Microgrid Fault Detection and Classification: Machine Learning Based Approach, Comparison, and Reviews
Abstract
:1. Introduction
- We design a novel network for distribution line FDC of MG based on the deep learning network together with the WT that enables the network to take out the relevant short circuit fault attribute from the faulted line signals effectively.
- We develop a hierarchical generative model with multiple layers of RBM that restrains overfitting of the training dataset with a prominent unsupervised pre-trained process.
- Both operating modes namely islanded and grid-connected/tied with two typologies (radial and loop) of MG are studied to measure the effectiveness of the proposed model.
- The dropout strategy is integrated with the proposed network to establish the robust performance of the developed DBN model over the noisy environment.
2. Materials and Methods
2.1. Types of Network Faults
2.1.1. Single PG Fault
2.1.2. Two PG Fault
2.1.3. PP Fault
2.1.4. Three PG Fault
2.2. System Modelling
2.3. Variation of Signal Energy with Feature Generation
2.3.1. Effect of Fault Distance on Signal Energy
2.3.2. Effect of Fault Resistance on Signal Energy
2.4. Fault Feature Generation with Wavelet Transform
2.5. Proposed Hierarchical Generative Fault Classification Model
2.5.1. Restricted Boltzmann Machine
2.5.2. Unsupervised Learning of the Proposed Network
2.5.3. Supervised Training of the Proposed Network
3. Results and Discussion
3.1. Performance Assessment of the DBN Based FDC Scheme
- TP: A label is correctly predicted by the classifier, and it belongs to the original class.
- TN: A label is correctly predicted by the classifier, and it does not belong to the original class.
- FP: A label is predicted as positive by the classifier. but it does not belong to the original class.
- FN: A label is predicted as negative by the classifier, but it belongs to the original class.
3.2. Effect of Sampling Resolution and Signal Type
3.3. Effect of Noise Present in the Measured Signal Data on the Classification Accuracy
3.4. Comparative Study
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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System Parameters | Types or Values | |
---|---|---|
Fault | Types of faults | a-g, b-g, c-g, ab-g, bc-g, ac-g, a-b, b-c, |
a-c, abc-g, non-fault | ||
Fault distance (km) | 1 to 19 with an increment of 0.5 | |
Fault resistance () | 0.1, 1, 5, 10, 20, 50, 100 | |
Line | Positive and zero sequence resistances (/km) | 0.0135 and 0.0424 |
Positive and zero sequence inductances (H/km) | 4.9869 × 10−5 and 1.39 × 10−4 | |
Positive and zero sequence capacitances (F/km) | 11.33 × 10−9 and 5.01 × 10−9 | |
Distance (km): Line 1-3; Line 1-2; Line 3-5; Line 3-4; Line 5-6 | 20 km each | |
Transformers (T) | T-1 | 79/13 kV, 10 MVA, 50 Hz |
T-2 and T-5 | 0.575/13 kV, 9 MVA, 50 Hz | |
T-3 and T-4 | 0.4/13 kV, 10 MVA, 50 Hz | |
Generators (G) | Main grid | 1000 MVA, 79 kV, 50 Hz |
G-1 (DFIG based wind farm) | Rated MVA: 9 MVA, Rated kV: 575 V | |
G-2 (Wind turbine with asynchronous machine) | Rated MVA: 1.5 MVA, Rated kV: 0.4 | |
G-3 ( Inverter based) | Rated MVA: 10 MVA, Rated kV: 13 | |
G-4 ( Inverter based) | Rated MVA: 10 MVA, Rated kV: 575 V |
System Configuration | Average Accuracy(%) | ||||
---|---|---|---|---|---|
Line 1-3 | Line 1-2 | Line 3-4 | Line 3-5 | Line 5-6 | |
Grid-connected radial mode | 99.70 | 99.71 | 99.36 | 99.68 | 99.21 |
Grid-connected loop mode | 99.65 | 99.69 | 99.35 | 99.62 | 99.07 |
Islanded radial mode | 99.59 | 99.48 | 99.13 | 99.42 | 98.80 |
Islanded loop mode | 99.56 | 99.51 | 98.97 | 99.39 | 98.82 |
Fault Class | Radial Mode | Loop Mode | ||||||
---|---|---|---|---|---|---|---|---|
Accuracy (%) | Precision | Recall | F1-Score | Accuracy (%) | Precision | Recall | F1-Score | |
a-g | 99.89 | 1.0 | 0.99 | 0.99 | 99.96 | 1.0 | 1.0 | 1.0 |
b-g | 100 | 1.0 | 1.0 | 1.0 | 99.96 | 1.0 | 1.0 | 1.0 |
c-g | 99.93 | 1.0 | 0.99 | 1.0 | 99.89 | 1.0 | 0.99 | 0.99 |
ab-g | 100 | 1.0 | 1.0 | 1.0 | 100 | 1.0 | 1.0 | 1.0 |
bc-g | 99.88 | 0.99 | 0.99 | 0.99 | 99.81 | 0.99 | 0.99 | 0.99 |
ac-g | 99.84 | 0.99 | 0.99 | 0.99 | 99.84 | 0.99 | 0.99 | 0.99 |
a-b | 99.89 | 0.99 | 1.0 | 0.99 | 99.93 | 0.99 | 1.0 | 1.0 |
b-c | 100 | 1.0 | 1.0 | 1.0 | 100 | 1.0 | 1.0 | 1.0 |
a-c | 100 | 1.0 | 1.0 | 1.0 | 100 | 1.0 | 1.0 | 1.0 |
abc-g | 99.96 | 1.0 | 1.0 | 1.0 | 99.95 | 1.0 | 0.99 | 1.0 |
nf | 100 | 0.99 | 1.0 | 1.0 | 99.95 | 0.99 | 1.0 | 1.0 |
Fault Class | Radial Mode | Loop Mode | ||||||
---|---|---|---|---|---|---|---|---|
Accuracy (%) | Precision | Recall | F1-Score | Accuracy (%) | Precision | Recall | F1-Score | |
a-g | 99.84 | 0.99 | 0.99 | 0.99 | 99.95 | 1.0 | 1.0 | 1.0 |
b-g | 100 | 1.0 | 1.0 | 1.0 | 99.91 | 1.0 | 0.99 | 1.0 |
c-g | 99.89 | 1.0 | 0.99 | 0.99 | 99.91 | 1.0 | 0.99 | 1.0 |
ab-g | 100 | 1.0 | 1.0 | 1.0 | 100 | 1.0 | 1.0 | 1.0 |
bc-g | 99.88 | 0.99 | 0.99 | 0.99 | 99.82 | 0.99 | 0.99 | 0.99 |
ac-g | 99.88 | 0.99 | 0.99 | 0.99 | 99.88 | 0.99 | 1.0 | 0.99 |
a-b | 99.98 | 0.99 | 1.0 | 0.99 | 99.95 | 0.99 | 1.0 | 1.0 |
b-c | 100 | 1.0 | 1.0 | 1.0 | 99.95 | 1.0 | 1.0 | 1.0 |
a-c | 99.96 | 1.0 | 1.0 | 1.0 | 100 | 1.0 | 1.0 | 1.0 |
abc-g | 99.93 | 1.0 | 0.99 | 1.0 | 99.88 | 1.0 | 0.99 | 0.99 |
nf | 99.93 | 0.99 | 1.0 | 1.0 | 99.88 | 0.99 | 1.0 | 0.99 |
SNR | Classification Accuracy (%) | |||
---|---|---|---|---|
MKELM | KELM | SVM | DBN with Dropout | |
30 dB | 98.29 | 98.15 | 98.24 | 99.38 |
Techniques | Methods | Advantages | Disadvantages |
---|---|---|---|
Classical strategies | Circuit theory, | 1. Simplicity | 1. Inaccurate |
Traveling waves, | 2. Easy implementation | 2. Limited fault type | |
Symmetrical | classification | ||
component | 3. Slow | ||
Signal processing | FFT, | 1. Direct fault analysis | 1. Decision threshold are |
WT | 2. Used for feature extraction | defined arbitrarily | |
and information compression | |||
Statistical | Traditional statistical | 1. High generalizability | 1. Take longer time to |
concepts | 2. Individual data patterns | make a decision | |
are clear and visible | 2. Validation of data | ||
is not guaranteed | |||
Knowledge based | 1. High precision | 1. The accuracy can not be | |
Fuzzy logic | 2. Rapid operation | guaranteed as the system is | |
3. Can handle uncertainty | based on the experts’ | ||
experience | |||
Artificial intelligence | 1. Detects the non-linear | 1. Slow convergence of | |
relationship between | training process | ||
ANN | independent and dependent | 2. Shallow architecture limit the | |
variables | capacity to learn the complex | ||
non-linear relationships | |||
1. Faster, even if the problem | 1. Choosing kernel function and | ||
SVM | is large-size | hyper parameters are difficult | |
2. Requires less heuristics | |||
1. Good at noisy environment | 1. Processes are random | ||
2. The simulation speed can | 2. Outputs are not consistent | ||
GANN | be improved | ||
3. Dimension of solution can | |||
be reduced | |||
Proposed method | 1. Robust hierarchical generative | ||
model | |||
DBN | 2. Restrain overfitting of the | ||
training data | |||
3. Discover the fundamental | |||
regularity of versatile features | |||
4. Powerful generalization | |||
capability |
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Rahman Fahim, S.; K. Sarker, S.; Muyeen, S.M.; Sheikh, M.R.I.; Das, S.K. Microgrid Fault Detection and Classification: Machine Learning Based Approach, Comparison, and Reviews. Energies 2020, 13, 3460. https://doi.org/10.3390/en13133460
Rahman Fahim S, K. Sarker S, Muyeen SM, Sheikh MRI, Das SK. Microgrid Fault Detection and Classification: Machine Learning Based Approach, Comparison, and Reviews. Energies. 2020; 13(13):3460. https://doi.org/10.3390/en13133460
Chicago/Turabian StyleRahman Fahim, Shahriar, Subrata K. Sarker, S. M. Muyeen, Md. Rafiqul Islam Sheikh, and Sajal K. Das. 2020. "Microgrid Fault Detection and Classification: Machine Learning Based Approach, Comparison, and Reviews" Energies 13, no. 13: 3460. https://doi.org/10.3390/en13133460
APA StyleRahman Fahim, S., K. Sarker, S., Muyeen, S. M., Sheikh, M. R. I., & Das, S. K. (2020). Microgrid Fault Detection and Classification: Machine Learning Based Approach, Comparison, and Reviews. Energies, 13(13), 3460. https://doi.org/10.3390/en13133460