Fire risk assessments and fire protection measures for wind turbines: A review

You, Fei; Shaik, Sujan; Rokonuzzaman, Md.; Rahman, Kazi Sajedur; Tan, Wen-Shan

doi:10.1016/j.heliyon.2023.e19664

Cited by 6 publications

(2 citation statements)

References 41 publications

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“…Due to difficulties in firefighting, multiple fire hazards, and complex fire types in wind turbines, once a wind turbine catches fire, it will cause the loss of the whole unit, which easily causes great economic losses, and the rescue possibility is almost zero [11]. In addition, due to the complexity of the operating conditions of offshore wind turbines and the severity of the operating environment [12], the main fire-extinguishing agents and water mist technology used at present are limited and cannot achieve good fire control effects [13]. Therefore, a fire warning for offshore wind turbines is crucial to protecting the security of on-site staff and the safe operation of the wind turbines.…”

Section: Introductionmentioning

confidence: 99%

Using Transfer Learning and XGBoost for Early Detection of Fires in Offshore Wind Turbine Units

Wan,

Du,

Gong

et al. 2024

Energies

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To improve the power generation efficiency of offshore wind turbines and address the problem of high fire monitoring and warning costs, we propose a data-driven fire warning method based on transfer learning for wind turbines in this paper. This paper processes wind turbine operation data in a SCADA system. It uses an extreme gradient-boosting tree (XGBoost) algorithm to build an offshore wind turbine unit fire warning model with a multiparameter prediction function. This paper selects some parameters from the dataset as input variables for the model, with average cabin temperature, average outdoor temperature, average cabin humidity, and average atmospheric humidity as output variables. This paper analyzes the distribution information of input and output variables and their correlation, analyzes the predicted difference, and then provides an early warning for wind turbine fires. This paper uses this fire warning model to transfer learning to different models of offshore wind turbines in the same wind farm to achieve fire warning. The experimental results show that the prediction performance of the multiparameter is accurate, with an average MAPE of 0.016 and an average RMSE of 0.795. It is better than the average MAPE (0.051) and the average RMSE (2.020) of the prediction performance of a backpropagation (BP) neural network, as well as the average MAPE (0.030) and the average RMSE (1.301) of the prediction performance of random forest. The transfer learning model has good prediction performance, with an average MAPE of 0.022 and an average RMSE of 1.469.

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Section: Introductionmentioning

confidence: 99%

Using Transfer Learning and XGBoost for Early Detection of Fires in Offshore Wind Turbine Units

Wan,

Du,

Gong

et al. 2024

Energies

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“…The UDPA is currently utilized in a variety of applications, including integrated underground tube corridors [11,12], wind turbine enclosures [13,14], and high-rise structures [15,16]. It has been observed that the moisture absorption and aggregation of particles have a significant impact on the diverse characteristics of the UDPA during storage and usage, serving as pivotal contributors to its failure [17,18].…”

Section: Introductionmentioning

confidence: 99%

Study on Flowability Enhancement and Performance Testing of Ultrafine Dry Powder Fire Extinguishing Agents Based on Application Requirements

Lu,

Zhao,

Zhou

et al. 2024

Fire

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Flowability greatly affects the application of ultrafine dry powder fire extinguishing systems, while hydrophobicity and acute inhalation toxicity are concerns for fire extinguishing agents. In the present study, we examined the impact of hydrophobic fumed silica on the hydrophobicity and flow properties of ammonium dihydrogen phosphate as the base. Our findings revealed that incorporating 6 wt.% of hydrophobic fumed silica resulted in optimal flowability, accompanied by a hydrophobicity angle of 126.48°. The excessive inclusion of hydrophobic fumed silica impeded powder flow within the ammonium dihydrogen phosphate particles. Furthermore, the investigations indicated that the incorporation of a small quantity of bentonite (0.5 wt.%) amongst the three functional additives—bentonite, magnesium stearate, and perlite—offered further enhancements in powder flowability. In fire extinguishing experiments’ total flooding conditions (1 m3), the designed UDPA exhibited a minimum required extinguishing concentration of merely 41.5 g/m3, which is better than the publicly reported value. Moreover, observations on the well-being of mice subjected to nearly three times the extinguishing concentration at 60 s, 10 min, and 3 days, respectively, demonstrated the absence of acute inhalation toxicity associated with the designed UDPA. Collectively, the developed ultrafine dry powder fire extinguishing agent displayed promising performance and possesses broad applicability.

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