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Transmission loss allocation using ANN

Transmission loss allocation using ANN. OBJECTIVES. To allocate loss in the transmission line To implement Artificial Neural Network(back propagation). Transmission line loss. Loss occurs due to current flowing in line.

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Transmission loss allocation using ANN

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  1. Transmission loss allocation using ANN

  2. OBJECTIVES • To allocate loss in the transmission line • To implement Artificial Neural Network(back propagation)

  3. Transmission line loss • Loss occurs due to current flowing in line. • Loss is nonlinear function of total power and the voltage and other system parameters. • For different transmission lines, due to difference in parameters, loss is different • Manual calculation needs time and it is complex • Artificial intelligence can be used to allocate the loss in transmission line

  4. Data preparation • For loss calculation, current and resistance of that line is necessary. • Load flow analysis is suitable for data preparation. • Load flow analysis provides data pair with input and output. • Incremental load flow analysis can be used to learn the neural network (supervised learning)‏

  5. Why multilayer perceptron network with backpropagation algorithm? • It is effective and easy to learn. • It has ability to provide solutions for highly nonlinear systems and also for systems with ill-defined problems.

  6. Processes involved for speed up the convergence. • Initialization of weight

  7. Contd... • Adapting different learning rate for each weight direction

  8. contd... • Adapting threshold values

  9. contd... • Use of dual activation function

  10. Optimum hidden neurons • Hidden neurons are selected such that the input/output characteristic match with minimum error

  11. Weight update with momentum term.

  12. Results • No of iterations=19701. • Amplitudes of activation functions • a=0.1116 for real loss allocations • a=0.5115 for reactive loss allocations • b=0.61 for both the activation functions. • η = 0.85 • α = 0.48

  13. Result contd... • Mean squared error is used for checking the accuracy • Mean square error = 5E -8 • Trained network is tested with 838 test patterns • Results obtained from ILFA and trained network matches with good accuracy.

  14. THANK YOU

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