My presentation on "An Intelligent protection scheme for microgrid using data-mining and machine learning."

2. CONTENTS
 Preface
 Microgrid Test System
 Microgrid Modeling with RTDS
 Fault Data Generation
 No-Fault Data Generation
 Feature Extraction
 Deep-Learning
 Simulation Results
 RTDS
 Classification
 Conclusions
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3. PREFACE
 A data-mining based intelligent protection scheme for fault detection is developed
 The proposed relaying scheme is developed on a real time digital simulator (RTDS) platform
 One cycle post-fault current signal samples are retrieved
 Samples are pre-processed using S-transform to obtain statistical features
 Deep learning is used to classify fault and no fault from extracted features
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4. MICROGRID TEST SYSTEM
 Two synchronous generators
 One photovoltaic (PV) module
 One wind farm
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 DGs are connected to the corresponding buses with breaker
 So they can be disconnected
 Microgrid can operate in both radial and mesh topology using circuit breakers

5. MICROGRID MODELING WITH RTDS
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Wind
Turbine
Photovoltaic
Synchronous
Generator
Synchronous
Generator
Load
Capacitor
Bank
Topology
Switches
PCC
Breaker

6. FAULT DATA GENERATION
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 Fault and no-fault scenarios are generated using RSCAD script
 The script file controls the operation of the simulator and analyses the data without user interaction
 Batch mode of operation for all combination of scenarios is implemented.
 Different type of faults at different locations of the microgrid can be implemented
Fault types:
 phase-to-ground
 phase-to-phase-ground
 three-phase-to-ground faults
1. Fault resistance: 0.1, 10 , 30, 100 Ohms
2. Fault in different lines.
3. Fault position: 20%, 40%, 60% and 80% of line length.
4. microgrid topology: Radial and mesh
5. Mode of operation: grid connected, islanded.
6. DG outage: Synchronous generator at bus 3.

7. FAULT GENERATION SCRIPT
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DG
Breake
r
Capacitance
Switch
PCC
Breake
r
Location
of Fault
Line
Resistance
Fault Type
No initial
fault
scenario generation by
index in the loops
Save cycle
samples

8. NO-FAULT DATA GENERATION
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 No-fault scenarios are generated using the same RSCAD script
 The script file changes the parameters of the microgrid without user interaction
No Fault Operational Scenarios:
1. Load variations from normal load to 120% overload.
2. Capacitor switching at PCC and Buses 2 and 6.
3. Microgrid topology: Radial and mesh
4. Mode of operation: grid connected, islanded.
5. DG outage: Synchronous generator at bus 3.

9. S-TRANSFORM IN FEATURE
EXTRACTION
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 Three phase current signals of both ends of the respective feeder are retrieved
 Samples are processed using S-transform to extract features.

10. EXTRACTED FEATURES
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11. DEEP LEARNING
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 Deep learning is used for the data classification
 All classification tasks depend upon labeled datasets
 Two classes of fault and no fault are considered
 80% of the generated data is used for Deep learning training
 Remaining 20% is used for test
200 nodes:
sigmoid 100 nodes:
tanh
100 nodes:
softsign
50 nodes:
sigmoid
0/1

12. SIMULATION RESULTS: NORMAL
CONDITION
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13. LINE1-3, A-G FAULT, GRID CONNECTED
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14. LINE1-3, B-C-G FAULT, GRID
CONNECTED
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15. LINE1-3, A-B-C-G FAULT, GRID
CONNECTED
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16. LINE1-3,B-G FAULT, LOOP AND GRID
CONNECTED
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17. SIMULATION RESULTS: DATA
CLASSIFICATION
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• The data is classified into two classes of fault and no fault
• The classification results for 748 fault and 625 no-fault scenarios are shown below.
 The results show the classification has about 97% accuracy.
 The 3% misclassification :
 18 faulty and 18 non-faulty scenarios are classified incorrectly

18.  In this project, the simulated IEC microgrid in RSCAD and its components have been described.
 Different types of conditions including fault resistance, fault locations, and various levels of DG penetration within
different topologies of microgrid are studied.
 The faults are simulated at different location of the line between two buses and the current is measured.
 Then, deep learning has been applied for classification for the protection scheme.
 In the next step, the performance of the proposed method will be evaluated for high impedance faults (HIFs) which are
difficult to detect.
 Also, the effects of communication failure will be assessed.
 Compare with other data-mining based approaches.
CONCLUSION
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