An online maximum power point capturing technique for high-efficiency power generation of solar photovoltaic systems

Zhang, Lijun; Yu, Samson Shenglong; Fernando, Tyrone; Iu, Herbert Ho-Ching; Wong, Kit Po

doi:10.1007/s40565-018-0440-2

Cited by 25 publications

(9 citation statements)

References 23 publications

(48 reference statements)

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“…Additionally, electric power with little noise and vibration in the operation process is allowable and accepted by the public. With the closed-loop control strategy, grid fluctuations and harmonics are inevitably produced [25]. Wavelets for smoothing are also added because wavelet transforms have been applied as filters in power systems with many merits.…”

Section: A Sampling and Smoothingmentioning

confidence: 99%

Fault Detection Method for Permanent Magnet Synchronous Generator Wind Energy Converters Using Correlation Features Among Three-phase Currents

Tan

Zhang

Zhou

2020

Journal of Modern Power Systems and Clean Energy

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In a permanent magnet synchronous generator (PMSG) system, conversion systems are major points of failure that create expensive and time-consuming problems. Fault detection is usually used to achieve a steady system. This paper presents a full analysis of a PMSG system for wind turbines (WT) and proposes a fault detection method using correlation features. The proposed method is motivated by the balance among the three-phase currents both before and after an opencircuit fault occurs in a converter of the PMSG system. It is unnecessary to analyze the output waveforms of a converter during fault detection. In this study, two correlation features of stator currents, the mean and covariation, are extracted to train an artificial neural network (ANN), thereby enhancing the performance of the proposed method under different wind speed conditions. Moreover, additional sensors and the collection of a massive amount of data are not required. Model simulations of an ideal inverter and a PMSG system are conducted using PSCAD software. The simulation results show that the proposed method can detect the locations of faulty switches with a diagnostic rate greater than 99.4% for the ideal inverter, and the PMSG drives settings at different wind speeds. Index Terms-Permanent magnet synchronous generator (PMSG), artificial neural network (ANN), mixed logical dynamical (MLD) theory, conversion system.

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Section: A Sampling and Smoothingmentioning

confidence: 99%

Fault Detection Method for Permanent Magnet Synchronous Generator Wind Energy Converters Using Correlation Features Among Three-phase Currents

Tan

Zhang

Zhou

2020

Journal of Modern Power Systems and Clean Energy

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“…Both step-change modes can track the MPP and the output voltage and current of the PV battery within about 0.01 s. The parameters (light intensity G and temperature T) of the system change at 0.1 s, which occurs in about 1 ms. Once again, by tracking the MPP and reaching a steady state, the dynamic characteristics and stability of the system can reflect changes in light intensity in a timely and smooth manner, and the system can quickly trace the maximum power output of the PV battery. (16)…”

Section: Simulation Resultsmentioning

confidence: 99%

Simulation and Analysis of Power-point Tracking via Photovoltaic Sensors

Fu-jian¹,

Ye²,

Liu³

et al. 2021

Sensors and Materials

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The photovoltaic (PV) cell is the basic component of a solar power system as it converts light energy into electricity by the PV effect. To increase the efficiency of conversion, the maximum power point (MPP) of the battery must be tracked in real time. We have established a system model based on the main characteristics of the PV power generation system and PV cells that considers the nonlinearity of the PV cell output. The mathematical model of a PV array is established by MATLAB/Simulink and combined with the common equivalent circuit of a PV cell and the mathematical model of PV power generation. This model enables the maximum power point tracking (MPPT) control of the PV cell through the simulation of its current-voltage (I-V) characteristic curve, which shows the cell's operation within its electrical circuit. We propose a MPPT control algorithm based on this model as an improved perturbation observation method. This algorithm controls the working current of the inverter of the PV cell using a PV sensor. Changes in light intensity or working temperature do not affect the ability of the tracking system to reliably track the MPP. This method does not need complex analysis and the control is based on a simple principle. Simulation results show that the algorithm has good dynamic performance in a steady state. The proposed method can track the MPP and improve the PV power generation efficiency by reflecting changes in illumination intensity with time.

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“…2) High steady-state error, power oscillation at MPP and slow transient response. 3) Inability to find GMPP, trapping at local MPP and incorrect perturbation direction under PSC or sudden irradiance change due to MPPT failure [10].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

Yap

Sarimuthu

Lim

2020

Journal of Modern Power Systems and Clean Energy

144

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In the last decade, artificial intelligence (AI) techniques have been extensively used for maximum power point tracking (MPPT) in the solar power system. This is because conventional MPPT techniques are incapable of tracking the global maximum power point (GMPP) under partial shading condition (PSC). The output curve of the power versus voltage for a solar panel has only one GMPP and multiple local maximum power points (MPPs). The integration of AI in MPPT is crucial to guarantee the tracking of GMPP while increasing the overall efficiency and performance of MPPT. The selection of AI-based MPPT techniques is complicated because each technique has its own merits and demerits. In general, all of the AI-based MPPT techniques exhibit fast convergence speed, less steady-state oscillation and high efficiency, compared with the conventional MPPT techniques. However, the AI-based MPPT techniques are computationally intensive and costly to realize. Overall, the hybrid MPPT is favorable in terms of the balance between performance and complexity, and it combines the advantages of conventional and AI-based MPPT techniques. In this paper, a detailed comparison of classification and performance between 6 major AI-based MPPT techniques have been made based on the review and MATLAB/Simulink simulation results. The merits, open issues and technical implementations of AI-based MPPT techniques are evaluated. We intend to provide new insights into the choice of optimal AI-based MPPT techniques. Index Terms-Maximum power point tracking (MPPT), artificial intelligence (AI), fuzzy logic control (FLC), artificial neural network (ANN), genetic algorithm (GA), swarm intelligence (SI), machine learning (ML).

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An online maximum power point capturing technique for high-efficiency power generation of solar photovoltaic systems

Cited by 25 publications

References 23 publications

Fault Detection Method for Permanent Magnet Synchronous Generator Wind Energy Converters Using Correlation Features Among Three-phase Currents

Fault Detection Method for Permanent Magnet Synchronous Generator Wind Energy Converters Using Correlation Features Among Three-phase Currents

Simulation and Analysis of Power-point Tracking via Photovoltaic Sensors

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

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