Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms - A Review

In the wind energy industry, the power curve represents the relationship between the “wind speed” at the hub height and the corresponding “active power” to be generated. It is the most versatile condition indicator and of vital importance in several key applications, such as wind turbine selection, capacity factor estimation, wind energy assessment and forecasting, and condition monitoring, among others. Ensuring an effective implementation of the aforementioned applications mostly requires a modeling technique that best approximates the normal properties of an optimal wind turbines operation in a particular wind farm. This challenge has drawn the attention of wind farm operators and researchers towards the “state of the art” in wind energy technology. This paper provides an exhaustive and updated review on power curve based applications, the most common anomaly and fault types including their root-causes, along with data preprocessing and correction schemes (i.e., filtering, clustering, isolation, and others), and modeling techniques (i.e., parametric and non-parametric) which cover a wide range of algorithms. More than 100 references, for the most part selected from recently published journal articles, were carefully compiled to properly assess the past, present, and future research directions in this active domain

Multivariate Relevance Vector Regression based Degradation Modeling and Remaining Useful Life Prediction

Relevance Vector Regression (RVR) is a useful tool for degradation modeling and Remaining Useful Life (RUL) prediction. However, most RVR models are for one-dimensional degradation processes and can only handle univariate observations. This paper proposes a degradation path based RUL prediction framework using a dynamic Multivariate Relevance Vector Regression (MRVR) model. Specifically, a multi-step regression model is established for describing the degradation dynamics and extends the classical RVR into a multivariate one with consideration of the multivariate environment. The paper introduces a matrix Gaussian distribution based RVR approach and then estimates the hyperparameters with Nesterov's accelerated gradient method to avoid the exhausting re-estimation phenomenon in seeking analytical solutions. It further forecasts the degradation path for monitoring the degradation status. Based on the forecasted path, the RUL is predicted by the First Hitting Time (FHT) method. Finally, the proposed methods are illustrated by two case studies, one is presented in the paper and the other in the supplement, both of which investigate the capacitors' performance degradation in the traction systems of high-speed trains

Stage-based online quality control for batch processes

A stage-based quality control scheme, which combines an online adjustment strategy with the authors' previous works on stage partial least squares (PLS) modeling and quality prediction, is developed for within-batch control of end-product quality for batch processes. Considering the inherent time-specific nature of process trajectories to the end-product quality, a critical-to-quality-control stage is introduced for quality control and stability improvement, together with guidelines on the manipulating variable selection and no-control region. The effectiveness and feasibility of the proposed scheme are illustrated on an injection molding process

Stage-based process analysis and quality prediction for batch processes

A process analysis and quality prediction scheme is proposed based on a stage-based PLS modeling for batch processes. Without any requirement of prior process knowledge, the scheme first divides a batch process into stages of different process characteristics. Subsequently, a strategy is developed to identify stages that have critical influences on concerned qualities, defined as critical-to-quality stages. Within these critical-to-quality stages, an algorithm is then further developed to identify the variables that have significant contributions to the quality variations. Finally, based on the identified nature of quality and stage relationships, a set of algorithms is developed for online quality prediction. The applications of the proposed scheme to injection molding show that the proposed analysis and quality prediction are not only effective but are also able to enhance process understanding and identify specific variables and periods for quality improvement

Data-Driven Incipient Sensor Fault Estimation with Application in Inverter of High-Speed Railway

Incipient faults in high-speed railway have been rarely considered before developing into faults or failures. In this paper, a new data-driven incipient fault estimate (FE) methodology is proposed under multivariate statistics frame, which incorporates with Kullback-Leibler divergence (KLD) in information domain and neural network approximation in machine learning. By defining one sensitive fault indicator (SFI), the incipient fault amplitude can be precisely estimated. According to the experimental platform of China Railway High-speed 2 (CRH2), the proposed incipient FE algorithm is examined, and the more sensitivity and accuracy to tiny abnormality are demonstrated. Followed by the incipient FE results, several factors on FE performance are further analyzed

Combination method of principal component and wavelet analysis for multivariate process monitoring and fault diagnosis

Product quality and operation safety are important aspects of industrial processes, particularly those with large numbers of correlated process variables. Principal component analysis (PCA) has been widely used in multivariate process monitoring for its ability to reduce process dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults with similar time-domain process characteristics. A wavelet-based time-frequency approach is developed in this paper to improve PCA-based methods by extending the time-domain process features into time-frequency information. Subsequently, a similarity measure is presented to compare process features for on-line process monitoring and fault diagnosis. Simulation results show that the proposed multivariate time-frequency process feature is effective in both fault detection and diagnosis, illustrating the potentials for real-world application