Duy C. Huynh

Bio: Duy C. Huynh is an academic researcher from Ho Chi Minh City University of Technology. The author has contributed to research in topics: Particle swarm optimization & Photovoltaic system. The author has an hindex of 9, co-authored 24 publications receiving 222 citations. Previous affiliations of Duy C. Huynh include Vietnam National University, Ho Chi Minh City & Heriot-Watt University.

Development and Comparison of an Improved Incremental Conductance Algorithm for Tracking the MPP of a Solar PV Panel

Abstract: This paper proposes an adaptive and optimal control strategy for a solar photovoltaic (PV) system. The control strategy ensures that the solar PV panel is always perpendicular to sunlight and simultaneously operated at its maximum power point (MPP) for continuously harvesting maximum power. The proposed control strategy is the control combination between the solar tracker (ST) and MPP tracker that can greatly improve the generated electricity from solar PV systems. Regarding the ST system, the paper presents two drive approaches including open- and closed-loop drives. Additionally, the paper also proposes an improved incremental conductance algorithm for enhancing the speed of the MPP tracking of a solar PV panel under various atmospheric conditions as well as guaranteeing that the operating point always moves toward the MPP using this proposed algorithm. The simulation and experimental results obtained validate the effectiveness of the proposal under various atmospheric conditions.

Global MPPT of solar PV modules using a dynamic PSO algorithm under partial shading conditions

Abstract: This paper proposes a novel global maximum power point tracking (MPPT) strategy for solar photovoltaic (PV) modules under partial shading conditions using a dynamic particle swarm optimisation (PSO) algorithm. Solar PV modules have non-linear V-P characteristics with local maximum power points (MPPs) under partial shading conditions. In order to continuously harvest maximum power from solar PV modules, it always has to be operated at its global MPP which is determined using the proposed dynamic PSO algorithm. The obtained simulation results are compared with MPPs achieved using the standard PSO, and Perturbation and Observation (P&O) algorithms to confirm the effectiveness of the proposed algorithm under partial shading conditions.

Maximum power point tracking of solar photovoltaic panels using advanced perturbation and observation algorithm

Abstract: An efficient maximum power point tracking (MPPT) scheme is necessary to improve the efficiency of a solar photovoltaic (PV) panel. This paper proposes an advanced perturbation and observation (P&O) algorithm for tracking the maximum power point (MPP) of a solar PV panel. Solar PV cells have a non-linear V-I characteristic with a distinct MPP which depends on environmental factors such as temperature and irradiation. In order to continuously harvest maximum power from the solar PV panel, it always has to be operated at its MPP. The proposed P&O algorithm can reduce the main drawbacks commonly related to the P&O algorithm. This is achieved with determining the short-circuit current before each perturbation and observation stage. The obtained simulation results are compared with MPPs achieved using the conventional P&O algorithm under various atmospheric conditions. The results show that the advanced P&O algorithm is better than the conventional P&O algorithms for tracking MPPs of solar PV panels. Additionally, it is simple and can be easily implemented in digital signal processor (DSP).

Comparison between open- and closed-loop trackers of a solar photovoltaic system

Abstract: Solar energy is one of the renewable energy sources which is widely used to provide heat, light and electricity. The solar tracking controller used in solar photovoltaic (PV) systems to make solar PV panels always perpendicular to sunlight. This approach can greatly improve the generated electricity of solar PV systems. There are popularly two drive approaches including open- and closed-loop drives. This paper analyses and compares the open- and closed-loop trackers of a solar PV system. The obtained experimental results are to validate the effectiveness of each tracker.

Chaos PSO algorithm based economic dispatch of hybrid power systems including solar and wind energy sources

Abstract: This paper proposes a novel application of a chaos particle swarm optimization (PSO) algorithm for economic dispatch (ED) of a hybrid power system including the solar and wind energy sources. The algorithm is seeking to minimize total operating costs of the hybrid power system. The proposed chaos PSO algorithm is one of the standard PSO algorithm variants which has been used a logistic map for initializing random values of generators, as well as the inertia weight in the velocity update equation of the standard PSO algorithm. This results in the best convergence capability and search performance during the evolution process of the algorithm. The chaos PSO algorithm based ED problem of the hybrid power system with and without solar and wind powers is considered on a standard IEEE 30-bus 6-generator 41-transmission line test power system. The simulation results demonstrate the capabilities of the proposed algorithm to generate optimal dispatch solutions of the ED problem considering the renewable energy resources. The comparison with the standard PSO algorithm demonstrates the superiority of the proposed algorithm and confirms its potential to solve the ED problem.

An Overall Distribution Particle Swarm Optimization MPPT Algorithm for Photovoltaic System Under Partial Shading

Abstract: Solar photovoltaic (PV) systems under partial shading conditions (PSCs) have a nonmonotonic P – V characteristic with multiple local maximum power points, which makes the existing maximum power point tracking (MPPT) algorithms unsatisfactory performance for global MPPT, if not invalid. This paper proposes a novel overall distribution (OD) MPPT algorithm to rapidly search the area near the global maximum power points, which is further integrated with the particle swarm optimization (PSO) MPPT algorithm to improve the accuracy of MPPT. Through simulations and experimentations, the higher effectiveness and accuracy of the proposed OD-PSO MPPT algorithm in solar PV systems is demonstrated in comparison to two existing artificial intelligence MPPT algorithms.

Improved Differential Evolution-Based MPPT Algorithm Using SEPIC for PV Systems Under Partial Shading Conditions and Load Variation

Abstract: Photovoltaic arrays subject to partial shading conditions have more than one maximum power point (MPP), and conventional algorithms are unable to track the global maximum power point (GMPP) accurately. Thus, an improved global search space differential evolution algorithm for tracking the GMPP is introduced in this paper. The main contribution of the proposed algorithm are the following: capability in tracking GMPP and faster respond against load variation; optimization algorithm can search for the GMPP within a larger operating region as it is implemented by using a single-ended primary-inductor converter; and easy tuning as less parameter has to be set in the algorithm. The proposed system is first simulated in PSIM to ensure its capability. The feasibility of the approach is validated through physical implementation and experimentation. Results demonstrate that the proposed algorithm has the capability to track the GMPP within 2 s with an accuracy of 99% and respond to load variation within 0.1 s.

An Experimental Estimation of Hybrid ANFIS–PSO-Based MPPT for PV Grid Integration Under Fluctuating Sun Irradiance

Abstract: To enhance the photovoltaic (PV) power-generation conversion, maximum power point tracking (MPPT) is the foremost constituent. This article introduces an adaptive neuro-fuzzy inference system–particle swarm optimization (ANFIS–PSO)-based hybrid MPPT method to acquire rapid and maximal PV power with zero oscillation tracking. The inverter control strategy is implemented by a space vector modulation hysteresis current controller to get quality inverter current by tracking accurate reference sine-shaped current. The ANFIS–PSO-based MPPT method has no extra sensor requirement for measurement of irradiance and temperature variables. The employed methodology delivers remarkable driving control to enhance PV potential extraction. An ANFIS–PSO-controlled Zeta converter is also modeled as an impedance matching interface with zero output harmonic agreement and kept between PV modules and load regulator power circuit to perform MPPT action. The attainment of recommended hybrid ANFIS–PSO design is equated with perturb and observe, PSO, ant colony optimization, and artificial bee colony MPPT methods for the PV system. The practical validation of the proposed grid-integrated PV system is done through MATLAB interfaced dSPACE interface and the obtained responses accurately justify the proper design of control algorithms employed with superior performance.

Critical Review on PV MPPT Techniques: Classical, Intelligent and Optimisation

Abstract: Maximum power extraction from the photovoltaic (PV) system plays a critical role in increasing efficiency during partial shading conditions (PSC's). The higher cost and low conversion efficiency of the PV panel necessitate the extraction of the maximum power point (MPP). So, a suitable maximum power point tracking (MPPT) technique to track the MPP is of high need, even under PSC's. This study gives an extensive review of 23 MPPT techniques present in literature along with recent publications on various hardware design methodologies. MPPT classification is done into three categories, i.e. Classical, Intelligent and Optimisation depending on the tracking algorithm utilised. During uniform insolation, classical methods are highly preferred as there is only one peak in the P–V curve. However, under PSC's, the P–V curve exhibits multiple peaks, one global MPP (GMPP) and the remaining are local MPPs. Hence, Intelligent and Optimisation techniques came into limelight to differentiate the GMPP out of all LMPPs. Every MPPT technique has its advantages and limits, but a streamlined MPPT is drafted in numerous parameters like sensors required, hardware implementation, tracking in PSC's, cost, tracking speed and tracking efficiency. This present study aimed to address the advancement in this area for further research.

A critical evaluation on maximum power point tracking methods for partial shading in PV systems

Abstract: As photovoltaic (PV) system suffers considerable energy loss due to partial shading (PS), various approaches have been proposed to mitigate this problem. Among these, improving the maximum power point tracker (MPPT) algorithm seems to be the most feasible and economical solution. To date, there appears to be an absence of a single review paper that critically evaluates the performance of various types of MPPT algorithms during PS. To fill this gap, fifty prominent works on PS are analyzed from the theoretical and operational point of view. In particular, the paper will closely address the accurate detection for PS occurrence and the efficiency of global peak tracking. For certain selected cases, in-depth analysis is carried out to allow for an improved understanding on the operational intricacies of the algorithm. It is envisaged that this review paper would be a valuable one-stop reference to enable PV professionals to make more informed decisions when designing or choosing new MPPT algorithms for their inverters.