Risk assessment study of hydrogen energy storage system based on KPCA-TSO-LSSVM

Wind turbines (WTs) usually work in harsh environments and complex operating conditions, it leads to operating data of WTs with non-stationary, randomness and outlier. These imperfect data will inevitably impact the fault diagnosis of WTs. In this work, a fault diagnosis method for WT generators using normal behavior model (NBM) based on multi-task learning (MTL) is proposed. The imperfect operating data come from WTs are preprocessed and divided into different conditions. Each divided condition is constructed as a task, and a special deep neural network (DNN) is employed to train each task. Different tasks share some parameters through $L_{2}$ regularization at the bottom layer, and are designed independently at the upper layer. Multiple tasks learn at the same time and promote each other’s performance. We employ actual operational data to test the validity for NBM of WTs. The comparative results indicate that the proposed method overcomes the deficiency of imperfect data and is suitable for fault diagnosis under complex operating conditions. Note to Practitioners—Traditional fault diagnosis methods do not divide imperfect data under complex operating conditions, resulting in poor generalization and robustness in practical engineering applications. This work proposes a data-driven fault diagnosis method, which regards the imperfect data under each operating condition as a task and employs MTL to improve the performance of each task. Experiments results show that the proposed method is feasible, but the method has not been tested on other types of wind turbines. Next, we will extend this method to other mechanical components under complex operating conditions. At the same time, we also consider embedding the proposed algorithm into SCADA system as an Active X control object, which can automatically analyze the imperfect data.

Fault Diagnosis for Wind Turbine Generators Using Normal Behavior Model Based on Multi-Task Learning

Non Invasive Fault Detection of Offshore Wind Turbines Using Deep Network-Based Thermogram Features

ABSTRACT In recent times, the Automated Teller Machine (ATM) scam has increased widely in this society. The technology has emerged to steal money or hack the service during the ATM transaction. Many thieves are using the strategy of skimming and trapping to steal money from ATM devices. In order to secure the transaction in ATMs, several authentication frameworks with recognition have been implemented. This safety work could be dealt with through biometric identification such as face, retina, and fingerprint recognition of the user. Due to high demand for security and reliable authentication schemes, the multimodal biometric system has emerged. The multimodal biometric system needs more than one biometric trait of an individual for identification and security purposes. Therefore, an integrated bank ATM transactions security model using a deep learning‐based multi‐modal biometric authentication system is developed to provide higher security during ATM transactions. Initially, the ATM card with Personal Identification Number (PIN) is given as the input for making ATM transactions in integrated banks. If the PIN is verified, then the appropriate person is identified through the biometric information of the user. The biometric information of the users includes Fingerprint, Face recognition, Retina, and speech. The speech information is in the format of signal, and hence the de‐noising is performed to eliminate the noises from the input signal. The de‐noised signal is given to Short Time Fourier Transform (STFT) to perform a signal transformation. After applying STFT, the spectrogram of images is attained. Finally, both the fingerprint, face, Retina, and spectrogram images are combined and given as the input for the recognition stage. Here, the Adaptive and Attentive‐based Mobilenet‐v3 (AAMNet) network is used for the recognition of input images, where the parameters from the Mobilenet‐v3 are optimized using the Enhanced Archerfish Hunting Optimizer (EAHO) to improve the recognition performance. After recognizing the biometric information of the users, the money transactions in ATMs are completed. Therefore, the security of the integrated banking ATM transaction is highly improved, and the illegal transaction is avoided. The experimental result of the developed security model in ATM transactions is validated with the traditional models to ensure the effectiveness of the developed system. Hence, the effective results of the proposed model attain nearly 93% for accuracy, sensitivity, specificity, and also nearly 6% for FPR and FNR, respectively. This result could be used for practical applications like banking and the financial sector, money transactions, funding management, and so forth. 

A Framework for Integrated Bank <scp>ATM</scp> Transactions Security Based on Multi‐Modal Biometric Authentication Using Adaptive and Attentive Assisted Mobilenet‐V3

The wind turbines (WT) are installed both onshore and offshore. The offshore WTs are more productive as compared to the onshore WTs as the availability of the wind speed is much higher offshore, but this productivity comes at the cost of an increase in faults, a higher maintenance requirement, and difficulty in accessibility. It is very difficult to gather the signatures with invasive sensors from the offshore WT due to the surrounding conditions. The position of the sensors changes, leading to unreliable signatures. A non-invasive sensor-based infrared thermography technique has been proposed to solve this problem. In this work, an approach for non-invasive condition monitoring of offshore WT for inter-turn short circuit (ITSC) faults has been developed using deep neural network (DNN)-based features of thermograms. Pre-trained deep learning (DL) models such as Alex Net have frequently been used for the diagnosis of motor faults. These pre-trained DL models are tested on particular data, which has a different classification problem and does not provide a reliable and high-efficacy model. Along with the previous drawback, these models also suffer from high processing times. To solve these problems, in this work, pre-trained models are modified with the incorporation of the feature selection technique and a shallow learning classifier at the output layer. The proposed methodology is able to classify the ITSC, rotor, and cooling fan faults with an accuracy of 99.7%. 

Efficient condition monitoring of off shore wind turbines using deep networks

The use of failure recognition technology can detect unusualness timely and deal with them properly to ensure safe and stable operation of wind turbine (WT). An effective troubleshooting method can quickly distinguish the type of WT fault and reduce wind farm operation and maintenance costs. At present, the relevant data required for fault diagnosis methods come from the supervisory control and data acquisition (SCADA) system, because the SCADA data contains information associated with the operating characteristics of WT, which can provide a rich source of data for WT fault diagnosis. A deep belief network (DBN) is commonly used the deep learning method. In the present study, an optimized DBN based on the modified tuna swarm optimization (MTSO) algorithm was established to construct a MTSO-DBN WT fault diagnostic model, so as to address the problem that the selection of DBN hyperparameters may affect the classification results. After preprocessing the WT fault data acquired by SCADA, the MTSO-DBN model was used to classify the WT faults. The experimental results reveal that, compared with the support vector machine (SVM), extreme learning machine (ELM), DBN, PSO-DBN, and TSO-DBN classification models, the MTSO-DBN model could effectively improve the accuracy of WT faults for wind farms.

Fault classification in wind turbine based on deep belief network optimized by modified tuna swarm optimization algorithm

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Fault classification in wind turbine based on deep belief network optimized by modified tuna swarm optimization algorithm