Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions

Highlights

• An artificial bee colony based MPPT under partially shaded conditions is proposed.

• Photovoltaic systems are considered.

• A co-simulation methodology combining Simulink and Pspice has been adopted.

• Excellent efficiency and tracking performance compared to the PSO-based MPPT.

• The effectiveness of the proposed method has been confirmed experimentally.

Abstract

Artificial bee colony (ABC) algorithm has several characteristics that make it more attractive than other bio-inspired methods. Particularly, it is simple, it uses fewer control parameters and its convergence is independent of the initial conditions. In this paper, a novel artificial bee colony based maximum power point tracking algorithm (MPPT) is proposed. The developed algorithm, does not allow only overcoming the common drawback of the conventional MPPT methods, but it gives a simple and a robust MPPT scheme. A co-simulation methodology, combining Matlab/Simulink™ and Cadence/Pspice™, is used to verify the effectiveness of the proposed method and compare its performance, under dynamic weather conditions, with that of the Particle Swarm Optimization (PSO) based MPPT algorithm. Moreover, a laboratory setup has been realized and used to experimentally validate the proposed ABC-based MPPT algorithm. Simulation and experimental results have shown the satisfactory performance of the proposed approach.

Introduction

Photovoltaic (PV) energy sources are becoming a mature technology while their applications are spreading, ranging from supplying small electronic devices to large power plants connected to medium and low voltage grids. However, some problems remain a challenge for PV systems as improving overall efficiency, maximize the available power, minimize the return period of the installation cost, detection and diagnosis of faults, etc. [1], [2], [3], [4]. Several research works and specialized laboratory reports have addressed the issue of low yields and power losses in PV systems facilities and all have approved the use of a power optimizer known as maximum power point tracking (MPPT). Typically, the maximum power point (MPP) is achieved by adjusting the operating point of the PV array using a DC–DC converter.

Most MPPT techniques are based on the assumption that all cells in the same module and all modules in the same string receive the same irradiance. Perturb and observe (P&O) and incremental conductance are the most popular algorithms implemented in commercial charge regulators and grid connected inverters and can accurately track the MPP under uniform illuminating conditions [5], [6], [7]. However, PV modules are often subject to partial shade conditions (PSC), being the real problem responsible of most output power reduction and mismatch [8], [9], [10]. When PV array is operating under this conditions, the power voltage curves are characterized by the apparition of multiple local peaks which are due to the activation of bypass diodes avoiding shaded cells from damage [11], [12], [13]. In such case, conventional MPPT algorithms may miss the goal by converging to a local maximum rather than the global one, giving a rise to a significant loss in the output power and consequently to a low yield of the overall system. In order to cope with the effects of shading on the P–V curves, a number of improvements of conventional MPPT algorithms have been proposed. Some of them are topology based and need extra additional power circuits to perform global MPPT (GMPPT) [14], [15], [16]. Thus, the overall efficiency is reduced and the total cost will be increased. Some other techniques are algorithm based, such as fuzzy logic with polar controller [17], sequential extremum seeking control [18], dividing rectangle (DIRECT) search control [19]. These techniques are costly, time-consuming processes and need a complex hardware for their implementation.

Recently, bio-inspired (BI) methods such as Artificial Neural Networks (ANN), evolutionary computation and swarm intelligence algorithms have emerged as powerful optimization algorithms for solving complex problems and providing optimum solutions. Parallel, dynamic, decentralized, asynchronous and self-organizing behavior of the nature inspired algorithms is best suited for soft computing applications [20], [21]. Indeed, BI-inspired techniques were used in several applications as an efficient tool to solve complex optimization problems and design sophisticated controllers [22], [23], [24], [25].

The effectiveness of BI-inspired methods in handling complex nonlinearities, such that encountered in PV array behavior, and their implementation simplicity make them very attractive to solve the MPPT problem of PV systems, especially in the case of partial shading and module mismatches [26], [27]. Artificial Neural Networks (ANN), are one of the bio-inspired methods that was used in MPPT techniques. Typically, they were used to estimate the MPP with respect to the randomly changing weather conditions [28], and to improve the P&O and IC algorithms [29]. It is known that, using ANN with larger number of hidden nodes leads to more accurate results, but at the expense of longer computational time and complex hardware implementation. Evolutionary computation techniques, such as Genetic algorithm (GA) [30] and Differential Evolution (DE) [31], have been also proposed to deal with the MPPT problem. Commonly, the parameters setting of these algorithms are achieved using trial-and-error process which leads to a large computational time [32].

Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are the most swarm intelligence techniques used in the development of MPPT schemes [33], [34]. The PSO convergence significantly depends on the initial position of the agents which gives a poor convergence rate in some situation. In [35] the authors have used the ant colony based optimization technique. Though the authors have demonstrated the applicability of the method using simulation, it is difficult to be implemented and realized in hardware platform. In addition, both of PSO and ACO have five parameters to be determined, which make them inflexible and complex. In [36] the authors have used fuzzy logic to dynamically adapt some important parameters in PSO and ACO approaches. Better results than those of the original methods have been obtained, but the developed MPPT algorithms are complex and time-consuming.

Artificial bee colony (ABC) is a relatively new member of swarm intelligence techniques. It was proposed by Karaboga [37], based on foraging behavior, learning, memorizing and information sharing characteristics of honeybees. Several research papers have already evaluated the performances of ABC algorithm against different approaches like PSO, ACO and several other optimization techniques [38], [39], [40], [41]. Their finding states that the main advantages of the ABC algorithm are its simplicity [42], light computing complexity, high solutions accuracy [38], [43], [44], convergence independent of the initial conditions [45] and its ability to deal with local minima [37]. At present, the ABC algorithm has been successfully applied in distinct fields of science such as electrical engineering: in power flow optimization and optimal sizing of photovoltaic systems [46], [47], control engineering: in enhancement of control algorithms [48], image processing: in block matching techniques [49], mechanical engineering: in optimization of design approaches [50] and many others.

In this work, a novel ABC-based MPPT with direct control method for PV systems working under PSC is proposed. With this MPPT control scheme, the duty cycle is adjusted directly by the algorithm without the need of using a linear controller. The proposed MPPT scheme advances the state-of-the art through the following contributions:

• Excellent tracking capability with a good accuracy.

• No requirement of knowledge about the characteristics of the PV array.

• The use of just two control parameters, allowing great flexibility and simplicity.

The rest of this paper is organized as follows. Section 2 presents an overview of partial shading on PV modules and its impact on MPP location. Section 3 briefly describes the principle of the ABC algorithm and how it is applied to MPPT in PV systems. Section 4 provides simulation and experimental results of the proposed approach. A comparison of the proposed MPPT algorithm with the PSO-based MPPT method is also presented in this section. Finally, the drawn conclusions from this work are provided.