Multi-layered feed-forward back propagation neural network approach for solving short-term thermal unit commitment

Authors

  • V. Pavan Kumar PG Student, Department of Electrical Engineering, VNIT, Nagpur
  • P. S. Kulkarni Associate Professor, Department of Electrical Engineering, VNIT, Nagpur

Keywords:

Artificial Neural Networks (ANN), Dynamic Programming (DP), Multi-layered Feedforward Back propagation Neural Network (FF-BPNN), Lagrangian Relaxation (LR), Priority List (PL), Unit Commitment (UC)

Abstract

This paper presents an approach for solving the short-term thermal unit commitment (UC) problem using a multi-layered Feed-forward Back propagation Neural Network (FF-BPNN). The main focus of the paper is on finding the schedule of committed thermal units within a short computational timesuch that the total operating cost is minimized. The proposed method is implemented and tested on a 3-unit and 10-unit systems for a scheduling period of 4-hours and 24-hours respectively in MATLABTM software using the Neural Network toolbox. Comparison of simulation results of the proposed method with the results of previous published methods shows that the proposed FF-BPNN method provides better solution with less computational time.

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Published

2015-06-02

How to Cite

Pavan Kumar, V., & Kulkarni, P. S. (2015). Multi-layered feed-forward back propagation neural network approach for solving short-term thermal unit commitment. Power Research - A Journal of CPRI, 277–286. Retrieved from https://cprijournal.in/index.php/pr/article/view/723

Issue

Volume 11, Issue 2, June 2015

Section

Articles

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