Power system operators/planners are always face problem regarding reactive power compensation. Reactive power plays an important role in maintaining voltage stability and system reliability. In this paper, a new algorithm based on back propagation neural network is used by using suitable number of layers and various constants is presented, for forecasting the active and reactive power consumed by various capacities Induction Motor. Firstly, Database of active power (P) and reactive power (Q) for different voltages and frequencies are generated through real time experiment on various capacities Induction Motor. Then, Back propagation Neural Network (BPNN) is designed to predict the P and Q drawn by in induction motor for different voltages and frequency condition. Back Propagation technique is used for training. These trained BPNN models are used to predict P & Q for many unseen operating conditions and the results are found to be coming fast and very accurate.

In this paper, we propose a new region-based Active Contour Model (ACM) that employs signed pressure force (SPF) as a level set function. Further, a flood fill algorithm is incorporated along with SPF function for robust object extraction. Signed pressure force (SPF) parameters, is able to control the direction of evolution of the region. The proposed system shares all advantages of the C-V and GAC models. The proposed ACM has an additional advantage i.e. of selective local or global segmentation. Flood Fill framework is employed for retrieving the object upon successful detection in the image. In addition, the computer simulation results show that the proposed system could address object detection within an image and its extraction with highest order of efficiency.