This study proposes a method for detecting possible faults in wind turbine systems in advance such that the operating state of the fan can be changed or appropriate maintenance steps taken. In the proposed method, a chaotic synchronisation detection method is used to transform the vibration signal into a chaos error distribution diagram. The centroid (chaotic eye) of this diagram is then taken as the characteristic for fault diagnosis purposes. Finally, a grey prediction model is used to predict the trajectory of the feature changes, and an extension theory pattern recognition technique is applied to diagnose the fault. Notably, the use of the chaotic eye as the fault diagnosis characteristic reduces the number of extracted features required, and therefore greatly reduces both the computation time and the hardware implementation cost. From the experimental results, it is shown that the fault diagnosis rate of the proposed method exceeds 98%. Moreover, it is shown that for oil leaks in the gear accelerator system, the proposed method achieves a detection accuracy of 90%, whereas the multilayer neural network method achieves a maximum accuracy of just 80%.
The detection of islanding effect is one of the important issues for photovoltaic (PV) power system since islanding is dangerous to utility equipment and workers, and result in severe injuries and death. The novelty of this study is combining extension neural networks and Chaos synchronisation (CS) on islanding detection of grid connected PV systems based on non-autonomous Chua's circuit. Combining CS and extension neural network type-2 (ENN-2), this research is to propose a novel detection method at detecting and distinguishing the occurrence of islanding effect based on non-autonomous Chua's circuit. Simulation and experimental designs through powersim (PSIM) were applied to mimic PV power system to demonstrate the effectiveness of the proposed method. Results show that the accuracy of ENN-2 achieves 98.4% on detecting the islanding effect for PV power system.
With the rapid development of solar energy, the photovoltaic (PV) module fault detection plays an important role in knowing how to enhance the reliability of the solar photovoltaic system and knowing the fault type when a system problem occurs. Therefore, this paper proposed the hybrid algorithm of chaos synchronization detection method (CSDM) with convolutional neural network (CNN) for studying PV module fault detection. Four common PV module states were discussed, including the normal PV module, module breakage, module contact defectiveness and module bypass diode failure. First of all, the defects in 16 pieces of 20W monocrystalline silicon PV modules were preprocessed, and there were four pieces of each fault state. When the signal generator delivered high frequency voltage to the PV module, the original signal was measured and captured by the NI PXI-5105 high-speed data acquisition system (DAS) and was calculated by CSDM, to establish the chaos dynamic error map as the image feature of fault diagnosis. Finally, the CNN was employed for diagnosing the fault state of the PV module. The findings show that after entering 400 random fault data (100 data for each fault) into the proposed method for recognition, the recognition accuracy rate of the proposed method was as high as 99.5%, which is better than the traditional ENN algorithm that had a recognition rate of 86.75%. In addition, the advantage of the proposed algorithm is that the mass original measured data can be reduced by CSDM, the subtle changes in the output signals are captured effectively and displayed in images, and the PV module fault state is accurately recognized by CNN.
This study applied an extension algorithm combined with the Chaos Theory to the fault diagnosis of the three-phase synchronous generator. First, the three-phase synchronous generator is classified, including normal, carbon brush fault, three-phase unbalance, and insulation deterioration, and then by means of hardware measurement circuit and device, electrical signals are measured for each category and a chaotic error scatter map is built through the Chaos Theory to get the chaotic eye coordinates under specific fault categories. Next, the extension algorithm is used to carry out the correlation function and the normalization calculation, evaluating the type of fault to which it belongs. The analysis results show that the proposed method can effectively identify the fault types of three-phase synchronous generators and significantly reduce the amount of feature extraction data, so as to effectively detect the change of fault signals, allowing us to know the operation state of three-phase synchronous generators.
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