Hardware‐Based Open Switch Fault Diagnosis in Three‐Phase Voltage Source Inverters Using Single Neuron Implementation

Park's Vector Transform, Discrete Wavelet Transform, Artificial Neural Network, Fuzzy Logic, and other methods are used to diagnose faults in the power converter in both single and multiple open switch situations. These methods are implemented on the digital signal processor or controller, which needs additional hardware and consumes more processing time. This paper presents a hardware-based open switch fault diagnostic method in a three-phase voltage source inverter to minimize fault diagnosis time and cost. An innovative hardware-based approach that utilizes a single neuron for open switch fault diagnosis in three-phase voltage source inverters has been successfully implemented without using digital signal processor or controller. A gradient descent algorithm calculates the weight and bias values of a single processing neuron. Furthermore, a high-speed multiplier and adder circuit seamlessly integrate with the single processing neuron, enabling rapid real-time fault diagnosis. This method is capable of diagnosing single and multiple switches open circuit faults in switching devices under variable load conditions at different frequencies. The results are presented for different combinations of single and multiple open switch faults under variable load conditions at different frequencies. Compared to existing algorithms, the proposed approach significantly accelerates computation while maintaining uncompromised accuracy and stability in fault diagnostics, even under dynamic load conditions in permanent magnet synchronous motor drives. The combination of a hardware-based solution, single neuron architecture, real-time gradient descent adaptation, and rapid fault diagnosis makes this method very innovative and impactful for fault diagnostics.