A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

Qian, Peng; Tian, Xinshou; Kanfoud, Jamil; Lee, Joash Lap Yan; Gan, Tat-Hean

doi:10.3390/en12183411

Cited by 33 publications

(20 citation statements)

References 34 publications

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“…However, the reliability of the obtained results strongly depends on the choice of the type of neural network and its settings [15]. Deep neural networks are now becoming one of the most popular methods of machine learning [4,[16][17][18][19]. They show better results in comparison with alternative methods in recognition problems [20].…”

Section: Methodsmentioning

confidence: 99%

Assessment of the Condition of Pipelines Using Convolutional Neural Networks

et al. 2020

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Pipelines are structural elements of many systems. For example, they are used in water supply and heat supply systems, in chemical production facilities, aircraft manufacturing, and in the oil and gas industry. Accidents in piping systems result in significant economic damage. An important factor for ensuring the reliability of energy transportation systems is the assessment of real technical conditions of pipelines. Methods for assessing the state of pipeline systems by their vibro-acoustic parameters are widely used today. Traditionally, the Fourier transform is used to process vibration signals. However, as a rule, the oscillations of the pipe-liquid system are non-linear and non-stationary. This reduces the reliability of devices based on the implementation of classical methods of analysis. The authors used neural network methods for the analysis of vibro-signals, which made it possible to increase the reliability of diagnosing pipeline systems. The present work considers a method of neural network analysis of amplitude-frequency measurements in pipelines to identify the presence of a defect and further clarify its variety.

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Section: Methodsmentioning

confidence: 99%

Assessment of the Condition of Pipelines Using Convolutional Neural Networks

et al. 2020

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“…Furthermore, to store the data information that is used in the long-term storage in hidden layers, the "cell-states" were introduced. As presented in Equations ( 10) and (11), f t and i t introduce the forget and input gates for controlling the input and output of each cell-state [36].…”

Section: Energy Harvestingmentioning

confidence: 99%

“…In this equation, the c index shows the current state of each parameter. Equation ( 13) obtains the current cell state, which is considered as using both forget and input gates [36].…”

Section: Energy Harvestingmentioning

confidence: 99%

Predicting the Parameters of Vortex Bladeless Wind Turbine Using Deep Learning Method of Long Short-Term Memory

et al. 2021

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From conventional turbines to cutting-edge bladeless turbines, energy harvesting from wind has been well explored by researchers for more than a century. The vortex bladeless wind turbine (VBT) is considered an advanced design that alternatively harvests energy from oscillation. This research investigates enhancing the output electrical power of VBT through simulation of the fluid–solid interactions (FSI), leading to a comprehensive dataset for predicting procedure and optimal design. Hence, the long short-term memory (LSTM) method, due to its time-series prediction accuracy, is proposed to model the power of VBT from the collected data. To find the relationship between the parameters and the variables used in this research, a correlation matrix is further presented. According to the value of 0.3 for the root mean square error (RMSE), a comparative analysis between the simulation results and their predictions indicates that the LSTM method is suitable for modeling. Furthermore, the LSTM method has significantly reduced the computation time so that the prediction time of desired values has been reduced from an average of two and a half hours to two minutes. In addition, one of the most important achievements of this study is to suggest a mathematical relation of output power, which helps to extend it in different sizes of VBT with a high range of parameter variations.

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“…Step 2: Construct a comparison judgement matrix: The judgement matrix A is formed by pairwise comparisons between factors of the same level to evaluate their relative degree of importance, as shown in Equation (9). The definitions and explanations of the evaluation scales are shown in Table 1.…”

Section: Calculation Of the Subjective Weightsmentioning

confidence: 99%

Research on CBM of the Intelligent Substation SCADA System

Liu

Chou

et al. 2019

Energies

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An equipment status management and maintenance platform of an intelligent substation monitoring and control system is built in the Tai-Tam substation of the Taipower company. The real-time operating status of the equipment, such as the server, supervisory control and data acquisition (SCADA) human–machine interface (HMI) software, switches, intelligent electronic devices (IEDs), merging units (MUs), as well as the entire SCADA system, are evaluated comprehensively. First, the status information of all equipment is collected, and the theory of relative deterioration degree (RDD) and fuzzy theory (FT) are applied to calculate the fuzzy evaluation matrix of the equipment influencing factors. Then, the subjective analytic hierarchy process (AHP) and the objective entropy method for weighting are combined to calculate the comprehensive weights of the equipment influencing factors. Finally, the result of the equipment status evaluation is obtained using the fuzzy comprehensive evaluation (FCE) method and is presented at the equipment status management and maintenance platform. Such equipment status evaluation results can be used by the inspection and maintenance personnel to determine the priority for equipment maintenance and repair. The result of this study may serve as a valuable reference to utility companies when making maintenance plans.

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A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

Cited by 33 publications

References 34 publications

Assessment of the Condition of Pipelines Using Convolutional Neural Networks

Assessment of the Condition of Pipelines Using Convolutional Neural Networks

Predicting the Parameters of Vortex Bladeless Wind Turbine Using Deep Learning Method of Long Short-Term Memory

Research on CBM of the Intelligent Substation SCADA System

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