This paper investigates the sensitivity of statistical fingerprints to different phase resolution (PR) and amplitude bins (AB) sizes of partial discharge (PD) phi-q-n (phase-amplitude-number) patterns. In particular, this paper compares the capability of the nsemble neural network (ENN) and the single neural network (SNN) in recognizing and distinguishing different resolution sizes of phi-q-n discharge patterns. The training fingerprints for both the SNN and ENN comprise statistical fingerprints from different f-q-n measurements. The result shows that there exists statistical distinction for different PR and AB sizes on some of the statistical fingerprints. Additionally, the ENN and SNN outputs change depending on training and testing with different PR and AB sizes. Furthermore, the ENN appears to be more sensitive in recognizing and discriminating the resolution changes when compared with the SNN. Finally, the results are assessed for practical implementation in the power industry and benefits to practitioners in the field are highlighted.
- partial discharge
- ensemble neural network
- phase resolution and amplitude bin sizes