Machine Learning Controller for DFIG Based Wind Conversion System

Srinivasan, P.; Jagatheeswari, P.

doi:10.32604/iasc.2023.024179

Cited by 5 publications

(5 citation statements)

References 18 publications

(19 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, machine learning (ML) algorithms, specifically supervised learning, are used for the rotor and GSC in wind energy conversion systems. The ML algorithm is developed and trained in MATLAB, and simulation results demonstrate the efficiency of the proposed system, as demonstrated in 47 . These methods offer better adaptability but often at the cost of computational complexity and system overhead.…”

Section: Introductionmentioning

confidence: 93%

See 1 more Smart Citation

A novel intelligent control approach for wind energy conversion systems with synchronous reluctance generators

Selma,

Bounadja,

Belmadani

et al. 2024

Circuit Theory & Apps

View full text Add to dashboard Cite

SummaryThis study addresses a significant research gap in the field of wind energy, focusing on the underutilized potential of synchronous reluctance generators (SynRG) in wind power conversion systems (WPCS). Despite notable advancements in wind energy controls, the capabilities of SynRG within these systems have not been fully explored. In response, we introduce an innovative model‐free control (MFC) approach, termed intelligent proportional (iP), specifically designed and optimized for WPCS equipped with SynRG. This research encompasses the modeling, control, and simulation of SynRG‐based WPCS, aiming to optimize system performance and enhance the quality of grid power supply. The proposed vector control (VC) method utilizes a MFC approach (VC‐iP) to address the limitations inherent in traditional VC methods with proportional‐integral (PI) controllers (VC‐PI). Although widely used for their simplicity, VC‐PI methods often lead to power fluctuations and decreased power and current quality, consequently reducing overall system efficiency. In contrast, VC‐iP markedly enhances power quality and system efficiency, especially in situations involving fluctuating power demand and variable wind speeds. The efficacy of VC‐iP is demonstrated through simulations in two distinct test scenarios that include variations in power demand and random wind speed fluctuations. The results affirm the superiority of VC‐iP in maintaining stable, high‐quality power output from WPCS. This method notably minimizes fluctuations, improves current quality, reduces harmonic distortion and steady‐state error, ensures reliable grid integration, and contributes to a more efficient and sustainable energy system.

show abstract

Section: Introductionmentioning

confidence: 93%

“…The ML algorithm is developed and trained in MATLAB, and simulation results demonstrate the efficiency of the proposed system, as demonstrated in. 47 These methods offer better adaptability but often at the cost of computational complexity and system overhead.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel intelligent control approach for wind energy conversion systems with synchronous reluctance generators

Selma,

Bounadja,

Belmadani

et al. 2024

Circuit Theory & Apps

View full text Add to dashboard Cite

show abstract

“…The RSC maintains the active and reactive power flow between the DFIG and the grid. In terms of controlling the power converters, several techniques have been proposed to control the RSC of DFIG, such as backstepping control [3], machine learning-based control [4], event-triggered robust control [5], feedback linearization-based PI control [6], model predictive control [7,8], and fuzzy control [9]. However, despite the desired control performance of the approaches mentioned above in preserving the continuous operation of WECSs, their sensitivity to parameter uncertainties and external disturbances has led to a gradual investigation of more efficient methods during the past decade.…”

Section: Introductionmentioning

confidence: 99%

“…In [23], the RSC control of DFIG-based WECS was addressed via a fast adaptive TSMC considering diverse and challenging situations. In another study [4], the recurrent high-order neural network trained with the extended Kalman filter was developed to build up the DFIG models. Then, a super-twisting-based higher-order high-order SMC with reduced chattering was synthesized to improve the quality of the generated power.…”

Section: Introductionmentioning

confidence: 99%

Observer-Based High-Order Sliding Mode Control of DFIG-Based Wind Energy Conversion Systems Subjected to Sensor Faults

Mousavi,

Bevan,

Kucukdemiral

et al. 2024

IEEE Trans. on Ind. Applicat.

View full text Add to dashboard Cite

Recent advances in technology have paved the way for the increased penetration of wind energy conversion systems (WECSs) into the grid, worldwide. However, the existence of model uncertainties and intermittency of wind power can lead to malfunction of the stabilizing controllers and degrade the WECSs' power production performance. In this work, a compound control scheme comprising active fault-tolerant fractional-order nonsingular terminal sliding mode controllers (AFTSMCs) and a sliding mode observer (SMO) is developed to enhance the robustness of doubly-fed induction generator (DFIG) -based WECSs against uncertainties and maintain their desired performance. The developed AFTSMCs alleviate the chattering problem and overcome the compromise between fast response and the undesirable chattering problem. At the same time, it performs the speed trajectory tracking and rotor current regulation tasks. Moreover, under inevitable false fault detections due to unavoidable gradual performance degradations in the current sensors, a tolerance boundary is circumscribed for actual faults occurrence, allowing the developed robust SMO to estimate and reconstruct the rotor current during sensor faults with a high level of reliability. Evaluations of comparative performance are provided and validate the cooperative fault-tolerant method's superior control performance over other advanced approaches.

show abstract

“…It mainly consists of a wind wheel, a generator, and a power electronic converter [5,6]. The power electronic converter in wind turbines is also called a wind power converter, which can integrate the power generated by the generator into the grid [7,8]. The IGBT power module is the most vulnerable component in wind power converters, and 34 percent of the converter system failures are related to its failure [9].…”

Section: Introductionmentioning

confidence: 99%

Junction Temperature Prediction of Insulated-Gate Bipolar Transistors in Wind Power Systems Based on an Improved Honey Badger Algorithm

et al. 2022

View full text Add to dashboard Cite

To reduce carbon dioxide emissions, wind power generation is receiving more attention. The conversion of wind energy into electricity requires frequent use of insulated-gate bipolar transistors (IGBTs). Therefore, it is important to improve their reliability. This study proposed a method to predict the junction temperature of IGBTs, which helps to improve their reliability. Limited by the bad working environment, the physical temperature measurement method proposed by previous research is difficult to apply. Therefore, a junction temperature prediction method based on an extreme learning machine optimized by an improved honey badger algorithm was proposed in this study. First, the data of junction temperature were obtained by the electro-heat coupling model method. Then, the accuracy of the proposed method was verified with the data. The results show that the average absolute error of the proposed method is 0.0303 °C, which is 10.62%, 11.14%, 91.67%, and 95.54% lower than that of the extreme learning machine optimized by a honey badger algorithm, extreme learning machine optimized by a seagull optimization algorithm, extreme learning machine, and back propagation neural network model. Therefore, compared with other models, the proposed method in this paper has higher prediction accuracy.

show abstract

Machine Learning Controller for DFIG Based Wind Conversion System

Cited by 5 publications

References 18 publications

A novel intelligent control approach for wind energy conversion systems with synchronous reluctance generators

A novel intelligent control approach for wind energy conversion systems with synchronous reluctance generators

Observer-Based High-Order Sliding Mode Control of DFIG-Based Wind Energy Conversion Systems Subjected to Sensor Faults

Junction Temperature Prediction of Insulated-Gate Bipolar Transistors in Wind Power Systems Based on an Improved Honey Badger Algorithm

Contact Info

Product

Resources

About