Raja Zolkiply, Raja Ezham Shariffudin
(2003)
Application of artificial neural network for solving unit commitment problem / Raja Ezham Shariffudin Raja Zolkiply.
Student Project.
Faculty of Information Technology and Quantitative Sciences, Shah Alam.
(Submitted)
Full text not available from this repository.
Abstract
Artificial Neural Networks (ANNs) are general purpose optimization techniques based
on principles inspired from biological neurons in the brain which consists of a number
of simple and highly interconnected processors (neurons). ANN has proved to be able
to solve optimization problems in power system. One of the areas in power system
operation that requires optimal solution is the Unit Commitment (UC) problem. The
UC problem involves determination of startup and shutdown schedule of generating
units, and indirectly determines the optimum power should be generated by each unit
committed over a period of time to meet the forecasted load demand at minimum cost.
Besides that, the commitment schedule must satisfy other constraints in order to
minimize the total production cost. Therefore, this constitutes a problem to the
operators, where they find it difficult to make the decision manually on which unit to
keep online, and which unit to switch to offline, in order to minimize the production
cost. The ANN approach has proved to be able to solve the UC problem but involve
several problems such as divergence, excessive computation time, too much iteration
for solving small task and so on. In order to improve the implementation of ANN for
solving UC problem in power systems, this paper presents a comparison study
between standard backpropagation algorithm, extended backpropagation algorithms
and hybrid approach. The ANN used to forecast the power of four generating units in
a small power system consists of multilayer neural network which consists of three
input nodes, several hidden nodes and five output nodes. Besides that, input
parameters involve are current stage of load demand (first stage to sixth stage), current
load demand (megawatt) and previous load demand (megawatt), while the outputs of
the neural netwoiic are power generated by four thermal units at current stage and total
cost.
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