Artificial intelligence solution to transmission loss allocation problem

The problem of transmission loss allocation of deregulated power system has been solved through the application of artificial neural network (ANN). Two network structures namely Levenberg–Marquardt back propagation (LMBP) and Bayesian regularization back propagation (BRBP) have been trained and their performance compared. It has been found that LMBP network gives faster solution for same accuracy level. As the working range of power flow transaction is quite vast, a huge volume of data need to be stored and processed for the training of neural network. The time needed for training of neural network against such huge data is prohibitive for real time application of the ANN based solution tool where raw data are used for training. A simple filtering technique has been found to be very effective to improve the solution time and training data volume requirement and make the proposed technique suitable for real time applications. With the use of filtered data for training both the training network have shown comparable performance.

Research highlights
► Shapley value allocates the losses to the user as per their involvement in the process.
► 3 layers MPL provides excellent result for the transmission loss allocation problem.
► LMBP gives better performance over BRBP algorithm.
► Proposed data filtering technique considerably reduces the ANN training time.
► Proposed ANN solution tool for loss allocation can be employed for real time solutions.