# Distinction Between Interturn Short-Circuit Faults and Unbalanced Load in Transformers
###### Abstract  
Transformers are essential in electrical networks, and their failure can lead to the shutdown of a section or the entire grid. This study proposes a combination of techniques for early fault detection, distinguishing between small load imbalances and incipient interturn short circuits. An experimental setup was developed using a three-phase transformer bank with three single-phase dry-type transformers. One transformer was modified to create controlled short circuits of two and four turns and to simulate a load imbalance by reducing the winding by four turns. The main contribution of this research is the development of a combined diagnostic approach using instantaneous space phasor (ISP) spectral analysis and infrared thermal imaging to differentiate between load imbalances and incipient interturn short circuits in transformers. This method enhances early fault detection by identifying distinctive electrical and thermal signatures associated with each condition. The results could improve transformer monitoring, reducing the risk of failure and enhancing grid reliability. fault detection; fault diagnosis; turn-to-turn fault; interturn short circuit; infrared imaging; instantaneous space vector; transformers 2025 8 50  
[PERSON], R.A.  
high-voltage winding. In any case, when a fault is incipient, it is very difficult to detect, as it appears suddenly and grows gradually. Consequently, classical detection methods are ineffective until the fault becomes significant, placing the transformer at imminent risk of total failure due to short circuit [3,4]. Currently, various methodologies are used for the detection of incipient short-circuit faults, including vibration signal analysis [5], which is useful in identifying mechanical problems in transformers, but its sensitivity is limited when it comes to detecting electrical faults, especially in the early stages. The analysis of active and reactive power under no-load conditions [6] and frequency response analysis [7,8] are two methodologies that are typically applied with the transformer completely offline, enabling initial detection but not monitoring during operation. Current signal analysis [9] often struggles to distinguish between similar phenomena, such as load imbalances and early-stage faults, particularly under noisy or variable network conditions. Infrared thermal imaging analysis [10], on its own, is a useful tool, but it requires the processing of many images to detect a fault, which entails high economic and computational costs. These faults have also been studied using different methodologies involving artificial intelligence techniques [8,11,12,13], although AI-based approaches require large volumes of labeled data and may not generalize well across different transformer types or operating conditions. There is a large variety of methods for the detection of incipient turn-to-turn winding faults in transformers; however, few studies address how to distinguish between different types of faults or how to differentiate an inherent imbalance in the power grid or load from an incipient short-circuit fault. The authors of reference [14] study the distinction between internal faults and transient effects occurring in the network or load. Among the internal faults analyzed, incipient interturn short circuits are included, while the transient effects used for comparison include inrush currents, current transformer saturation, and the switching of nonlinear loads. The proposed method analyzes the waveform distortion caused by each condition, based on the duration of the event. Since these transients are brief compared to an incipient short circuit, the method can differentiate them based on their temporal profiles. The interturn short-circuit size considered in the study ranges from 20% to 60% of the winding, which corresponds to a severe short circuit, making it difficult to classify as incipient. The comparison between load imbalances and incipient interturn short circuits is also studied in [15].