Elsevier

Applied Soft Computing

Volume 13, Issue 11, November 2013, Pages 4320-4332
Applied Soft Computing

Development and analysis of adaptive fuzzy controllers for photovoltaic system under varying atmospheric and partial shading condition

https://doi.org/10.1016/j.asoc.2013.06.021Get rights and content

Highlights

  • A modified incremental conduction MPPT algorithm is proposed for solar PV system under partial shading conditions.

  • An adaptive fuzzy modulator is developed to provide PWM pluses to the DC–DC converter.

  • An adaptive hysteresis current control algorithm is proposed for DC–AC inverter in the PV system.

  • The hardware implementation of proposed algorithms using Xilinx spartran-3 FPGA is presented.

Abstract

Control of power electronics converters used in PV system is very much essential for the efficient operation of the solar system. In this paper, a modified incremental conduction maximum power point tracking (MPPT) algorithm in conjunction with an adaptive fuzzy controller is proposed to control the DC–DC boost converter in the PV system under rapidly varying atmospheric and partial shading conditions. An adaptive hysteresis current controller is proposed to control the inverter. The proposed current controller provides constant switching frequency with less harmonic content compared with fixed hysteresis current control algorithm and sinusoidal PWM controller. The modeling and simulation of PV system along with the proposed controllers are done using MATLAB/SIMSCAPE software. Simulation results show that the proposed MPPT algorithm is faster in transient state and presents smoother signal with less fluctuations in steady state. The hardware implementation of proposed MPPT algorithm and inverter current control algorithms using Xilinx spartran-3 FPGA is also presented. The experimental results show satisfactory performance of the proposed approaches.

Introduction

The photovoltaic system converts sunlight directly into electricity. At a unique point on the IV or PV curve of a PV cell, called the maximum power point (MPP), the PV system operates with maximum efficiency and produces maximum output power. The maximum power point tracking (MPPT) module should usually be integrated with photovoltaic (PV) systems so that the PV arrays are able to deliver maximum available power. There are a number of MPPT techniques proposed in the literature namely, perturb and observe [1], incremental conduction [2], neural network [3], fuzzy logic [4], genetic algorithm [5], etc. These algorithms assume that there is only one peak power point in the PV curve. The varying irradiation and partial shading condition result in multiple peaks in PV curve, i.e. local maximum power point (local MPP) and global maximum peak power point (global MPP) and multiple steps in an IV curve. The effectiveness of the existing MPP tracking algorithms deteriorates under these conditions.

The reference paper [6] has experimentally obtained the IV characteristics of the PV module to analyze the varying shading effect but, the work is limited to module-level study only and does not discuss the shading effects on an entire PV array. The reference paper [7] has adopted single-stage three-phase PV system that features an enhanced MPPT capability, but it has not studied the controlling methods of PV inverter. In [8], particle swarm optimization (PSO) based MPPT was proposed under varying atmosphere condition. This algorithm needs many variables that have to be set by the engineer. The reference paper [9] has proposed a MATLAB based PV module and P&O MPPT algorithm to track global MPP, and P controller to provide duty cycle for DC–DC converter in PV system. This approach uses a complex subroutine to track global MPP. This paper proposes a modified incremental conduction algorithm to track the global MPP and an adaptive fuzzy controller to provide duty cycle to the DC–DC converter. The authors of the research paper [10], [11] have proposed adaptive fuzzy hysteresis current controller for filter application. Dai [12] proposes adaptive hysteresis controller for grid connected PV inverter but does not consider varying atmospheric conditions.

In this work the DC–AC inverter is controlled by an adaptive hysteresis current controller. The adaptive hysteresis controller provides output with less harmonics and constant switching frequency. The PV system and the control algorithm are developed using Matlab/Simscape and Simulink software.

This paper is organized as follows. Section 2 presents the modeling of PV cell and its characteristics. Section 3 describes the PV system in detail. Section 4 presents the details of the proposed modified incremental conduction method and adaptive fuzzy logic controller. Section 5 explains the details of the adaptive hysteresis current controller. Section 6 discusses the simulation and experimental results. Conclusions are presented in the last section.

Section snippets

Modeling of PV cell and its characteristics

A PV cell can be represented by an equivalent circuit shown in Fig. 1.

The expression for current in the above equivalent circuit is given byIIPHIS(e(V+IRS/NVt)1)where Is represents the saturation current of diodes; Vt represents thermal voltage; N represents the quality factor and IPH represents light generated current. The value of IPH is in proportion to light intensity (Ir), and is given byIPH=IrIPH0Ir0In this expression, Ir is light intensity (irradiation), IPH0 is the light generated

System description

Fig. 3 shows the block diagram representation of a PV system, which includes a solar photovoltaic array with DC to DC converter, single phase inverter and load. The solar photovoltaic array produces electricity when the photon of the sunlight strikes the PV cell array. The output of the PV panel is directly connected to the DC to DC boost converter to step up the DC output of photovoltaic panel. Then it is fed to an inverter which converts DC into AC power at the desired voltage and frequency.

Proposed MPPT algorithm and adaptive fuzzy controller

Each PV cell has an individual operating point where it can provide the highest electrical power, the maximum power point (MPP). Under rapidly varying atmospheric condition and partial shading condition, the PV array characteristics get complex due to the presence of multiple peaks (multiple local as well as a global maximum). Under this condition in order to improve efficiency, it is essential to use an effective MPPT algorithm. One of the most successful and simplest methods for MPPT is

Adaptive hysteresis current controller

An adaptive hysteresis current controller is used to control the inverter in the PV system. This controller adjusts the hysteresis bandwidth, as a function of reference compensator current variation to optimize switching frequency and THD of supply current [11].

Fig. 5(a) shows the schematic diagram of a grid connected PV system along with the various controllers. As shown in the diagram, an adaptive hysteresis current controller is used to control the inverter. Synchronous reference frame (SRF)

Simulation of PV module

This section presents the details of the simulation carried out on a PV system along with the proposed controllers. The PV system is simulated using the built-in PV modules of Matlab/Simscape software. Each module consists of 36 photocells connected in series. The parameters of the simulated photovoltaic modules are Isc = 5.29 A, Voc = 45.2 V, Ir0 = 1000 W/m2, N = 1.2 × 36 and Rs =0.65 Ω. The arrays of photovoltaic modules are established by connecting two sets of 9 panels in series, and then containing the

Conclusion

In this paper, a PV system under the partial shading and varying atmospheric condition has been investigated. The modified incremental conduction technique has been proposed to track the maximum power point and an adaptive fuzzy controller is developed to produce the PWM pulses for the DC–DC converter. The DC–AC inverter is controlled by means of an adaptive hysteresis current control algorithm. The effectiveness of the proposed adaptive hysteresis current controller has been demonstrated

References (12)

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