The wind turbine power curve is an important indicator of the performance of a wind turbine. Modeling and monitoring the power curve can detect wind turbine operating abnormalities and degradation in a timely manner. This paper firstly points out the drawbacks of the standard binned power curve modeling method of IEC-61400-12-1. Multiple factors that influence the wind energy capture and power output of a wind turbine are analyzed in detail and used as the power curve model inputs. A multivariable power curve model is constructed with a modified Cholesky decomposition Gaussian Process (GP) and validated using wind turbine SCADA data. A Sequential Probability Ratio Test (SPRT) with two groups of hypotheses is introduced to analyze and detect abnormal changes in GP power curve prediction residuals and thus detect abnormal operation. In order to locate failed components when an alarm is identified, longitudinal and transverse data comparisons are proposed to check the operation of specific components. The modeling and monitoring methods proposed in this paper successfully identify faults and locate the faulty component for two wind turbines with anemometer failure and pitch system failure respectively.

Condition Monitoring (CM) of wind turbines can greatly reduce the maintenance costs for wind farms, especially for offshore wind farms. A new condition monitoring method for a wind turbine gearbox using temperature trend analysis is proposed. Autoassociative Kernel Regression (AAKR) is used to construct the normal behavior model of the gearbox temperature. With a proper construction of the memory matrix, the AAKR model can cover the normal working space for the gearbox. When the gearbox has an incipient failure, the residuals between AAKR model estimates and the measurement temperature will become significant. A moving window statistical method is used to detect the changes of the residual mean value and standard deviation in a timely manner. When one of these parameters exceeds predefined thresholds, an incipient failure is flagged. In order to simulate the gearbox fault, manual temperature drift is added to the initial Supervisory Control and Data Acquisitions (SCADA) data. Analysis of simulated gearbox failures shows that the new condition monitoring method is effective.

With appropriate vibration modeling and analysis the incipient failure of key components such as the tower, drive train and rotor of a large wind turbine can be detected. In this paper, the Nonlinear State Estimation Technique (NSET) has been applied to model turbine tower vibration to good effect, providing an understanding of the tower vibration dynamic characteristics and the main factors influencing these. The developed tower vibration model comprises two different parts: a sub-model used for below rated wind speed; and another for above rated wind speed. Supervisory control and data acquisition system (SCADA) data from a single wind turbine collected from March to April 2006 is used in the modeling. Model validation has been subsequently undertaken and is presented. This research has demonstrated the effectiveness of the NSET approach to tower vibration; in particular its conceptual simplicity, clear physical interpretation and high accuracy. The developed and validated tower vibration model was then used to successfully detect blade angle asymmetry that is a common fault that should be remedied promptly to improve turbine performance and limit fatigue damage. The work also shows that condition monitoring is improved significantly if the information from the vibration signals is complemented by analysis of other relevant SCADA data such as power performance, wind speed, and rotor loads.

Wind turbine condition-monitoring and fault diagnosis have important practical value for wind farms to reduce maintenance cost and improve operating level. Due to the special distribution law of the operating parameters of similar turbines, this paper compares the instantaneous operation parameters of four 1.5 MW turbines with strong correlation of a wind farm. The temperature-power distribution of the gearbox bearings is analyzed to find out the main trend of the turbines and the deviations of individual turbine parameters. At the same time, for the huge amount of data caused by the increase of turbines number and monitoring parameters, this paper uses the huge neural network and multi-hidden layer of a convolutional neural network to model historical data. Finally, the rapid warning and judgment of gearbox bearing over-temperature faults proves that the monitoring method is of great significance for large-scale wind farms.

Based on an analysis of the working principles of the hydraulic variable pitch system of a wind turbine, a novel Petri net model and reliability evaluation method are proposed. First, Petri net theory is adopted to build a model for each discrete state of the operation of the hydraulic pitch system of the wind turbine and at the same time a fault Petri net model is established. Then through qualitative analysis and quantitative calculations based on the fault Petri net, the system reliability indexes are obtained. During the qualitative analysis process, in order to more conveniently find the minimal cut sets of the fault Petri net, a Visual C++ 6.0-based algorithm is compiled and the minimal cut sets are tested correctly with another method. During the quantitative calculation process, the fault probability has been obtained from the equations according to the fault probability of libraries and transitions between different states. Not only does the proposed Petri net describe the structure, function and operation of the hydraulic pitch system with a graphic language, but the fault Petri net model can also clearly express the logical relations among faults. The novel Petri net model offers simple calculations and the prospect of broad applicability and the new reliability evaluation method provides an important reference for the performance evaluation of these systems.

CSTR is a nonlinear chemical reactor widely used in chemical industry and can be simplified as an affine nonlinear system. Hopfield network is a neural network with rich dynamic characteristics. In this paper, affine nonlinear system is treated as black box, and is identified with Hopfield network. After obtaining the relative degree of the nonlinear system from the network, state feedback linearization method is used to transform CSTR to a one-order linear system. The state variables and Lie derivatives needed in the transform can be obtained from the Hopfield network. Finally, a PFC controller is designed to control the linear system. Simulations prove that the new method has good control performance