Abstract
Partial shading condition (PSC) is the major threat to the building-integrated PV systems as they are sorely affected in terms of drastic reduction in PV output power and efficacy. To enhance the maximum power production capability and efficacy, the PV system needs a robust maximum power point tracking (MPPT) controller capable of tracking global maximum power peak (GMP) under PSCs. Many conventional algorithms, i.e., incremental conductance (Inc) , perturb and observe (P&O), etc., are reported in literature, but they are failed to track GMP and also create significant power oscillations in steady state during PSCs. Hence, this paper proposes metaheuristic algorithms-based TCT-configured PV MPPT system. In this, metaheuristic algorithms such as artificial bee colony (ABC), grey wolf optimization (GWO), and particle swarm optimization (PSO) techniques are applied to TCT-configured PV array to operate at GMP under four dynamic PSCs. All the metaheuristic algorithm-based MPPT methods are simulated in MATLAB/Simulink platform and their performances are compared with each other and also with conventional P&O and Inc techniques with respect to achieved GMP, tracking speed/convergence time, efficiency, and oscillations at GMP. The presented simulation results confirm that PSO algorithm outperforms other methods by achieving the highest GMP, efficiency, less convergence time, and reduced oscillations around GMP.
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Bonthagorla, P.K., Mikkili, S. (2023). Power Enhancement of Total-Cross-Tied Configured PV Array During Dynamic Irradiance Change Using Metaheuristic Algorithm-Based MPPT Controllers. In: Dash, R.N., Rathore, A.K., Khadkikar, V., Patel, R., Debnath, M. (eds) Smart Technologies for Power and Green Energy. Lecture Notes in Networks and Systems, vol 443. Springer, Singapore. https://doi.org/10.1007/978-981-19-2764-5_21
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