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Hierarchical temporal memory (htm)approach for fault detection in transmission line

• Authors Details :  
• Engr. Umuroh O. Richard ,  
• Assoc. Prof. Omeje O. Crescent

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This study was conducted to proposea hierarchical temporal memory (HTM) approach for fault detection in the Onitsha-Alaoji transmission line in Nigeria. Using a mixed research method, the study employed the Hawkins HTM model with two objectives and their corresponding research questions. The study gathered primary and secondary data to detect and evaluate faults in the Onitsha-Alaoji transmission line in Nigeria using HTM and compares its efficacy to current fault detection methods. With the use of simulation and descriptive methods of data analysis, results showed that partial discharge (PD) is the fault type that is being detected and it is commonly found as a fault leading to transmission line errors. More so, fault detection simulations were conducted at 40 km using typical power spectral density analysis. The first fundamental shifted from about 10 kHz to roughly 13 kHz during a fault. The HTM model outperformed sequence learning methods, resulting in a 90% mean test classification accuracy (CA) over extreme learning machine(ELM) and online sequential learning–extreme learning machine (OS-ELM), with OS-ELM performing poorly.The study concluded and recommended that the proposed HTM model be used to identify various PD fault types that plague the Onitsha-Alaoji transmission line in Nigeria. With the increased efficacy and reliability of the proposed model compared to existing methods, it is recommended for future implementation in this transmission line and potentially other fault-prone power transmission lines in Nigeria.

Article Subject Details

• Engineering
• Engineering management
• Materials
• Materials Science

Article Keywords Details

• Fault detection
• Hierarchical temporal memory (HTM)
• Onitsha-Alaoji transmission line
• Online sequential learning–extreme learning machine
• Transmission line errors

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