a. dameshghi

Renewable energy, like wind turbines, is growing rapidly in the world today due to environmental pollution, so their maintenance plans are very important. Fault diagnosis and fault-tolerant approaches are typical methods to reduce the cost of energy production and downtime of Wind Turbines (WTs). In this paper, a new Hardware In the Loop (HIL) simulator based on Double Feed Induction Generator (DFIG) for fault diagnosis and fault-tolerant control is proposed. The system developed as a laboratory bed uses a generator with a power of about 90 kW, which is connected from two sides to a back-to-back power converter with a power of one-third of the generator power. The generator is connected to a motor as a propulsion and wind energy replacement with a power of about 110 kW, and this connection is established through a gearbox with a gear ratio of more than three. The effectiveness of the proposed simulator is evaluated based on different fault representations back-to-back converter and generator. The experiment shows that the Condition Based Maintenance (CBM) is improved by the proposed simulator and the fault is modeled before serious damage occurs. This setup is effective for the development of wind turbine fault analysis software. As the testing on real WTs is very expensive, to improve and develop the research fields of condition monitoring and WT control, this low-cost setup is effective.

One of the major faults in Doubly-Fed Induction Generator (DFIG) is the Inter-Turn Short Circuit (ITSC) fault. This fault leads to an asymmetry between phases and causes problems to the normal state between current lines. Faults diagnosis from non-stationary signals for the Wind Turbine (WT) is difficult. Therefore, the strategy of fault diagnosis must be robust against instability. In this paper, a new intelligent strategy based on multi-level fusion is proposed for diagnosis of DFIG inter-turn stator winding fault. Firstly, to overcome the non-stationary nature of the vibration signals of the WT, empirical mode decomposition (EMD) method is performed in time-frequency domains to extract best fault features from information power sensor and information current sensor. Moreover, a feature evaluation technique is used for the input of the classifier to choose the best subset features. Secondly, Least Squares Wavelet Support Vector Machines (LS-WSVM) classifier is trained to classify fault types based on feature level fusion (FLF) from different sensors. The main parameters of SVM and the kernel function are optimized by Genetic Algorithm (GA). Finally, Dempster-Shafer evidential reasoning (DSER) is used for fusing the GA-LS-WSVM results based on decision level fusion (DLF) of individual classifiers. In order to evaluate the proposed strategy, a DFIG WT test rig is developed. The experimental results show the efficiency of the proposed structure compared to other ITSC fault diagnosis methods. The results show that the classification accuracy of DSER-GA-LS-WSVM is 98.27%.

In this paper, a new method is provided for Fault-Tolerant Control (FTC) of wind turbine pitch systems. One of the common faults in wind turbines is the defects of the pitch sub-system. Each blade of wind turbines tracks a reference signal; it is generated by the main controller unit, defects of actuators, or disturbance decrease of the reference signal quality. Classic controllers cannot deal with the disturbance and compensate for the faults to maintain system performance in normal operating conditions. For this purpose, a novel method based on Optimal Robust Model Reference Adaptive Control (ORMRAC) is presented, the output of the proposed method is a new adaptive rule. The ORMRAC method is robust, optimal, and fast at the same time. The proposed structure includes Fault Detection (FD) and FTC units. FD acts based on the generation and evaluation of residuals. The residual generation is based on Artificial Neural Network (ANN) model. When there is disturbance or fault in the pitch system and residual exceeds the certain threshold, the FT unit is activated. The proposed FT method is tested and evaluated using a wind turbine simulator based on practical data. The results indicated the proper performance of the proposed method in comparison with conventional MRAC and some other methods.

Geometric Dilution of Precision (GDOP) is a coefficient for constellations of Global Positioning System (GPS) satellites. These satellites are organized geometrically. Traditionally, GPS GDOP computation is based on the inversion matrix with complicated measurement equations. A new strategy for calculation of GPS GDOP is construction of time series problem; it employs machine learning and artificial intelligence methods for problem-solving. In this paper, the Time Delay Neural Network (TDNN) is introduced to the GPS satellite DOP classification. The TDNN has a memory for archiving past event that is critical in GDOP approximation. The TDNN approach is evaluated all subsets of satellites with the less computational burden. Therefore, the use of the inverse matrix method is not required. The proposed approach is conducted for approximation or classification of the GDOP. The experiments show that the approximate total RMS error of TDNN is less than 0.00022 and total performance of satellite classification is 99.48%.