Power Systems Application of Artificial Neural Networks. (ANN)

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Power Systems Application of Artificial Neural
Networks. (ANN)

• Introduction
• Brief history.
• Structure
• How they work
• Sample Simulations. (EasyNN)
• Why use them (Merits and Demerits)?
• Current Applications
• NN Power Systems
• Conclusion

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Introduction

• Derived from biological neuron.
• Connection of processing nodes that transfer
  activity to the next.
• Likened to the Brain.
• Mostly one-way connection.
• Architecture
• Feedforward
• Feedback
• Network Layers
• Perceptron

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Architecture

• Feedforward Perceptron
• Network

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How They Work.

• Inputs to NN compared with pre-processed data.
• Outputs compared to desired response.
• NN learn system patterns.
• NN are trained to respond.
• Learning
• Supervised
• Unsupervised
• Reinforced
• EasyNN software.

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EasyNN

• Not the best model for NN

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Output Graph
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Why Use NN (Merits)?

• Adaptive Learning
• Self Organization
• Real-Time Organization.
• Fault Tolerance.

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Current Applications.

• Sales Forecasting
• Industrial Process Control
• Data Validation
• Risk Management (Insurance)
• Banking (loan processing).
• Medical

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Power Systems Application.

• Integrated Power Systems.
• Load Forecasting (Blackout)
• Power Plants.
• Many other areas.

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Conclusion.

• NN great potential for future systems.
• Need for much more research.
• Could prove vital in power management and
  distribution.