No abstract

The authors review the techniques applied to diagnose oil aging. Further, the authors put forward a new diagnostic method. It stipulates for an additional high-frequency measuring loop formed in an operating transformer. This dielcometric measuring capacitor cell includes a stray capacitance generated by the transformer winding and core. The monitoring of the dependence between the physico–technological oil parameters and the measuring cell capacity is fundamental for the procedures for determining the composition and properties of the transformer oil filling this cell. High-frequency low-voltage is the output signal. To prevent the cross-impact of low-frequency high-voltage and high-frequency low-voltage circuits, the pilot high-frequency low-voltage is excited by a special coupling capacitor; the output to the power feeder is conducted through an appropriate low-frequency choke, where the measuring capacitor cell does not disturb the normal transformer operation. The key physical processes used for the monitoring are analyzed and described in detail. The authors develop an algorithm to compute the current resistances of both the transformer oil and its impurities. The transformer state is estimated by comparing the parameters specified with preset permissible limits. A structure flowchart based on two synchronous quadrature detectors is proposed for a high-frequency measuring loop. The monitoring system considered allows for determining the following insulating oil properties by using the algorithm for processing the recorded data: moisture content; dielectric losses due to the accumulation of aging products in the oil and its pollution; and the content of dissolved gases in the oil. The monitoring system operability and efficiency are confirmed by appropriate experimental studies. The experiments are conducted using a TM-25-6/0.4 oil-filled transformer with a capacity of 25 kVA in a steady-state operating mode at a load current of 25 A. It is found that the proposed control system allows for identifying a critical defect of increased moisture content in the oil with no more than 10% error, and a sensitivity threshold in the order of tenths of ppm.

Implementation of the smart transformer concept is critical for the deployment of IIoT-based smart grids. Top manufacturers of power electrics develop and adopt online monitoring systems. Such systems become part of high-voltage grid and unit transformers. However, furnace transformers are a broad category that this change does not affect yet. At the same time, adoption of diagnostic systems for furnace transformers is relevant because they are a heavy-duty application with no redundancy. Creating any such system requires a well-founded mathematical analysis of the facility’s condition, carefully selected diagnostic parameters, and setpoints thereof, which serve as the condition categories. The goal hereof was to create an expert system to detect insulation breach and its expansion as well as to evaluate the risk it poses to the system; the core mechanism is mathematical processing of trends in partial discharge (PD). We ran tests on a 26-MVA transformer installed on a ladle furnace at a steelworks facility. The transformer is equipped with a versatile condition monitoring system that continually measures apparent charge and PD intensity. The objective is to identify the condition of the transformer and label it with one of the generally recognized categories: Normal, Poor, Critical. The contribution of this paper consists of the first ever validation of a single generalized metric that describes the condition of transformer insulation based on the online monitoring of the PD parameters. Fuzzy logic algorithms are used in mathematical processing. The proposal is to generalize the set of diagnostic variables to a single deterministic parameter: insulation state indicator. The paper provides an example of calculating it from the apparent charge and PD power readings. To measure the indicativeness of individual parameters for predicting further development of a defect, the authors developed a method for testing the diagnostic sensitivity of these parameters to changes in the condition. The method was tested using trends in readings sampled whilst the status was degrading from Normal to Critical. The paper also shows a practical example of defect localization. The recommendation is to broadly use the method in expert systems for high-voltage equipment monitoring.

Implementing the concept of a “smart furnace transformer” should stipulate its information support throughout its life cycle. This requires improving techniques for estimating the transformer’s health and forecasting its remaining useful life (RUL). A brief review of the problem being solved has shown that the known RUL estimation techniques include processing the results of measuring the facility state parameters using various mathematical methods. Data processing techniques (deep learning, SOLA, etc.) are used, but there is no information on their application in online monitoring systems. Herewith, fast (shock) changes in the resource caused by the failures and subsequent recoveries of the facility’s health have not been considered. This reduces the RUL forecasting accuracy for the repairable equipment, including transformers. It is especially relevant to consider the impact of sudden state changes when it comes to furnace transformers due to a cumulative wear effect determined by their frequent connections to the grid (up to 100 times a day). The proposed approach is based on calculating the RUL by analytical dependencies, considering the failures and recoveries of the facility state. For the first time, an engineering RUL forecasting technique has been developed, based on the online diagnostic monitoring data results provided in the form of time series. The equipment’s relative failure tolerance index, calculated with analytical dependencies, has first been used in RUL forecasting. As a generalized indicator, a relative failure tolerance index considering the facility’s state change dynamics has been proposed. The application of the RUL forecasting technique based on the results of dissolved gas analysis of a ladle furnace unit’s transformer is demonstrated. The changes in the transformer state during the operation period from 2014 to 2022 have been studied. The RUL was calculated in the intensive aging interval; the winding dismantling results were demonstrated, which confirmed developing destructive processes in the insulation. The key practical result of the study is reducing accidents and increasing the service life of the arc and ladle furnace transformers. The techno-economic effect aims to ensure process continuity and increase the metallurgical enterprise’s output (we cannot quantify this effect since it depends on the performance of a particular enterprise). It is recommended to use the technique to forecast the RUL of repairable facilities equipped with online monitoring systems.

Making “digital twins” for rolling processes and mill equipment should begin with the development of mathematical models of the deformation zone. The deformation zone of two-high flat mill rolling have been studied in detail, relevant models are available in many academic papers. However, the same cannot be said about the most complex deformation zones in stands with multi-roll gauge. Therefore, the task of their reliable mathematical description is of immediate interest. The development of mathematical models is necessary for the design of new wire mills and rolling-drawing units. The combination of rolling in stands with multi-roll gauge and drawing is a promising direction in the production of wire from difficult-to-form steels and alloys. Digital models for pressure-based metal treatment are also necessary for calculating the rolling-mill power parameters during the development of new assortments at the operating mills. The models of deformation zones present the basis for developing the multivariable control systems of process conditions of continuous mills. This research is devoted to the study of the deformation zone and the development of a procedure for calculating the power parameters of rolling in a stand with four-roll passes. The solution of these challenges is given using the example of an operating five-stand wire mill. The authors analysed the known analytical dependencies for calculating the rolling mill force and torque. A mathematical model of the deformation zone and a program for calculating the power parameters have been developed. The paper compares the results obtained from calculations based on analytical dependence and on modelling. A comparison with the experimental parameters obtained at the mill is given. The authors assess the feasibility of using the known formulas and analyse the impact of the front and rear tensions on the power parameters of rolling mill. The problem of developing an automatic tension control system for continuous mills with multi-roll groove is substantiated.