N. Fujii

Bio: N. Fujii is an academic researcher from Sophia University. The author has contributed to research in topics: Maximum power point tracking & Photovoltaic system. The author has an hindex of 2, co-authored 2 publications receiving 554 citations.

Maximum Power Point Tracking of Multiple Photovoltaic Arrays: A PSO Approach

Abstract: Multiple photovoltaic (PV) modules feeding a common load is the most common form of power distribution used in solar PV systems. In such systems, providing individual maximum power point tracking (MPPT) schemes for each of the PV modules increases the cost. Furthermore, its v-i characteristic exhibits multiple local maximum power points (MPPs) during partial shading, making it difficult to find the global MPP using conventional single-stage (CSS) tracking. To overcome this difficulty, the authors propose a novel MPPT algorithm by introducing a particle swarm optimization (PSO) technique. The proposed algorithm uses only one pair of sensors to control multiple PV arrays, thereby resulting in lower cost, higher overall efficiency, and simplicity with respect to its implementation. The validity of the proposed algorithm is demonstrated through experimental studies. In addition, a detailed performance comparison with conventional fixed voltage, hill climbing, and Fibonacci search MPPT schemes are presented. Algorithm robustness was verified for several complicated partial shading conditions, and in all cases this method took about 2 s to find the global MPP.

A Novel maximum power point tracker controlling several converters connected to photovoltaic arrays with particle swarm optimization technique

Abstract: This paper deals with maximum power point tracking control of photovoltaic generators. Photovoltaic 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 using conventional techniques is a difficult task. To overcome the difficulty, finding the MPP, the authors have proposed a novel MPPT algorithm by introducing Particle Swarm Optimization technique. Further, the proposed algorithm uses only one pair of sensors to control multiple PV arrays. This results in lower cost, higher overall efficiency and also the algorithm is simple. Proposed MPPT algorithm is verified through experimental studies. Several partial shading conditions were tested and in all these cases the algorithm takes about one second to reach the global MPP. The reachability to MPP is good in both shading and unshading.

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

Abstract: 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.

Optimum design of hybrid renewable energy systems: Overview of different approaches

Abstract: Public awareness of the need to reduce global warming and the significant increase in the prices of conventional energy sources have encouraged many countries to provide new energy policies that promote the renewable energy applications. Such renewable energy sources like wind, solar, hydro based energies, etc. are environment friendly and have potential to be more widely used. Combining these renewable energy sources with back-up units to form a hybrid system can provide a more economic, environment friendly and reliable supply of electricity in all load demand conditions compared to single-use of such systems. One of the most important issues in this type of hybrid system is to optimally size the hybrid system components as sufficient enough to meet all load requirements with possible minimum investment and operating costs. There are many studies about the optimization and sizing of hybrid renewable energy systems since the recent popular utilization of renewable energy sources. In this concept, this paper provides a detailed analysis of such optimum sizing approaches in the literature that can make significant contributions to wider renewable energy penetration by enhancing the system applicability in terms of economy.

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

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

Abstract: 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.