This paper presents a new modified variable step size Fractional Order Incremental Conductance (FOIC) with maximum power point tracking using Fractional Order PID controller tuned by bio-inspired Particle Swarm Optimization (PSO) to find optimal gain values of fractional integrator order (λ) and fractional derivative order (μ). The classical incremental conductance and FOIC show drawbacks under changing irradiance, oscillation around maximum power point (MPP) which decreases its convergence speed. To resolve these prone a variable step size FOIC is proposed to achieve an adaptive duty cycle via tuning of FOPID through PSO. The robustness of the proposed technique is judged by its steady-state and dynamic response with fast converges, less response time, overshoot, and ripples under changing environmental conditions. Furthermore, the performance of the proposed technique is evaluated by comparing it with a fixed step size conventional incremental conductance algorithm and FOIC.

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In this paper, cabling constraints of different photovoltaic (PV) configurations is addressed in three steps: 1) a cable selection criterion is developed in accordance with metric system defined by American Wire Gauge (AWG), 2) a mathematical model is designed, which estimates the cable length for any array size, and 3) cost functions are devised owing to estimate the cables capital expenditure. The aforementioned steps are developed for Series (S), Parallel (P), Series-Parallel (SP), Total-cross-tied (TCT), Bridge-Link (BL) and HoneyComb (HC). A comprehensive mathematical model is developed for each of the above mentioned architectures in the context of cabling cost versus energy payback time. This cost analysis provides a clear snapshot to the designers of PV plant about the capital investments of cabling system of specific architecture and its potential energy payback time. At the end, a design example for each configuration is presented against an array size of 10×2 PV panels. INDEX TERMS Series-parallel (SP); Bridge-Linked (BL); HoneyComb (HC); Total Cross tied (TCT), Cabling Constraints, Cost Analysis NOMENCLATURE WM Width of module LM Length of module BL Base Length RSP Distance of row spacing ISC Short Circuit Current of PV Module L MM Cable length between two consecutive modules within a string LStr-Str Cable length between two consecutive strings LCab(M_M) Total cable length for module-to-module LCab(Str-Str) Total cable length for string-to-string L RT Extra round trip of cable length to reach inverter θ1 PV Tilt Angle θ2 Solar Elevation Angle NM Number of Modules in a String Nstr Number of Strings in an Array NHC No of Inter Strings Connection in Honey Comb Array IM_M Module-to-module current IStr-Str String-to-string current TCF Temp. Correction Factor TH Highest temperature of installation field (−) Rated current module-to-module (−) Rated current module-to-module CCab(M-M) Cost of cable module-to-module CCab(Str-Str) Cost of cable string-to-string CTotal Total cost of cabling This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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Oversampling is commonly encountered in orthogonal frequency division multiplexing (OFDM) systems to ease various performance characteristics. In this paper, we investigate the performance and complexity of one tap zero-forcing (ZF) and minimum mean-square error (MMSE) equalizers in oversampled OFDM systems. Theoretical analysis and simulation results show that oversampling not only reduces the noise at equalizer output but also helps mitigate ill effects of spectral nulls. One tap equalizers therefore yield improved symbol-error-rate (SER) performance with the increase in oversampling rate, but at the expense of increased system bandwidth and modest complexity requirements.