Evaluation of the Performance of the State-of-the-art Meta-Heuristics Techniques for Varying Insolation

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1 Evaluaton of the Performance of the State-of-the-art Meta-Heurstcs Technques for Varyng Insolaton Puneet Josh 1 and Sudha Arora 2 1 Department of Electrcal Engneerng, COT, GBPUAT, Pantnagar, Uttarakhand, Inda. Emal: 2 Department of Electrcal Engneerng, COT, GBPUAT, Pantnagar, Uttarakhand, Inda. Emal: arora.sudha@gmal.com Artcle Receved: 05 October 2017 Artcle Accepted: 21 October 2017 Artcle Publshed: 30 October 2017 AB ST R ACT Development of effcent and effectve converter for Maxmum Power Pont Trackng (MPPT) under varyng nsolaton s a great challenge for researchers workng on solar photovoltac systems. Meta-heurstc technques are clamed to be effectve but stll scope for mprovement does exst. To ths end, a comparatve study of popular approaches may be helpful for selecton of best approach. In ths paper, an evaluaton of the behavor of the state-of-the-art meta-heurstcs based maxmum power pont trackng (MPPT) technques has been presented. Partcle Swarm Optmzaton (PSO), Gravtatonal Search Algorthm (GSA) and a hybrd co-evolutonary scheme PSO-GSA have been compared for varous nsolaton levels. Statstcal ndces and a test statstc (t-test) have been appled to compare and determne the scheme havng superor performance. From the smulaton results, t s evdent that the hybrd PSO-GSA may outperform other algorthms for PV systems for changng envronments. Ths work wll also serve as a source to nstgate wth the applcaton of modern optmzaton methodologes n the doman of MPPT of PV systems. Keywords: Solar PV Systems, MPPT, Converter and Meta-Heurstcs Technques. 1. INTRODUCTION Solar energy s a polluton free, unlmted, clean and nexhaustble source of Renewable energy. PV systems are desgned for both stand-alone and hybrd applcatons. these nclude street lghtng, water pumps, electrc vehcles, solar-wnd hybrd systems, Mcrogrds, etc. Nonetheless, one of the major problems assocated wth the PV systems s the effcency of the solar panels whch s very low ((17-21%) [1]. In addton, the performance of solar panels degrades exponentally under low rradance levels. Major challenge n extractng power from the solar panels s due to the nonlnear behavor of a PV cell wth varyng rradance and temperature, Fg.1 On the P-V curve, there les a pont, called the Maxmum Power Pont (MPP) at whch the complete PV system operates wth maxmum effcency, Fg. 1. But the trouble les n the fact that ths pont s not known and the task s to track ths pont by applyng the Maxmum Power Pont Trackng (MPPT) algorthms [2]. Many conventonal MPPT algorthms have been proposed n lterature; vz., Perturb and Observe (P&O), Incremental Conductance, Incremental Resstance, Temperature method. However, a more modern approach to ths problem s to consder t as an optmzaton problem where the ask s to maxmze power subject to constrants lke duty rato. Approaches lke PSO, GSA, Ant Colony Optmzaton [2], Dfferental Evoluton [3], Cuckoo Search [4] etc. fall under ths category. In ths artcle, the more effcent and modernzed MPPT algorthms have been scrutnzed and compared under the categores: energy producton and convergence rate. The three MPPT algorthms are PSO, GSA & PSO-GSA. These methods have been chosen due to the ncapablty of the classcal approaches to trace the MPPT durng rapdly changng envronment and oscllatons. Besdes these are easer to carry out n hardware as compared to ther peers. The MPPT technques are nvestgated usng the MATLAB/SIMULINK toolbox consderng varety of nsulatons. Wthout the loss of generalty stand-alone PV system s taken, n the analyss, by pluggng nto a boost converter as shown n Fg P a g e Webste:

2 2. PV ARRAY/ SYSTEM OVERVIEW Generally, PV systems consst of PV panels, a DC-DC converter, battery and controllers. PV panels convert the solar energy to the electrcal energy whch s then fed to the chopper crcutry controlled by the MPPT controllers. The controller, at all tmes, matches the load characterstcs wth those of the PV panels. Battery s an all-mportant component n case of grd nterconnectons, snce, t bestows stablzed voltage levels correspondng to dfferent loads at the nverter nput termnals, n addton, t also delvers power durng low rradances. Fg.3 shows the equvalent crcut of a PV cell. A solar panel comprses of several seres parallel combnatons of such cells. Wth reference to fg.3, (1) descrbes the I-V characterstcs of a solar panel: V+IR s V+IR s I=I ph -I o (exp( )-1)- (1) nsvt Rp Rp s very large, n modern PV cells typcally, t s >100kΩ. Thus, current through t has been overlooked for the sake of restrant n the computaton. Mathematcally, current at MPP s gven by: V mpp +Impp Rs V mpp +Impp Rs I mpp = I ph - Io exp( ) -1 - nsv T Rp (2) 1kW/m 2 0.8kW/m 2 0.6kW/m 2 0.4kW/m 2 Fg.1(a) current vs voltage wth varyng rradance 50 0 C 40 0 C 25 0 C 15 0 C 5 0 C Fg.1 (c) Current vs voltage wth varyng temperature 115 P a g e Webste:

3 1kW/m 2 0.8kW/m 2 0.6kW/m 2 0.4kW/m 2 Fg.1 (b) Power vs voltage wth varyng rradance 5 0 C 15 0 C 25 0 C 40 0 C 50 0 C Fg.1(d) Power vs voltage wth varyng temperature Fg.2 MATLAB/SIMULINK based modellng of stand-alone PV system 116 P a g e Webste:

4 I R + S I ph I d Rsh Fg.3 Equvalent crcut of a PV cell V _ However analytcal soluton of (2) s Table I ndcates the parameters of the PV panel used for the smulaton purposes. Table I. Solar Array Specfcatons (25 0C, 1000 W/m 2) Parameters Defntons Maxmum Power Voltage at Maxmum Power Pont, V MPP Current at Maxmum Power Pont, I MPP 2.30 Short Crcut Current, I SC 2.43 Open Crcut Voltage, V OC 24.9 Nomenclature: I ph Photoelectrc current (A) I o Dode reverse saturaton current (A) q Charge of an electron, C K Boltzmann constant, J/K T Standard Operatng Temperature (Kelvn) n Dode factor (1 n 2) G Insolaton (W/m 2 ) R s Seres resstance ( ) R p Shunt resstance ( ) n s Number of cells n seres V oc Open Crcut Voltage I sc Short Crcut Current V mpp Voltage at Maxmum Power Pont I mpp Current at Maxmum Power Pont I d Dode current I PV module Current (A) V PV module Voltage (V) E g Slcon gap energy of semconductor v T Thermal voltage equvalent (V) STC Standard Temperature Condton 3. META-HEURISTICS BASED MPPT APPROACHES Over the years, several meta-heurstcs approaches have been proposed, but some have reached enormous popularty and credt. Broadly any optmzaton method starts wth a set number of practcable solutons, known as, populaton. Grounded along the problem n hand and the technque appled these solutons are adopted, teratvely, to acheve the destnaton. Once the desred output s obtaned, the procedure s stopped by some stoppng crtera. Thus, ntalzaton and stoppng crtera are an mportant ssue. More often than not, n case of the MPPT method, 117 P a g e Webste:

5 ntalzaton conssts of dstrbutng populaton unformly between the multples of the VOC. Possbly, a varety of convergence crtera can be adopted. It may be set as threshold for velocty of the partcles. Lmtng the number of teratons can also act as a complementary convergence crteron. For re-ntalzaton of the process, usually, a relatve change n power can be employed as a measure. Some other way to reboot, s to set up a set tme pont. After ths tme, the technque s automatcally ntalzed wth new, random postons of the partcles. Hereafter, three such popular approaches have been dscussed. 3.1 Partcle Swarm Optmzaton PSO starts wth ndependent random selecton of the agents. In between teratons, the agents share the acqured knowledge n ts respectve endeavour. Each agent, referred to as a partcle, tres to mprove ts own accomplshment aganst the best partcle n the swarm. In ths manner, each partcle ultmately attans an optmal or a nearly optmal soluton. START Generate Intal Populaton Evaluate Ftness for each Agent Update lbest and gbest Update veloctes and poston NO Meet End Crteron? YES STOP Fg.4 Flowchart for PSO The standard PSO s formally nterpreted by: t+1 t t+1 x =x +v (3) Where t+1 t t+1 t t+1 t v =wv +c 11 r (localbest -x )+c 2 r 2 (globalbest -x ) (4) Where, x s the partcle poston, v s the velocty of th partcle; w s an nertal weght; t s the teraton number; and r2 are random selected values between [0,1]. c1 s the cogntve coeffcent and c2 s the socal factor. The localbest varable stores the best value that an ndvdual has acheved tll the th teraton, and globalbest stores the 118 P a g e Webste:

6 best soluton obtaned by all the partcles collectvely [5]-[8]. The flowchart of a basc PSO algorthm s exemplfed n fg. 4. The algorthm of a basc PSO method s dentfed as follows: 3.2 Gravtatonal Search Algorthm GSA s based along the prncple of Newtonan gravtaton. As mentoned by the authors n [9], the populaton n each step updates through cooperaton, competton and self-adaptaton. The performance of any agent s ndcated by ts mass. All agents engage each other by the gravty force gven by: t t M p M d t aj d d F j (t)=g (X j (t)-x (t)) t R j +ε (5) Where (-α ter/max_ter) G(t)=G e 0 (6) α and G0 are descendng coeffcent, ter s the current teraton, and max_ter s upper lmt of teratons. Rj(t) s the Eucldean dstance between agents and j, ε s a constant, x represents the poston n the search space, M are the actve gravtatonal masses gven by: t t t ft -worst m = t t best -worst (7) t t m M = m (8) d d F (t) a (t)= (9) t M d d d v (t+1)=rand v (t)+a (t) (10) START GENERATE INTIAL POPULATION EVALUATE FITNESS FOR EACH AGENT CALCULATE Force, mass & acceleraton FOR ALL AGENTS UPDATE VELOCITIES AND POSITION NO MEET END CRITERION? YES STOP Fg.5 Flowchart for GSA Henceforth, next poston s calculated by usng (3). The flowchart for the method s shown n Fg.5 [10-11]. 3.3 PSO-GSA PSO-GSA ntegrates the capablty of socal thnkng (explotaton) n PSO wth the local exploraton potental of GSA. It uses a hybrd approach whch s low-leveled, co-evolutonary and nhbts heterogenety. The hybrd s 119 P a g e Webste:

7 low-level because the functonalty of both algorthms s blended. It s co-evolutonary because the two approaches run n ncognto and concurrence. It s heterogeneous because there are two dfferent algorthms that are requred to get a soluton. [12] Resultant forces among agents s gven by (5). Henceforth, the acceleraton of partcles s calculated by (9) and the best soluton(globalbest) so far s updated. Through the globalbest, the veloctes of all agents can be computed as follows: t+1 t t t =w v +c 1 rand a + c 2 rand ( globalbest -x ) v (11) The agents new poston s agan calculated by (3). 4. RESULTS AND DISCUSSION A Smulnk model shown n fg.2 was used to smulate the system as specfed n Table I. From the smulatons results Table II was summarzed and followng conclusons were nferred: All methods are able to acheve Global MPP at all rradances wth fewer partcles requred (~3). All methods have very fast convergence rate (3-5 teratons max.). PSO and PSO-GSA have no oscllatons, whereas, GSA suffers from mnute oscllatons n output power. PSO requres least computatons and s thus, easer to program n hardware. PSO and PSO-GSA produce almost same output at all rradances. Table II. Comparson of Dfferent MPPT Methods at Varous Irradance Levels Irradance Method Optmum Duty Max. Power PSO GSA PSO-GSA PSO GSA PSO-GSA PSO GSA PSO-GSA PSO Table III. Varous statstcal ndces for the three methods Method Mean Medan Best Worst PSO Power Duty GSA Power Duty PSO-GSA Power Duty P a g e Webste:

8 All methods produce almost same results, thus, the choce for any method depends on the ease n hardware mplementaton. However, another method to determne the superorty of a partcular method s to form a hypothess and then apply some test statstc to check t. 30 samples for each method were obtaned for 1000 W/m2 and 250C. Table III summarzes the results for 30 samples. It s shown that PSO-GSA was able to perform better than the other two approaches. In addton, two sampled t-test was also appled for whch the level of sgnfcance for the test was set to 95%. Through the test t was deduced that PSO and PSO-GSA have almost same performance and they prove to be a better choce over GSA. However, as stated earler due to ease n hardware mplementaton PSO may be preferred over PSO-GSA. 5. CONCLUSION In ths paper, PSO, GSA and PSO-GSA methods were compared for trackng MPP of a PV system. The three approaches are very effcent compared to the classcal approaches. As they requre fewer steps and populaton sze to converge to the GMPP. Oscllatons exstng n the conventonal methods, were removed by means of socal thnkng and local search capablty. Despte the mathematcal and programmng complexty, the proposed methods offer remarkable accuracy and speed. Furthermore, the methods can be easly mplemented n real tme usng a mcrocontroller. In future, newer meta-heurstcs based algorthms may be developed for MPPT or hardware based performance analyss of the exstng schemes may be carred out. REFERENCES [1] D. P. Hohm, M. E. Ropp, Comparatve study of maxmum power pont trackng algorthms usng an expermental, programmable, maxmum power pont trackng test bed, Photovolt. Spec. Conf Conf. Rec. Twenty-Eghth IEEE, pp , [2] L. L. Jang, D. L. Maskell, and J. C. Patra, A novel ant colony optmzaton-based maxmum power pont trackng for photovoltac systems under partally shaded condtons, Energy Buld., vol. 58, pp , [3] M. F. N. Tajuddn, S. M. Ayob, Z. Salam, and M. S. Saad, Evolutonary based maxmum power pont trackng technque usng dfferental evoluton algorthm, Energy Buld., vol. 67, pp , [4] J. Ahmed, Z. Salam, A Maxmum Power Pont Trackng (MPPT) for PV system usng Cuckoo Search wth partal shadng capablty, Appl. Energy, vol. 119, pp , [5] N. Khaehntung, A. Kunakorn, and P. Srsuk, A novel fuzzy logc control technque tuned by partcle swarm optmzaton for maxmum power pont trackng for a photovoltac system usng a current-mode boost converter wth bfurcaton control, Int. J. Control. Autom. Syst., vol. 8, no. 2, pp , P a g e Webste:

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