Power Loss Minimization using Optimal Power Flow based on Firefly Algorithm

Conventional methods are commonly used to solve optimal power flow problems in power system networks. However, conventional methods are not suitable for solving large and non-linear optimal power flow problems as they are influenced by initialization values and more likely be trapped in local optimum. Hence, heuristic optimization methods such as Firefly Algorithm have been widely implemented to overcome the limitations of the conventional methods. These methods often use random strategy that can provide better solutions to avoid being trapped in the local optimum while achieving global optimum. In this study, the load flow analysis was performed using the conventional method of Newton-Raphson technique to calculate the real power loss. Next, Firefly Algorithm was implemented to optimize the control variables for minimizing the real power loss of the transmission system. Generator bus voltage magnitudes, transformer tap settings and generator output active power were taken as the control variables to be optimized. The effectiveness of the proposed Firefly Algorithm was then tested on the IEEE 14-bus and 30-bus system using MATLAB software. The simulated results were then analyzed and compared with Particle Swarm Optimization’s results based on the consistency and execution time. Implementation of the Firefly Algorithm has successfully produced minimum real power loss with faster computational time as compared to Particle Swarm Optimization. For the IEEE 14-bus system, the active power loss for the Firefly Algorithm is 6.6222 MW and the calculation time is 18.2372 seconds. Therefore, the application of optimal power flow based on Firefly Algorithm is a reliable technique, in which the optimal settings with respect to power transmission loss can be determined effectively.

@article{Jun2021,
  title     = {Power Loss Minimization using Optimal Power Flow based on Firefly Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {9},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Chia Shu Jun and Syahirah Abd Halim and Hazwani Mohd Rosli and Nor Azwan Mohamed Kamari},
  doi       = {10.14569/IJACSA.2021.0120940},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120940}
}