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High Impedance fault detection classification using Neural Network in MATLAB

High Impedance fault detection classification using Neural Network in MATLAB

In today's discussion, we'll explore the fascinating realm of detecting high impedance faults in distribution systems. A high impedance fault occurs when a conductor comes into contact with the ground, creating a path with high impedance. This situation can lead to dangerous scenarios, including the risk of fire. Let's delve into the details and understand how we can detect and classify high impedance faults in our distribution system.


Understanding High Impedance Faults

High impedance faults often occur when a conductor is touched by a tree or the ground, leading to the creation of a high impedance path. These faults can result in fires, making their detection crucial for the safety of the distribution system. The examples include imbalance faults, concrete surface faults, and brass surface faults.

To detect these faults, we need to analyze the impedance characteristics of the system during such occurrences and identify which line is affected. We'll achieve this by creating a high impedance fault in our distribution system using MATLAB and employing neural network techniques for fault detection.

Simulation Model Development

Referring to a relevant paper, we've developed a simulation model in MATLAB to test the impedance fault in the distribution system. The model includes two ends, sending and receiving, with both operating at 33 kilovolts. The voltage is stepped down from 33 kilowatts to 11 kilowatts using a Step Down Transformer, and feeder lines are present. Additionally, the voltage is stepped up from 11 kilovolts to 33 kilowatts before reaching the receiving end.

The high impedance fault is introduced in one of the three phases (A, B, or C), simulating a real-world scenario. The goal is to detect and classify the high impedance fault using a neural network.

Neural Network Training

Data collection involves gathering information on sending end voltage, sending end current, and the corresponding fault scenario. Target data is structured to indicate which phase is affected by the fault. Using this dataset, we train a neural network to recognize and classify high impedance faults.

The training process involves loading input and target data, configuring the neural network, and then simulating the network for fault detection. The trained model can then be used to analyze different fault scenarios in the distribution system.

Final Model and Testing

The final model integrates input data such as sending end voltage and RMS current. The neural network processes this information and provides logic outputs, indicating the presence of a fault and its classification. Testing the model involves introducing faults in different phases and observing the network's accuracy in fault detection and classification.

Results and Conclusion

The simulation demonstrates the effectiveness of the neural network in detecting and classifying high impedance faults. By adjusting parameters and testing various scenarios, we can fine-tune the model for optimal performance in real-world scenarios.

In conclusion, the integration of simulation models and neural networks offers a powerful solution for detecting and classifying high impedance faults in distribution systems. This technology plays a crucial role in preventing serious incidents such as fires.

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