Solar Chimney Power Plants for Sustainable Air Quality Management Integrating Photocatalysis and Particulate Filtration: A Comprehensive Review

Mandal, Dipak Kumar; Bose, Sharmistha; Biswas, Nirmalendu; Manna, Nirmal K.; Cuce, Erdem; Benim, Ali Cemal

doi:10.3390/su16062334

Cited by 4 publications

(1 citation statement)

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“…The advantages of wind power generators are as follows. Clean energy production is possible, and wind power generators do not generate air pollutants because they use wind to generate electricity without combustion reactions [6]. Thus, the depletion of energy resources is expected to be alleviated.…”

Section: Introductionmentioning

confidence: 99%

LSTM-Autoencoder Based Anomaly Detection Using Vibration Data of Wind Turbines

Lee,

Park,

Kim

et al. 2024

Sensors

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The problem of energy depletion has brought wind energy under consideration to replace oil- or chemical-based energy. However, the breakdown of wind turbines is a major concern. Accordingly, unsupervised learning was performed using the vibration signal of a wind power generator to achieve an outlier detection performance of 97%. We analyzed the vibration data through wavelet packet conversion and identified a specific frequency band that showed a large difference between the normal and abnormal data. To emphasize these specific frequency bands, high-pass filters were applied to maximize the difference. Subsequently, the dimensions of the data were reduced through principal component analysis, giving unique characteristics to the data preprocessing process. Normal data collected from a wind farm located in northern Sweden was first preprocessed, and a long short-term memory (LSTM) autoencoder, and outlier detection was performed. The LSTM Autoencoder is a model specialized for time-series data that learns the patterns of normal data and detects other data as outliers. Therefore, we propose a method for outlier detection through data preprocessing and unsupervised learning, utilizing the vibration signals from wind generators. This will facilitate the quick and accurate detection of wind power generator failures and provide alternatives to the problem of energy depletion.

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Section: Introductionmentioning

confidence: 99%