There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Power transformer, as one of the most important apparatus in power system, must be protected against the turn-to-turn faults. Although the various methods based on the terminal currents and/or voltages have been presented for transformer protection, the flux-based methods can be used to achieve the more accurate, sensitive, and secure results. Since the symmetrical form of the magnetic flux distribution will be disturbed due to any fault occurrence in the transformer windings, it can be considered as an appropriate criterion to achieve a suitable protection algorithm, which can detect the fault occurrence and identify the faulty phase/region. In this paper, a new leakage flux sensor is introduced and then a flux-based technique is proposed to detect and identify the faulty phase in transformers to apply in online conditions. By using the proposed technique, the faulty region can be identified in offline conditions, as well. The experimental results (which are done on a distribution transformer) show that this technique is secure in the face of overfluxing and transformer energizing conditions and can detect turn-to-turn faults with high sensitivity. Moreover, tap changer operation, imbalanced loads, and/or unsymmetrical voltage sources cannot influence the proposed technique security.

A New Leakage Flux-Based Technique for Turn-to-Turn Fault Protection and Faulty Region Identification in Transformers

Transformers are utilized in generation, transmission, and distribution power system network, and face an enormous number of hazards during their course of operation. Frequency response analysis (FRA) is an inexpensive, accurate, and non-destructive technique to explore the transformer mechanical integrity very fast. However, FRA results interpretation is not being automated yet. This study introduces a new setup for FRA measurement that can assist to leave the conventional FRA data interpretation techniques and obtain smart interpretation. Hence, FRA setups and interpretation techniques are studied and formulated in this paper. A new measurement technique is introduced and discussed in detail. Practical studies are performed over distribution and power transformers and FRA data are recorded for inter-disk fault. The analysis of fault severity, which is obatined in this paper, is an advantage of the proposed measurement technique. In this regard, the techniques from machine learning and numerical analysis are employed to train a predictive engine for smart interpretation of FRA data. It is revealed that the proposed intelligent technique is capable of interpreting, detecting, and classifying the transformer winding inter-disk fault and its severity. The new introduced FRA measurement setup is also able to support the online FRA data assessment.

A New Transformer FRA Measurement Technique to Reach Smart Interpretation for Inter-Disk Faults

Designing a composite deep learning based differential protection scheme of power transformers

Monitoring of winding faults is of great significance for the assessment of the transformer maintenance status. In this study, the time series analysis is combined with frequency response analysis (FRA) for better interpretation and analysis of FRA results. Here, healthy baseline residual data are modeled using the time series analysis for faults detection of the transformer winding. Different statistical software, such as R 4.0.2 and ITSM2000, are used to analyze measured data. To detect the transformer’s winding faults, various analyses, such as the autocorrelation and partial autocorrelation functions plot, the Box–Pierce test, the Ljung–Box test, the McLeod–Li test, the turning points test, the Wallis and Moore phase–frequency test, and the Jarque–Bera test, and the order of minimum corrected Akaike’s information criteria are used. The simulation results show that the suggested method is able to classify and discriminate mechanical and electrical faults of the transformer’s winding with good accuracy.

Transformer Winding Faults Detection Based on Time Series Analysis

Since the internal turn-to-turn faults (TTFs), as a common cause of the transformer failures, result in minor changes on terminal currents/voltages, they maybe undetected by the conventional protective relays. In this study, a simple, sensitive and robust linkage flux-based technique is proposed to protect the power transformers against TTFs. In this approach, some separated multi-turn windings as search coils (SCs) have to be wrapped around the transformer core legs to sense the related passing flux. Since passing equal flux through a transformer core leg in normal condition induces equal voltages in the related SCs, variation of the induced voltages indicates the fault occurrence in that phase. Against the traditional differential protection schemes, current transformer error and saturation, tap changer operation, energising inrush current, over-fluxing etc. cannot impact the proposed technique performance. Therefore, it can be introduced as a comprehensive protection technique for power transformers.To confirm the proposed technique performance, it is tested on a real 50 kVA, 20 kV/400 V three-phase distribution transformer, in various conditions, too.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-gtd.2015.0738

Flux‐based turn‐to‐turn fault protection for power transformers

Internal turn-to-turn faults (TTF) are considered as a common cause of transformer failures. Such defects (especially minor TTFs) may result in minor changes in terminal currents/voltages as these are undetectable by conventional current and/or voltage-based methods. In this study, a simple, sensitive and robust method is proposed to identify the TTF locations in power transformers by using core flux based technique. In this approach, a few turns must be wrapped around the transformer core legs to sense and detect the related core flux. Passing asymmetrical flux through a transformer core leg (due to a TTF) induces different voltages in the related sensors, which are located at different places. Variation of the core flux in the corresponding sensors indicates the faulty phase and the TTF location on that phase as well. The proposed technique is verified by some simulations and experimental tests. The obtained results show that the proposed technique successfully (i) detects all the TTFs, (ii) identifies the faulty phase, and (iii) specifies the faulty region on the related phase. Although the proposed method requires a few search coils to be installed on the transformer core legs, installing these thin wires will not change the transformer design significantly.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-gtd.2015.1180

Flux-based method to diagnose and identify the location of turn-to-turn faults in transformers

An overview of various faults detection methods in synchronous generators

List of symbols and abbreviations: CT, Cu Negative Sequence; OLTC, On‐Load Tap‐Cha Sequence Component; SC, Search Coil; SFRA Symbols: N1, Primary winding turns number ZPos, Positive sequence impedance; VNeg, Ne Transformer turn ratio; iz, Short circuit curre Phase‐to‐phase voltages at the triangle side; transformer star winding; L12, Leakage induc sequence current at star side; 3u0, Sum of the in the triangle winding; ie1, ie2, ie3, Summati primary side; T, Sampling interval; ESC, Induc

Mohsen Mostafaei

Papers

A New Leakage Flux-Based Technique for Turn-to-Turn Fault Protection and Faulty Region Identification in Transformers

Flux‐based turn‐to‐turn fault protection for power transformers

Flux-based method to diagnose and identify the location of turn-to-turn faults in transformers

An overview of various faults detection methods in synchronous generators

Turn-to-turn fault monitoring methods in electrical power transformers—State of the art