Experimental prototype of a novel feed forward compensation for DC‐link voltage stabilization in grid‐tied PV system

Self Cite

Stabilization of DC-link voltage is indispensable for the satisfactory operation of three-phase electronically coupled photovoltaic (PV) system. Practically, a challenge is to promptly and effectively settle the DC-link voltage with minimum peak magnitude and settling time to its pre-disturbance level when exposed to disturbances in the load and solar insolation level. The classical methods of DClink voltage stabilization employ a proportional-plus-integral (PI) compensator, and its gain coefficients are modulated through a cumbersome trial-and-error approach. Nevertheless, this approach does not yield superior results, especially under the wide interruptions in operating conditions due to nonlinear and timevarying characteristic of the PV module. These concerns have resulted in the emergence of multiple meta-heuristic techniques to optimally tune PI compensator gain coefficients. Nevertheless, most of these methods call for the ingrained paradox between computational efficiency and dynamic performance. Driven by this drawback, a proposal based on grey wolf optimizer (GWO-PI) is described in this paper to serve the following control targets: (a) be able to improve harmonic filtering with computational effectiveness, and (b) to ensure superior performance under exposure to interferences in the load and solar insolation level. Good agreement in MATLAB/Simulink simulation studies and experimental evaluations establish that the proposed GWO-PI outperforms the existing standardized optimization techniques such as particle swarm optimization (PSO-PI) and gravitational search algorithm (GSA-PI) in aspects of peak magnitude and settling time. Besides, it practically enables an improved harmonic filtering LIST OF SYMBOLS AND ABBREVIATIONS: K p , proportional gain coefficient of PI-controller; K i , integral gain coefficient of PI-controller; i LR , i LY , i LB , load currents; i L0 , zero-sequence component of the current; i Lα , i Lβ , i L0 , stationary reference frame currents; i Ld , active current component; i Lq , reactive current component; φ, grid voltage phase-angle; i * dc , d-axis reference current; i dc , DC-link VCL output; i Ldh , load harmonic current; i α , i β , i 0 , reference compensating currents; i Cα , i Cβ , i C0 , actual compensating current; V DC , measured DC-link voltage; V DCref , reference DC-link voltage; E SS , steady-state error; O f , objective function; M p , peak magnitude; T s , settling time; α, alpha; β, beta; δ, delta; ω, omega; D !

Section: Test 4-performance Comparison Of the Dc-link Voltage Stabimentioning

confidence: 95%

Section: Simulation Verificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Electronically coupled photovoltaic system with grey wolf optimizer enabled DC ‐link voltage control loop

Patel

Kumar

Gupta

2020

Self Cite

“…The deviation in energy can be represented as 19,26

italicδE = C * ([], {(U_{italicdc, italicref})}^{2} - {(U_{italicdc, italicmeas})}^{2}) / 2

where C is the total capacitance, δ E is the energy received by capacitor.

\approx C * ((), U_{dc, ref}) * ((), δ U_{italicdc, italicmeas})

…”

Section: Detailed Analysis Of the Designed Control Schemementioning

confidence: 99%

Control and analysis of a 3‐level diode‐clamped split source inverter in the applications of grid‐tied photovoltaic systems

Nannam

Babu

Banerjee

2020

Back ground and Aim In this work, a new control scheme is developed to track the peak power from a solar energized grid‐tied three‐level diode‐clamped split source inverter (DCSSI). Material and method Two isolated photovoltaic sources (PV) are considered as sources for the topology. The impedance network serves as a voltage booster and enhances the input PV voltage. Two individual perturb and observe maximum power tracking algorithms are used to pursue the peak power and to generate two individual dc‐link voltage references. A new dc‐link voltage reference is developed by the addition of the two individual voltage references. Results The proposed control scheme is tested with different cases to investigate the optimum performance of the DCSSI. Primarily, the control scheme is examined with MATLAB/SIMULINK. To validate experimentally, a 5KVA DCSSI prototype is designed along with two individual impedance networks. An SP‐110 pyranometer is used to sense the variable irradiance. A Spartan‐6 XC6SLX25 field programmable logic array (FPGA) is used to generate the control algorithm for the DCSSI. Conclusion Simulation and experimental results demonstrate the robustness of the proposed control scheme.

“…Standalone systems are generally costly with a high levelized cost of electricity (LCOE) due to storage requirements . However, in counties (and regions) with stable grids, grid‐tied systems are generally deployed with no mandatory requirement for storage . The energy produced by the panels in these settings can be directly fed to the grid (through inverter).…”

Section: Introductionmentioning

confidence: 99%

Analysis on inverter selection for domestic rooftop solar photovoltaic system deployment

Ali

Khan

2020

Summary Partial shading is a common occurrence in residential and commercial photovoltaic (PV) installations. It causes mismatch losses, particularly in string and central inverter‐based systems, leading to output power loss and in turn lower performance ratio under partial shading conditions. The choice of string configuration itself is critical in lowering mismatch losses and the levelized energy cost. While a range of commercial inverters with different string configurations and prices are available, many standard string topologies are not optimum for common settings under various shading scenarios. Therefore, in this article, we evaluate various string configurations attached to state‐of‐the‐art commercial inverters widely used in the industry from both mismatch loss and cost perspective. We quantify the impact of various shading scenarios on multiple inverter configurations to ascertain the relative performance of PV systems under symmetrical shading as well as random shading in field settings. For multiple PV system configurations (on a single rooftop), the mismatch loss varied up to 2.3% and 6% under symmetrical shading and field settings, respectively. The levelized cost of electricity also varies from 0.062$/kWh to 0.041$/kWh and is dependent on the type of inverter and string configurations.