Maximum power point tracking using a variable antecedent fuzzy logic controller

Kwan, Trevor Hocksun; Wu, Xiaofeng

doi:10.1016/j.solener.2016.08.008

Cited by 52 publications

(19 citation statements)

References 26 publications

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“…Non-self-optimization algorithms mainly include curve fitting [46] and other methods. Furthermore, there are artificial intelligence techniques, such as fuzzy logic [12,47,48] and particle swarm optimization [12,[49][50][51][52], that are combined with conventional MPPT methods to achieve a high tracking accuracy.…”

Section: Maximum Power Point Tracking (Mppt)mentioning

confidence: 99%

Simulation and Analysis of Perturbation and Observation-Based Self-Adaptable Step Size Maximum Power Point Tracking Strategy with Low Power Loss for Photovoltaics

2018

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Photovoltaic (PV) techniques are widely used in daily life. In addition to the material characteristics and environmental conditions, maximum power point tracking (MPPT) techniques are an efficient means to maximize the output power and improve the utilization of solar power. However, the conventional fixed step size perturbation and observation (P&O) algorithm results in perturbations and power loss around the maximum power point in steady-state operation. To reduce the power loss in steady-state operation and improve the response speed of MPPT, this study proposes a self-adaptable step size P&O-based MPPT algorithm with infinitesimal perturbations. This algorithm combines four techniques to upgrade the response speed and reduce the power loss: (1) system operation state determination, (2) perturbation direction decision, (3) adaptable step size, and 4) natural oscillation control. The simulation results validate the proposed algorithm and illustrate its performances in operational procedures.

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Section: Maximum Power Point Tracking (Mppt)mentioning

confidence: 99%

Simulation and Analysis of Perturbation and Observation-Based Self-Adaptable Step Size Maximum Power Point Tracking Strategy with Low Power Loss for Photovoltaics

2018

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“…However, when the state trajectory reaches the sliding surface, it is difficult to slide strictly along the sliding surface toward the balance point, but to cross back and forth on both sides of the sliding surface, resulting in vibration and power loss. In references [8][9][10], fuzzy control is used for MPPT, which does not require an accurate mathematical…”

Section: Introductionmentioning

confidence: 99%

Maximum Power Point Tracking Technology of Photovoltaic Array under Partial Shading Based On Adaptive Improved Differential Evolution Algorithm

Zhang

Sui

2020

Energies

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In the case of partial shading conditions, there will be more than one maximum power point (MPP) in photovoltaic (PV) array. The traditional maximum power point tracking (MPPT) methods are easy to get in the local maximum power point (LMPP) and fail. Based on the standard differential evolution (DE) algorithm, the mutation strategy, scaling factor F, and cross factor CR of the algorithm are optimized. Also, the population position variance δ 2 is used to prevent falling into LMPP. Finally, the conditions for algorithm termination and restart are set. It is verified by simulation that the method has fast convergence speed, high accuracy, and can adapt well to changes in the external environment. The improved DE algorithm has a great advantage in MPPT.Energies 2020, 13, 1254 2 of 15 model and has strong robustness. However, it is difficult to establish a fuzzy rule because it needs to accumulate a lot of operation experience of power point tracking. In references [11,12], neural networks are used to track the MPP. This method also requires the distribution of power extremum points under many different shadows, so it is not practical. In references [13,14], Fibonacci is used to track the GMPP; this method also depends on the learning to the peak distribution, and it is easy to fall into LMPP. Reference [15] proposed a dichotomy, the first step is to find the vicinity of GMPP, and the second step is to use traditional MPPT method to track MPPs, but the dichotomy will fail in the case of complex shading. Reference [16] proposed a MPPT method using shading detection and the trend of slopes from each section of the curve. After the short-term and long-term testing, this method shows the advantages of high precision and short tracking time. In reference [17] a modified P&O MPPT algorithm is proposed for accurate detection of PSC, which stabilizes the system output voltage without compromising the power efficiency. Reference [18] proposed an algorithm that is based on bio-inspired Whale Optimization. This algorithm eliminates the computational burden faced by the hybrid MPPT algorithms as discussed in various references and reduces the power oscillation during the change in operating conditions. Reference [19] proposed a modified P&O MPPT that can be used under PSC effectively, by integrating the Artificial Bee Colony algorithm in the first stage and P&O algorithm in the second stage. However, the algorithm needs many parameters to be adjusted, which is not conducive to control.Based on the advantages and disadvantages of the above methods, the DE algorithm is used to optimize the output of PV array. DE only needs two adjustable parameters. Firstly, according to the method proposed in reference [20], the initial positions of particles are selected near the voltages corresponding to the possible MPPs to avoid falling into the LMPP. The mutation direction, mutation operator, and crossover operator of particles are optimized based on the traditional DE algorithm.

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“…This is because it heavily depends on the good knowledge of PV systems, resulting in inaccurate membership functions. To address this issue, many modifications have been proposed, for example, an adaptive and optimized membership function of the traditional FLC-MPPT, as evidence in [7,[15][16][17]. However, the implementation is become over complex.…”

Section: Introductionmentioning

confidence: 99%

Design of an intelligent MPPT based on ANN using a real photovoltaic system data

Al-Majidi

Abbod

Al-Raweshidy

2019

2019 54th International Universities Power Engineering Conference (UPEC)

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Maximum power point tracking (MPPT) methods are an essential part in photovoltaic (PV) system design for increasing the output power of a PV array. Whilst several methods have been introduced, the artificial neural network (ANN) is one of the most powerful method for MPPTs due to its less oscillation and fast response. However, accurate training data is a big challenge to design an optimized ANN-MPPT technique. In this paper, an ANN-MPPT technique based on a large experimental training data is proposed to avoid the system from having a high training error. Those data are collected during one year from experimental tests of a grid-connected PV system installed at Brunel University, London, United Kingdom. The irradiance and temperature of weather conditions are selected as the input, and the maximum power from the PV system as the output of the ANN model. To assess the performance, the Perturb and Observe (P&O) and the proposed ANN-MPPT methods are simulated using a MATLAB/Simulink model for a PV system. The results show that the proposed ANN method accurately tracks the optimal maximum power point and avoids the phenomenon of drift problem, whilst achieving a higher output power when compared with P&O-MPPT method.

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Maximum power point tracking using a variable antecedent fuzzy logic controller

Cited by 52 publications

References 26 publications

Simulation and Analysis of Perturbation and Observation-Based Self-Adaptable Step Size Maximum Power Point Tracking Strategy with Low Power Loss for Photovoltaics

Simulation and Analysis of Perturbation and Observation-Based Self-Adaptable Step Size Maximum Power Point Tracking Strategy with Low Power Loss for Photovoltaics

Maximum Power Point Tracking Technology of Photovoltaic Array under Partial Shading Based On Adaptive Improved Differential Evolution Algorithm

Design of an intelligent MPPT based on ANN using a real photovoltaic system data

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