A new study from ACTOM High Voltage Equipment has demonstrated that machine learning techniques can significantly improve the identification of lightning impulse failure modes in power transformers a process that is traditionally slow, destructive and dependent on specialist experience. The research, presented by ACTOM Engineer Bafana Nyandeni at CIGRE Southern Africa 2025 on October 15, compares multiple algorithms to determine which offer the most accurate and practical diagnostic support during transformer impulse testing.
Lightning impulse testing is used to confirm whether transformer insulation can withstand transient over-voltages by applying full wave impulses at defined basic insulation levels BILs. Resultant waveforms include parameters such as front time, tail time, peak voltage, time to chop and overshoot. When failures occur, isolating the fault within the complex winding structure requires interpretation of these non-stationary, time-varying waveforms a task that normally depends on specialist analysts and, in some cases, destructive inspection.
Nyandenis study investigates whether machine learning models can interpret these waveform characteristics more systematically. The comparative assessment reviewed artificial neural networks ANNs, decision trees DTs and support vector machines SVMs. ANN-based models were found to be sensitive to mismatches between training and fault samples while DT models exhibited reduced precision when additional samples increased the depth and imbalance of the tree structure.