Power Loss Minimization using Optimal Power Flow based on Firefly Algorithm

Abstract

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.

Multi-objective Optimal Power Flow and Emission Index Based Firefly Algorithm

“…The randomization coefficient α is ranges from 0 to 1 and ε is the vector of random numbers taken from Gaussian distribution ranges from 0 to 0.5 [47]. At the end of all generations, the firefly with the highest brightness, i.e., the best fitness value is concluded as the optimal solution to the problem [48]. The Cartesian distance is used to calculate the distance between fireflies i and j as given in Eq.…”

A novel solution for the optimal reactive power dispatch problem using an artificial neural network integrated with the firefly optimization algorithm

“…The flickering lights develop a network that can be improved in conjunction with desired functions. Some criteria apply while implementing the firefly algorithm (Jun et al, 2021).…”

“…Various power system problems can be solved in real time by implementing the firefly optimization technique (VC and T., 2018;Olabode et al, 2023). The proposed firefly algorithm is effective in minimizing the objective functions while considering the power system constraints and obtains the optimal reactive power dispatch solution (Balachennaiah et al, 2018;Jun et al, 2021). The gray wolf algorithm that has been implemented to solve the power loss problem in a radial distribution system is also attractive, as given in astronomical models (RoutrayMistry and Raj Arya, 2020).…”