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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.

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Power Systems Application of Artificial Neural Networks . (ANN)

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  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.

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