Transmission loss allocation using ANN

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.

13. 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

14. 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.

16. THANK YOU