Dynamic performance evaluation and improvement of PV energy generation systems using Moth Flame Optimization with combined fractional order PID and sliding mode controller
Photovoltaic (PV) energy generation systems;
Dynamic performance;
Global Maximum Power Point Tracking (GMPPT);
Moth Flame optimization (MFO);
Fractional Order Controller;
Sliding Mode Controller (SMC);
PHOTOVOLTAIC MODULES;
CLIMATIC CONDITIONS;
DIFFERENTIATOR;
PERTURBATION;
SIMULATION;
DESIGN;
D O I:
10.1016/j.solener.2020.02.055
中图分类号:
TE [石油、天然气工业];
TK [能源与动力工程];
学科分类号:
0807 ;
0820 ;
摘要:
The output power of Photovoltaic (PV) energy generation systems depends mainly on operating conditions that include climatic conditions and occurrence of faults. Current-Voltage characteristic curves show different operating regions that are characterized by different transient responses in varying operating conditions and in the special case of the presence of the partial shading condition. In this paper, a dynamic performance evaluation of the PV system is performed both in open loop and in the presence of a feedback controller. Analysis shows that the PV system is affected when operating in different regions and environmental conditions and is characterized by a complex dynamic behavior. To improve the PV system dynamics, an adapted control strategy is proposed where the PV voltage is regulated using a Fractional Order PID controller tuned in various regions and partial shading patterns using Particle Swarm Optimization, whereas the PV current is regulated using a sliding mode controller that does not require using Pulse Width Modulation (PWM). In the present study, it is also shown that MPPT algorithms are affected by the conventional tuning approach of the feedback controller. To remedy to this issue, the Moth Flame Optimization based MPPT technique is implemented associated with the proposed control strategy to improve the performance PV system in different operating conditions. The proposed dynamic performance improvement strategy shows excellent transient responses in various operating scenarios.