ECONOMIC LOAD DISPATCH BY INTEGRATION OF LAGRANGE NEURAL NETWORK AND FEEDFORWARD NEURAL NETWORK
Abstract
This paper presents a novel integrated approach to find the load dispatch for economic
power generation in a grid of thermal power plants. A synergy of feedforward neural network
(FNN) and Lagrange neural network (LNN) has been introduced in an innovative way.
Backpropagation algorithm is used for training the FNN. The data required for training and testing
FNN is obtained by LNN. Transmission loss, equality and inequality constraints of economic power
generation are appropriately considered in the formulation of solution algorithm. The cost of
electricity generation by a generating unit is expressed exponentially with respect to active power
output of the corresponding generating unit. The generation schedule obtained by LNN is most
economical whereas a FNN trained by backpropagation algorithm produced results practically in
no time. Therefore, the proposed approach produced most economical results in less computation
time
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