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Neural network based Fault Tolerant System for Cascaded Multilevel Inverters

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Neural network based Fault Tolerant System for Cascaded Multilevel Inverters


1. Understanding the Fault Tolerance Concept

Overview of Cascaded Multilevel Inverters

  1. Cascaded Multilevel Inverter Configuration

  • For our 15-level Cascaded Multilevel Inverter (CMLI), we require seven H-bridge inverters (H1, H2, H3, H4, H5, H6, and H7).

  • Each H-bridge is responsible for generating a specific level of output voltage.

  1. Types of Faults

  • Inverter Failure: Complete failure of an H-bridge inverter.

  • Switch Failure: Individual switches within the inverter may fail.

  • Battery Failure (DC Source Failure): Issues with the DC power source affecting inverter operation.

  • These faults need to be detected and corrected to maintain stable output voltage.



Fault Detection and Correction

  1. Neural Network for Fault Detection

  • A neural network will be trained to detect faults based on the voltage measurements across each H-bridge and the load.

  • The network will identify which specific H-bridge is failing and trigger appropriate corrective actions.

  1. Corrective Actions

  • When a fault is detected, a backup H-bridge inverter will be activated to compensate and maintain the required output voltage.

2. Implementing the Fault Tolerance System

Simulink Model Setup

  1. Building the Model

  • Create a Simulink model with seven H-bridge inverters.

  • Incorporate ideal switches and constants to simulate normal and fault conditions.

  1. Fault Creation

  • Implement fault conditions by changing constants to simulate inverter failures.

  • Include an additional H-bridge inverter (Aary H-bridge) that will be activated during fault conditions to maintain output voltage.

Data Collection for Neural Network Training

  1. Gathering Data

  • Measure voltages across each H-bridge and the load under normal and fault conditions.

  • Collect data for various scenarios, including normal operation and faults in different H-bridges.

  1. Preparing Data for Training

  • Create a dataset with both normal and fault conditions.

  • Generate a significant amount of data (e.g., 10 sets of samples) for effective neural network training.

  1. Labeling Data

  • Assign labels to the data indicating the specific fault or normal condition.

  • For example, if a fault occurs in H-bridge 3, label the data accordingly.

3. Training the Neural Network

Neural Network Training Process

  1. Setting Up the Neural Network

  • Use MATLAB's Neural Network Toolbox to train the neural network.

  • Input data includes voltage measurements, and output data includes fault classification.

  1. Training and Validation

  • Train the neural network using the collected data.

  • Validate the network to ensure accurate fault detection and classification.

  • Ensure that the network output matches the expected results for various fault conditions.

4. Simulation and Results

Running the Simulation

  1. Simulating Normal Operation

  • Run the model with all toggles set to simulate normal operation.

  • Verify that the system outputs the correct 15-level voltage.

  1. Simulating Fault Conditions

  • Introduce faults in various H-bridges (e.g., H-bridge 1, 2, 3, etc.).

  • Observe the network’s ability to detect faults and activate the backup H-bridge inverter.

Results and Analysis

  1. Fault Detection

  • The neural network accurately detects faults and activates the backup inverter as needed.

  • The output voltage remains stable at the required level despite faults.

  1. System Performance

  • The fault tolerance system maintains the 15-level output voltage even when faults occur.

  • The neural network effectively identifies faults and ensures continuous power supply to the load.

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