Limited Power Point Tracking for a Small-Scale Wind Turbine Intended to Be Integrated in a DC Microgrid

Aourir, Jamila; Locment, Fabrice

doi:10.3390/app10228030

Cited by 15 publications

(9 citation statements)

References 36 publications

(62 reference statements)

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“…The angle between voltage and current of stationary load should be stable. Grid-connected systems of photovoltaic, direct drive fan and other new energy sources, as well as flexible DC power transmission can be regarded as converter grid-connected models, which have multi-time scale characteristics, and their instability phenomenon shows broad frequency characteristics [23][24][25] . The main criteria for taking phase locked loop as the leading link www.nature.com/scientificreports/ include: stability criteria for extracting the main loop of phase locked loop; Generalized impedance criterion of phase angle impedance is extracted.…”

Section: Analysis Methods Of Voltage and Current Angle And Impedance ...mentioning

confidence: 99%

Research on high proportion of clean energy grid-connected oscillation risk prediction technology based on CNN and trend feature analysis

Pu,

Yingnan,

Chongjuan

et al. 2023

Sci Rep

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Oscillations, commonly known as a universal, propagative, and intricate event in the new power system, often give rise to generator tripping and load shedding, not only adversely affecting the power flow limit and the power angle stability but also posing threats to the lines of defense for stability and protection. Traditionally, emphasis has been laid on post-fault oscillation management, an emergency measure to deal with the impact and damage that have already affected the power grid. As such, this paper focuses on an oscillation prediction technique to detect oscillation energy early and intervene proactively to prevent further faults. This technique effectively lessens the damage caused by impacts and disconnects to the power grid. Firstly, this paper proposes the concept of disturbance power density and establishes the correlation between disturbance energy and the time domain, thereby exploring a method for evaluating the pattern of electrical quantities before power system oscillation. Secondly, it speeds up the time it takes to detect faults by catching nuances of voltage-current phase angle and impedance. Lastly, it puts forward a technique to cope with the intricacy and variety of power grid equipment using the convolutional neural network (CNN). This technique incorporates an integrated attention mechanism within a one-dimensional CNN model to capture the implicit mapping between voltage, active power, and reactive power at any time in the power system. This enables the model to self-learn multi-device characteristics and enhances the possibility of using theory in practical ways. Moreover, practical case studies also show that the prediction technique proposed in this paper can effectively issue warnings eight minutes before the occurrence of oscillation.

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Section: Analysis Methods Of Voltage and Current Angle And Impedance ...mentioning

confidence: 99%

Research on high proportion of clean energy grid-connected oscillation risk prediction technology based on CNN and trend feature analysis

Pu,

Yingnan,

Chongjuan

et al. 2023

Sci Rep

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show abstract

“…Another case where experiments were involved in a PV system showed that conventional P&O techniques are very sensitive to step size (which is related to the disadvantages of this method) [51]. Despite the fact that P&O is one of the most popular MPPT algorithm in industry due to its simplicity [54,55]. Regardless of these assets, the major downsides are high chattering when the MPP is reached and the lack of success to achieve this point [56].…”

Section: Perturb and Observationmentioning

confidence: 99%

Maximum Power Point Tracking Techniques for Photovoltaic Panel: A Review and Experimental Applications

et al. 2021

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This article contains a review of essential control techniques for maximum power point tracking (MPPT) to be applied in photovoltaic (PV) panel systems. These devices are distinguished by their capability to transform solar energy into electricity without emissions. Nevertheless, the efficiency can be enhanced provided that a suitable MPPT algorithm is well designed to obtain the maximum performance. From the analyzed MPPT algorithms, four different types were chosen for an experimental evaluation over a commercial PV system linked to a boost converter. As the reference that corresponds to the maximum power is depended on the irradiation and temperature, an artificial neural network (ANN) was used as a reference generator where a high accuracy was achieved based on real data. This was used as a tool for the implementation of sliding mode controller (SMC), fuzzy logic controller (FLC) and model predictive control (MPC). The outcomes allowed different conclusions where each controller has different advantages and disadvantages depending on the various factors related to hardware and software.

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“…Up to now, three main MPPT control schemes for wind energy conversion systems (WECS) have been developed including hill-climb search (HCS) method [5][6][7][8][9][10], tip speed ratio (TSR) method [11][12][13], and power signal feedback (PSF) method [14,15]. In the HCS control method, based on the locality of the operating point, the maximum power point of the wind-generator is continuously searched.…”

Section: Introductionmentioning

confidence: 99%

A novel digital‐signal‐processor‐based maximum‐power‐point tracking control design for a vertical‐axis wind‐turbine generation system using neural network compensator

Tsai

Tseng

Hung

et al. 2022

IET Electric Power Appl

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This paper presents a novel maximum‐power‐point tracking algorithm of a vertical‐axis wind‐turbine (VAWT) generation system using neural network compensator on the basis of a digital signal processing chip. First, the mechanical power versus rotation‐speed curve of a 3‐kW VAWT generator was constructed by means of the measurement data in various wind speeds. Because the slope of the wind‐turbine output power is a non‐linear function of the generator output current, air density, wind speed, and the characteristics of the wind‐turbine generator, the analytic solution for the maximum power is difficult to obtain due to its complexity, nonlinearity, and uncertainties of parameters. The innovation of this study is to obtain the maximum power of the wind‐turbine system using a recurrent neural network (RNN) by transferring the maximum‐power‐point tracking problem into a unit‐step‐command regulation problem despite the variation of the wind speed, ambient air density, and the load electrical characteristics. The uncertainties in the system are compensated by the RNN, which is composed of three‐layer networks with three neurons and feedback loops in the hidden layer for capturing the characteristics of the wind‐turbine generator system. Without the necessity of wind speed sensor, it only needs the generator output current and rotation speed as the inputs of the input layer. This structure containing only six neurons totally is simpler than the existing work. The duty cycle of the boost converter is determined by a proportional‐integral controller, the parameters of which are determined via a genetic algorithm by the minimisation of a performance index function with the help of MATLAB simulation tool. From the simulation and experimental results, the validity of the proposed control scheme was verified under various wind speed conditions. The performance efficiency and the tip speed ratio in each case of the experiment is near the optimal values of 0.28 and 4.3, respectively. The output power achieves 2048.4 W at the wind speed of 11 m/s in the steady state.

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Limited Power Point Tracking for a Small-Scale Wind Turbine Intended to Be Integrated in a DC Microgrid

Cited by 15 publications

References 36 publications

Research on high proportion of clean energy grid-connected oscillation risk prediction technology based on CNN and trend feature analysis

Research on high proportion of clean energy grid-connected oscillation risk prediction technology based on CNN and trend feature analysis

Maximum Power Point Tracking Techniques for Photovoltaic Panel: A Review and Experimental Applications

A novel digital‐signal‐processor‐based maximum‐power‐point tracking control design for a vertical‐axis wind‐turbine generation system using neural network compensator

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