Search Results (6)

Detecting power transformer winding degradations at an early stage is very important for the safe operation of nuclear power plants. Most transformer failures are caused by insulation breakdown; the winding turn-to-turn short circuit fault is frequently encountered. Experience has shown that routine testing techniques, e.g., winding resistance, leakage inductance, and sweep frequency response analysis (SFRA), are not sensitive enough to identify minor turn-to-turn short defects. The SFRA technique is effective only if the fault is in such a condition that the flux distribution in the core is prominently distorted. This paper proposes the frequency domain reflectometry (FDR) technique for detecting and locating transformer winding defects. FDR measures the wave impedance and its change along the measured windings. The wire over a plane model is selected as the transmission line model for the transformer winding. The effectiveness is verified through lab experiments on a twist pair cable simulating the transformer winding and field testing on a real transformer. The FDR technique successfully identified and located the turn-to-turn short fault that was not detected by other testing techniques. Using FDR as a complementary tool for winding condition assessment will be beneficial. Full article

In this study, we discussed how the increasing demand for electrical energy results in higher loads on transformers, creating the need for more effective testing and maintenance methods. Accurate fault classification is essential for the reliable operation of transformers. In this context, Sweep Frequency Response Analysis (SFRA) has emerged as an effective method for detecting potential faults at an early stage by examining the frequency responses of transformers. In this study, we used artificial intelligence (AI) and machine learning (ML) techniques to analyze the data generated by SFRA tests. These tests typically produce large datasets, making manual analysis challenging and prone to human error. AI algorithms offer a solution to this issue by enabling fast and accurate data analysis. In this study, three different transformer conditions were analyzed: a healthy transformer, a transformer with core failure, and a transformer with winding slippage. Six different machine learning algorithms were applied to detect these conditions. Among them, the Gradient Boost Classifier showed the best performance in classifying faults. This algorithm accurately predicted the health status of transformers by learning from large datasets. One of the most important contributions of this study is the use of gradient boosting algorithms for the first time to analyze SFRA test results and facilitate preventive maintenance through the early detection of transformer failures. In conclusion, this study presents an innovative approach. The interpretation of offline SFRA results through various artificial intelligence-based analysis methods will contribute to achieving the ultimate goal of reliable online SFRA applications. Full article

In traditional nonintrusive load monitoring (NILM) systems, the measurement device is installed upstream of an electrical system to acquire the total aggregate absorbed power and derive the powers absorbed by the individual electrical loads. Knowing the energy consumption related to each load makes the user aware and capable of identifying malfunctioning or less-efficient loads in order to reduce consumption through appropriate corrective actions. To meet the feedback needs of modern home, energy, and assisted environment management systems, the nonintrusive monitoring of the power status (ON or OFF) of a load is often required, regardless of the information associated with its consumption. This parameter is not easy to obtain from common NILM systems. This article proposes an inexpensive and easy-to-install monitoring system capable of providing information on the status of the various loads powered by an electrical system. The proposed technique involves the processing of the traces obtained by a measurement system based on Sweep Frequency Response Analysis (SFRA) through a Support Vector Machine (SVM) algorithm. The overall accuracy of the system in its final configuration is between 94% and 99%, depending on the amount of data used for training. Numerous tests have been conducted on many loads with different characteristics. The positive results obtained are illustrated and commented on. Full article

Asynchronous motors represent a large percentage of motors used in the electrical industry. Suitable predictive maintenance techniques are strongly required when these motors are critical in their operations. Continuous non-invasive monitoring techniques can be investigated to avoid the disconnection of the motors under test and service interruption. This paper proposes an innovative predictive monitoring system based on the online sweep frequency response analysis (SFRA) technique. The testing system applies variable frequency sinusoidal signals to the motors and then acquires and processes the applied and response signals in the frequency domain. In the literature, SFRA has been applied to power transformers and electric motors switched off and disconnected from the main grid. The approach described in this work is innovative. Coupling circuits allow for the injection and acquisition of the signals, while grids feed the motors. A comparison between the transfer functions (TFs) of healthy motors and those with slight damage was performed with a batch of 1.5 kW, four-pole induction motors to investigate the technique’s performance. The results show that the online SFRA could be of interest for monitoring induction motors’ health conditions, especially for mission-critical and safety-critical applications. The overall cost of the whole testing system, including the coupling filters and cables, is less than EUR 400. Full article

A power transformer is an essential device for stable and reliable power transfer to customers. Therefore, accurate modeling of transformers is required for simulation-based analysis with the model. The paper proposes an efficient and straightforward parameter estimation of power transformers based on sweep frequency response analysis (SFRA) test data. The method first develops a transformer model consisting of repetitive RLC sections and mutual inductances and then aligns the simulated SFRA curve with the measured one by adjusting parameters. Note that this adjustment is based on individual parameter impacts on the SFRA curve. After aligning the two curves, the final transformer model can be obtained. In this paper, actual single-phase, three-winding transformer model parameters were estimated based on field SFRA data, showing that SFRA curves simulated from the estimated model are consistent with the measured data. Full article

The power transformer is a key object in the electrical power system. The working principle hasn’t changed since its discovery. The main work nowadays is focused on the rising of the reliability of transformers and lowering power losses. The replacement of new materials instead of conventionally used ones can provide a solution. This procedure can improve factors, like a reduction of power losses, but also influence others normally neglected, like proper work in higher frequencies. The article presents the measurement results of the frequency characteristics of model test coils made with different magnetic materials cores (silicon steel, amorphous material, and nanocrystalline material), the measurements based on the sweep frequency response analysis (SFRA) method used for the determination of chosen frequency characteristics. The analysis presents the impact of different coil deformation levels on frequency characteristics. Results show that the replacement of conventional silicon steel with thinner high permeability materials can modify the state-of-the-art frequency response analysis (FRA) interpretation guidelines. The replacement of a new type of magnetic material as a magnetic core in the existing design of power transformer should lead to a full analysis of its behavior in the high-frequency domain. Full article