementation, model testing, forecasting, and data visualization. Random Forest (RF), k-Nearest Neighbors (KNN), Feedforward Neural Network (FFNN), Support Vector Regression (SVR), and eXtreme Gradient Boosting models (XGBoost) are implemented and evaluated. The KNN model was found to be superior achieving a relative Root Mean Squared Error (RMSE), relative Mean Absolute Error (MAE), and R-Squared (R\u0000<sup>2</sup>\u0000) of 5.77%, 4.51% and 0.89 respectively on testing data. The variable importance analysis revealed that temperature (X!) exerted the greatest influence on predicting solar irradiance, accounting for 44% of the predictive power. The KNN model is suitable to inform solar systems design and financing decisions. Directions for future studies are identified and suggestions for areas of exploration are provided to contribute to the advancement of solar irradiance predictions.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"115 3","pages":"99-108"},"PeriodicalIF":1.4,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10551303","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-06DOI: 10.23919/SAIEE.2024.10551304
Lindizgani K. Ndovie;Emmanuel Masabo
Malawi’s economy heavily depends on agriculture, including both commercial and subsistence farming. Smallholder and small-medium enterprises leading the production of tomatoes in Malawi cannot satisfy local demand due to problems such as pests, diseases, unstable markets, and high costs. Many farmers lack the expertise to effectively manage these threats. To address the problem of tomato leaf disease identification, this research aimed to develop an automated system for tomato leaf disease detection by utilizing data augmentation techniques, MobileNetV3, and Convolutional Neural Network algorithms. We trained models on secondary data collected from the public PlantVillage dataset and tested the resultant classifiers on primary data of local farm images. The experimental results demonstrate that both models tested better on the PlantVillage dataset. Additionally, with an accuracy of 92.59% and a loss of 0.2805, the pre-trained MobileNetV3 model conventionally performs better than a CNN model. However, when tested on the primary field dataset, the models did not meet expectations for generalization, with the pre-trained MobileNetV3 achieving an accuracy of 9.2%, and loss of 12.91 and the CNN achieving an accuracy of 10.14%, and loss of 8.11. The experiments aided in showing that the models trained on the PlantVillage dataset are not as effective when applied in real-world scenarios. Further improvements are needed to enhance the models’ generalization in real-world scenarios.
{"title":"Leveraging MobileNetV3 for In-Field Tomato Disease Detection in Malawi via CNN","authors":"Lindizgani K. Ndovie;Emmanuel Masabo","doi":"10.23919/SAIEE.2024.10551304","DOIUrl":"https://doi.org/10.23919/SAIEE.2024.10551304","url":null,"abstract":"Malawi’s economy heavily depends on agriculture, including both commercial and subsistence farming. Smallholder and small-medium enterprises leading the production of tomatoes in Malawi cannot satisfy local demand due to problems such as pests, diseases, unstable markets, and high costs. Many farmers lack the expertise to effectively manage these threats. To address the problem of tomato leaf disease identification, this research aimed to develop an automated system for tomato leaf disease detection by utilizing data augmentation techniques, MobileNetV3, and Convolutional Neural Network algorithms. We trained models on secondary data collected from the public PlantVillage dataset and tested the resultant classifiers on primary data of local farm images. The experimental results demonstrate that both models tested better on the PlantVillage dataset. Additionally, with an accuracy of 92.59% and a loss of 0.2805, the pre-trained MobileNetV3 model conventionally performs better than a CNN model. However, when tested on the primary field dataset, the models did not meet expectations for generalization, with the pre-trained MobileNetV3 achieving an accuracy of 9.2%, and loss of 12.91 and the CNN achieving an accuracy of 10.14%, and loss of 8.11. The experiments aided in showing that the models trained on the PlantVillage dataset are not as effective when applied in real-world scenarios. Further improvements are needed to enhance the models’ generalization in real-world scenarios.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"115 3","pages":"74-85"},"PeriodicalIF":1.4,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10551304","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-06DOI: 10.23919/SAIEE.2024.10551310
{"title":"Editors and reviewers","authors":"","doi":"10.23919/SAIEE.2024.10551310","DOIUrl":"https://doi.org/10.23919/SAIEE.2024.10551310","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"115 3","pages":"72-72"},"PeriodicalIF":1.4,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10551310","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-06DOI: 10.23919/SAIEE.2024.10551314
Bhakti Y. Suprapto;Suci Dwijayanti;Dimsyiar M.A. Hafiz;Farhan A. Ardandy;Javen Jonathan
Autonomous electric vehicles use camera sensors for vision-based steering control and detecting both roads and objects. In this study, road and object detection are combined, utilizing the YOLOv8x-seg model trained for 200 epochs, achieving the lowest segmentation loss at 0.53182. Simulation tests demonstrate accurate road and object detection, effective object distance measurement, and real-time road identification for steering control, successfully keeping the vehicle on track with an average object distance measurement error of2.245 m. Route planning for autonomous vehicles is crucial, and the A-Star algorithm is employed to find the optimal route. In real-time tests, when an obstacle is placed between nodes 6 and 7, the A-Star algorithm can reroute from the original path (5, 6, 7, 27, and 28) to a new path (5, 6, 9, 27, and 28). This study demonstrates the vital role of sensor fusion in autonomous vehicles by integrating various sensors. This study focuses on sensor fusion for object-road detection and path planning using the A * algorithm. Real-time tests in two different scenarios demonstrate the successful integration of sensor fusion, enabling the vehicle to follow planned routes. However, some route nodes remain unreachable, requiring occasional driver intervention. These results demonstrate the feasibility of sensor fusion with diverse tasks in third-level autonomous vehicles.
{"title":"Designing an Autonomous Vehicle Using Sensor Fusion Based on Path Planning and Deep Learning Algorithms","authors":"Bhakti Y. Suprapto;Suci Dwijayanti;Dimsyiar M.A. Hafiz;Farhan A. Ardandy;Javen Jonathan","doi":"10.23919/SAIEE.2024.10551314","DOIUrl":"https://doi.org/10.23919/SAIEE.2024.10551314","url":null,"abstract":"Autonomous electric vehicles use camera sensors for vision-based steering control and detecting both roads and objects. In this study, road and object detection are combined, utilizing the YOLOv8x-seg model trained for 200 epochs, achieving the lowest segmentation loss at 0.53182. Simulation tests demonstrate accurate road and object detection, effective object distance measurement, and real-time road identification for steering control, successfully keeping the vehicle on track with an average object distance measurement error of2.245 m. Route planning for autonomous vehicles is crucial, and the A-Star algorithm is employed to find the optimal route. In real-time tests, when an obstacle is placed between nodes 6 and 7, the A-Star algorithm can reroute from the original path (5, 6, 7, 27, and 28) to a new path (5, 6, 9, 27, and 28). This study demonstrates the vital role of sensor fusion in autonomous vehicles by integrating various sensors. This study focuses on sensor fusion for object-road detection and path planning using the A\u0000<sup>*</sup>\u0000 algorithm. Real-time tests in two different scenarios demonstrate the successful integration of sensor fusion, enabling the vehicle to follow planned routes. However, some route nodes remain unreachable, requiring occasional driver intervention. These results demonstrate the feasibility of sensor fusion with diverse tasks in third-level autonomous vehicles.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"115 3","pages":"86-98"},"PeriodicalIF":1.4,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10551314","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-06DOI: 10.23919/SAIEE.2024.10551320
{"title":"Notes for authors","authors":"","doi":"10.23919/SAIEE.2024.10551320","DOIUrl":"https://doi.org/10.23919/SAIEE.2024.10551320","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"115 3","pages":"109-109"},"PeriodicalIF":1.4,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10551320","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-08DOI: 10.23919/SAIEE.2024.10463749
{"title":"Editors and reviewers","authors":"","doi":"10.23919/SAIEE.2024.10463749","DOIUrl":"https://doi.org/10.23919/SAIEE.2024.10463749","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"115 1","pages":"2-2"},"PeriodicalIF":1.4,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10463749","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140066450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-08DOI: 10.23919/SAIEE.2024.10463748
S. Achari;A. Y. Yang;J. Goodhead;B. Swanepoel;L. Cheng
Visible light communication is a present technology which allows data to be transmitted by modulating information onto a light source. It has many advantages over traditional radio frequency communication and up to 10,000 times larger bandwidth. Many existing research in visible light communication assumes a synchronized channel, however, this is not always easily achieved. This paper proposes a novel synchronized VLC system with the potential to ensure reliable communication in both intra- and inter-vehicle communication. The protocol achieves synchronization at the symbol level using the transistor-transistor logic protocol and achieves frame synchronizations with markers. Consequently, the deployment of the system in both intra- and inter-vehicle communication may present numerous advantages over existing data transmission processes. A practical application where visible light communication is used for media streaming is also previewed. In addition, various regions of possible data transmission are determined to infer forward error correction schemes to ensure reliable communication.
{"title":"Self-synchronizing on-off-keying visible light communication system for intra- and inter-vehicle data transmission","authors":"S. Achari;A. Y. Yang;J. Goodhead;B. Swanepoel;L. Cheng","doi":"10.23919/SAIEE.2024.10463748","DOIUrl":"https://doi.org/10.23919/SAIEE.2024.10463748","url":null,"abstract":"Visible light communication is a present technology which allows data to be transmitted by modulating information onto a light source. It has many advantages over traditional radio frequency communication and up to 10,000 times larger bandwidth. Many existing research in visible light communication assumes a synchronized channel, however, this is not always easily achieved. This paper proposes a novel synchronized VLC system with the potential to ensure reliable communication in both intra- and inter-vehicle communication. The protocol achieves synchronization at the symbol level using the transistor-transistor logic protocol and achieves frame synchronizations with markers. Consequently, the deployment of the system in both intra- and inter-vehicle communication may present numerous advantages over existing data transmission processes. A practical application where visible light communication is used for media streaming is also previewed. In addition, various regions of possible data transmission are determined to infer forward error correction schemes to ensure reliable communication.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"115 1","pages":"4-15"},"PeriodicalIF":1.4,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10463748","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140067551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-08DOI: 10.23919/SAIEE.2024.10463751
A. D. Asiegbu;M. T. E. Kahn;A. M. Almaktoof
The need for sustainable and environmentally friendly energy sources is very important if future generations are to be spared of the misdoings of past generations. Climate change, environmental pollution, radiation pollution, and altering of the balanced ecology of the green planet earth is now a significant challenge, because if not mitigated, some island nations will disappear in the future, due to human activities energized by unsustainable fossil fuels. Based on these facts, Germany has taken concrete and important steps to stem the tide of this ill wind by embracing the Green Hydrogen system. This is more obvious in the transport system that produces more than 40% of Green House Gases in Europe. Because Germany is an industrialized European nation, the government has introduced Green Hydrogen for transportation in accordance with European Union energy policy. Therefore, this paper provides a clear, concise analysis and implementation of Green Hydrogen energy in the smart energy system. The paper presents Green Hydrogen analysis in transportation in four sections, namely, Geographical, Chemical, social, and economic aspects, respectively. The last section of this paper is the discussion of results from cost/feasibility analysis, which shows that although the expected or forecasted GH cost is US$7.11per kg from 2020 to 2050, the current growth in the green hydrogen infrastructure and technology, reduced the overall cost of GH in 2020 to $1.60/kg. An erudite conclusion and recommendations are made at the end of this research.
{"title":"Green hydrogen: A clean energy solution for Germany's transportation sector","authors":"A. D. Asiegbu;M. T. E. Kahn;A. M. Almaktoof","doi":"10.23919/SAIEE.2024.10463751","DOIUrl":"https://doi.org/10.23919/SAIEE.2024.10463751","url":null,"abstract":"The need for sustainable and environmentally friendly energy sources is very important if future generations are to be spared of the misdoings of past generations. Climate change, environmental pollution, radiation pollution, and altering of the balanced ecology of the green planet earth is now a significant challenge, because if not mitigated, some island nations will disappear in the future, due to human activities energized by unsustainable fossil fuels. Based on these facts, Germany has taken concrete and important steps to stem the tide of this ill wind by embracing the Green Hydrogen system. This is more obvious in the transport system that produces more than 40% of Green House Gases in Europe. Because Germany is an industrialized European nation, the government has introduced Green Hydrogen for transportation in accordance with European Union energy policy. Therefore, this paper provides a clear, concise analysis and implementation of Green Hydrogen energy in the smart energy system. The paper presents Green Hydrogen analysis in transportation in four sections, namely, Geographical, Chemical, social, and economic aspects, respectively. The last section of this paper is the discussion of results from cost/feasibility analysis, which shows that although the expected or forecasted GH cost is US$7.11per kg from 2020 to 2050, the current growth in the green hydrogen infrastructure and technology, reduced the overall cost of GH in 2020 to $1.60/kg. An erudite conclusion and recommendations are made at the end of this research.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"115 1","pages":"16-23"},"PeriodicalIF":1.4,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10463751","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140067561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-08DOI: 10.23919/SAIEE.2024.10463754
J. V. Bissett;P. A. van Vuuren;J. J. Walker
Reliable cable systems are of utmost importance for electricity distribution- and grid networks. Cable terminations are key components of cable systems but also contribute significantly towards unwanted cable failures and power outages. Although well-developed standards exist in the electricity industry, it is not always possible to execute the most effective tests at power frequency due to financial or logistical reasons. Hence, withstand voltage- and Partial Discharge tests at power frequency are only conducted at a low percentage of electricity utilities. This paper presents the evaluation of unconventional partial discharge fault detection techniques and Very Low Frequency Tan Delta measurements on different cable termination defects. Five artificial cable termination defects were created on single core 6.35/11kV cross linked polyethylene cables, with one “defect-free” termination and five defective ones. Power frequency voltage was applied at increased voltage steps to initiate aging acceleration due to partial discharge. This resulted in insulation deterioration and eventual termination failure. Different data sets were obtained from the Very Low Frequency Tan Delta measurements as well as for the unconventional Partial Discharge measurements. The sequence of failures is compared between the two data sets and evaluated against the root cause failures of the terminations as evaluated by an industry expert. High Frequency Current Transformer and capacitive coupling sensors were used together with an advanced partial discharge data acquisition system. Partial discharge analysis was done to relate the specific on-line trending data with termination failure. Good correlation was found between the two data sets which supports the utilization of unconventional partial discharge measurement for cable acceptance testing. It would be beneficial for the electricity industry to consider unconventional Partial Discharge technology as a suitable alternative for on-site acceptance tests for cable terminations.
可靠的电缆系统对配电和电网网络至关重要。电缆终端是电缆系统的关键部件，但也是造成电缆故障和停电的重要原因。虽然电力行业已制定了完善的标准，但由于资金或物流原因，并不总是能在工频下执行最有效的测试。因此，只有很少一部分电力公司会进行工频耐压和局部放电测试。本文介绍了非常规局部放电故障检测技术和超低频 Tan Delta 测量对不同电缆终端缺陷的评估。在单芯 6.35/11kV 交联聚乙烯电缆上人为制造了五个电缆终端缺陷，其中一个为 "无缺陷 "终端，五个为有缺陷终端。以增加的电压阶跃施加工频电压，以启动局部放电导致的老化加速。这导致绝缘老化，最终导致终端失效。从甚低频 Tan Delta 测量和非常规局部放电测量中获得了不同的数据集。比较了两组数据的故障顺序，并根据行业专家评估的终端故障根本原因进行了评估。高频电流互感器和电容耦合传感器与先进的局部放电数据采集系统一起使用。通过局部放电分析，将特定的在线趋势数据与终端故障联系起来。发现这两个数据集之间存在良好的相关性，从而支持将非常规局部放电测量用于电缆验收测试。考虑将非常规局部放电技术作为电缆终端现场验收测试的合适替代方案，对电力行业大有裨益。
{"title":"Evaluation of unconventional partial discharge and tan delta assessment techniques on medium voltage cable terminations with artificial defects","authors":"J. V. Bissett;P. A. van Vuuren;J. J. Walker","doi":"10.23919/SAIEE.2024.10463754","DOIUrl":"https://doi.org/10.23919/SAIEE.2024.10463754","url":null,"abstract":"Reliable cable systems are of utmost importance for electricity distribution- and grid networks. Cable terminations are key components of cable systems but also contribute significantly towards unwanted cable failures and power outages. Although well-developed standards exist in the electricity industry, it is not always possible to execute the most effective tests at power frequency due to financial or logistical reasons. Hence, withstand voltage- and Partial Discharge tests at power frequency are only conducted at a low percentage of electricity utilities. This paper presents the evaluation of unconventional partial discharge fault detection techniques and Very Low Frequency Tan Delta measurements on different cable termination defects. Five artificial cable termination defects were created on single core 6.35/11kV cross linked polyethylene cables, with one “defect-free” termination and five defective ones. Power frequency voltage was applied at increased voltage steps to initiate aging acceleration due to partial discharge. This resulted in insulation deterioration and eventual termination failure. Different data sets were obtained from the Very Low Frequency Tan Delta measurements as well as for the unconventional Partial Discharge measurements. The sequence of failures is compared between the two data sets and evaluated against the root cause failures of the terminations as evaluated by an industry expert. High Frequency Current Transformer and capacitive coupling sensors were used together with an advanced partial discharge data acquisition system. Partial discharge analysis was done to relate the specific on-line trending data with termination failure. Good correlation was found between the two data sets which supports the utilization of unconventional partial discharge measurement for cable acceptance testing. It would be beneficial for the electricity industry to consider unconventional Partial Discharge technology as a suitable alternative for on-site acceptance tests for cable terminations.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"115 1","pages":"24-26"},"PeriodicalIF":1.4,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10463754","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140067562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：