Leena Srivastava
TERI University, Department of Policy Studies, Faculty Member
On-line monitoring of power system voltage security has become a very demanding task in competitive power market operation and fast estimation of bus voltage is essential for this. In this paper, a novel parallel radial basis function... more
On-line monitoring of power system voltage security has become a very demanding task in competitive power market operation and fast estimation of bus voltage is essential for this. In this paper, a novel parallel radial basis function neural network (PRBFN) which is a multistage network, in which stages operate in parallel rather than in series during testing, has been developed
Research Interests:
Power system security is one of the vital concerns in competitive electricity markets due to the delineation of the system controller and the generation owner. This paper presents an approach based on radial basis function neural network... more
Power system security is one of the vital concerns in competitive electricity markets due to the delineation of the system controller and the generation owner. This paper presents an approach based on radial basis function neural network (RBFN) to rank the contingencies expected to cause steady state bus voltage violations. Euclidean distance-based clustering technique has been employed to select the
Research Interests:
Research Interests:
Voltage stability problems have been one of the major concerns for electric power utilities due to increased interconnections and loading of the present day power system. Fast estimation of loadability margin is essential for evaluating... more
Voltage stability problems have been one of the major concerns for electric power utilities due to increased interconnections and loading of the present day power system. Fast estimation of loadability margin is essential for evaluating on-line voltage stability condition of a power system. In this paper, an approach based on parallel self-organizing hierarchical neural network is presented to predict a