Automated Diagnostic System for Power Transformers using a QR Code

This paper discusses approaches and methods of monitoring, diagnostics, and assessment for the effective maintenance, repair, and extension of the service life of transformers without the loss of reliability. It considers the newly developed option of the full automation of the power transformer diagnostics system using automated control systems. The system of monitoring, control, and diagnostics of electrical system transformer equipment is combined with an automated electrical equipment control system and an automated information measurement system for the commercial accounting of electric power. The novelty of the developed system consists of the use of QR codes and the Team Viewer program for the operational elimination of accidents and the detection of any abnormal operation of power transformers. The automation of the power transformer diagnostic system will reduce the time needed to perform repair work and restore the necessary protective modes as well as allow the efficient use of material and labor resources.

Diagnostic System; Effective maintenance; QR code

       An analysis of the damageability of the power transformer stations belonging to the operators of the electrical and intersystem grids in Russia shows that the specific quantity of technological disturbances that led to automatic shutdowns by the action of protective devices or forced shutdowns by personnel on an emergency request is 1.8% per year. At the same time, about 30% of the total number of such technological malfunctions were accompanied by the occurrence of internal short circuits (Rozhentcova et al., 2019).
     Since power transformers are some of the most expensive elements in the power grid, it is necessary to identify the initial stage of defect development and the pre-emergency and emergency modes of transformer equipment. In most cases, the decision is made to keep long-life transformers in operation. Therefore, the search for new approaches and methods of monitoring, diagnostics, and assessment for the effective maintenance, repair, and extension of the service life of transformers without the loss of reliability is becoming urgent (Alyunov et al., 2019; Dhini et al., 2020; Nemirovskiy et al., 2020; Wang et al., 2020). The issue of identifying defects at an early stage of their occurrence in normally operating transformers  and,  especially,  in those that have reached  the standard longevity of power equipment is an acute problem. Also, the introduction of an automated diagnostic system for power transformers is a prerequisite for the implementation of smart grid technology in industrial electrical networks. The currently existing tools and methods for diagnosing the state of a power transformer’s insulation do not allow one to fully identify defects at an early stage of their formation.
    In modern conditions, the role of diagnostics in the operation of equipment is increasing significantly. At the same time, it is known that the diagnostic system for transformers and other electrical equipment should have complete information, technical and regulatory support, and a decision-making strategy for the feasibility of the further operation of the equipment and the need to take it out for repair (Mackenzie et al., 2010; Junior et al., 2011; Rozhentcova et al., 2019). Currently, in world energy practice, one of the most effective ways to improve the reliability of transformer equipment is the introduction of systems for the continuous monitoring of changes in the main parameters of transformers during operation. Based on the analysis of control results, measures are developed to prevent the unfavorable development of defects and, ultimately, emergency shutdown.

    The aim of this work is to develop a monitoring, control, and diagnostics system for transformer equipment in conjunction with an automated control system for electrical equipment and an automated information and measurement system for commercial metering of electricity using a QR code. This paper presents the main components and algorithms of the developed Supervisory control and data acquisition (SCADA)-based system. The novelty of this work consists of the use of QR codes and remote monitoring and control, which enables efficient elimination of equipment emergency modes and early detection of the abnormal operation of transformers.

Damage to power transformers disrupts the operation of the power system and affects electricity consumers, and abnormal and emergency modes create the likelihood of damage or instability in the power system. To provide consumers with uninterrupted power supply and ensure trouble-free operation of the power system, it is necessary as quickly as possible to detect the cause of failure and restore the damaged area of the network to normal working conditions. Dangerous consequences of abnormal conditions can be prevented by the timely detection of deviations from normal operation and by taking measures to eliminate them (reducing the current during its buildup, reducing the voltage when it is increasing, etc.).

An analysis of the current statistics of failures reveals that about 40% of transformers in use have passed their peak performance time and continue to work with increased losses, and 60% of transformer stations need to be overhauled (Denisova et al., 2019).

The implementation of systems such as we have proposed, which combines an automated system for the monitoring, control, and diagnostics of transformers, an automated control system for electrical equipment, an automated information and measurement system for the use of commercial metering of electricity using QR codes and remote monitoring, and the use of the TeamViewer remote administration program to control and track all of the above systems, will help to increase efficiency in the elimination of emergency modes and in the early detection of abnormal operation, making production more efficient, saving time for examining certain equipment defects, and reducing the cost of operating the equipment.

        The automation of the power transformer diagnostic system will reduce the time needed for performing repair work and restoring the necessary protective modes as well as allow the efficient use of material and labor resources. Thus, we have proposed the full automation of the power transformer diagnostics system using automated control systems.

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