This paper provides a comprehensive review of the maximum power point tracking (MPPT) techniques applied to photovoltaic (PV) power system available until January, 2012. A good number of publications report on different MPPT techniques for a PV system together with implementation. But, confusion lies while selecting a MPPT as every technique has its own merits and demerits. Hence, a proper review of these techniques is essential. Unfortunately, very few attempts have been made in this regard, excepting two latest reviews on MPPT [Salas, 2006], [Esram and Chapman, 2007]. Since, MPPT is an essential part of a PV system, extensive research has been revealed in recent years in this field and many new techniques have been reported to the list since then. In this paper, a detailed description and then classification of the MPPT techniques have made based on features, such as number of control variables involved, types of control strategies employed, types of circuitry used suitably for PV system and practical/commercial applications. This paper is intended to serve as a convenient reference for future MPPT users in PV systems.

This paper presents a maximum power point tracking (MPPT) design for a photovoltaic (PV) system using a grey wolf optimization (GWO) technique. The GWO is a new optimization method which overcomes the limitations such as lower tracking efficiency, steady-state oscillations, and transients as encountered in perturb and observe (P&O) and improved PSO (IPSO) techniques. The problem of tracking the global peak (GP) of a PV array under partial shading conditions (PSCs) is attempted employing the GWO-based MPPT technique. The proposed scheme is studied for a PV array under PSCs which exhibits multiple peaks and its tracking performance is compared with that of two MPPT algorithms, namely P&O-MPPT and IPSO-MPPT. The proposed GWO-MPPT algorithm is implemented on a PV system using MATLAB/SIMULINK. Furthermore, an experimental setup is developed to verify the efficacy of the proposed system. From the obtained simulation and experimental results, it is observed that the proposed MPPT algorithm outperforms both P&O and IPSO MPPTs.

This paper proposes a new Hybrid MPPT algorithm combining Grey Wolf Optimization (GWO) and Perturb & Observe (P&O) technique for efficient extraction of maximum power from a Photovoltaic (PV) system subjected to rapid variation of solar irradiance and partial shading conditions (PSCs). GWO handles the initial stages of maximum power point (MPP) tracking followed by application of the P&O algorithm at the final stage in view of achieving faster convergence to the global peak (GP). This MPPT thus overcomes the computational overhead as encountered in the case of a GWO based MPPT algorithm reported earlier [15]. The idea behind using the hybrid technique is to scale down the search space of GWO which helps to speed up for achieving convergence towards the GP. The proposed MPPT algorithm is first implemented using MATLAB/SIMULINK and subsequently an experimental setup is prepared for its practical implementation. From the obtained results, it is confirmed that the proposed MPPT provides superior tracking performance in any weather conditions compared to both GWO and PSO+PO based MPPT algorithms.

This brief proposes a new maximum power point tracker (MPPT) for a stand-alone photovoltaic (PV) system using the concept of double integral sliding mode controller (DISMC). Performance of a sliding mode controller (SMC) is greatly influenced by the choice of the sliding surface. A double integral SMC uses double integral of tracking voltage error term in its sliding surface to eliminate steady-state error apart from providing robust control actions in face of system uncertainties. However, there is still difficulty of increased chattering and slow transient response in DISMC. In this brief, the objective is to exploit the merits of the DISMC for designing an MPPT for PV system whilst the disadvantages of chattering and slow transient response are alleviated by choosing a new sliding surface. The design of this new DISMC-MPPT system considers the uncertainties in weather conditions and variations in load. This MPPT has been implemented using a pulsewidth modulator controlled dc/dc converter to keep the switching frequency constant. Thus, designing the control and filter circuits becomes simpler. The effectiveness of the proposed DISMC has been validated using simulation and experimental results using a prototype PV control system setup.

The underwater path planning problem deals with finding an optimal or sub-optimal route between an origin point and a termination point in marine environments. The underwater environment is still considered as a great challenge for the path planning of autonomous underwater vehicles (AUVs) because of its hostile and dynamic nature. The major constraints for path planning are limited data transmission capability, power and sensing technology available for underwater operations. The sea environment is subjected to a large set of challenging factors classified as atmospheric, coastal and gravitational. Based on whether the impact of these factors can be approximated or not, the underwater environment can be characterized as predictable and unpredictable respectively. The classical path planning algorithms based on artificial intelligence assume that environmental conditions are known apriori to the path planner. But the current path planning algorithms involve continual interaction with the environment considering the environment as dynamic and its effect cannot be predicted. Path planning is necessary for many applications involving AUVs. These are based upon planning safety routes with minimum energy cost and computation overheads. This review is intended to summarize various path planning strategies for AUVs on the basis of characterization of underwater environments as predictable and unpredictable. The algorithms employed in path planning of single AUV and multiple AUVs are reviewed in the light of predictable and unpredictable environments.