## 5 Experiment Results and Discussion
### Identification Results Improved by Phase-Resolved 3D Patterns  
For further improving the recognition rates of IHHT, additional PD features extracted from phase-resolved 3D patterns are taken into account. The detailed procedure of feature extraction from a phase-resolved 3D pattern can refer to authors' previous research [26]. The measured PD field data are first transferred to \(n\)-\(q\)-\(\varphi\) 3D pattern, and the fractal features and mean-discharge are then extracted from the phase-resolved 3D patterns. Figure 15 shows the typical 3D patterns as transferred from the measured PD data for each experiment model. The parameters of the 3D patterns are number of discharge \(n\), discharge magnitude \(q\), and phase angle \(\varphi\). We can observe that the amplitude (\(pC\)) and numbers of discharge (\(n\)) in Type IV and VI are less than the other defect types. In Type I,  
Figure 14: Feature distributions for time-frequency-energy 3D spectrums by IHHT with the \(z\) axis representing the discharged energy level.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ModelNoise** & **0\%** & **\(\pm\)10\%** & **\(\pm\)20\%** & **\(\pm\)30\%** \\ \hline Type I & 100 & 100 & 100 & 100 \\ Type II & 100 & 95 & 95 & 90 \\ Type III & 100 & 90 & 65 & 70 \\ Type IV & 100 & 95 & 85 & 65 \\ Type V & 100 & 95 & 90 & 90 \\ Type VI & 95 & 65 & 75 & 55 \\ Average & 99.2 & 90 & 85 & 78.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Identification results by IHHT versus features FD-\(\Lambda\)-MD-E.  
the discharge amplitude is larger than that in the other defects, the maxima discharge can even reach 350 \(pC\). The experiment Type II and Type V have the similar 3D pattern but discharge amplitude Type V is larger than Type II. According to the 3D PD pattern in each experiment model, we found that the features are different between the defect models. Demonstrated in Figure 16 are the features distributions of the fractal dimension, lacunarity and mean-discharge extract from 3D phase-resolved PD pattern. The patterns belonging to a particular class are close to each other, and the distribution of Type I is very clearly different with other modes. The recognition rates of combining IHHT with phase-resolved _n-q-p_ 3D PD pattern are shows in Table 4. There are totally seven features as input to extension method for pattern recognition. It can be obviously observed that combining IHHT with phase-resolved PD pattern not only improves recognition rates but also enhances noise tolerance.  
As illustrated in Figure 17, a relatively high identification result is provided by IHHT in contrast with HHT as a consequence of the removal of mode confusions, purification of IMFs and features extracted by a fractal theory. Due to the fact that a partial discharge signal is sensed as an extremely weak signal, the marginal difference in 3D spectrums analyzed by both HHT and IHHT is not distinctive enough unless the features are extracted in IHHT by a fractal theorem. This research also combines IHHT with phase-resolved 3D patterns for further improving recognition rates. The results show that more representative features added can effectively raise the recognition rates. However, more complex procedure for both features extraction and pattern recognition are comparatively required.