A photovoltaic (PV) generation system (PGS) is becoming increasingly important as renewable energy sources due to its advantages such as absence of fuel cost, low maintenance requirement, and environmental friendliness. For large PGS, the probability for partially shaded condition (PSC) to occur is also high. Under PSC, the P-V curve of PGS exhibits multiple peaks, which reduces the effectiveness of conventional maximum power point tracking (MPPT) methods. In this paper, a particle swarm optimization (PSO)-based MPPT algorithm for PGS operating under PSC is proposed. The standard version of PSO is modified to meet the practical consideration of PGS operating under PSC. The problem formulation, design procedure, and parameter setting method which takes the hardware limitation into account are described and explained in detail. The proposed method boasts the advantages such as easy to implement, system-independent, and high tracking efficiency. To validate the correctness of the proposed method, simulation, and experimental results of a 500-W PGS will also be provided to demonstrate the effectiveness of the proposed technique.

A Particle Swarm Optimization-Based Maximum Power Point Tracking Algorithm for PV Systems Operating Under Partially Shaded Conditions

In this paper, the concept of power tracking for PV systems is highlighted and an overview on 40 old and recent Maximum Power Point Tracking (MPPT) methods, available in the literature, is presented and classified. These methods are mathematically modeled and presented in such a way the reader can select the most appropriate method for his own application. A comparative table is presented at the end of the paper to simplify the classification of the different methods.

General review and classification of different MPPT Techniques

Conventional maximum power point tracking (MPPT) methods such as perturb-and-observe (P&O) method can only track the first local maximum point and stop progressing to the next maximum point. MPPT methods based on particle swarm optimization (PSO) have been proposed to track the global maximum point (GMP). However, the problem with the PSO method is that the time required for convergence may be long if the range of the search space is large. This paper proposes a hybrid method, which combines P&O and PSO methods. Initially, the P&O method is employed to allocate the nearest local maximum. Then, starting from that point on, the PSO method is employed to search for the GMP. The advantage of using the proposed hybrid method is that the search space for the PSO is reduced, and hence, the time that is required for convergence can be greatly improved. The excellent performance of the proposed hybrid method is verified by comparing it against the PSO method using an experimental setup.

A Maximum Power Point Tracking Method Based on Perturb-and-Observe Combined With Particle Swarm Optimization

A photovoltaic (PV) array has non-linear I–V (current–voltage) characteristics and its output power varies with solar insolation level and ambient temperature. There exists only one point, called maximum power point (MPP), on the P–V (power–voltage) curve, where power is maximum and this point varies with the changing atmospheric conditions. Moreover, energy conversion efficiency of PV module is very low and mismatch between source and load characteristics causes significant power losses. Consequently, maximization of power output with greater efficiency is extremely important. Maximum power point tracking (MPPT) is a technique employed to extract maximum power available from the PV module. It traces the PV operating voltage corresponding to the MPP and locks the operating point at MPP and extract maximum power from the array. Till date, many algorithms for MPPT have been reported, each with its own features. In this paper, a comprehensive presentation of working principle of these techniques is made and they are compared against each other in terms of some critical parameters like: number of variables used, complexity, accuracy, speed, hardware implementation, cost, tracking efficiency and so on. This study is aimed at providing a compendium on MPPT techniques for an appropriate selection, based on application requirements and system constraints.

Maximum power point tracking control techniques: State-of-the-art in photovoltaic applications

Maximum power point tracking (MPPT) is an important function in all photovoltaic (PV) power systems. The classical “hill climbing” and “incremental conductance” MPPT algorithms are widely applied in many papers and applications. Both algorithms perturb the operating conditions of the PV array and detect the changes in generated power. Since the detected change in generated power also could be a result of changes in irradiance, both algorithms are prone to failure in case of large changes in irradiance. This paper starts to discuss the size of the perturbation of the operating conditions for both algorithms, based on the single-diode model. The result is used to select the updating frequency for the two algorithms, in order not to run away under certain dynamic conditions. Both algorithms are implemented in an inverter and tested over 16 days of simultaneous operation. Basic statistical procedures, the paired t-test, have been applied to the data with the conclusion that the two algorithms perform equally good.

Evaluation of the “Hill Climbing” and the “Incremental Conductance” Maximum Power Point Trackers for Photovoltaic Power Systems

In this paper, a novel photovoltaic (PV) maximum power point tracking (MPPT) based on biological swarm chasing behavior is proposed to increase the MPPT performance for a module-integrated PV power system. Each PV module is viewed as a particle, and as a result, the maximum power point is viewed as the moving target. Thus, every PV module can chase the maximum power point (MPP) automatically. A 525 W prototype constructed by three parallel-connected 175 W PV modules is implemented to assess the MPPT performance. Comparing with a typical perturb and observe (P & O) MPPT method, the MPPT efficiency ?MPPT is improved about 12.19% in transient state by the proposed MPPT as theoretical prediction.

Chih-Hui Tsai

Papers

A Biological Swarm Chasing Algorithm for Tracking the PV Maximum Power Point