comparing of artificial intelligent algorithms for maximum power point tracking in photovoltaic systems using and fuzzy mcdm

The photovoltaic system has the highest efficiency in power generation when the system is at the maximum power point (mpp). one of the key issues in using solar cells is the maximum power point tracking (mppt). it should be noted that the output power of solar cells depends on two main factors: 1) radiation intensity and 2) temperature changes. given these two factors, the mpp is subject to change. in recent years, mathematical algorithms have been used to track the mpp with respect to the two preceding factors. these algorithms are very efficient in tracking the mpp of photovoltaic (pv) systems. for this aim, four algorithms are used: bat algorithm (ba), perturb & observe (p&o) algorithm, particle swarm optimization (pso) algorithm and harmony search (hs) algorithm. one of the main problems of researchers in this field is to use the appropriate algorithm among the algorithms used. to solve this problem, a hybrid model based on two multi criteria decision making (mcdm) techniques called analytic network process )anp( and vlse kriterijumsk optimizacija kompromisno resenje )vikor( under fuzzy environment is used. finally, according to the implementation, quality, and efficiency indicators, the po algorithm has been obtained as the appropriate and superior alternative. the pso, hs and ba algorithms are chosen as the second, third and fourth alternatives, respectively.