Implementation and Analysis of Grey Wolf Optimization Technique for PV Fed Switched Coupled Inductor Quasi Z-Source Cascaded Multilevel Inverter
DOI:
https://doi.org/10.13052/dgaej2156-3306.3755Keywords:
Perturb & Observe, grey wolf optimization, switched coupled inductor, quasi Z-Source, cascaded multilevel inverter, partial shading conditionsAbstract
A new type of photovoltaic (PV) fed cascaded multilevel topology is proposed in this work. The proposed topology is an integration of Switched Coupled Inductor (SCL) quasi Z-Source network (qZS) to Cascaded Multilevel Inverter (CMI). In order to extract maximum power with high tracking efficiency under various conditions from the PV system, Grey Wolf Optimization (GWO) algorithm is implemented in the proposed topology. The main aspect of GWO is to control the duty cycle through iterations for better performance. The GWO has the potential to achieve global peak under any climatic conditions. The iterations are done in three steps mainly hunting, encircling for prey and, attacking prey. To prove the effectiveness of GWO, its performance is compared with traditional Perturb & Observe (P & O) MPPT technique. The proposed technique is implemented, tested for various Partial Shading Conditions (PSC) using MATLAB/ Simulink results are verified with experimental set up of 1 kW by utilizing My-RIO embedded FPGA processor board.
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Zhang Z, Wang P (2020) Research on Synchronous Control Method for Suppressing Nonlinear Impulse Perturbation of Photovoltaic Grid-Connected Inverter. IEEE Access 8:43497–43507.
Gupta KK, Ranjan A, Bhatnagar P, Sahu LK, Jain S (2016) Multilevel Inverter Topologies With Reduced Device Count: A Review. IEEE Transactions on Power Electronics 1:135–151.
Panda RK, Mohapatra A, Srivastava SC (2019) Enhancing inertia of solar photovoltaic-based microgrid through notch filter-based PLL in SRF control. IET Generation, Transmission & Distribution 14:379–388.
Nour AMM, Hatata AY, Helal AA, El-Saadawi M (2019) Review on voltage-violation mitigation techniques of distribution networks with distributed rooftop PV systems. IET Generation, Transmission & Distribution 14:349–361.
Zeng J, Ning J, Du X, Kim T, Yang Z, Winstead V (2020) A Four-Port DC–DC Converter for a Standalone Wind and Solar Energy System. IEEE Transactions on Industry Applications 56:446–454.
Rajvikram M (2019). The Motivation for Renewable Energy and its Comparison with Other Energy Sources: A Review. European Journal of Sustainable Development Research.
Sangwongwanich A, Yang Y, Sera D, Blaabjerg F (2020) Mission Profile-Oriented Control for Reliability and Lifetime of Photovoltaic Inverters. IEEE Transactions on Industry Applications 56:601–610.
Pashajavid E, Golkar MA (2013) Optimal placement and sizing of plug in electric vehicles charging stations within distribution networks with high penetration of photovoltaic panels. Journal of Renewable and Sustainable Energy 5:512–520.
Gong Q, Midlam-Mohler S, Marano V, Rizzoni G (2012) Study of PEV charging on residential distribution transformer life. IEEE Trans. Smart Grid 3:404–412.
Podder AK, Roy NK, Naruttam Kumar Roy, Pota HR (2019) MPPT methods for solar PV systems: a critical review based on tracking nature. IET Renewable Power Generation 13:1615–1632.
Ishaque K, Salam Z, Shamsudin A, Amjad M (2012) A direct control based maximum power point tracking method for photovoltaic system under partial shading conditions using P & O algorithm. Appl. Energy 99:414–422.
Chen K, Tian S, Cheng Y (2014) An improved MPPT controller for photovoltaic system under partial shading condition. IEEE Trans. Sustain. Energy 5:978–984.
Kumar A, Gupta N, Gupta V (2019) Experimental prototype of a novel feed forward compensation for DC-link voltage stabilization in grid-tied PV system. International Transactions on Electrical Energy Systems 30:1–26.
Bastidas-Rodriguez JD, Franco E, Petrone G, Ramos-Paja CA, Spagnuolo G (2014) Maximum power point tracking architectures for photovoltaic systems in mismatching conditions: a review. IET Power Electronics 7:1396–1413.
Jana S, Kumar N, Mishra R, Sen D, Saha TK (2019) Development and implementation of modified MPPT algorithm for boost converter-based PV system under input and load deviation. International Transactions on Electrical Energy Systems 30:1–18.
Husev O, Vinnikov D, Roncero-Clemente D, Chub A, Romero-Cadaval E (2021) Single-Phase String Solar qZS-based Inverter: Example of Multi-Objective Optimization Design. IEEE Transactions on Industry Applications 3: 3120–3130.
Jiang LL, Maskell DL, Patra JC (2013) A novel ant colony optimization based maximum power tracking for photovoltaic systems under partially shaded conditions. Energy and Buildings 58:227–236.
Liu L, Liu C, Gao H (2013) A novel improved particle swarm optimization maximum power point tracking control method for photovoltaic array by using current calculated predicted arithmetic under partially shaded conditions. Journal of Renewable and Sustainable Energy 5.
Bingol O, Ozkaya B (2019) Analysis and comparison of different PV array configurations under partialvshading conditions. Solar Energy 160:336–343.
Sridhar V, Umashankar S (2017) A comprehensive review on CHB MLI based PV inverter and feasibility study of CHB MLI based PV-STATCOM. Renewable and Sustainable Energy Reviews 78:138–156.
Raghavendra Rajan V, Premalatha L (2017). Quasi-Z-Source Inverter Topologies with Reduced Device Rating: a Review. International Journal of Power Electronics and Drive System 8:325–334.
Furqan Ahmed H, Cha H, Kim SH, Kim HG (2016) Switched-Coupled-Inductor quasi-Z-Source Inverter. IEEE Transactions on Power Electronics 31:1241–1254.
Zhang M, Li H, Hao Y, Li K, Ding X (2021) A Modified Switched-Coupled-Inductor Quasi-Z-Source Inverter. IEEE Journal of Emerging and Selected Topics in Power Electronics 9: 3634–3646.
Laxman B, Annamraju A, Srikanth NV (2021) A grey wolf optimized fuzzy logic based MPPT for shaded solar photovoltaic systems in microgrids. International Journal of Hydrogen Energy 46: 10653–10665.
Meng K, Tang Q, Zhang Z, Yu C (2021) Solving multi-objective model of assembly line balancing considering preventive maintenance scenarios using heuristic and grey wolf optimizer algorithm. Engineering applications of artificial intelligence 1: 1–15.