Presented Paper: https://ieeexplore.ieee.org/document/9375568 Github Repository: https://github.com/ACRRL/Oil-Project

1. Daniel Firouzimagham, Pouya Aminaie, Zeinab Shayan, Mohammad Sabouri,
Mohammad Hassan Asemani
Applied Control and Robotics Research Lablatory, Shiraz University
Presenter: Pouya Aminaie
Online Transformer Oil Analysis Based on
Spectroscopy Technique and Machine Learning
Classifier: Experimental Setup
Dec 31, 2020

2. 2
Introduction
• Experimental Setup
• Machine Learning Algorithm
1.Proposed Method
1.Results
Conclusion

3. One of the most critical components of a transformer is
insulating oil. Investigation of oil ageing can improve the status
of the transformers.
 Role:
• Electrical Insulation
• Heat Transfer
3

4. Electrical Chemical Physical
Dielectric Breakdown Voltage Moisture
Density
Resistivity Acidity
Viscosity
Dielectric Loss Factor Color
4
 Oil Properties:
Target: Spectrometry technique in visible region

5.  Literature Review:
5
Reference Method Year
[24] The effect of oil ageing on the UV-Visible spectrum 2014
[27] Correlation between the color index of oil and the
bandwidth of UV-Visible spectrum
2016
[32] Dissolved Gas Analysis (DGA) based on FTIR
spectrum
2018
[30] The effect of paper degradation condition on FTIR
spectrum
2019
[31] Detection of dissolved acetylene in transformer oil
based on FTIR spectrum
2019
[26] The effect of oil ageing on the FTIR spectrum 2020
[28] The effect of paper aging on NIR spectrum 2020

6. Disadvantages of previous study:
 Offline
 Shutting down the power during periodic sampling
Advantages of proposed method:
 Online
 Real-Time analysis
 Low-Cost implementation
6

7. Spectrometry Device is consist of:
1. Sample Chamber
2. LED RGB WS2812 Module
3. TSL2561 Light Sensor Module
7

8. 8
 Step 1: 5-class transformer oil samples are prepared
 Step 2: Producing the visible spectrum using WS2812
 Step 3: Measuring the intensity and send to the MQTT via NodeMCU
 Step 4: Classification of oil samples based on machine learning algorithms

9. 9

10.  Light Intensity
10

11.  Accuracy Comparison
Test
Accuracy
(%)
Train
Accuracy
(%)
Algorithm
Type
88
88
92
99.56
99.56
95.55
Fine Tree
Medium Tree
Coarse Tree
Decision Tree
88
76
92.5
92.5
Linear Discriminant
Subspace Discriminant
Discriminant
Analysis
92
96
98.67
98.5
Gaussian Naive Bayes
Kernel Naive Bayes
Naïve Bayes
92
92
92
98.5
95.5
94
Linear SVM
Medium Gaussian SVM
Coarse Gaussian SVM
SVM
11

12. New Oils
Predicted Label
Coarse Tree
Linear
Discriminant
Kernel
Naive
Bayes
Linear
SVM
A1 4 4 4 4
A2 5 1 1 4
A3 4 4 4 2
A4 4 1 1 4
A5 5 1 5 4
Validation
Accuracy
60 % 20 % 20 % 80 %
12
 Prediction Result

13. 13
 Linear SVM Scatter Plot

14.  Validation
14
New Oils Correlation
with oil1
Correlation
with oil2
Correlation
with oil3
Correlation
with oil4
Correlation
with oil5
Maximum
Correlation
A1 0.8340 0.8482 0.9391 0.9395 0.9384 0.9395, oil4
A2 0.8449 0.8374 0.9482 0.9484 0.9478 0.9484, oil4
A3 0.8668 0.8461 0.9425 0.9427 0.9396 0.9427, oil4
A4 0.8495 0.8417 0.9498 0.9503 0.9494 0.9503, oil4
A5 0.8525 0.8431 0.9483 0.9488 0.9474 0.9488, oil4

15.  Age of transformer oil effects on the oil color and amount of
passing visible light spectrum
 Linear SVM is considered as the most efficient algorithm to
classify the ageing of transformer oils
 IOT-based remote monitoring can solve the problems of
offline monitoring
15

16. 16
Any question or feedback is appreciated !