Condition Monitoring System For Transformers Research Articles

Rancang Bangun Sistem Monitoring dan Uji Analisis Performansi Tranformator berbasis Internet of Things (IoT)

Maintenance on transformers is currently carried out on a monthly basis, which activities are carried out manually so that it is difficult to detect transformers experiencing abnormal conditions if there is a disturbance outside the maintenance schedule. Therefore, this research was carried out with the aim of making a prototype of a transformer condition monitoring system and monitoring parameters on the transformer remotely through the Blynk application in real time. In making this tool, the PZEM-004T sensor is used to read electrical parameters, the DS18B20 sensor to read the temperature on the transformer winding and the Arduino MEGA 2560 With WiFi Built-in - ESP8266 to process the data read by the sensor which is then sent to the Blynk application. The results of this study indicate that the average error value for the comparison of current readings is 0.68%, the average error value for the comparison between phase to neutral voltage readings is 0.91%, the average error value for the comparison of voltage readings between phases is 1, 00%, the average error value of the Cosφ reading comparison is 0.02% and the average temperature reading comparison error value is 1.73%.

A Novel Power Transformer Condition Monitoring System Based on Wide-Band Measurement of Core Earth Signals and Correlation Analysis With Multi-Source Data

On-line condition monitoring and real-time fault detection are essential to protect power transformers from unscheduled outage of services. The core earth signals (CES) are valuable for fault detection and diagnosis of power transformers, as they are related to multiple transformer defects and faults, such as core multi-point earthing fault and core deep saturation. Moreover, the CES also contain valuable information of transient impulses intruded into power transformers such as very fast transient overvoltage (VFTO) impulses. This paper develops a novel power transformer condition monitoring system (NCMS), which makes full use of CES to detect and diagnose the external impulses and internal defects and faults. Firstly, the NCMS carries out the wide-band measurement of CES. Secondly, the NCMS obtains data from supervisory control and data acquisition system (SCADAS), dissolved gases analysis (DGA) system, and other monitoring systems, and correlates these data using grey relational analysis (GRA). Finally, the NCMS detects the impulses and diagnoses defects and faults based on the correlation relationships, then presents the monitoring results to the users. This novel system has been applied to the field and successfully diagnosed many defects and faults. It has high accuracy (<; 3%) at the power and harmonic frequencies and possesses the capability to capture VFTO-pulses.

Analysis of Partial Discharge Signal Characteristics of Nano-Mineral Oil for Transformer Condition Monitoring Applications

Transformer is an expensive and key component in an electric power transmission network. Most of the failure of transformer occurs due to degradation of insulation oil, which further leads to major economical losses and power shutdown. Advancements in nanotechnology has given horizons for the development of nanofluids for transformer insulation applications. Understanding the enhancements in dielectric properties of nanofiller added mineral oil is a hot research area. In recent times, partial discharge (PD) detection and analysis is a well recognized insulation condition monitoring technique for transformers. However, reports on PD signal characteristics of nano filler added mineral oil is scanty. This paper attempts to investigate the PD characteristics of SiO2 nanofiller added mineral oil at different %wt concentrations of nanofiller material. Major PD source normally experienced in transformers i.e. corona discharge due to non-uniform electric field was simulated in the laboratory using needle-plane electrode configuration and the corresponding PD signals were measured using wide band PD detection system. Important PD characteristics such as inception voltage, stable PD formation voltage, PD amplitude with respect to increase in voltage stress and PD signal frequency spectrum were evaluated. Results clearly show that addition of SiO2 nanofiller material in the base mineral oil enhances PD performance of mineral oil and these results will be useful for electrical utilities for developing effective condition monitoring system for transformers.

The Design of a Multi-Agent Transformer Condition Monitoring System

Online diagnostics and online condition monitoring are important functions within the operation and maintenance of power transformers. This paper describes how a multi-agent system (MAS) for transformer condition monitoring has been designed to employ the data generated by the ultra high frequency (UHF) monitoring of partial discharge activity. It describes the rationale behind the use of multi-agent techniques, and the problems overcome through this technology. Every aspect of the MAS design is discussed. In addition, the design and performance of the intelligent interpretation techniques are detailed.