A Distributed Approach to Maximum Power Point Tracking for Photovoltaic Sub-Module Differential Power Processing

A Distributed Approach to Maximum Power Point Tracking for Photovoltaic Sub-Module Differential Power Processing

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  www.projectsatbangalore.com 09591912372 1 A DistributedApproach to Maximum Power Point Tracking for Photovoltaic Sub-Module Differential Power Processing Shibin Qin, Student Member, IEEE, Stanton T. Cady, Student Member, IEEE, Alejandro D. Dom´ınguez-Garc´ıa, Member, IEEE, Robert C.N. Pilawa-Podgurski, Member, IEEE Abstract—This paper presents the theory and implementation of a distributed algorithm for controlling differential power processing converters in photovoltaic (PV) applications. This distributed algorithm achieves true maximum power point track- ing (MPPT) of series-connected PV sub-modules by relying only on local voltage measurements and neighbor-to-neighbor communication between the differential power converters. Com- pared to previous solutions, the proposed algorithm achieves reduced number of perturbations at each step and potentially faster tracking without adding extra hardware; all these features make this algorithm well-suited for long sub-module strings. The formulation of the algorithm, discussion of its properties, as well as three case studies are presented. The performance of the distributed tracking algorithm has been verified via experiments, which yielded quantifiable improvements over other techniques that have been implemented in practice. Both simulations and hardware experiments have confirmed the effectiveness of the proposed distributed algorithm. I. INTRODUCTION IN photovoltaic (PV) energy systems, PV modules are often connected in series for increased string voltage. However, there is usually mismatch between the I-V characteristics of the series connected PV modules; this is typically the result of partial shading, manufacturing variability and thermal gradients. Since all modules in a series string share the same current, the string output power may be limited by underperforming modules. A bypass diode is often connected in parallel with each PV module to mitigate this mismatch and prevent PV hot spotting, but the efficiency loss is still significant when only a central converter is used to perform MPPT on the PV string. To address the mismatch problem, distributed power elec- tronics architectures that perform module-level MPPT, or even sub-module-level MPPT, have been proposed; the two most dominant architectures are DC optimizers [1]–[4], and micro- inverters [5]–[7], as shown in Fig. 1a and Fig. 1b, respectively. The major limitation of these two solutions, however, is that the distributed converters are connected in series with the PV modules and must process the full power output of every module. Even with highly efficient distributed converters, the overall power losses of the system can still be significant. The information, data, or work presented herein was funded in part by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award DE-AR0000217. The authors are with the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA. E-mail: {sqin3, scady2, aledan, pilawa}@ILLINOIS.EDU. Differential power processing (DPP) for PV applications has gained significant attention recently due to its substantial improvements over conventional solutions in terms of effi- ciency, reliability, and cost. The structure of a DPP-based system in shown in Fig. 1c; an overview of the DPP concept can be found in [8]. In contrast to DC optimizers or micro- inverters, DPP converters, or differential power processors (DPPs), are configured in parallel with the PV string. The bulk power that is common to all PV modules is directly processed by the central inverter without any intermediate conversion. DPP converters only need to process the power difference between series connected PV modules, which is often just a small fraction of the bulk power. This results in high efficiency of the system, small size and low power ratings of the power electronics circuit components. Due to these advantages, DPP has been further extended to apply to sub-module-level applications [9]–[13]. In order to compare the efficiency of a DPP-based system with micro-inverters or DC optimizers, we consider a system with 10 PV modules (i.e., 30 PV sub-modules) as an example. The irradiances on the PV sub-modules are set randomly by drawing from a Gaussian distribution with a standard deviation equal to 10% of the mean; the resulting maximum power of each PV sub-module is displayed in Fig. 2 using striped red bars. The differential power, i.e., the power difference between the maximum power of each PV module and the power common to all PV modules, is also shown in Fig. 2 using dotted blue bars. In a DC optimizer-based system, the DC optimizers have to process the full power of all PV sub- modules (represented by the striped red bars), which adds up to 2218 W. In a DPP-based system, each DPP only needs to process the differential power (represented by the dotted blue bars), which only adds up to 242.5 W. Both the DC optimizer-based system and the DPP-based system then use a string-level inverter to convert DC string voltage into AC voltage. In a micro-inverter system, the PV module voltage is directly converted to AC, but the efficiency of a micro- inverter is typically lower and its per-watt cost is typically higher than that of a string-level inverter or a central inverter. Table I summarizes the power losses of these three different solutions for the irradiance condition in this example. DC optimizers are assumed to have an average efficiency of 96%, while that of DPP converters is assumed to be 92% since they often operate in light-load conditions. For both the DC optimizer and DPP-based system, a string inverter efficiency