2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA)最新文献_第10页

Artisan Development and Training - An analysis of the Apprentice, Learnership and ARPL Trade Test Results of Candidates Tested at TEK-MATION Training Institute 技工发展与训练——在泰克-泰克培训学院参加学徒、学徒及ARPL技术测试的考生成绩分析

2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA) Pub Date : 1900-01-01 DOI: 10.1109/ROBOMECH.2019.8704795

Therasinamurthie P. Govender, I. Davidson

引用次数: 1

Comparison of Different Wind Speed Prediction Models for Wind Power Application 风电应用中不同风速预测模型的比较

2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA) Pub Date : 1900-01-01 DOI: 10.1109/ROBOMECH.2019.8704793

T. Ayodele, R. Olarewaju, J. Munda

引用次数: 6

Application of Artificial Intelligence Technique in Predicting 7-Days Solar Photovoltaic Electrical Power 人工智能技术在7天太阳能光伏发电功率预测中的应用

2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA) Pub Date : 1900-01-01 DOI: 10.1109/ROBOMECH.2019.8704810

Raymond O. Kene, S. Chowdhury, T. Olwal

{"title":"Application of Artificial Intelligence Technique in Predicting 7-Days Solar Photovoltaic Electrical Power","authors":"Raymond O. Kene, S. Chowdhury, T. Olwal","doi":"10.1109/ROBOMECH.2019.8704810","DOIUrl":"https://doi.org/10.1109/ROBOMECH.2019.8704810","url":null,"abstract":"To be able to dispatch electrical power effectively to consumers using solar photovoltaic (SPV) cells, there is a need to have information about the SPV power generation. This information is best derived from predicting the SPV power ahead of any supply. Artificial neural network intelligence technique is employed in this study with the aim of predicting SPV electrical power for a period of 7 days. The maximum power produced on a daily basis is been identified as well as the daily average power that is produced and predicted. With this information, the short-term availability of daily solar irradiation can be maximized. A statistical regression analysis has been used to establish the relationship between the produced and predicted power, using statistical functions like the mean bias error (MBE), the mean square error (MSE), root mean square error (RMSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE), and the correlation coefficient (CC). The algorithm used in training the network is the backpropagation algorithm with feed-forward neural network. A total of 14,300 datasets have been used to establish this study with the application of artificial neural network (ANN) for prediction analysis. The result indicates that, the uncertainty in SPV power generation can be mitigated using ANN to predict its performance, thereby creating visibility as to what the SPV system can generate. This enables load balancing, efficient power dispatch and accurate scheduling.","PeriodicalId":344332,"journal":{"name":"2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA)","volume":"16 s3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113956965","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

A Support Vector Machine Based Fault Diagnostic Technique In Power Distribution Networks 基于支持向量机的配电网故障诊断技术

2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA) Pub Date : 1900-01-01 DOI: 10.1109/ROBOMECH.2019.8704768

K. Moloi, A. Yusuff

{"title":"A Support Vector Machine Based Fault Diagnostic Technique In Power Distribution Networks","authors":"K. Moloi, A. Yusuff","doi":"10.1109/ROBOMECH.2019.8704768","DOIUrl":"https://doi.org/10.1109/ROBOMECH.2019.8704768","url":null,"abstract":"In this paper, a method for detection and classification of faults in an electrical power distribution system is presented. Digsilent Power Factory software was used to model a section of a 66 kV power system. Fault incidents were instantiated on the model. The signal obtained from fault incidences were subsequently fed as input to discrete wavlet transform in order to obtained fault features and subsequently the features were then used as inputs for a support vector machine (SVM) and artificial neural network (ANN) for fault classification and detection. In addition, a Gaussian Process Regression (GPR) technique was employed for estimation of fault locations along the distribution line. Fault detection, classification and location estimation scheme were developed in MATLAB. The method showed that most faults on electric power distribution network can be classified with a good accuracy and minimum fault estimation error. The method is further validated on a real world power system. A hybrid method is thus proposed for detection, classification and estimation of fault location in a distribution network.","PeriodicalId":344332,"journal":{"name":"2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132558676","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: 4

Enhancing edge-based image descriptor models through colour unification 通过颜色统一增强基于边缘的图像描述符模型

2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA) Pub Date : 1900-01-01 DOI: 10.1109/ROBOMECH.2019.8704732

Dumusani Kunene, Vusi Skosana

{"title":"Enhancing edge-based image descriptor models through colour unification","authors":"Dumusani Kunene, Vusi Skosana","doi":"10.1109/ROBOMECH.2019.8704732","DOIUrl":"https://doi.org/10.1109/ROBOMECH.2019.8704732","url":null,"abstract":"The lack of suitable robust appearance models hinders the performance of most image descriptors. Descriptors often rely on pieces of information in images called image features to discriminate the contents of images. Most successful descriptors use gradient images for determining the overall shapes of objects. Consequently, the inferred features are often susceptible to the noise caused by shadows, reflections and inner textures within the object. Significant efforts have been made towards improving the performance of image classifiers, yet generic object detection remains an open problem. In this paper, a method aimed at improving existing appearance models is proposed. The focus is on enhancing the acquired information at fundamental stages to improve the robustness of common statistical learning classifiers, as seen with the work of Holger Winnemoller et al. with human subjects.The selective Gaussian blur filter was applied to several human classification datasets to reduce the amount of ambiguous low-frequency noise. Experiments were then conducted to determine whether the unification of similar colours in local image regions could improve the acquired image features. The classification results that were obtained with the processed images were competitive to the results obtained with the original images, however inconclusive for demonstrating the benefits of image smoothing.","PeriodicalId":344332,"journal":{"name":"2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128841859","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: 2

Comparative Analysis of the Fault Ride-Through Capabilities of the VSG Methods of Microgrid Inverter Control under Faults 故障情况下微网逆变器控制VSG方法故障穿越能力的比较分析

2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA) Pub Date : 1900-01-01 DOI: 10.1109/ROBOMECH.2019.8704754

E. Buraimoh, I. Davidson

{"title":"Comparative Analysis of the Fault Ride-Through Capabilities of the VSG Methods of Microgrid Inverter Control under Faults","authors":"E. Buraimoh, I. Davidson","doi":"10.1109/ROBOMECH.2019.8704754","DOIUrl":"https://doi.org/10.1109/ROBOMECH.2019.8704754","url":null,"abstract":"The Distributed Generators (DGs) based on Renewable Energy System (RES) lack the inertia (rotating mass) and damping features of conventional power system, where fossil fuel based synchronous generators are dominant. The consequence of insignificant inertia and damping on grid stability and dynamic performance is further compounded with growing intermittent RES introduction into the grid. The use of RES based converters with appropriate Virtual Synchronous Generator (VSG) control strategy offers the necessary inertia support which culminates exceptional grid stability enhancement. However, the existing VSG studies focused on inverters in steady-state and under balanced grid voltage without emphasis on VSG dynamic performance during fault and other transients. Conversely, under fault conditions, it’s imperative to investigate the dynamic performance of the VSG control strategies while ensuring the protection of inverters owing to their low overvoltage and overcurrent tolerance capacities. Consequently, this study investigated the two methods of Virtual Synchronous Machine (VISMA) and carried out a comparative analysis to observe how the two methods ensure the VSG-inverter’s sustained grid connection under grid fault. Fault-Ride-Through (FRT) is the ability of electrical generating units to remain grid connected in the brief periods of fault and after fault clearance. Conclusions are drawn as to which VISMA strategies provide a better performance in terms of fault ride-through capability, current-limiting and recovery from faults.","PeriodicalId":344332,"journal":{"name":"2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124652707","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: 9

Performance Prediction for an Electric Vehicle 电动汽车的性能预测

2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA) Pub Date : 1900-01-01 DOI: 10.1109/ROBOMECH.2019.8704770

C. Landman, A. Rix

引用次数: 1

A hybrid method for high impedance fault classification and detection 一种高阻抗故障分类与检测的混合方法

2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA) Pub Date : 1900-01-01 DOI: 10.1109/ROBOMECH.2019.8704765

K. Moloi, J. Jordaan, Y. Hamam

引用次数: 5

Reduction of Inverter-Induced Shaft Voltages Using Electrostatic Shielding 利用静电屏蔽降低逆变器感应轴电压

2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA) Pub Date : 1900-01-01 DOI: 10.1109/ROBOMECH.2019.8704702

S. Gerber, R. Wangi

引用次数: 8

Segmentation of Optic Cup and Disc for Diagnosis of Glaucoma on Retinal Fundus Images 视杯盘分割在青光眼诊断中的应用

2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA) Pub Date : 1900-01-01 DOI: 10.1109/ROBOMECH.2019.8704727

A. O. Joshua, F. Nelwamondo, G. Mabuza-Hocquet

引用次数: 18