Development and analysis of adaptive fuzzy controllers for photovoltaic system under varying atmospheric and partial shading condition
Graphical abstract
Introduction
The photovoltaic system converts sunlight directly into electricity. At a unique point on the I–V or P–V 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 P–V curve. The varying irradiation and partial shading condition result in multiple peaks in P–V curve, i.e. local maximum power point (local MPP) and global maximum peak power point (global MPP) and multiple steps in an I–V curve. The effectiveness of the existing MPP tracking algorithms deteriorates under these conditions.
The reference paper [6] has experimentally obtained the I–V 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 bywhere 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 byIn 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)
- et al.
FPGA-based implementation of a fuzzy controller (MPPT) for photovoltaic module
Energy Conversion and Management
(2011) - et al.
Comparison between conventional methods and GA approach for maximum power point tracking of shaded solar PV generators
Solar Energy
(2013) - et al.
Optimization of perturb and observe maximum power point tracking method
IEEE Transactions on Power Electronics
(2005) - et al.
Simulation and hardware implementation of incremental conductance MPPT with direct control method using Cuk converter
IEEE Transactions on Industrial Electronics
(2011) The new maximum power point tracking algorithm using ANN-based solar PV systems
IEEE Network Conference
(2010)- et al.
A novel MPPT controlling several converters connected to PV arrays with PSO technique
Proceedings of Power Electronics Applications in European Conference
(2007)
Cited by (39)
Comment on “Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids”
2023, Applied Soft ComputingReinforcement learning with fuzzified reward approach for MPPT control of PV systems
2021, Sustainable Energy Technologies and AssessmentsDeep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids
2020, Applied Soft ComputingPartial shading detection for PV arrays in a maximum power tracking system using the sine-cosine algorithm
2020, Energy for Sustainable DevelopmentCitation Excerpt :To track the global maximum operation point (GMOP), various complex evolutionary algorithms have been implemented to identify the best fitting maximum power point objective function, including the optimal balancing control (Chen & Tsai, 2015), particle swarm optimization (PSO) (Ishaque & Salam, 2012), artificial bee colony (Benyoucef, Chouder, Kara, Silvestre, & Sahed, 2015), bat algorithm (Wu & Yu, 2018), fuzzy system (Punitha, Devaraj, & Sakthivel, 2013), and random search techniques (Palani, Peddapati, & Sundareswaran, 2014). In Lyden and Haque (2016), Pilawa-Podgurski and Perreault (2013), Chen and Tsai (2015), Ishaque and Salam (2012), Benyoucef et al. (2015), Wu and Yu (2018), Punitha et al. (2013), and Palani et al. (2014), the authors discussed GMOP tracking and how fast an algorithm can track the GMOP. Also in the above techniques, the MPPT algorithm is executed in deterministic intervals irrespective of the occurrence of the PSC, and is set by the system operator.
Improved MPPT method to increase accuracy and speed in photovoltaic systems under variable atmospheric conditions
2019, International Journal of Electrical Power and Energy SystemsCitation Excerpt :methods are combined with the FL, which are the commonly used structure in order to design membership functions of the FLC and fuzzy rules easily [36]. Danandeh and Mousavi [37] and Punitha et al. [38] have combined Inc. Cond.
Novel bio-inspired memetic salp swarm algorithm and application to MPPT for PV systems considering partial shading condition
2019, Journal of Cleaner Production