State Monitoring and Fault Diagnosis of HVDC System via KNN Algorithm with Knowledge Graph: A Practical China Power Grid Case
Abstract
:1. Introduction
2. HVDC System Knowledge Graph
2.1. Knowledge Acquisition
2.2. Knowledge Analysis
2.3. Knowledge Base Establishment
2.4. Graph Construction
2.5. Knowledge Service
2.6. Knowledge Application
3. Fault Classification
3.1. AC Fault
3.2. DC Fault
3.3. Converter Valve Fault
3.4. Commutation Failure
4. Principle of KNN Algorithm
- (1).
- Mahalanobis distance
- (2).
- Chebyshev distance
- (3).
- ED
- (4).
- Manhattan distance
5. Fault Diagnosis Model
- (1)
- Data processing, normalize the data of 15 channels in each type of fault data as follows:
- (2)
- The data of 15 channels of each sample are connected in series head to tail and stacked according to the number of samples of fault type to form all fault datasets;
- (3)
- Label the fault data;
- (4)
- Data classification: randomly divide 80% of all fault data into training sets and the remaining 20% into test sets;
- (5)
- Establish KNN fault diagnosis model, set the appropriate KNN algorithm parameter K value, and select the appropriate distance function;
- (6)
- 80% of data are substituted into the fault diagnosis model for fault diagnosis training, and the remaining 20% of data are substituted into the trained model for verification;
- (7)
- Obtain the test data label and compare the diagnosed label with the real label of the test data, and then calculate the fault diagnosis accuracy rate and draw a visual confusion matrix diagram. The accuracy rate formula is as follows.
6. Case Study
7. Discussion and Limitations
7.1. Discussion
7.2. Limitations
8. Conclusions
Author Contributions
Funding
Conflicts of Interest
Nomenclature
AC | alternating current |
BC | Bayesian classifier |
CNN | convolutional neural networks |
DC | alternating current |
ED | Euclidean distance |
ES | expert system |
HVDC | High-voltage direct current |
KG | knowledge graph |
KNN | K-Nearest Neighbor |
SVM | support vector machine |
K | number of adjacent points |
d | the distance between samples |
N | the number of samples |
Si | the standard deviation of the sample data in the i-th dimension |
x | the horizontal coordinate position of the sample |
y | the vertical coordinate position of the sample |
w | classification weight of samples |
x* | normalized sample data |
p | accuracy of fault diagnosis |
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1: Establish KNN algorithm model; |
2: Set KNN algorithm parameters: K, ; 3: Import data and select the test set; 4: Calculate similarity; 5: Calculate the distance between training data and unknown data according to the selected ; 6: Calculate weight and judge similarity according to ; 7: select front K data; 8: Record the times of each category; 9: Use the category with the most occurrences as the category of unknown data; 10: Repeated to judge all test data. |
Signal | Description Meaning | Signal | Description Meaning |
---|---|---|---|
UACA(V) | A-phase AC voltage | IACD_L3(A) | C-phase AC current of D-bridge valve side |
UACB(V) | B-phase AC voltage | UDL(V) | DC line voltage |
UACC(V) | C-phase AC voltage | UDN(V) | Neutral bus voltage |
IACY_L1(A) | A-phase AC current of Y-bridge valve side | IDN(A) | Neutral bus current |
IACY_L2(A) | B-phase AC current of Y-bridge valve side | IDE(A) | Grounding pole bus current |
IACY_L3(A) | C-phase AC current of Y-bridge valve side | IDH(A) | High-voltage bus current |
IACD_L1(A) | A-phase AC current of D-bridge valve side | IDL(A) | DC line current |
IACD_L2(A) | B-phase AC current of D-bridge valve side |
Method | Parameter Name | Parameter Setting |
---|---|---|
KNN | Neighbors: K | 7 |
Metric distance | Euclidean distance | |
Weight type | Inverse distance | |
SVM | Penalty coefficient: C | 1 |
Kernel | Gaussian | |
Decision function shape | One-versus-one | |
BC | Nuclear type | Gaussian |
Test Sample | Number of Samples | Number of Positive Samples | Number of Negative Data | Accuracy | ||||||
---|---|---|---|---|---|---|---|---|---|---|
KNN | SVM | BC | KNN | SVM | BC | KNN | SVM | BC | ||
Y1 | 12 | 10 | 9 | 8 | 2 | 3 | 4 | 83.3% | 75% | 66.7% |
Y2 | 44 | 44 | 39 | 33 | 0 | 5 | 11 | 100% | 88.6% | 75% |
Y3 | 56 | 56 | 48 | 50 | 0 | 8 | 6 | 100% | 85.7% | 89.3% |
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Chen, Q.; Li, Q.; Wu, J.; He, J.; Mao, C.; Li, Z.; Yang, B. State Monitoring and Fault Diagnosis of HVDC System via KNN Algorithm with Knowledge Graph: A Practical China Power Grid Case. Sustainability 2023, 15, 3717. https://doi.org/10.3390/su15043717
Chen Q, Li Q, Wu J, He J, Mao C, Li Z, Yang B. State Monitoring and Fault Diagnosis of HVDC System via KNN Algorithm with Knowledge Graph: A Practical China Power Grid Case. Sustainability. 2023; 15(4):3717. https://doi.org/10.3390/su15043717
Chicago/Turabian StyleChen, Qian, Qiang Li, Jiyang Wu, Jingsong He, Chizu Mao, Ziyou Li, and Bo Yang. 2023. "State Monitoring and Fault Diagnosis of HVDC System via KNN Algorithm with Knowledge Graph: A Practical China Power Grid Case" Sustainability 15, no. 4: 3717. https://doi.org/10.3390/su15043717