An efficient DC grid based PV — wind power generation using modified P&amp;O tracking technique

Manmohan, Atul; Prasad, Akhil; Karthikeyan, S. Prabhakar; Raglend, I. Jacob

doi:10.1109/ipact.2017.8245197

Cited by 3 publications

(1 citation statement)

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“…However, it produces power loss oscillation around MPP during steady‐state, while it tends to confuse under varying weather conditions 8 . Similarly, the P&O method is also implemented with Wind sources, and it tends to exhibits the same drawbacks with wind sources as in the case of PV sources 9 . The conventional methods do not possess the capacity and capability through which they can mitigate the challenges posed by the grid‐side of PV/Wind system in the context of power flow and utility.…”

Section: Introductionmentioning

confidence: 99%

Multi control adaptive fractional orderPIDcontrol approach forPV/wind connected grid system

Sibtain

Murtaza

Ahmed

et al. 2021

Int Trans Electr Energ Syst

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Summary This paper introduces a novel genetic optimize multi‐control adaptive fractional order PID (AFOPID) for Photovoltaic (PV) and Wind connected grid system. The proposed AFOPID controller is optimized by a genetic algorithm (GA) to initialize the controller parameters. The renewable energy sources are mathematically modeled using multi control approach (MCA). In the proposed work, the MCA involves the maximum power point tracking (MPPT), dc link voltage, and current control and quadrature axis modeling. Furthermore, the current control functionalities are performed through an adaptive approach of AFOPID, where the controlling parameters are updated by measured error at every instant. The idea behind the research is to improve the tracking efficiency by introducing better control in order to gain maximum power from the source with minimized total harmonic distortion (THD). The proposed control scheme is tested using computer‐aided experimentation by varying the output of renewable energy sources, inverter uncertainty, and grid voltage variations. The results are benchmarked against the conventional fuzzy logic controllers, fractional‐order PID, and PI controllers. Moreover, to evaluate the effectiveness of the proposed controller, the MCA‐AFOPID is compared with ant colony optimization (ACO) and particle swarm optimization (PSO) optimized FOPID controller respectively. The proposed controller outperforms as compared to other controller.

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Section: Introductionmentioning

confidence: 99%