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The data loss and corresponding false fault features of single-phase or multiphase detection signals caused by sensors is a relatively troublesome problem, which can increase the difficulty to the fault diagnosis of switch fault and open-phase fault of inverter, and even can lead to false alarm. For these problems, a complementary virtual mirror fault diagnosis method for microgrid inverter is proposed to improve them. First, the virtual mirrors are constructed, which contain fault information in different angle. Second, a cross comparison processing method is used to gain the cross variables and corresponding mirror cross variables by the obtained virtual mirrors and detected signals, which can extract and normalize fault components from different angle in the same way, respectively. Thus, it can reduce the impact of data loss. Third, the self-correction fault degree (scf) and averages fault degree (af) are calculated. Specially, scf can further reduce the influence of data loss and false fault features through the complementarity of cross variables and mirror cross variables. For af, it is a steady-state expression of fault degree, which can reduce the fluctuation of fault degree variable and the possibility of false alarm. Then, fault detection is implemented through the joint decision of scf and af. Finally, the fault is located by the fault detection results and the extracted mirror complementary location information. Compared with the existing fault diagnosis methods, it not only can diagnose switch fault of inverter, but also can have certain robustness for single-phase or multiphase data loss. Meanwhile, it also has relatively fewer parameters and the lower difficulty of debugging, which are conducive to practical application. The effectiveness of the proposed method is validated by the experiment results.

In this article, a robust inverter fault diagnosis algorithm is proposed under microgrid environment considering unbalanced state and overcurrent component interference. First, the detected signals are transformed into different data triangles by the proposed slidable triangularization processing which can conveniently and effectively extract the multiscale trend features and data jitter components. Further, the signed features are obtained by the proposed jitter signing processing method, which can reduce the influence of amplitude, and facilitate the logical operation and mixed operation. Then, a quantitative trend fault degree is calculated by the signed information to reflect the fault degree and location information, which has the characteristics of quantitative change. It can reduce the fluctuation and quantity of fault data features. Finally, echo state network is used to implement the intelligent classification. Because the design of hidden layer and the network training speed is simple and fast, which makes the design, implementation, and debugging of fault diagnosis more convenient. Compared with the existing fault diagnosis algorithms, it is robust to unbalanced state and overcurrent component interference of microgrid. These features make it more suitable for inverter fault diagnosis of microgrid environment. The effectiveness of fault diagnosis algorithm is verified by the experiments and comparisons.

Fault diagnosis of a microgrid inverter is susceptible to asymmetric interference such as overcurrent component and bias component. It may lead to uncertain fluctuation of diagnosis features, even false alarm and erroneous triggering of protection units. Therefore, for a microgrid inverter, a robust fault diagnosis algorithm is necessary to cope with such asymmetric interference. Motivated by this observation, first, a new fault feature extraction method is proposed in this paper by the trend relationship of adjacent fold lines for data curve. It can be used to extract the fault feature and it is not affected by asymmetric interference. Second, a trend encoding method is proposed to encode the trend feature of a fold line, which is well visualized, easily identified, stored, and calculated. Third, two main indexes are calculated by the encoded features to represent the degree of abnormal and location information. Fourth, the diagnosis results are realized through the logical relation operations. Compared with the existing fault diagnosis algorithms, multiswitches fault can be accurately diagnosed in the case of asymmetric interference. Finally, the detailed experiment results and comparisons are shown to validate the proposed algorithm.

For most existing aperiodic fault diagnosis algorithms of microgrid inverter, because of the common aperiodic processing features, they have relatively higher amount of algorithm startup, calculation, and complexity. These features increase the hardware requirements and realization difficulty, greatly affect the practicability. In order to improve above-mentioned problems, a practical fault diagnosis algorithm is investigated. In this paper, first, aperiodic corrected-second low-frequency processing method is proposed to get aperiodic small low-frequency data (ASLFD) by a simple way in the real time, which greatly reduces the amount of algorithm startup and corresponding calculation. Second, these ASLFD are processed by the real-time normalization method. Next, the degree of asymmetry and distortion degree of root mean square are extracted, respectively. Furthermore, the feature variables and results are realized through the logical operations. Compared with the existing fault diagnosis algorithms, the proposed algorithm has lower amount of startup and calculation, smaller complexity, and easy realization, which are conducive to practical applications. The detailed experimental results and comparisons are shown to validate the proposed algorithm.

For the inverter fault diagnosis of permanent magnet synchronous machine drives, it is difficult to simply and conveniently use the small low-frequency data without needing additional hardware and affecting the accuracy of the diagnosis results. This problem often leads to unnecessary computation and wastes a lot of corresponding system memory, increases the hardware requirements and realization difficulty, and affects practicability. In order to improve this problem, to make the design, debugging, and implementation of fault diagnosis more convenient and at low cost, and further to improve the practicability of the algorithm, a novel multi-switches fault diagnosis algorithm is proposed. First, a second low frequency processing method is used to obtain the small low-frequency data simply from the feedback signals of the controller. These small low-frequency data contain the main feature information of different switch states. Second, these small low-frequency data are effectively normalized by the single extremum normalization method, which is based on the symmetry features of different state data. These processed data can keep the asymmetry feature of the fault state of an inverter very well. Third, the main fault components and features are extracted from the distortion part and envelope change of the processed small low-frequency data. Fourth, echo state network is used by combining it with the extracted features to implement the intelligent classification. Different from the traditional neural network, the design of structure of hidden layer network is simplified, and the network training speed is very fast. Therefore, the debugging of this network is very convenient. Compared with the existing fault diagnosis algorithms, the proposed algorithm is convenient and at low cost to realize the multi-switches fault diagnosis. The effectiveness of fault diagnosis algorithm is verified by the experiment.

This paper presents a new open-circuit fault diagnosis algorithm for multiple switch of microgrid inverter in different load change condition. From the analysis of the waveform features and frequency composition for the main fault component under the different switch states, a low frequency sampling principle of the main fault component is developed. For the proposed fault diagnosis algorithm, first, based on above principle, small sampling data are obtained by the second low frequency mean sample processing, which can be used to reflect the main component features of different switch states and reduce the amount of the processed data. Because the data of the second low frequency mean sample processing comes from the obtained system data, it does not add any additional hardware and has the lower cost. Moreover, the obtained low frequency sampling data are divided into different feature data to adopt corresponding data processing scheme. Thus, the information of the main fault component is retained and the impact of load change is removed for any feature data. Furthermore, the feature values of the processed low frequency sampling data are extracted from the viewpoint of data attenuation. Finally, neural network is used to implement intelligent classification. The dependency and the number of threshold can be reduced, and low frequency sampling data are fully utilized to reflect the overall features. Compared with the existing fault diagnosis algorithms, the actual computational quantity is effectively reduced without affecting the accuracy and stability of the diagnosis results for microgrid inverter under load change condition. Finally, the effectiveness of fault diagnosis algorithm is verified through the detailed simulation and experiment results.

In this paper, a multiscale adaptive fault diagnosis (MAFD) method based on signal symmetry reconstitution preprocessing (SSRP) is proposed to realize fault diagnosis for any switch of the microgrid inverter under changing load condition. The MAFD method is composed of SSRP, multiscale features extraction, and artificial neural network (ANN). First, the SSRP method is used to generate the input signals of multiscale features extraction, which can reduce the impact of the changing load. Then, the multiscale features extraction is realized by the means of multilevel signal decomposition and coefficients reconstruction to extract energy content of different frequency groups signal. It can represent the detailed signal change laws at different levels for three-phase current. Finally, in order to achieve data-based adaptive fault diagnosis, ANN is used to detect the type and the location of the inverter switch fault. Compared to conventional fault diagnosis methods, the proposed fault diagnosis method can accurately detect and locate fault for any switch of the microgrid inverter under changing load condition. The effectiveness of the proposed fault diagnosis method is verified through detailed simulation and experimental results.

Multilevel feature moving average ratio method is proposed to realize an open-switch fault diagnosis for any switch of the microgrid inverter. The main steps of the proposed method include multilevel signal decomposition, coefficient reconstruction, absolute average ratio process and artificial neural network U+0028 ANN U+0029 classification. Specifically, multilevel signal decomposition is realized by using the means of multi resolution analysis to obtain the different frequency band coefficients of the three-phase current signal. The related coefficient reconstruction is executed to achieve signals decomposition in different levels. Furthermore, according to the obtained data, the absolute average ratio process is used to extract absolute moving average ratio of signal decomposition in different levels for the three-phase current. Finally, to intelligently classify the inverter switch fault and realize the adaptive ability, the ANN technology is applied. Compared to conventional fault diagnosis methods, the proposed method can accurately detect and locate the open-switch fault for any location of the microgrid inverter. Additionally, it need not set related threshold of algorithm and does not require normalization process, which is relatively easy to implement. The effectiveness of the proposed fault diagnosis method is demonstrated through detailed simulation results.