Power Data Preprocessing Method of Mountain Wind Farm Based on POT-DBSCAN

Zhu, Anfeng; Xiao, Zhao; Zhao, Qiancheng

doi:10.32604/ee.2021.014177

Cited by 5 publications

(4 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fuzzy logic, machine/deep learning, and the expert system are reviewed here and discussed more closely to recognize the collective understanding, useful implications, and further research prospects in applying AI tools for grid-connected DFIG-based winddriven turbine control systems. The literature studies demonstrate that machine learning (ML) techniques [45,[140][141][142][143] are very useful for small power quality (PQ) data analysis. Artificial neural networks [77,79,[144][145][146] have been used for voltage dip characterization and classifications.…”

Section: Ai Methodologies In Dfig Power Converter Control Systemmentioning

confidence: 99%

“…An additional abstract detailing the metaheuristic methods in [156] indicates the optimum solutions. GA [141,142,176], and PSO [166,171,177] are the best common metaheuristic approaches tuned to power electronic converter systems, as revealed in Figure 6. Table 1 indicates the applications of the metaheuristic method's superiority.…”

Section: Metaheuristic Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence Control System Applied in Smart Grid Integrated Doubly Fed Induction Generator-Based Wind Turbine: A Review

Behara

Saha

2022

Energies

View full text Add to dashboard Cite

Wind-driven turbines utilizing the doubly-fed induction generators aligned with the progressed IEC 61400 series standards have engrossed specific consideration as of their benefits, such as adjustable speed, consistent frequency mode of operation, self-governing competencies for voltage and frequency control, active and reactive power controls, and maximum power point tracking approach at the place of shared connection. Such resource combinations into the existing smart grid system cause open-ended problems regarding the security and reliability of power system dynamics, which needs attention. There is a prospect of advancing the art of wind turbine-operated doubly-fed induction generator control systems. This section assesses the smart grid-integrated power system dynamics, characteristics, and causes of instabilities. These instabilities are unclear in the wind and nonlinear load predictions, leading to a provisional load-rejection response. Here, machine learning computations and transfer functions measure physical inertia and control system design’s association with power, voltage, and frequency response. The finding of the review in the paper indicates that artificial intelligence-based machine and deep learning predictive diagnosis fields have gained prominence because of their low cost, less infrastructure, reduced diagnostic time, and high level of accuracy. The machine and deep learning methodologies studied in this paper can be utilized and extended to the smart grid-integrated power context to create a framework for developing practical and accurate diagnostic tools to enhance the power system’s accuracy and stability, software requirements, and deployment strategies.

show abstract

Section: Ai Methodologies In Dfig Power Converter Control Systemmentioning

confidence: 99%

Section: Metaheuristic Methodsmentioning

confidence: 99%

Artificial Intelligence Control System Applied in Smart Grid Integrated Doubly Fed Induction Generator-Based Wind Turbine: A Review

Behara

Saha

2022

Energies

View full text Add to dashboard Cite

show abstract

“…When the non-stationary signal is denoised by wavelet threshold, the following steps should be experienced: 1 The appropriate wavelet basis function should be selected according to the characteristics of signals, the wavelet system with orthogonality and compact support should be generally selected; 2 To consider the appropriate number of decomposition layers, however, there is no fixed empirical formula for the selection of decomposition layers, it is necessary to carry out repeated tests and verification according to the characteristics of signals; 3 Select the threshold function, and use the selected threshold form to process the detail component to achieve the effect of removing noise; 4 The signal is reconstructed to get the signal after noise reduction.…”

Section: Theory Of Wavelet Transformmentioning

confidence: 99%

“…The measured strain signals showed typical non-stationary and unsteady characteristics with the variation of crosswind angle. However, there are few studies on blade strain signal processing under continuous crosswind condition, simple methods such as taking average value and Butterworth low-pass filtering are mostly used, which is extremely difficult to accurately analyze the influence of crosswind on strain [3]. Actual wind turbine operation, on the other hand, in the process of the strain signal is affected by factors such as environment, test system, with high frequency noise, the noise can make the original signal frequency, amplitude distortion, make the analysis error, unable to guarantee the reliability of the strain analysis results.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Strain Characteristic Signals from Wind Turbine Blades Based on EEMD-WT

Wang¹,

Liu²,

Wang³

et al. 2023

Energy Engineering

View full text Add to dashboard Cite

Analyzing the strain signal of wind turbine blade is the key to studying the load of wind turbine blade, so as to ensure the safe and stable operation of wind turbine in natural environment. The strain signal of the wind turbine blade under continuous crosswind state has typical non-stationary and unsteady characteristics. The strain signal contains a lot of noise, which makes the analysis error. Therefore, it is very important to denoise and extract features of measured signals before signal analysis. In this paper, the joint algorithm of ensemble empirical mode decomposition (EEMD) and wavelet transform (WT) is used for the first time to achieve sufficient noise reduction and effectively extract the feature signals of non-stationary strain signals. The application process of EEMD-WT is optimized. This optimization can avoid the repeated selection of wavelet basis function and the number of decomposition layers due to different crosswind conditions. EEMD adaptively decomposes the strain signal into intrinsic mode functions, to judge the frequency of IMFs, remove the high-frequency noise components, retain the useful components. The useful components are denoised twice by the wavelet transform, the components and residual terms after the secondary denoising are reconstructed to obtain the characteristic signal. The EEMD-WT was applied to process the simulating signals and measured the strain signals. The results were compared with the results of the EEMD. The results showed that the EEMD-WT method has better noise reduction performance, and can effectively extract the characteristics of strain signals, which lays a solid foundation for accurate analysis of wind turbine blade strain signals under crosswind conditions.

show abstract

Condition Monitoring of Wind Turbine Anemometers Based on Combined Model Deep Learning

Zhu,

Zhao,

Yang

et al. 2024

Communications in Computer and Information Science

View full text Add to dashboard Cite

Power Data Preprocessing Method of Mountain Wind Farm Based on POT-DBSCAN

Cited by 5 publications

References 26 publications

Artificial Intelligence Control System Applied in Smart Grid Integrated Doubly Fed Induction Generator-Based Wind Turbine: A Review

Artificial Intelligence Control System Applied in Smart Grid Integrated Doubly Fed Induction Generator-Based Wind Turbine: A Review

Extraction of Strain Characteristic Signals from Wind Turbine Blades Based on EEMD-WT

Condition Monitoring of Wind Turbine Anemometers Based on Combined Model Deep Learning

Contact Info

Product

Resources

About