Theses and Dissertations (Power Engineering)

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Browsing Theses and Dissertations (Power Engineering) by Author "Sutherland, G., Dr."

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    Development of a multi-protocol equipment interface for use in renewable energy systems
    (Vaal University of Technology, 2023-01-31) Benson, M; Sutherland, G., Dr.; Joubert, A., Dr.
    Modern renewable energy systems be it off-grid, grid-connected, hybrid or a combination consisting of different equipment allowing data communication through different wired and wireless methods. The combination of equipment by different manufacturers has an impact on the communication and functionality of these energy systems. The aim of the research is to develop a multi-protocol equipment interface allowing communication between different renewable energy equipment using different communication mediums and communication protocols. The communication protocols to be focused on were Modbus and SunSpec using RS-232, RS-485 and Ethernet as wired communication mediums between the equipment interface and renewable energy devices. The research methodology utilized allowed for multiple cycles to develop the equipment interface. The first cycle focused on the hardware design and the second cycle focused on the design and development of the software algorithm required to retrieve the data from various renewable energy equipment. The equipment interface has shown that it was able to obtain a single data register value using Modbus and SunSpec and that it can gather data values from multiple registers as required by the user for Modbus and SunSpec. The user has to specify communication medium and the registers which are required along with the data types and scale factors depending on communication protocol used.
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    Modelling of transmission line faults using machine learning methods
    (Vaal University of Technology, 2023-03-30) Makgai, Maphale Lucas; Sutherland, G., Dr.; Joubert, A., Prof.
    Electrical transmission lines are a link between the generation station and the consumer. Any abnormality in transmission lines may result in a prolonged total electricity shutdown if not detected and identified accurately. Malfunctioning of transmission lines contributes to economic destruction and leads to wildfires and damage to electrical equipment. The existing literature has confirmed that 70 to 80 percent of the abnormalities that usually occur in transmission lines are single line-to-ground (L-G) faults. Detecting and identifying such faults will minimise restoration time. Machine learning-based models' accuracy has never been comprehensively tested in detecting and identifying L-G faults in transmission lines under noisy conditions. This study tested a MATLAB machine learning-based model to detect and identify L-G faults when introducing noise on these transmission lines.