## 1 Introduction  
Compared with traditional two-level inverters, T-type three-level (T\({}^{2}\)3L) inverters have higher operating efficiency and lower switching losses. These advantages have resulted in their widespread adoption in industrial applications, including renewable energy systems, electric vehicles (EVs), and uninterruptible power supplies (UPSs) [1, 2, 3, 4]. In industrial applications, the reliability and stability [5, 6, 7, 8] of power converters remain crucial factors. Industry surveys indicate that semiconductor device failures account for 38% of power converter faults [9]. Evidently, semiconductor device failures have emerged as the primary threat to the stable operation of power converters. Therefore, efficient identification of power switch faults is the core issue to ensure the stable and safe operation of T\({}^{2}\)3L inverters. Faults in power switches are primarily categorized as either short-circuit (SC) faults or open-circuit (OC) faults. SC faults typically result in extremely high abnormal currents in the circuit within a short period, causing the destruction of related components. To reduce the detrimental impact of such faults, hardware protection devices, such as fast-acting fuses, are typically used to transform SC faults into OC faults [10]. Open-circuit faults are less likely to cause serious short-time damage to the system, generally manifesting as distorted current and voltage. If not addressed promptly, these deviations may trigger secondary failures in adjacent components, potentially leading to an emergency system shutdown [11; 12]. Consequently, rapid and accurate detection and localization of OC faults are imperative. Currently, the diagnosis methods of power switches OC faults mainly involve data-driven [13; 14; 15], signal analysis-based [16; 17; 18; 19], and analytical model-based methods [20; 21; 22; 23]. Data-driven methods achieve fault classification by processing large volumes of raw data to extract hidden features and feeding this useful feature information into classifiers. Reference [13] proposed a fault diagnosis method for three-phase inverters through Bayesian networks. Phase voltage fault information is extracted using Fast Fourier Variation and combined with principal component analysis for dimensionality reduction. In [14], transfer learning was applied to enhance the generalization of diagnostic models across systems with the same topology. The initial diagnostic model is constructed using fluid feature learning combined with an extreme learning machine. Reference [15] proposed a multimodal deep residual network for fault diagnosis in a T\({}^{2}\)3L inverter. A low-rank matrix provides a unified feature representation of voltage and current, and the cross-transformer method produces fault results. However, data-driven methods heavily rely on the quantity and quality of the employed database and suffer from high algorithmic complexity and low real-time performance, hindering their widespread adoption in practical industrial applications. The signal analysis-based methods achieve fault diagnosis by extracting features from voltage or current signals and establishing their correspondence with inverter operating states. Reference [16] achieved fault diagnosis of neutral point clamped (NPC) inverters through the radius of the Concordia current pattern, but it requires approximately two fundamental periods. For T\({}^{2}\)3L inverters, reference [17] combined the neutral-point voltage with the average value of phase currents for fault diagnosis. However, under light loads, the slow response of DC-link capacitors weakens the neutral-point voltage deviation, prolonging diagnosis time. Reference [18] detected and located faulty switches using the average voltage vector and pertinent factors, including the eigenvector angle, normalized eigenvector modulus, and neutral-point potential.