Fast and simple open-circuit fault detection method for interleaved DC-DC converters

Photovoltaic (PV) power generation systems work chronically in various climatic outdoor conditions, therefore, faults may occur within the PV arrays in PV systems. Online fault detection for the PV arrays are important to improve the system’s reliability, safety and efficiency. In view of this, a fault-detection method based on voltage and current observation and evaluation is presented in this paper to detect common PV array faults, such as open-circuit, short-circuit, degradation and shading faults. In order to develop this detection method, fault characteristic quantities (e.g., the open-circuit voltage, short-circuit current, voltage and current at the maximum power point (MPP) of the PV array) are identified first to define the voltage and current indicators; then, the fault-detection thresholds are defined by utilizing voltage and current indicators according to the characteristic information of various faults; finally, voltage and current indicators evaluated at real-time voltage and current data are compared with the corresponding thresholds to detect potential faults and fault types. The performances of the proposed method are simulated verifying by setting eight different fault patterns in the PV array. Simulation experimental results show the effectiveness of the proposed method, especially the capacities of distinguishing the degradation faults, partial shading faults and variable shading faults.

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Asymmetric Multilevel Inverter Topology and Its Fault Management Strategy for High-Reliability Applications

Energies ◽

10.3390/en14144302 ◽

2021 ◽

Vol 14 (14) ◽

pp. 4302

Author(s):

Mohammad Fahad ◽

Mohd Tariq ◽

Adil Sarwar ◽

Mohammad Modabbir ◽

Mohd Aman Zaid ◽

...

Keyword(s):

Fault Tolerance ◽

Fault Detection ◽

Detection Method ◽

Fault Tolerant ◽

High Reliability ◽

Open Circuit ◽

Multilevel Inverter ◽

Level Control ◽

Machine Learning Classification ◽

Inverter Topology

As the applications of power electronic converters increase across multiple domains, so do the associated challenges. With multilevel inverters (MLIs) being one of the key technologies used in renewable systems and electrification, their reliability and fault ride-through capabilities are highly desirable. While using a large number of semiconductor components that are the leading cause of failures in power electronics systems, fault tolerance against switch open-circuit faults is necessary, especially in remote applications with substantial maintenance penalties or safety-critical operation. In this paper, a fault-tolerant asymmetric reduced device count multilevel inverter topology producing an 11-level output under healthy conditions and capable of operating after open-circuit fault in any switch is presented. Nearest-level control (NLC) based Pulse width modulation is implemented and is updated post-fault to continue operation at an acceptable power quality. Reliability analysis of the structure is carried out to assess the benefits of fault tolerance. The topology is compared with various fault-tolerant topologies discussed in the recent literature. Moreover, an artificial intelligence (AI)-based fault detection method is proposed as a machine learning classification problem using decision trees. The fault detection method is successful in detecting fault location with low computational requirements and desirable accuracy.

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Open-Circuit Fault Detection Method for Submodule in Modular Multilevel Converters

2018 37th Chinese Control Conference (CCC) ◽

10.23919/chicc.2018.8483747 ◽

2018 ◽

Cited By ~ 1

Author(s):

Yuqing Yang ◽

Longzhang Ke ◽

Yi Liu

Keyword(s):

Fault Detection ◽

Detection Method ◽

Open Circuit ◽

Multilevel Converters ◽

Modular Multilevel Converters

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A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset207430 ◽

2020 ◽

pp. 129-139

Author(s):

Weihai Sun ◽

Lemei Han

Keyword(s):

Fault Detection ◽

Signal Analysis ◽

Energy Analysis ◽

Detection Method ◽

Sound Signal ◽

Practical Significance ◽

Time Frequency ◽

Detection Approach ◽

Machine Fault ◽

Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

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