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Dynamic security contingency screening and ranking using neural networks.

Y Mansour¹, E Vaahedi, M A El-Sharkawi

¹British Columbia Hydro, Burnaby, BC.

IEEE Transactions on Neural Networks

|January 1, 1997

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Neural networks show promise for dynamic security assessment in power systems. After initial challenges, adjustments significantly improved their ability to screen and rank contingencies for BC Hydro and Hydro Quebec.

Area of Science:

Electrical Engineering
Computational Intelligence

Background:

Dynamic security assessment is crucial for power grid stability.
Contingency screening and ranking identify critical events impacting system security.

Purpose of the Study:

To evaluate the application of neural networks for dynamic security contingency screening and ranking.
To assess the performance of trained neural networks on large-scale power systems.

Main Methods:

Trained two neural networks using 1691 detailed transient stability simulations.
Utilized an energy margin calculation module for each simulation.
Implemented corrective measures to improve neural network performance.

Main Results:

Initial neural network performance was poor.

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Corrective measures, including output effectiveness, feature selection, and system partitioning, significantly improved results.

Final models demonstrated good potential for dynamic security assessment.

Conclusions:

Neural networks can be effectively applied to dynamic security assessment for contingency screening and ranking.
Careful consideration of network architecture and training data is essential for optimal performance.
The approach shows potential for enhancing the reliability of large-scale power grids.