Journal article Open Access
Introduction. To attain high efficiency and reliability in the field of clean energy conversion, power electronics play a significant role in a wide range of applications. More effort is being made to increase the dependability of power electronics systems. Purpose. In order to avoid any undesirable effects or disturbances that negatively affect the continuity of service in the field of energy production, this research provides a fault detection technique for insulated-gate bipolar transistor open-circuit faults in a three-level diode-clamped inverter of a wind energy conversion system predicated on a doubly-fed induction generator. The novelty of the suggested work ensures the regulation of power exchanged between the system and the grid without faults, advanced intelligence approaches based on a multilayer artificial neural network are used to discover and locate this type of defect; the database is based on the module and phase angle of three-phase stator currents of induction generators. The proposed methods are designed for the detection of one or two open-circuit faults in the power switches of the side converter of a doubly-fed induction generator in a wind energy conversion system. Methods. In the proposed detection method, only the three-phase stator current module and phase angle are used to identify the faulty switch. The primary goal of this fault diagnosis system is to effectively detect and locate failures in one or even more neutral point clamped inverter switches. Practical value. The performance of the controllers is evaluated under different operating conditions of the power system, and the reliability, feasibility, and effectiveness of the proposed fault detection have been verified under various open-switch fault conditions. The diagnostic approach is also robust to transient conditions posed by changes in load and speed. The proposed diagnostic technique's performance and effectiveness are both proven by simulation in the SimPower /Simulink® MATLAB environment.
Diagnosis and localization of fault for a neutral point clamped inverter in wind energy conversion system using artificial neural network technique.pdf
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