干旱区生态环境知识资源中心

In the deregulated power system, the Independent System Operator (ISO) has to update the value of Available Transfer Capability (ATC) on Open Access Same Time Information System (OASIS) for the secure bilateral/multilateral transaction planning. The off-line methods for calculating ATC requires large computation time and or not suitable for on-line estimation, hence the on-line updating of ATC requires an accurate method with lesser computation time. In this paper, Radial Basis Function Neural Network (RBFNN) with input feature reduction has been proposed for on-line ATC estimation for both bilateral and multilateral transactions under normal condition. Multiple and Multi Neural Network is developed and their performance is analyzed. The training data for Neural Network is generated using Repeated Power Flow (RPF) Algorithm. One of the challenges in the development of Neural Network in the power system is the selection of suitable input variables because a power system contains thousands of variables. For this purpose, a straight forward and quick procedure called the Sequential Feature Selection (SFS) is used to extract the most influenced variables, as features from a large set of variables. Simulation work is performed on standard IEEE 24 bus Reliability Test System (RTS) and IEEE 118 bus system. The feasibility of implementation of the proposed Neural Network for on-line ATC evaluation is discussed. The result of the proposed model is compared with the conventional RPF and developed BPA models. Test result shows the effectiveness of the Neural Network approach for on-line estimation of ATC.