# Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features

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## Abstract

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## 1. Introduction

## 2. Proposed Solution

## 3. EMI Measurement Technique

## 4. Classification Theory

#### 4.1. Adaptive Local Iterative Filtering

#### 4.2. Permutation Entropy

#### 4.3. Dispersion Entropy

#### 4.4. Support Vector Machine

- SVM generates an optimal line that separates the two different data features, such that feature clusters of one class are grouped on one side of the feature space and the remaining ones are grouped on the other side. This yields to the SVM model which is used in future data classification.
- SVM classifies the training data set based on the trained model in the previous step; this is known as the testing phase.

## 5. Application to EMI Data

- Site 1: The data was measured at the neutral earth cable of a 661 MVA hydrogen/water cooled synchronous generator operating at 23.5 kV, 19 kA, 3 phase, 50 Hz, 3000 RPM, 0.85 lag/0.95 lead power factor and 2 pole. A total of 13 signals were identified to contain E+mPD, C+E, C, N, PN and mPD.
- Site 2: similar to the previous site, the measurements were taken at the neutral earth of different assets including a General Step-Up (GSU) Transformer operating at 430/15.5 kV, 444/12329 A, 3 phase, 50 Hz, 331 MVA, IPB and Station Transformer (Sta XFMR). The events identified in the GSU transformer are mPD+mA, PD+mA, PD+A, PD and the events identified in the IPB are PD and PN. Finally, the DM event was identified in the Sta XFMR.
- Site 3: The data was measured at the neutral earth cable of an H2 cooled generator, operating at 294.25 MVA, 15 kV, 0.85 PF, 2 pole and 3000 RPM, from which seven signals were selected with an additional signal selected from an H2 cooled Steam Turbine Generator (STG) operating at 15 kV, 2 pole and 3000 RPM. The labelled events found at the neutral earth cable are PN, PD, NVFD, E and the ones found at the STG are: PD, E+mPD and E+PD.

## 6. Results and Discussion

## 7. Conclusions

## Author Contributions

## Conflicts of Interest

## Abbreviations

A | Arcing |

ALIF | Adaptive Local Iterative Filtering |

C | Corona |

DE | Dispersion Entropy |

DM | Data Modulation |

E | Exciter |

EMI | Electro-Magnetic Interference |

GSU | General Step-Up |

HFCT | High Frequency Current Transformer |

HV | High Voltage |

IMF | Intrinsic Mode Function |

IPB | Isolated Phase Bus |

MCSVM | Multi-Class SVM |

NVFD | Non Variable Frequency Drive |

PD | Partial Discharge |

PDE | Partial Differential Equation |

PDS200 | Partial Discharge Surveyor 200 |

PE | Permutation Entropy |

PN | Process Noise |

PRPD | Phase Resolved PD |

SD | Stopping Distance |

STG | Step Up Generator |

Sta XFMR | Station Transformer |

SVM | Support Vector Machine |

UHF | Ultra-High-Frequency |

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**Figure 1.**Overall process diagram of the proposed approach from data acquisition to pattern recognition of Electro-Magnetic Interference (EMI) events: Partial Discharge (PD), Corona (C), Process Noise (PN) and Data Modulation (DM).

**Figure 4.**Support Vector Machine (SVM) linear space mapping using second-order polynomial kernel function.

**Figure 5.**Example PD signal decomposed into Intrinsic Mode Functions (IMFs) using the Adaptive Local Iterative Filtering (ALIF) algorithm.

Signal | Feature | IMF1 | IMF2 | IMF3 | IMF4 |
---|---|---|---|---|---|

PD | |||||

PE | 1.75 | 1.45 | 1.24 | 1.06 | |

DE | 1.13 | 1.42 | 1.50 | 1.34 | |

PN | |||||

PE | 1.79 | 1.39 | 1.14 | 0.95 | |

DE | 2.01 | 1.82 | 1.64 | 1.39 |

Case | Classification Accuracy % |
---|---|

Site 1 | 91 |

Site 2 | 100 |

Site 3 | 100 |

Common data subset | 100 |

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**MDPI and ACS Style**

Mitiche, I.; Morison, G.; Nesbitt, A.; Hughes-Narborough, M.; Stewart, B.G.; Boreham, P. Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features. *Sensors* **2018**, *18*, 406.
https://doi.org/10.3390/s18020406

**AMA Style**

Mitiche I, Morison G, Nesbitt A, Hughes-Narborough M, Stewart BG, Boreham P. Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features. *Sensors*. 2018; 18(2):406.
https://doi.org/10.3390/s18020406

**Chicago/Turabian Style**

Mitiche, Imene, Gordon Morison, Alan Nesbitt, Michael Hughes-Narborough, Brian G. Stewart, and Philip Boreham. 2018. "Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features" *Sensors* 18, no. 2: 406.
https://doi.org/10.3390/s18020406