1 / 11

Power Systems Application of Artificial Neural Networks . (ANN)

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. Introduction. Derived from biological neuron.

manton
Télécharger la présentation

Power Systems Application of Artificial Neural Networks . (ANN)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. 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

  3. Architecture Feedforward Perceptron Network

  4. 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.

  5. EasyNN • Not the best model for NN

  6. Output Graph

  7. Why Use NN (Merits)? • Adaptive Learning • Self Organization • Real-Time Organization. • Fault Tolerance.

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

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

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

More Related