Raja Zolkiply, Raja Ezham Shariffudin
(2003)
*Application of artificial neural network for solving unit commitment problem / Raja Ezham Shariffudin Raja Zolkiply.*
[Student Project]
(Submitted)

## 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 start-up and shut-down 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.

## Metadata

Item Type: | Student Project |
---|---|

Creators: | Creators Email / ID Num. Raja Zolkiply, Raja Ezham Shariffudin UNSPECIFIED |

Divisions: | Universiti Teknologi MARA, Shah Alam > Faculty of Computer and Mathematical Sciences |

Keywords: | Artificial Neural Networks, neurons, backpropagation |

Date: | 2003 |

URI: | https://ir.uitm.edu.my/id/eprint/1985 |