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This study combined a Convolutional Neural Network (CNN) with the chaos theory and the Empirical Mode Decomposition (EMD) method for the attenuation fault recognition of power capacitors. First, it built six capacitor analysis models, including normal capacitors, failed capacitors, and normal capacitors attenuated by 20–80%. Then a power testing machine was used for an applied voltage test of the capacitor. The EMD method was combined with the chaos synchronisation detection method to chart the discharge signals of the voltage and current that was captured by a high frequency oscilloscope into a 3D chaotic error scatter plot, as the fault diagnosis feature image. Finally, the CNN algorithm was used for the capacitor fault detection. The advantages of the proposed method are that big data are compressed to extract meaningful feature images, the operating state of the power capacitor can be detected effectively, and faults can be diagnosed according to the electrical signal change of the power capacitor. The actual measurement results showed that the accuracy of the proposed method was as high as 97% and has a high efficiency of noise rejection ability, which indicates that the method could be applied to other power‐related fields in the future. [ABSTRACT FROM AUTHOR]

The detection of islanding effect is a highly important topic for photovoltaic (PV) power system. The islanding effect occurs when the distributed power source is disconnected from the main supply while the power is still supplied in partial load area, which may injure the set maintenance personnel or damage the equipment. Combining chaotic synchronization and extension theory, this research is to propose a novel detection method to distinguish the occurrence of islanding effect based on nonautonomous Chua’s circuit. To demonstrate the effectiveness of the proposed method, this paper applies PSIM to simulate the PV power system. The experimental results show that the accuracy of the proposed method achieves 98% on islanding effect. [ABSTRACT FROM AUTHOR]

This study develops and applies a chaos synchronization-based detection method for an engineering application of monitoring power quality disturbance. The new method can detect minor dynamic changes in signals. Likewise, prominent characteristics of system signal disturbance can be extracted by this technique. The method is then combined with the extension recognition algorithm to accurately apply to signal clustering of power disturbance. According to extensive computer simulation results and a comparison among three typical chaotic systems, it is confirmed that the proposed method is well applicable using various chaotic systems, mostly with very high accuracies. As compared with other traditional methods, the new method is shown to have higher accuracy, faster computing speed and better expandability. It is foreseen that if the method can be implemented by system-on-chip in the near future, it will find many real engineering applications such as hand-held power quality analyzers and auxiliary means for on-line real-time detection, among others. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]