Modeling and neural networks based control of power converters associated with a wind turbine

Jday, Mariem; Haggège, Joseph

doi:10.1109/gecs.2017.8066248

Cited by 6 publications

(6 citation statements)

References 2 publications

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“…The authors in [10], suggest that sliding mode observer outperforms in tracking error than ANN-based observer as a sensorless control of PMSG in WECS; however, sliding mode observer deteriorates with chattering issue. To control the back-to-back power converter of a WECS, space vector-based pulse width modulation (SVPWM) has been developed through a feedforward ANN by obtaining lower total harmonic distortion (THD) [11]. On the other hand, an ANN-based voltage estimation for the calculation of THD is presented for multi-bus islanded AC microgrids [12].…”

Section: Related Literaturementioning

confidence: 99%

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Rüzgâr Enerjisi Dönüşüm Sistemlerinde bir DC-DC Güç Dönüştürücüsünün Denetimli Öğrenme Destekli Kontrolü

Akpolat

Dursun

Kuzucuoğlu

2021

International Journal of Advances in Engineering and Pure Sciences

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Over the last decades, to adopt high penetration of renewable energy sources (RESs) in electrical energy systems, distributed energy resources (DERs) have become prominent. Due to easy attainability status of small wind turbines (WTs), wind energy conversion systems (WECSs) are feasible applications for small customers, especially in windy areas. The next decade is likely to witness a considerable rise in DERs. In this context, WECSs are preferred broadly, thus harvesting wind energy into electrical energy effectively is a substantial issue. WTs can be got involved in the grid-connected or autonomous mode with a variety of topologies. In this paper, we examine to control of DC-DC boost converter of a WECS with the help of artificial intelligence (AI)-aided PI controller based on supervised learning method. Regarding the proposed method, artificial neural networks (ANNs) as a subset of AI are utilized. To test and ensure the applicability of the proposed control method, a small WECS with a permanent magnet synchronous generator (PMSG) connected a DC bus was implemented in MATLAB/Simulink environment. The proposed ANN scheme has reached a high accuracy rate with an overall mean squared error (MSE) equal to 7.4e-08. The results present that dynamic response and less complexity with a high accuracy rate have been obtained under study. The main target of this study is to reduce the number of sensors in the control layer. Thus, a costeffective and more reliable structure is obtained with fewer sensor requirements.

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Section: Related Literaturementioning

confidence: 99%

“…Besides, the electromagnetic torque (Tem) of the generator can be expressed as equation (11) unless Lq is not equal to Ld, * ,…”

Section: The Turbine Characteristics Can Vary For Different Values Of the Pitch Angle βmentioning

confidence: 99%

Rüzgâr Enerjisi Dönüşüm Sistemlerinde bir DC-DC Güç Dönüştürücüsünün Denetimli Öğrenme Destekli Kontrolü

Akpolat

Dursun

Kuzucuoğlu

2021

International Journal of Advances in Engineering and Pure Sciences

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“…In Figure 4 a list of variables and data-driven methods, used by some case studies and scholars, is provided by considering the MCS monitoring of this work. [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44] which can be adopted to monitor the relevant variables.…”

Section: Static Risk Assessmentmentioning

confidence: 99%

“…For this reason, a dynamic risk assessment is developed in the next section that monitors the probability over time in its evolution. ) CNN [28] 2) ANN + GA [29] 3) ANN/SVM [30] 4) ANN [ Minimal cut sets of AHU components with methods present in the literature [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44] which can be adopted to monitor the relevant variables.…”

Section: The Average Consequence Of the Block Of The Air Flow (𝐶mentioning

confidence: 99%

A New Risk Management Methodology for Infrastructure Based on Real-Time Monitoring and Dynamic Interventions: An Example Application on an Air Handling Unit

et al. 2020

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For an effective risk management of complex buildings it is required to dynamically estimate the risk on the service and take proper responsive measures to contrast it. This implies being able to estimate the evolving probabilities of failures over time and the way their occurrence is trust in affecting the service. This is now possible thanks to the advent of new sensing technologies and data-driven models to estimate failure probabilities, as well as solid risk management methodologies to estimate their effect on the service. However, it needs to be considered that the implementation of a dynamic risk management in standard building operation has to consider the reconfiguration of some processes to include the use of enabling technologies. In this paper a new dynamic risk management methodology is proposed to consistently (i) model the service, estimate the risk, first (ii) statically, using fault tree analysis, and then (iii) dynamically, using sensing technologies for data gathering and data-driven models for dynamic probability estimate, and finally (iv) implement the required intervention measures to minimize the risk. Then an application of the methodology is presented, for the risk management of an air handling unit, using a convolutional neural network, and its outcomes discussed. Conclusions are also drawn on the implications of integrating such a methodology in the current whole building risk management process and several outlooks are proposed.

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“…In such a structure, the output leg voltage is produced by a specific association of switching cells. The main advantage of MSC is to increase the degree of freedom of the conversion structure and then to contribute to the reduction of the voltage constraints on the switches, such as the reduce of the harmonic distortion rate which lead to obtain a high quality output voltage [2], [3]. Nevertheless, MSC structures have inner capacitors.…”

Section: Introductionmentioning

confidence: 99%

Minimal Order Linear Functional Observers Design for Multicell Series Converter

Jday¹,

Vidal²,

Haggège³

2020

IJACSA

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The requirement of high voltage-power level for many applications like the energy conversion system give rise to use a structure of a multilevel converter like the multicell series converter. To benefit as much as possible from this power converter an appropriate voltage distribution for each cell must be performed, hence the need to estimate this voltages. This paper aims to design a minimal single linear functional observer for a multicell series converter to estimate the capacitor voltages. Based on its hybrid model, an observability study prove the ability to estimate this capacitor voltages. Also a linear functional observers are proposed using a direct procedure without solving the Sylvester equation and based on an operation mode classification approach. Simulations of four-cells multicell converter are given in order to check the efficiency of the converter's hybrid model and the performance of the proposed minimal single linear functional observers.Recently, [9] detailed the design of the functional observer for switched discrete system.

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Modeling and neural networks based control of power converters associated with a wind turbine

Cited by 6 publications

References 2 publications

Rüzgâr Enerjisi Dönüşüm Sistemlerinde bir DC-DC Güç Dönüştürücüsünün Denetimli Öğrenme Destekli Kontrolü

Rüzgâr Enerjisi Dönüşüm Sistemlerinde bir DC-DC Güç Dönüştürücüsünün Denetimli Öğrenme Destekli Kontrolü

A New Risk Management Methodology for Infrastructure Based on Real-Time Monitoring and Dynamic Interventions: An Example Application on an Air Handling Unit

Minimal Order Linear Functional Observers Design for Multicell Series Converter

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