In this research, a modified Osprey optimization algorithm (MOOA) is presented to optimize droop control parameters. MOOA is a modification of the Osprey optimization algorithm by adding levy flight which has the advantage of exploiting a wider space and being adaptive to environmental changes. This research also modifies droop control, Proportional Integral Derivative (PID) is applied to secondary control. PID has flexibility in responding to changes in system conditions and fast response in dealing with system changes. The PID parameters are optimized using MOOA and are called MOOA-PID. The MOOA method is validated using 23 CEC2017 benchmarks-function and performance on DC microgrid systems. This research uses the latest algorithms as a comparison, namely One-to-One Based Optimizer (OOBO), Preschool Educational Optimization Algorithm (PEOA), and the red-tailed hawk (RTH) algorithm in testing 23 CEC2017 benchmark functions. From the simulation of the 23 CEC2017 benchmark function, it is known that the MOOA method has better capabilities. MOOA has advantages in 15 out of 23 benchmark functions. In DC microgrid system testing, MOOA-PID is compared with the Proportional Integral (PI) method which is optimized with MOOA and is called MOOA-PI. Testing on the microgrid is aimed at determining the performance of the transient response of power, voltage and current in the system. Tests on DC microgrid systems found that the application of MOOA-PID in secondary control had better capabilities than MOOA-PI. The average value of voltage overshoot from MOOA-PID is 9.828% better than MOOA-PI. The average ITSE MOOA-PID score is 22.3% better than MOOA-PI.

Original languageEnglish
Pages (from-to)804-820
Number of pages17
JournalJournal of Robotics and Control (JRC)
Issue number3
Publication statusPublished - 2024


  • DC Microgrid
  • Droop Control
  • Metaheuristic
  • Optimization
  • Secondary Control

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