Voltage characteristic as a parameter for surge arrester diagnosing

An alternative method for field MOV surge arresters diagnosing was observed, the controlled characteristic was the surge voltage of a gap arrester. The condenser that was connected in series with gap arrester was applied as voltage measurement sensor. Electrical aging of active elements (MOV), surge arrester insulation degradation and other types of electric faults causes to voltage increase at capacitor. The voltage value can be measured directly or the energy stored in capacitor can be transformed to electromagnetic signal and, then, registered remotely by specific radio transceiver. The capacitor connected in series with the surge arrester can also be used for leakage current limitation during all the life period of surge arrester. Shunted with a spark gap and presented as the low-current gap arrester with pre-sated discharge voltage glass (porcelain) pin-cap insulator can be the simplest, but reliable sensor. Taking into consideration modern technologies the surge arrester statement continuous monitoring system can be designed. It also allows locating the place of damaged arrester that is particularly true for remote maintenance of equipped with surge protection devices electrical

Transformer Oil Quality Assessment Using Random Forest with Feature Engineering

Machine learning is widely used as a panacea in many engineering applications including the condition assessment of power transformers. Most statistics attribute the main cause of transformer failure to insulation degradation. Thus, a new, simple, and effective machine-learning approach was proposed to monitor the condition of transformer oils based on some aging indicators. The proposed approach was used to compare the performance of two machine-learning classifiers: J48 decision tree and random forest. The service-aged transformer oils were classified into four groups: the oils that can be maintained in service, the oils that should be reconditioned or filtered, the oils that should be reclaimed, and the oils that must be discarded. From the two algorithms, random forest exhibited a better performance and high accuracy with only a small amount of data. Good performance was achieved through not only the application of the proposed algorithm but also the approach of data preprocessing. Before feeding the classification model, the available data were transformed using the simple k-means method. Subsequently, the obtained data were filtered through correlation-based feature selection (CFsSubset). The resulting features were again retransformed by conducting the principal component analysis and were passed through the CFsSubset filter. The transformation and filtration of the data improved the classification performance of the adopted algorithms, especially random forest. Another advantage of the proposed method is the decrease in the number of the datasets required for the condition assessment of transformer oils, which is valuable for transformer condition monitoring.

Particle Filtering Based Remaining Useful Life Prediction for Electromagnetic Coil Insulation

Electromagnetic coils are one of the key components of many systems. Their insulation failure can have severe effects on the systems in which coils are used. This paper focuses on insulation degradation monitoring and remaining useful life (RUL) prediction of electromagnetic coils. First, insulation degradation characteristics are extracted from coil high-frequency electrical parameters. Second, health indicator is defined based on insulation degradation characteristics to indicate the health degree of coil insulation. Finally, an insulation degradation model is constructed, and coil insulation RUL prediction is performed by particle filtering. Thermal accelerated degradation experiments are performed to validate the RUL prediction performance. The proposed method presents opportunities for predictive maintenance of systems that incorporate coils.