https://orcid.org/0000-0002-9169-6466
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ABSTRACT
This paper presents a Maximum Power Point Tracking (MPPT) algorithm using system identification (SI) for solar photovoltaic (PV) pumping system under partial shading (PS) conditions. The measurement of the PV panel current was not required for the proposed MPPT algorithm. The MPPT algorithm was developed for an electric motor with a nonlinear pump load. In this study, a high-efficiency synchronous reluctance motor (SynRM) was designed as a 4-inch submersible pump motor. The system aimed to the small-scale solar photovoltaic water pumping systems. The SynRM was designed and optimized to use at low voltage levels such as solar PV panels without requiring a boost converter. Thus, the motor could be connected directly with any low voltage leveled power supply. The motor was produced 0.55 kW power with an efficiency of 86.5%. Eight PS patterns were generated to obtain four different scenarios for analyzing of the proposed algorithm. The Maximum Power Point (MPP) was determined with high accuracy for global maximum for all PS conditions using the proposed algorithm. Thus, the motor was operated at the MPP in all conditions thanks to the developed algorithm. The overall system efficiency was obtained between 72% and 82% under these conditions.
Nomenclature
| MPP | = | Maximum Power Point |
| MPPT | = | Maximum Power Point Tracking |
| SI | = | System Identification |
| PV | = | Photovoltaic |
| PS | = | Partial Shading |
| SynRM | = | Synchronous Reluctance Motor |
| P&O | = | Perturb and Observe |
| InC | = | Incremental Conductance |
| CVC | = | Constant Voltage Controller |
| AI | = | Artificial Intelligence |
| FL | = | Fuzzy Logic |
| ANFIS | = | Adaptive neuro fuzzy inference system |
| LMPP | = | Local Maximum Power Point |
| GMPP | = | Global Maximum Power Point |
| RPC | = | Rectangular Power Comparison |
| AVR | = | Adaptive Voltage Reference |
| FEA | = | Finite Element Analysis |
| ARX | = | Auto-Regressive Model with Exogenous |
| GFI | = | Goodness of Fit Index |
| MSE | = | Mean Squared Error |
| MTPA | = | Maximum Torque Per Ampere |
Acknowledgments
The authors would like to thank the Scientific and Technological Research Council of Turkey (TUBITAK) for their financial support for the current study (Project No:116E116).
Disclosure statement
No potential conflict of interest was reported by the author(s).