On-line PD Monitoring System (PDLOOK) for XLPE Cables
Shigeru Akaiwa1*, Koji Miyagawa1, Takayuki Nakashima1, Tatsuya Sakoda2, Takuma Miyake2, Akira Takayama3
1 Kyushu Electric Power Co., Inc.
2 Electric Power Lab, University of Miyazaki
3 Kyuden Business Solutions

Introduction
Fig.1 Image of the relationship between PD occurrence
and insulation deterioration

Fig.2 Example of obtained signal waveforms

Fig.3 Signal obtained on a substation cable (1 phase)

PD Occurrence

Failure(1)PD-like signal

Same PD Occurrence

MASS DATA: Necessary to analyze every month: HARD WORK!

Insulation deterioration, Time (year)

Necessary to monitor PD occurrence constantly
to grasp the condition of insulation deterioration.

We have developed an Online Remote PD diagnosis system (PDLOOK) with
Automatic Analysis Function taking advantage of AI.

PDLOOK system
System Overview

Noise Filtering Function

Sites

Cloud System

CT sensor

AD conversion
board / edge
computer
100MHz sampling

database

Clusteringfunction

Remote maintenanceenvironment

Learning cycle
function• Noise filtering function reduces LTE traffic.
• AI PD judgement function enables real-time PD trend monitoring.

AI PD judgment Function• Set a “Gray” standard, which needs human judgment for theprobability range where the AI judgment is ambiguous.
• Adopting ensemble learning using multiple AIs with different
characteristics.

“Gray” standard
Fig.5 Standard of AI probability

0%

10%

Gray
20%

30%

40%

50%

PD
60%

70%

80%

90%

• Based on the variance of the peak section set the Judgment
Boundary for other sections.
• If the variances of all sections are below the Judgment Boundary,
the data is sent to the cloud system.
Table1. Performance of Noise filtering function

Number ofNumber of data removed byRatio
validation data
the noise filter
Noise1,146,529
1,145,426
99.90%
PD373,118
91
0.02%

Ensemble learning

To reduce judgment omissions for the PD signals

NoiseFig.4 Noise Filtering method using variance (Example of 5 sections)

Partial discharge
judgment function

４G

Noise filteringfunction

Noise(2)Noise-like signal

PD
100%

PD probability based on AI judgment:“Noise” <10% , 90%＝< “PD”

PD probability needed human judgment:

10% ＝< “Gray” <90%

• Adopt 3 judgment methods for 4 AI models.
• Implement them from [1] and end if “PD” result is reached.
[1] Priority: "PD“, if any one of AI model judged as "PD"[2] Majority: If majority AI models judged as “PD”, “PD”
[3] Average value: Threshold judgment with the average value of thejudgment probability of 3 models.
AI ModelCNN
GRU
Light GBM
Efficient GAN

Table 2. Threshold for each judgment method[1] Priority[2] Majority[3] Average value
PD: 0.9 or higher
NAPD:0.8 or higher
NAPD:0.9 or higher
Noise:0.2 or less
Noise:0.1 or less
NAPD: 0.038 or less
NANA

Table3. Judgment accuracy evaluation result of ensemble learning (evaluated with 718,335 data obtained from the field)

Accuracy (without Gray)
PD probability
True
True
False
False Precision Recall PD/Gray
Gray
Positive Negative Positive Negative (%)(%) Noiseratio
CNN
332,164 383,845
185
181
99.944 99.946 716,375 1,960 0.27%
GRU
322,292 375,646846
451
99.738 99.860 699,235 19,100 2.66%
Light GBM 205,722 340,564
46999.998 99.966 546,359 171,976 23.94%
Ensemble
332,513 380,936
2465099.926 99.985 713,745 4,590 0.64%
learning
AI ModelSummery

True ValuePDNoisePD True Positive False Positive
AINoise False Negative True Negative
Precision：Low False Positives for PD
TP / (TP + FP)
Recall：Low False Negatives for PD
TP / (TP + FN)• We have developed a continuous remote monitoring system for partial discharge, which consists of an AI judgment function and a noise
filtering function, focusing on the waveform of the partial discharge signal. We’re expanding services with this system in Japan.
• Accumulating big data from the system, we will try to clarify the correlation between cable lifetime and PD characteristics.