Performance improvement of dynamic PV array under partial shade conditions using M ² algorithm

Abstract

Photovoltaic (PV) plants operating under the partial shade condition show an imbalance in the array irradiance and produce less output power. To counteract this problem, reconfigurable PV array or dynamic PV array (DPVA) for changing the inter-connections of PV modules to balance the irradiance distribution has been proposed previously. This study introduces a new strategy, the maximum and minimum (M 2) algorithm, to identify global maximum irradiance configuration with a minimal number of interchanges among the PV modules. For the implementation of DPVA, this study introduces a double pole double throw (DPDT) switch network (SN) with less switch-count compared to a conventional SN. Simulations of PV array have been carried out on a 9 × 9 size PV array. Results are compared with the previously reported algorithms. Further, cost-benefit analysis of a 10 kW P grid-tied DPVA plant has been presented. Experimental tests on 4 × 2 size DPVA under different shade conditions are conducted to validate the proposed algorithm and DPDT SN. Nomenclature m number of tiers or rows in total-cross-tied (TCT) array n number of modules per tier or row in TCT array G ip input irradiance matrix g i j irradiance of the module at ith tier, jth column of PV array M ip input index matrix of the PV array M op output index matrix of the PV array G op output irradiance matrix G sum column matrix with row sum of each row S matrix with rows arranged in decreasing order of row sum IR ideal ideal irradiance balance (W/m 2) of PV array IR act actual irradiance balance (W/m 2) of PV array IR balance percentage irradiance balance of PV array R avg average irradiance (W/m 2) of a tier or row in PV array d i difference between the irradiance of ith tier and R avg (W/m 2) r number of elements to be exchanged between two tiers l number of ways to choose r elements from n Ẽ m × l new matrix of m × l size computed at each iteration

A Novel PV Array Reconfiguration Algorithm Approach to Optimising Power Generation across Non-Uniformly Aged PV Arrays by Merely Repositioning

“…In [19], an offline rearrangement approach was devised to make aged PV systems more energy efficient by inspecting J 2020, 3 34 the possible options for PV module rearrangement based on the identification of the maximum power point. Meanwhile, in [1] the ideal arrangement for balancing and attenuating the aging process of switches in the switching matrix was assessed on the basis of the Munkres algorithm [20,21]. Issues related to the restructuring of modules in PV arrays of different sizes can be managed via additional approaches proven to be efficient, although these are computationally too complex and time-consuming because they involve a search of every possible manner, in which restructuring can be achieved [22].…”

Gene Evaluation Algorithm for Reconfiguration of Medium and Large Size Photovoltaic Arrays Exhibiting Non-Uniform Aging

“…Another benefit is that only the affected PV modules require repositioning, whereas the others can remain in their original positions. This also has the advantage of keeping the number of relays to be used for switching to a minimum [20,21].…”

Performance analysis of PV array configurations (SP, BL, HC and TT) to enhance maximum power under non‐uniform shading conditions

“…The dynamic reconfiguration based M 2 algorithm is proposed to recognize the maximum insolation condition with less number of changes in module inter-connections. 21 This technique requires less number of switching networks compared to other reconfiguration methods. The D-MPPT technique is implemented in Reference 22.…”