# Turn-to-Turn Fault Diagnosis on Three-Phase Power Transformer Using Hybrid Detection Algorithm
## 1 Introduction  
Power transformers (PTs) are essential to maintain the stable operation of a power grid. There is a massive number of transformers in service, and internal faults occur in the long-term ageing process [1]. However, conducting maintenance with system outage or assessment of the transformer means considering the expenditure and time due to production loss. Energize levels at 765 kV, and even 400 kV, can generate inrush currents which can cause internal PT damage. There are several protection schemes required for effective discrimination between an internal fault and inrush [2]. The faulty components need to be fixed quickly as they can cause severe damage, loss, and power stability problems. Outages resulting from a fault in PTs cause significant revenue loss and affect consumer service. Therefore, online PT monitoring is needed to avoid these outages. If the faults are not detected, they will develop into more severe faults. Thus, detection of inter-turn faults is a concern in power systems [3, 4]. The result of a transformer failure survey, based on the investigation of 750 massive transformer failures from 1996 to 2010, confirmed the importance of short-circuit strength as almost half of the failures were correlated to winding and the insulating system [5, 6]. Accounting for transformer winding connections is required to apply differential protection principles to PTs. Concerning transformers of standard winding connections, these protection rules are commonly referred to as ratio matching, vector group compensation, and zero-sequence removal. The standard protection method is the differential protection current between the line-to-line. The protection method is used for protecting power transformers of 10 MVA and above [7]. However, the differential protection provides accurate results in most of the fault cases, and it suffers from the problem of accidental tripping during external events such as magnetizing inrush conditions [8]. The percentage differential scheme is used to restrainthe second harmonic component, and sometimes the fifth harmonic component, to avoid accidental tripping against magnetizing inrush conditions [2; 9]. Another problem when using conventional differential protection is the detection of the low-level winding turn-to-turn fault (TTF). If this fault happens, the terminal current is relatively unchanged, and the circulating current in the short turn area will increase and be dangerous. This protection only detects if the differential current exceeds 20% to 25% of the rated current [10]. Many methods are used to detect TTFs in PT windings. Solutions include optimization of the PT's estimation parameter problem [11; 12; 13; 14] and an artificial base using neural networks [15; 16] to detect TTFs. The optimization technique searches for the optimal PT parameter. [PERSON] et al. [17] applied the parameter identification of a transformer based on the PSO algorithm using port data. In this research, the internal transformer fault can be determined according to the model reference adaptive principle. The fitness function consists of the two models' response performance, and its corresponding circuit parameters are calculated using the PSO algorithm. The PSO algorithm is used to obtain the optimum value from the model's equivalent parameters and converge. The optimal parameter obtained from the optimization process is the equivalent parameter in the model to be identified. However, using the optimization method has a higher computational burden than using the analog signal-based method (terminal current signal). The negative sequence current scheme could be an alternative for fault detection and distinguish between an internal (TTF) and an external fault. The negative sequence current is a simple and efficient protection technique to detect minor internal TTFs in PTs. According to [10] and [7], the negative sequence detects TTFs involving 1% transformer winding.