Hasan Mohammadi

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.

Analyzing of detected partial discharge (PD) signals is a reliable technique to assess the electrical insulation in power transformers. In order to detect and locate the PD sources in power transformers, some distributed simple fine sensors are proposed and used, which are made by a few turns of thin enameled copper wire wrapped around the transformer core. They are distributed sensors, which sense PD voltage signals through capacitive coupling with the transformer windings. Two methods, including broadband and limited-band PD signal analyzing, are considered to identify defective phase and/or region on that phase through the proposed distributed sensors outputs. Transfer functions of these sensors are obtained and it is experimentally shown that although defective phase can be diagnosed, the expected priority to identify defective region is not regarded in broadband analysis, while a suitable limited frequency band can be found to achieve this aim. The idea is implemented on a sample transformer by injecting artificial PD signals to different high-voltage winding sections. The results show that not only PD signals can be detected through the mentioned sensors with high sensitivity, but also defective phase can be diagnosed using broadband analysis. Moreover, defective region can be accurately identified by limited-band analysis.

Monitoring of partial discharge (PD) and its progress as the main cause of the insulation deterioration process in power transformers can encompass valuable information about the related insulation condition and reveals degradation mechanisms. In this study, some simple sensors, which are made from a few turns wrapped around the transformer core, are used to detect PD signals. Moreover, a feasibility study is done to identify defective phase/region based on the captured PD signals waveforms through these sensors. The idea is implemented on a 50 kVA, 20 kV/400 V three‐phase transformer by injecting artificial and quasi‐real PD signals to different sections of HV windings. Also, various transformer connections are considered in the experimental studies to find the effect of them on the waveforms of the captured signals (e.g. peak‐to‐peak value and signal energy). The obtained experimental results show that small PDs (as low as 10 pC) is detectable by the mentioned sensors and defective phase can be identified in all of transformer connection cases. Furthermore, considering failure to identify defective regions by broadband analysis, a limited‐band approach is proposed through obtaining | H(s) | for the sensors. The results show that exact defective phase/region identification is feasible by selecting a frequency band compatible with IEC60270 recommendation.