This paper proposes an improved maximum power point tracking (MPPT) method for the photovoltaic (PV) system using a modified particle swarm optimization (PSO) algorithm. The main advantage of the method is the reduction of the steady- state oscillation (to practically zero) once the maximum power point (MPP) is located. Furthermore, the proposed method has the ability to track the MPP for the extreme environmental condition, e.g., large fluctuations of insolation and partial shading condition. The algorithm is simple and can be computed very rapidly; thus, its implementation using a low-cost microcontroller is possible. To evaluate the effectiveness of the proposed method, MATLAB simulations are carried out under very challenging conditions, namely step changes in irradiance, step changes in load, and partial shading of the PV array. Its performance is compared with the conventional Hill Climbing (HC) method. Finally, an experimental rig that comprises of a buck-boost converter fed by a custom-designed solar array simulator is set up to emulate the simulation. The soft- ware development is carried out in the Dspace 1104 environment using a TMS320F240 digital signal processor. The superiority of the proposed method over the HC in terms of tracking speed and steady-state oscillations is highlighted by simulation and experimental results.

An Improved Particle Swarm Optimization (PSO)–Based MPPT for PV With Reduced Steady-State Oscillation

This paper proposes a deterministic particle swarm optimization to improve the maximum power point tracking (MPPT) capability for photovoltaic system under partial shading condition. The main idea is to remove the random number in the accelerations factor of the conventional PSO velocity equation. Additionally, the maximum change in velocity is restricted to a particular value, which is determined based on the critical study of P-V characteristics during partial shading. Advantages of the method include: 1) consistent solution is achieved despite a small number of particles, 2) only one parameter, i.e., the inertia weight, needs to be tuned, and 3) the MPPT structure is much simpler compared to the conventional PSO. To evaluate the idea, the algorithm is implemented on a buck-boost converter and compared to the conventional hill climbing (HC) MPPT method. Simulation results indicate that the proposed method outperforms the HC method in terms of global peak tracking speed and accuracy under various partial shading conditions. Furthermore, it is tested using the measured data of a tropical cloudy day, which includes rapid movement of the passing clouds and partial shading. Despite the wide fluctuations in array power, the average efficiency for the 10-h test profile reaches 99.5%.

A Deterministic Particle Swarm Optimization Maximum Power Point Tracker for Photovoltaic System Under Partial Shading Condition

This paper presents a review on the state-of-the-art maximum power point tracking (MPPT) techniques for PV power system applications. The main techniques that will be deliberated are the Perturb and Observe, Incremental Conductance and Hill Climbing. The coverage will also encompass their variations and adaptive forms. In addition, the more recent MPPT approaches using soft computing methods such as Fuzzy Logic Control, Artificial Neural Network and Evolutionary Algorithms are included. Whilst the paper provides as thorough treatment of MPPT at normal (uniform) insolation, its focus will be on the applications of the abovementioned techniques during partial shading conditions. It is envisaged that this review work will be a source of valuable information for PV professionals to keep abreast with the latest progress in this area, as well as for new researchers to get started on MPPT.

A review of maximum power point tracking techniques of PV system for uniform insolation and partial shading condition

Particle swarm optimization is a stochastic optimization, evolutionary and simulating algorithm derived from human behaviour and animal behaviour as well. Special property of particle swarm optimization is that it can be operated in continuous real number space directly, does not use gradient of an objective function similar to other algorithms. Particle swarm optimization has few parameters to adjust, is easy to implement and has special characteristic of memory. Paper presents extensive review of literature available on concept, development and modification of Particle swarm optimization. This paper is structured as first concept and development of PSO is discussed then modification with inertia weight and constriction factor is discussed. Issues related to parameter tuning, dynamic environments, stagnation, and hybridization are also discussed, including a brief review of selected works on particle swarm optimization, followed by application of PSO in Solar Photovoltaics.

Review article: A review of particle swarm optimization and its applications in Solar Photovoltaic system

The characteristics of photovoltaic array under partial shading comprises multiple local MPPs and one global. The classical maximum power point tracking (MPPT) algorithms can’t reach to global MPP. Accordingly, this work aims to study the behavior performance of two optimization techniques. They have been developed for extracting the global MPP from the partially shaded PVPS. The two studied techniques include Particle Swarm Optimization (PSO) and Cuckoo Search (CS). A comprehensive assessment of the two techniques has been carried out against a conventional algorithm of INR−based tracker. The tracking performances of PSO and CS based trackers are evaluated for different partial shading patterns based on MATLAB software. Results confirm that PSO and CS based trackers guarantee the convergence to the global MPP. Furthermore, they have the best performance in comparison with the conventional one. Additionally; the obtained results show that the CS−based tracker has superiority compared with PSO. The tracking time in case of CS−tracker is reduced compared to PSO in all the studied cases.

A comparison of different global MPPT techniques based on meta-heuristic algorithms for photovoltaic system subjected to partial shading conditions

This paper deals with Maximum Power Point Tracking (MPPT) control of photovoltaic (PV) generators. PV generation systems need maximum power point tracker because the PV power output depends on the operating terminal voltage and current. Further, the PV array exhibits two or more MPP's under partial shading condition, and hence finding the MPP by using conventional techniques is a difficult issue. In order to overcome this difficulty, verifying the performance of an improved PSO-MPPT algorithm, the authors have made a series of experiments in various types of solar irradiation changes. As a result, it is proved that the new algorithm has high performance and is superior to the original PSO-MPPT methods.

Evaluation of extracted energy from PV with PSO-based MPPT against various types of solar irradiation changes

An effective PSO (Particle Swarm Optimization) of MPPT (Maximum Power Point Tracking) control method for photovoltaic(PV) power generation system was proposed in the past paper. In this paper, the authors propose the parameter tuning method of the controller based on an adjusting rule in order to extract power as large as possible under various insolation conditions with partial shading. In the result, PSO control makes PV output power increased.

The optimization of control parameters of PSO based MPPT for photovoltaics

Photovoltaic (PV) generation systems need maximum power point tracker because the PV power output depends on the operating voltage and current. Further, multiple local maximum power points (MPPs) appear in the power-voltage characteristics under partial shading conditions, hence finding the global MPP using conventional MPPT techniques is a difficult task. To overcome this difficulty, the authors improved MPPT algorithm by introducing a Particle Swarm Optimization (PSO) technique. PSO's feature is it uses only one pair of sensors to control and has extensibility in a multiple dimension. In this paper, the authors tried to verify the extensibility of the improved method, Initialization and Repulsion PSO (IR-PSO) in multiple photovoltaic arrays under various types of change in insolation and partial shading conditions. Thereby, these methods resulting in lower cost, higher overall efficiency and simplicity of its implementation. IR-PSO method was verified through simulation and experimental studies. The detailed performance comparisons with other methods, Initialization PSO (I-PSO), Repulsion PSO (R-PSO), are presented.

Takayoshi Kamejima

Papers

Evaluation of extracted energy from PV with PSO-based MPPT against various types of solar irradiation changes

The optimization of control parameters of PSO based MPPT for photovoltaics

Verification of efficacy of the improved PSO-based MPPT controlling multiple photovoltaic arrays