Wind Farm Fault Detection by Monitoring Wind Speed in the Wake Region

Tran, Minh-Quang; Li, Yi‐Chen; Lan, Chen-Yang; Liu, Mengkun

doi:10.3390/en13246559

Cited by 12 publications

(4 citation statements)

References 36 publications

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“…According to the various mentioned studies, binary versions of optimization algorithms have strengths over traditional feature selection algorithms. Some other recent notable works of interest which proposed techniques for feature selection based on fuzzy entropy such as in [51][52][53]. Other notable works of interest that proposed metaheuristics for the FS problem are discussed below.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Heap Based Optimizer Algorithm for Feature Selection

Ali

P.²,

Elminaam³

2022

Mathematics

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The heap-based optimizer (HBO) is an innovative meta-heuristic inspired by human social behavior. In this research, binary adaptations of the heap-based optimizer B_HBO are presented and used to determine the optimal features for classifications in wrapping form. In addition, HBO balances exploration and exploitation by employing self-adaptive parameters that can adaptively search the solution domain for the optimal solution. In the feature selection domain, the presented algorithms for the binary Heap-based optimizer B_HBO are used to find feature subsets that maximize classification performance while lowering the number of selected features. The textitk-nearest neighbor (textitk-NN) classifier ensures that the selected features are significant. The new binary methods are compared to eight common optimization methods recently employed in this field, including Ant Lion Optimization (ALO), Archimedes Optimization Algorithm (AOA), Backtracking Search Algorithm (BSA), Crow Search Algorithm (CSA), Levy flight distribution (LFD), Particle Swarm Optimization (PSO), Slime Mold Algorithm (SMA), and Tree Seed Algorithm (TSA) in terms of fitness, accuracy, precision, sensitivity, F-score, the number of selected features, and statistical tests. Twenty datasets from the UCI repository are evaluated and compared using a set of evaluation indicators. The non-parametric Wilcoxon rank-sum test was used to determine whether the proposed algorithms’ results varied statistically significantly from those of the other compared methods. The comparison analysis demonstrates that B_HBO is superior or equivalent to the other algorithms used in the literature.

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Section: Related Workmentioning

confidence: 99%

An Efficient Heap Based Optimizer Algorithm for Feature Selection

Ali

P.²,

Elminaam³

2022

Mathematics

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“…Some of the popular areas of application of computer vision techniques include robotics [2], autonomous vehicles [3], and environmental monitoring [4]. The measurement of wind speed and direction is a crucial aspect of environmental monitoring, especially for areas such as fault diagnosis [5] and wind energy monitoring [6]. Meteorologists also use wind information for climate pattern and climate trend research [6].…”

Section: Introductionmentioning

confidence: 99%

Wind Speed Measurement via Visual Recognition of Wind-Induced Waving Light Stick Target

Zhou

Kasimu

et al. 2023

Applied Sciences

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Wind measurement in confined spaces is a challenge due to the influence of the dimensions of anemometers in intrusive flow-field measurements where the anemometer probes directly contact and influence the near-probe flow field. In this work, a new wind speed detection methodology is proposed based on wind-induced motion of a stick via vision-based recognition. The target’s displacement in pixel coordinates is mapped to its angular displacement in world coordinates to derive wind speed and direction information by applying the calibration coefficients. Simulation experiments were carried out to validate the model, the error of which was within an angular displacement of 4.0° and 3.0° for wind speed and direction detections, respectively. When applied to the measurement of wind speed in the inner equipment cabin of a stationary high-speed train, the error was within ±1.1 m/s in terms of average RMSE. Thus, the proposed method provides an accurate and economic option for monitoring 2D wind in a confined space.

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“…Compared to other diagnostic techniques, this feature selection can improve classification accuracy as well as reduce computational time. Monitoring the wind speed in the wake zone to detect wind farm faults is proposed in [3], in which a feature selection algorithm finds the significant information and increases the classification accuracy. In the case of Multi-sensor data fusion for the milling chatter detection task, the approach in [4] incorporates the recursive feature elimination method to find the important chatter features.…”

Section: Introductionmentioning

confidence: 99%

A Critical Study on Stability Measures of Feature Selection with a Novel Extension of Lustgarten Index

Sen

Mandal

Chakraborty

2021

MAKE

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Stability of feature selection algorithm refers to its robustness to the perturbations of the training set, parameter settings or initialization. A stable feature selection algorithm is crucial for identifying the relevant feature subset of meaningful and interpretable features which is extremely important in the task of knowledge discovery. Though there are many stability measures reported in the literature for evaluating the stability of feature selection, none of them follows all the requisite properties of a stability measure. Among them, the Kuncheva index and its modifications, are widely used in practical problems. In this work, the merits and limitations of the Kuncheva index and its existing modifications (Lustgarten, Wald, nPOG/nPOGR, Nogueira ) are studied and analysed with respect to the requisite properties of stability measure. One more limitation of the most recent modified similarity measure, Nogueira’s measure, has been pointed out. Finally, corrections to Lustgarten’s measure have been proposed to define a new modified stability measure that satisfies the desired properties and overcomes the limitations of existing popular similarity based stability measures. The effectiveness of the newly modified Lustgarten’s measure has been evaluated with simple toy experiments.

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Wind Farm Fault Detection by Monitoring Wind Speed in the Wake Region

Cited by 12 publications

References 36 publications

An Efficient Heap Based Optimizer Algorithm for Feature Selection

An Efficient Heap Based Optimizer Algorithm for Feature Selection

Wind Speed Measurement via Visual Recognition of Wind-Induced Waving Light Stick Target

A Critical Study on Stability Measures of Feature Selection with a Novel Extension of Lustgarten Index

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