Research Article
Maximizing Electric Power Recovery through Advanced Compensation with MPPT Algorithms
Algorithm 1
Optimization algorithm by Harris Hawks.
| Inputs: Population size N and maximum number of iterations T | | Outputs: Rabbit location and fitness value Initialize random population π(i = 1, 2,..............., N) | | As long as (stopping condition not met) do | | Calculate vehicle speed values | | Set πππππππ‘ as vehicle location (best location) | | End | | For (each device( ππ )) do | | Update initial energy E0 and displacement force J βΊE0=2rand ()-1, J=2(1-rand ()) | | Update E using Eq. (3) | | If ( β₯ ) thenββββββββββββββββββββΊExploration phase | | Update the location vector using Eq. (2) | | If ( < ) then βΊExploration phase | | If (π β₯ 0 5 ππ β₯ 0 5 ππ₯π¨π«π¬)ββββββββββββββΊ gentle siege | | Update the location vector using Eq. (5) | | If no if (π β₯ 0 5 ππ < 0 5 ) thenββββββββββΊ Soft seat with fast Progressive dives (6) | | If not if (π < 0 5 ππ β₯ 0 5 ) thenβββββΊ Gentle siege with progressive fast dives updates the location vector using . . (Eq (5)) | | If not if (π < 0 5 ππ < 0 5 ) then βΊHard seating with progressive fast dives updates the location vector using (Eq (6)) | | Return πΏπππππ | | End. |
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