Power Systems Application of Artificial Neural Networks. (ANN)  Introduction  Brief history.  Structure  How they work  Sample Simulations. (EasyNN)

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Presentation transcript:

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

Architecture  FeedforwardPerceptron  Network

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.

EasyNN  Not the best model for NN

Output Graph

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

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

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

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