A brief review of techniques for fault diagnosis in microgrids with high contribution of solar energy is discussed in [6]. A method consisting of Hilbert-Huang transform (HHT) and decision tree to
This paper presents a review on the MG fault diagnosis techniques with their limitations and proposes a novel discrete-wavelet transform (DWT) based probabilistic generative model to explore the precise solution for
Fault detection, classification and location methods are reviewed for microgrid application and different methods applied for both fault location and fault classification are being classified by
Keywords: smart microgrid, fault diagnosis, VMD, CNN, De. Citation: Cao Y, Cheng X and Zhang Q (2022) An improved method for fault diagnosis of rolling bearings of power generation equipment in a smart
Accurate fault classification and detection for the microgrid (MG) becomes a concern among the researchers from the state-of-art of fault diagnosis as it increases the chance to rise the transient
This paper proposes an error-correcting particle swarm optimization back propagation microgrid fault diagnosis method for the diagnosis of short-circuit faults in microgrids that identifies the accuracy of alarm signals,
Accurate fault classification and detection for the microgrid (MG) becomes a concern among the researchers from the state-of-art of fault diagnosis as it increases the chance to rise the transient response.
The fault diagnosis methods for microgrid can be divided into three types:model-driven method, knowledge rule-driven method and data-driven method . The model-based method needs a deep understanding of the system model and lays a high requirement based on mathematics.
A cloud–edge framework-based intelligent fault diagnosis method for the microgrid is presented in this paper. An intelligent fault diagnosis platform is constructed based on the CloudPSS.Theoretical analyses and test results show the effectiveness of the proposed method. Besides, the proposed method is economical and reliable.
By includingheterogeneous sensors throughout the micro-grid, many fault detection and isolation methods can be developed to provide early indication of faults in the micro-grid infrastructure. For example, vibration or strain sensors could be installed along the transmission lines to monitor if unhealthy loads are passing through the lines.
A fault diagnosis method based on variational sparse Bayesian is proposed. From the two perspectives of weak feature extraction and nonlinear feature quantitative analysis, the time frequency and decomposition spectrum are used to analyse the fault signal, so as to realize the fault diagnosis of microgrid.
A level of considered noise is added with the sample data to test the robustness of the studied model. Results prove that the proposed fault detection and classification model has the ability to perform the precise diagnosis of MG faults.
