Power System Transient Stability Prediction Model using Neural Network
Introduction to Transient Stability in Power Systems
In power systems, faults can occur due to various reasons. After a fault occurs and is cleared, it is crucial to check the system's stability to ensure continuous power supply to the load and prevent further problems. We use a prediction model to determine whether the system remains stable post-fault.
The Role of Prediction Models
The prediction model assesses the power system's stability status after fault clearance. Based on this status, the power plant operator or an automatic control action can secure the power system, ensuring continuous power supply to customers.
Neural Network as a Prediction Model
To predict the transient stability of a power system, we use a neural network. We will utilize the IEEE 39-bus system for this demonstration. Detailed information about this system can be found on the referenced website. The 39-bus system consists of 10 generators and various buses. You can access more detailed technical information about this system in the provided article.
Implementing the Neural Network
We use a symbolic model of the 39-bus system and implement the neural network as the prediction model. The neural network has two inputs:
Rotor speed of the generator.
Stability index, calculated using the formula: Stability Index=360−Δmax360+Δmax\text{Stability Index} = \frac{360 - \Delta \text{max}}{360 + \Delta \text{max}}Stability Index=360+Δmax360−Δmax where Δmax\Delta \text{max}Δmax is the maximum power angle difference between the generators. The stability index should be greater than zero for the system to be considered stable.
Data Collection for Training
We collect data before and after a fault occurs, labeling the data as '1' for stable operation and '-1' for unstable operation. This data is used to train the neural network. We generate faults in the system and collect corresponding data on rotor speed, stability index, and status.
Training the Neural Network
Using the collected data, we train the neural network. The neural network's outputs are then integrated into the Simulink model to predict system stability.
Demonstrating Fault Conditions and Predictions
Let's create a fault in the system and observe the neural network's predictions. We create a fault that lasts for 10 cycles and check the system's stability post-fault. The neural network's output indicates whether the system is stable (1) or unstable (-1).
For a fault occurring at 1 second and clearing after 10 cycles, the system's status is unstable. This is confirmed by the neural network's output and the rotor angle exceeding 360 degrees, indicating instability.
Next, we change the fault duration to 5 cycles. The neural network correctly predicts the system as stable, as evidenced by the rotor angle and speed stabilizing post-fault.
Testing Different Fault Locations and Durations
We create faults at different locations and durations to test the neural network's accuracy. For faults lasting 40 cycles, the neural network accurately predicts instability when the rotor angle and speed exceed normal limits.
Conclusion
The neural network-based transient stability prediction model effectively predicts power system stability under various fault conditions. This model aids in taking timely control actions to maintain system stability and ensure continuous power supply.
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