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To more precisely assess the reliability of wind turbine gears, a competing failure model is established with consideration given to the degradation of internal fatigue strength and external random shocks. Firstly, the nonlinear Gamma process is adopted to depict the degradation of fatigue strength, and the strength‐stress interference theory is used to establish the threshold of degradation failure that changes over time. Also, the non‐homogeneous Poisson process is employed to depict the arrival of the shock, and classification of the shocks. Secondly, the correlation between degradation and shock is described, while the effect of shocks on degradation is demonstrated as a sudden increase in the extent of degradation. Besides, the decline rate of residual strength curve is introduced to describe the change in intensity parameters of the Poisson process, and then the effect of degradation on shocks is described. Finally, the sun gear in the wind turbine gearbox is an example. Monte Carlo simulation is used to calculate the reliability of the dependent competing failure model. This is then compared with the conventional stress‐strength interference model. The results verify that when the relationship between degradation and the shock process is disregarded, the reliability of gears may be overestimated. [ABSTRACT FROM AUTHOR]

Accurate status prediction is critical for detecting potential failures of Doubly‐fed induction generator (DFIG). A unique Digital Twin (DT) fault prediction paradigm is put forth in this research, which uses an edge intelligence paradigm to ensure high‐performance fault prediction. Edge‐based DT provides compute and storage capabilities on edge devices to enable effective data processing. The stator current spectrum and instantaneous power spectrum of the DFIG are simulated and assessed, followed by the development of a verification prototype on the DFIG and the qualitative observation of the internal performance of the DFIG. The encoder is constructed using Squeeze‐and‐Excitation (SE) Convolutional Neural Network (SE‐CNN) with the channel attention mechanism, and the decoder is constructed based on a long short‐term memory (LSTM) network to form the self‐encoding model named SE‐CNN‐LSTM. In order to forecast problems and provide a foundation for predictive maintenance, the status indicator is built based on the prediction residuals of the status variables. Based on the outcomes of edge‐cloud co‐simulation and data collected from the Supervisory Control and Data Acquisition system at the Da Bancheng Wind Farm in Xinjiang, China, it is demonstrated that the framework and technology are workable and capable of predicting generator failures. [ABSTRACT FROM AUTHOR]

The high penetration of uncertain resources challenges the security of power system operation. By taking the impact of rescheduling under contingencies into consideration, reliability‐constrained unit commitment (RCUC) is developed to address this challenge. Although several efforts have been made in modelling reliability constraints, the existing methods can only manage oversimplified low‐order temporal‐independent contingencies without considering wide‐range contingencies or their state‐transition‐process issue. To quantify the impact of rescheduling on the normal‐state scheduling process denoted by UC problem, this paper builds up a Bayesian inference method for encoding reliability constraints in wide‐range temporal‐dependent contingencies. Three predictors, for example, expected‐generator‐rescheduling‐power, expected‐energy‐not‐serviced and lost‐of‐load‐probability, are selected to describe the possible corrective behaviours in rescheduling process and quantified by using Bayesian inference method. Then, these predictors are reformatted as a set of linearized constraints to be incorporated into UC. The proposed RCUC comprehensively considers the effect of rescheduling in wide‐range temporal‐dependent contingencies. Therefore, it can reveal the influence of generator rescheduling in wide‐range contingencies and keep better reliability performance than those methods reported in previous RCUC studies. The modified IEEE 30‐bus test system and IEEE 118‐bus test system are used to show the proposed model's effectiveness. [ABSTRACT FROM AUTHOR]

Estimation of multiple directed graphs becomes challenging in the presence of inhomogeneous data, where directed acyclic graphs (DAGs) are used to represent causal relations among random variables. To infer causal relations among variables, we estimate multiple DAGs given a known ordering in Gaussian graphical models. In particular, we propose a constrained maximum likelihood method with nonconvex constraints over elements and element-wise differences of adjacency matrices, for identifying the sparseness structure as well as detecting structural changes over adjacency matrices of the graphs. Computationally, we develop an efficient algorithm based on augmented Lagrange multipliers, the difference convex method, and a novel fast algorithm for solving convex relaxation subproblems. Numerical results suggest that the proposed method performs well against its alternatives for simulated and real data. Statistical Analysis and Data Mining 2011 DOI: 10.1002/sam.11168 [ABSTRACT FROM AUTHOR]