Wind turbine fault diagnosis based on ReliefF-PCA and DNN

Wen, Xuezhi; Xu, Ziang

doi:10.1016/j.eswa.2021.115016

Cited by 80 publications

(31 citation statements)

References 24 publications

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“…The core content of the ReliefF algorithm is the correlation between features and dataset class marks [37]. ReliefF algorithm randomly selects a sample R from the training set.…”

Section: Relieff Algorithmmentioning

confidence: 99%

Research on Capacity Allocation Optimization of Commercial Virtual Power Plant (CVPP)

et al. 2022

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Commercial virtual power plants (CVPP) connect the form of renewable energy resource portfolio to the power market and reduce the risk of the unstable operation of a single renewable energy. Combining different kinds of large-scale renewable energy in CVPP to provide capacity services like base load, peak shaving, and valley-filling, etc., for the system loads is an urgent problem to be solved. Therefore, it is valuable to analyze the capacity allocation ratio of the CVPP to maximize the utilization of all kinds of energy, especially for the large-scale multi-energy base. This paper proposed a multi-energy coordinated operation framework by considering various load demands, including base load and peak shaving for the capacity allocation of CVPP based on the world’s largest renewable energy resource base on the upstream area of the Yellow River. The main procedures of this framework are as follows: (1) A paratactic model satisfying base load and peak shaving is proposed to determine the ability of the CVPP operation model’s capacity services to meet the different demands of the power system load. (2) A hybrid dimension reduction algorithm with a better convergence rate and optimization effect solves the proposed paratactic model based on the ReliefF and the Adaptive Particle Swarm Optimization (APSO). The results show that the large-scale CVPP with different compositions can achieve both of the goals of a stable base load output and stable residual load under different weather conditions. Compared with the operation on sunny days, the base load fluctuation and residual load fluctuation of CVPP on rainy days are reduced by 14.5% and 21.9%, respectively, proving that CVPP can alleviate renewable energy’s dependence on weather and improve energy utilization.

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“…The core content of the ReliefF algorithm is the correlation between features and dataset class marks [37]. ReliefF algorithm randomly selects a sample R from the training set.…”

Section: Relieff Algorithmmentioning

confidence: 99%

Research on Capacity Allocation Optimization of Commercial Virtual Power Plant (CVPP)

et al. 2022

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“…The Q-learning algorithm is simple to implement and widely used, but it still faces the problem of dimensional disaster. The algorithm usually stores Q-values as tables and is not suitable for reinforcement learning problems in highdimensional or continuous state space [33].…”

Section: Dqn-based Control Algorithmmentioning

confidence: 99%

A Deep $Q$ -Network-Based Collaborative Control Research for Smart Ammunition Formation

Shen

Zhang

Zhu

et al. 2022

International Journal of Aerospace Engineering

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The smart ammunition formation (SAF) system model usually has the characteristics of complexity, time variation, and nonlinearity. With the consideration of random factors, such as sensor error and environmental disturbance, the system model cannot be modeled accurately. To deal with this problem, this paper investigated an intelligent deep Q -network- (DQN-) based control algorithm for the SAF collaborative control, which deals with the high dynamics and uncertainty in the SAF flight environment. In the environment description of the SAF, we built a dynamic model to represent the system joint states, which referred to the smart ammunition’s velocity, the trajectory inclination angle, the ballistic deflection angle, and the relative position between different formation nodes. Next, we describe the SAF collaborative control process as a Markov decision process (MDP) with the application of the reinforcement learning (RL) technique. Then, the basic framework ε -imitation action-selecting strategy and the algorithm details were developed to address the SAF control problem based on the DQN scheme. Finally, the numerical simulation was carried out to verify the effectiveness and portability of the DQN-based algorithm. The average total reward curve showed a reasonable convergence, and the relative kinematic relationship among the formation nodes met the requirements of the controller design. It illustrated that the DQN-based algorithm obtained a novel performance in the SAF collaborative control.

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“…Training using analog circuits for diagnostic models in fault diagnosis is more valuable. With the continuous refinement of various data-analysis methods, the troubleshooting technology for analog circuits is also improving; common troubleshooting techniques, including PCA [1], Search Grid [2], particle swarm optimization (PSO) [3], ant colony algorithm (ACA) [4], simulated annealing (SA) [5], genetic algorithm (GA) [6], Back Propagation Neural Network (BP) [6], Self-organizing Maps (SOM) [7], Extreme Learning Machine (ELM) [8], decision tree [9], random forest [10] and SVM [11] all have good classification results to some extent [12].…”

Section: Introductionmentioning

confidence: 99%

TLSCA-SVM Fault Diagnosis Optimization Method Based on Transfer Learning

Zhang

2022

Processes

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In fault-diagnosis classification, a pressing issue is the lack of target-fault samples. Obtaining fault data requires a great amount of time, energy and financial resources. These factors affect the accuracy of diagnosis. To address this problem, a novel fault-diagnosis-classification optimization method, namely TLSCA-SVM, which combines the sine cosine algorithm and support vector machine (SCA-SVM) with transfer learning, is proposed here. Considering the availability of fault data, this thesis uses the data generated by analog circuits from different faults for analysis. Firstly, the data signal is collected from different faults of the analog circuit, and then the characteristic data are extracted from the data signals by the wavelet packets. Secondly, to employ the principal component analysis (PCA) reduces the feature-value dimension. Lastly, as an auxiliary condition, the error-penalty item is added to the objective function of the SCA-SVM classifier to construct an innovative fault-diagnosis model namely TLSCA-SVM. Among them, the Sallen–Key bandpass filter circuit and the CSTV filter circuit are used to provide the data for horizontal- and vertical-contrast classification results. Comparing the SCA with the five optimization algorithms, it is concluded that the performance of SCA optimization parameters has certain advantages in the classification accuracy and speed. Additionally, to prove the superiority of the SCA-SVM classification algorithm, the five classification algorithms are compared with the SCA-SVM algorithm. Simulation results showed that the SCA-SVM classification has higher precision and a faster response time compared to the others. After adding the error penalty term to SCA-SVM, TLSCA-SVM requires fewer fault samples to process fault diagnosis. Ultimately, the method which is proposed could not only perform fault diagnosis effectively and quickly, but also could run effectively to achieve the effect of transfer learning in the case of less failure data.

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Wind turbine fault diagnosis based on ReliefF-PCA and DNN

Cited by 80 publications

References 24 publications

Research on Capacity Allocation Optimization of Commercial Virtual Power Plant (CVPP)

Research on Capacity Allocation Optimization of Commercial Virtual Power Plant (CVPP)

A Deep $Q$ -Network-Based Collaborative Control Research for Smart Ammunition Formation

TLSCA-SVM Fault Diagnosis Optimization Method Based on Transfer Learning

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Wind turbine fault diagnosis based on ReliefF-PCA and DNN

Cited by 80 publications

References 24 publications

Research on Capacity Allocation Optimization of Commercial Virtual Power Plant (CVPP)

Research on Capacity Allocation Optimization of Commercial Virtual Power Plant (CVPP)

A Deep Q -Network-Based Collaborative Control Research for Smart Ammunition Formation

TLSCA-SVM Fault Diagnosis Optimization Method Based on Transfer Learning

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Product

Resources

About

A Deep $Q$ -Network-Based Collaborative Control Research for Smart Ammunition Formation