A High Performance Shade-Tolerant MPPT Based on Current-Mode Control
Abstract: This paper proposes a high performance shade tolerant maximum power point tracking (STMPPT) technique for DC-DC converter stage of photovoltaic (PV) applications. The average current-mode control (ACMC) is utilized to regulate the PV array current using two feedback control loops. The current mode control is a superior scheme in control of DC-DC power electronic converters. The proposed STMPPT technique operates in two modes. The ACMC with the perturb and observe (P&O) MPPT algorithm functions in a local MPPT (LMPPT) mode under normal irradiance condition. When the PV array is likely to be partially shaded, a global MPPT (GMPPT) subroutine effectively scans the PV profile to optimize the PV system operation. This is achieved by implementing simple innovations to the ACMC-based P&O algorithm. The innovations benefit from useful observations of I-V characteristics. The idea behind using the I-V characteristics is to significantly reduce the search space, make the algorithm independent of shading conditions and PV array configuration, and inherently recognize the occurrence of partial shading conditions (PSCs). The proposed STMPPT technique enables very fast and reliable tracking of global maximum power point (GMPP). In addition, it can stably work under dynamic environmental change without losing correct sense of tracking
direction. Its simplicity and independency would offer a viable solution for PV converter products. Simulation and experimental performance assessments are presented under different operating conditions that could happen in outdoor PV installations.1 Existing system: Although the algorithms could find the GMPP for a set of conditions, they suffer from problems such as considerable fluctuations and weak dynamic performance. The algorithms are also complex and require undesirable computational burden. Several studies investigated segmental GMPPT search techniques which initially choose an exploration range. Then, the exploration range is reduced in different steps up until the global peak is found. These techniques are originated from dividing rectangle (DIRECT) method in and Fibonacci search. The DIRECT technique progressively restricts the searching interval using the measurements of samples within the interval and a critical condition to specify the most promising interval holding the maximum value. In the Fibonacci GMPPT method, the length of the intervals is determined using the numbers in the Fibonacci sequence. Proposed system: Soft computing techniques have been proposed to find the global MPP (GMPP) of a PV array in PSC. The particle swarm optimization (PSO) algorithm and its modifications were used in several studies. A PSO method was used in to find the GMPP of modular PV systems with a centralized MPPT control. In, the PSO method and the conventional perturb and observe (P&O) technique was used to enhance the convergence time of PSO. In, a combination of PSO and differential evolution (DE) algorithm was investigated where the two algorithms were applied consecutively in odd and even iterations. A centralized GMPPT control approach based on PSO was presented in for distributed PV generation. Other soft computing techniques have been also studied such as artificial bee colony, grey wolf optimization, simulated annealing, fireworks algorithm, and natural cubic spline guided Jaya algorithm. Advantages:
The particle swarm optimization (PSO) algorithm and its modifications were used in several studies. A PSO method was used in to find the GMPP of modular PV systems with a centralized MPPT control. In, the PSO method and the conventional perturb and observe (P&O) techniques were used to enhance the convergence time of PSO. In, a combination of PSO and differential evolution (DE) algorithm was investigated where the two algorithms were applied consecutively in odd and even iterations. Disadvantages: Complex models of PV array and extra equipment (such as sensors and monitoring cells) are often used to solve the GMPPT problem. Although the modeling and additional sensors could increase the speed of controllers, they become system dependent and suggest limited application. In many cases, the GMPPT methods require detailed information of PV array configuration and placement of the bypass diodes. However, the power electronic conversion stage which incorporates the MPPT algorithm is manufactured without the detailed knowledge of a specific PV array configuration. Modules: Artificial Neural Network: The scanning process and the decisions are dependent on parameters of the PV modules and the PV array arrangement. A hybrid GMPPT is proposed using a combination of the artificial neural network (ANN) and a hill climbing MPPT. A GMPP region is predicted based on the current sampling within the I-V graph and the ANN classifier. This method requires a dataset which is dependent on laborious simulations of the PV array using a PV model. In, a trapezoidal area which contains all the possible peaks is initially introduced based on the PV string characteristics. The proposed GMPPT then progressively reduces the voltage range that contains the PV array GMPP to avoid scanning unnecessary voltage range. However, the study considers limited operating conditions of PV modules (irradiance: 100- 1000 W/m2 and temperature: 25-75 â—ŚC). Moreover, it needs detailed information about the PV module and PV array arrangement.
Dividing rectangle: Although the algorithms could find the GMPP for a set of conditions, they suffer from problems such as considerable fluctuations and weak dynamic performance. The algorithms are also complex and require undesirable computational burden. Several studies investigated segmental GMPPT search techniques which initially choose an exploration range. Then, the exploration range is reduced in different steps up until the global peak is found. These techniques are originated from dividing rectangle (DIRECT) method and Fibonacci search. The DIRECT technique progressively restricts the searching interval using the measurements of samples within the interval and a critical condition to specify the most promising interval holding the maximum value. In the Fibonacci GMPPT method, the length of the intervals is determined using the numbers in the Fibonacci sequence. Maximum power point tracking: Photovoltaic (PV) developers are always seeking ways to achieve better efficiency and more power from solar installations. A capability to harvest the maximum amount of energy is crucial which would help optimize return on a PV system investment. The maximum available power of a PV system changes over time as a result of ambient condition variation. In response to this challenge, power electronic conversion systems featuring maximum power point tracking (MPPT) control have been developed to optimize power production by operating at the maximum power point (MPP) of the nonlinear PV characteristics. The PV characteristics become more complicated in partial shading condition (PSC) if the PV array includes bypass diodes, and several MPPs are produced in the P-V curve. This situation would degrade the efficiency of the PV system significantly if the MPPT controller could not effectively track the optimal operation point. The issue of PSC in PV systems and shade-tolerant. Particle swarm optimization: The issue of PSC in PV systems and shade-tolerant MPPT (STMPPT) or global MPPT (GMPPT) solutions has been addressed in recent years. Soft computing techniques have been proposed to find the global MPP (GMPP) of a PV array in PSC. The particle swarm optimization (PSO) algorithm and its modifications were used in several studies. A PSO method was used in to find the GMPP of modular
PV systems with a centralized MPPT control. In , the PSO method and the conventional perturb and observe (P&O) technique were used to enhance the convergence time of PSO. In, a combination of PSO and differential evolution (DE) algorithm was investigated where the two algorithms were applied consecutively in odd and even iterations. A centralized GMPPT control approach based on PSO was presented in for distributed PV generation. Other soft computing techniques have been also studied such as artificial bee colony, grey wolf optimization, simulated annealing, fireworks algorithm, and natural cubic spline guided Jaya algorithm . However, the accuracy of these approaches is greatly.