Condition Assessment Of Power Transformers Research Articles

Development of fuzzy classifier for technical condition ranking of power transformer

The work aim is to develop a fuzzy classifier for technical condition ranking of power transformer under condition of vagueness and ambiguity diagnostic information. Methodology. The fuzzy classifier developing for technical condition ranking of power transformer was based on approach of using fuzzy set theory and optimization methods. The proposed approach for power transformer rank assessment by using a classifier was developed on the basis of Takagi-Sugeno fuzzy inference system. The input indicators choice is justified and their efficiency for classifier is evaluated by expert evaluation method. This makes it possible to formalize expert assessments regarding the development of power transformer defects. Results. The formalization of technical condition assessment of power transformer in knowledge base form, which implemented in expert system prototype for technical condition assessment, was carried out. The complex technical condition assessment for each functional unit of power transformer was determined based on expert evaluations with using the test and measurement parameters results. Originality. The considered approach to formalization of uncertainty regarding technical condition of power transformer allows building a deterministic decision-making scheme for further maintenance strategy, in which the ranking and decommissioning procedures for specific objects are implemented on the basis of objective criteria. Practical value. The proposed fuzzy classifier allows determination with a high probability degree of technical condition assessment of power transformer based on the test and measurement parameters results. Thus, an applied aspect of using the obtained scientific result is the possibility to objectively rank of power transformers park based on the identified possible defects and their development degree. This constitutes the prerequisites for determining the failure probability evaluation of power transformer at nearest observation period and emergency risk assessment in integrated electric power systems under power transformer failures.

Condition Assessment of Power Transformers through DGA Measurements Evaluation Using Adaptive Algorithms and Deep Learning

Condition assessment for critical infrastructure is a key factor for the wellbeing of the modern human. Especially for the electricity network, specific components such as oil-immersed power transformers need to be monitored for their operating condition. Classic approaches for the condition assessment of oil-immersed power transformers have been proposed in the past, such as the dissolved gases analysis and their respective concentration measurements for insulating oils. However, these approaches cannot always correctly (and in many cases not at all) classify the problems in power transformers. In the last two decades, novel approaches are implemented so as to address this problem, including artificial intelligence with neural networks being one form of algorithm. This paper focuses on the implementation of an adaptive number of layers and neural networks, aiming to increase the accuracy of the operating condition of oil-immersed power transformers. This paper also compares the use of various activation functions and different transfer functions other than the neural network implemented. The comparison incorporates the accuracy and total structure size of the neural network.

Health Index for Power Transformer Condition Assessment: A Comparison of Three Different Techniques.

In practice, the condition state of Power Transformers (PT) is quantified by using Health Index (HI). This paperanalyzes and compares three different state-of-the-art algorithms to compute HI. The first one uses a Weighted SumModel (WSM), the second is based on a Fuzzy Inference System (FIS), and the third combines both techniques, i.e.,WSM and FIS. These three approaches are tested in a PT fleet composed of 30 units. Results show that eachapproach produces different HI values for the same PTs. Therefore, decision making regarding the PT fleet willdepend on the selected approach for HI calculation. This work proposes merging the knowledge involved in eachanalyzed approach by using a K-means clustering technique to overcome this drawback. This solution could help theasset manager to make adequate decisions regarding the maintenance scheduling of PT when there is uncertaintyabout the appropriate approach to be selected.

Failure mapping and critical measurements for the operating condition assessment of power transformers

The causes of failure of critical component of the electrical power distribution network like the power transformers, has always attracted the interest of the scientific and technical community, since the failures of electrical transformers almost always have a great impact on the consumers, as they can lead to a black out situation. A reliable and coherent maintenance schedule, that would predict the causes and prevent un-expectable shutdowns of power transformers, is of outmost importance nowadays, since electrification is expanding in terms of volume and power, as well as in terms of production sources, voltage and location. Such a reliable and coherent maintenance schedule depends on many factors and this article describes the parts of oil-immersed power transformers that are prone to failure. It also illustrates the relationship of the well known dissolved gasses that appear in the insulating oil as a result of specific operating faults, with specific measurements that should be done periodically, in order to prevent a major failure. Additionally, it describes the relationship between these dissolved gases with specific maintenance actions that should be implemented during the preventive maintenance process.

Health index for power transformer condition assessment based on operation history and test data

Transformers are key equipment in a power grid. The stability of the power system is depending more and more on the safety and reliability of transformers. Health Index (HI) is a practical tool that combines complex condition data into a single value as a comparative indication of the overall condition of a transformer. Most of the existing health index procedures are based on laboratory or on-site test data, with few considering the actual operating time. In this research, a comprehensive assessment method of transformer operating condition is proposed. Based on the HI model, this method the first considers the aging process of the transformer insulation as well as the load and operating environment to form the theoretical health index HI1. The aging process is characterized by the operating time of the transformer, the load rate, and the pollution level. The test health index HI2 is then formed based on on-site tests. The final health index HI is obtained with the combination of HI1 and HI2 to characterize the health condition of the transformer. To make sure the proposed method can be readily adapted in the field, only traditional parameters with well-established interpretations are required. This method has been applied to assess multiple 330kV transformers in service and provided a useful method to transformer asset management.

Effective Oil-Immersed Paper Insulation Condition Assessment of Power Transformers

Effective Oil-Immersed Paper Insulation Condition Assessment of Power Transformers

DGA Method Implementation for Incipient Fault Analysis using Gas Concentrations

Power transformers are essential devices for the durable and reliable performance of an electrical system. the main objective of this study is to analyze three classical diagnosis techniques to identify incipient faults in Transformer oil using Rogers’s Ratio Method, Doernenburg Ratio Method, and ANN which is a type of artificial intelligence learning method. Implementation of the system in MATLAB software for each diagnosis method and compare their accuracy and efficiency and hence design three diagnosis methods of DGA for condition assessment of Power Transformer. And the analysis on the MATLAB software shall be carried so as to detect the best method for detection of a certain type of fault and the best suited method for overall fault analysis for a certain data sets out of the three methods. This technique utilizes the learning capacity of that artificial neural network has been shown to be more efficient in detecting different mistakes. The overall error detection accuracy of such gas neural network study was found to be 73.8 percent.

An adaptive fault diagnosis method of power transformers based on combining oversampling and cost‐sensitive learning

Dissolved gas analysis is an important technique for the insulation condition assessment and incipient fault diagnosis of power transformers. However, the performance of the traditional ratio methods can be hardly improved due to the overreliance on absolute ratio threshold. In this paper, a novel method combining oversampling and cost-sensitive learning is proposed to improve the diagnosis accuracy of all fault types of transformers. The radial-based oversampling (RBO) method is adopted to synthesise samples for the complex fault classes. With the newly generated samples, the deep belief network (DBN) can effectively learn the features of complex fault classes and distinguish them from the other fault classes. Moreover, by integrating a cost matrix into the loss function, the parameters of DBN are adaptively updated so as to ensure the correct classification of the fault class with less samples. Based on the oversampling and cost-sensitive learning, the proposed method can form suitable classification boundaries amongst thermal, discharge and complex fault classes. The effectiveness and generalisation capability of the proposed method are verified by case studies in a real-world fault dataset of power transformers with multi-source samples. The results demonstrate that the proposed method improves the classification accuracies in all fault classes, especially in the complex fault classes. The overall accuracy can be reach over 90% by applying both RBO and cost-sensitive learning.

A Robust Multi-Layer Framework for Online Condition Assessment of Power Transformers

Online continuous monitoring of dissolved gasses in transformer oil has become an emerging trend in the recent years. This way, in addition to sending the warning signal in the case of gas concentration, a fault diagnosis procedure can be conducted using standard Dissolved Gas Analysis (DGA) methods. However, since the fault diagnosis outcome and result of these methods are sometimes different, an appropriate solution should be suggested to accurately describe the transformer internal condition. To do so, a robust multi-layer framework has been proposed in this paper. Handling the measurement uncertainties and fusing the results of independent DGA methods without losing their diagnosis resolutions are the interesting features of this framework. To improve the overall accuracy of fused diagnosis result, an artificial neural network-based approach is proposed. It intelligently assigns the weight of each independent method in the fusion procedure according to its fault type detection accuracy for the range of input gasses concentrations. To evaluate performance of the proposed framework, comprehensive test studies have been conducted and the promising obtained results prove its superior performance for use in online applications.

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.

High voltage power transformer condition assessment considering the health index value and its decreasing rate

High voltage power transformer condition assessment considering the health index value and its decreasing rate

Analysis of the power transformer condition assessment in PLN Western Java Transmission unit

Transformers are critical equipment of the electrical power supply system and the most expensive asset in the electric power transmission network. Therefore, power transformer condition assessment method is needed to determine the actual condition of power transformer on operating. Indeed, they are several methods of condition assessment for power transformers have been implemented. The transformer data is divided into different levels and parts, so multi-parameter analysis is carried on both by direct condition data and by indirect computing history data. Some method use only one-step process, while others through many stages. This paper proposes a power transformer condition assessment with three stages. First step are Health Index and Susceptibility. Second step is Probability of Failure, that is combination between Health Index and Susceptibility. The last stage is Risk Based Priority as a result of combining Probability of Failure and Criticality Level. The outputs of condition assessment are analysis lifetime prediction, abnormality, recommendations for next action and determine maintenance or replacement priorities. This study has investigated 303 units of power transformers in Western Java Transmission unit. The result of study is useful for the utility company to improve their transformers asset quality.

Power Transformer Condition Assessment Method based on Improved Rough Fuzzy K-means Clustering

As an important equipment of power transmission and transformation, power transformer plays a direct role on the reliability of power supply. Considering the low proportion of power transformer fault data and the overlap boundary with normal data, an imbalance measurement of cluster size is introduced and a power transformer state assessment based on rough fuzzy k-means clustering is proposed. The developed method can not only judge the type of fault that has occurred, but also predict the possible future faults of power transformers. The effectiveness of the designed method is verified by experimental comparative analysis.

Review of Fiber Optic Diagnostic Techniques for Power Transformers

Diagnostic and condition monitoring of power transformers are key actions to guarantee their safe operation. The subsequent benefits include reduced service interruptions and economic losses associated with their unavailability. Conventional test methods developed for the condition assessment of power transformers have certain limitations. To overcome such problems, fiber optic-based sensors for monitoring the condition of transformers have been developed. Flawlessly built-up fiber optic-based sensors provide online and offline assessment of various parameters like temperature, moisture, partial discharges, gas analyses, vibration, winding deformation, and oil levels, which are based on different sensing principles. In this paper a variety and assessment of different fiber optic-based diagnostic techniques for monitoring power transformers are discussed. It includes significant tutorial elements as well as some analyses.

Comparative study of partial least squares and neural network models of near-infrared spectroscopy for aging condition assessment of insulating paper

Near-infrared spectroscopy (NIRS) is a rapid and non-destructive detection method for component determination and quantitative analysis with broad applications in numerous fields. In recent years, NIRS has started to be used in the aging condition assessment of power transformers. However, the real applications of NIRS are constrained by the lack of evaluation database and accurate prediction algorithms. In this paper, we aim at comparing different NIRS modeling methods and improving diagnostic accuracy. We build the evaluation database via the preparation of 230 specimens derived from three typical types of insulating paper. Calibration models are established by linear method-partial least squares (PLS) and nonlinear method-back propagation neural network (BPNN) to map the relationship between spectra and the degree of polymerization (DP). The DP prediction results show that using full NIR spectra as the input of the PLS model does not ensure a high prediction accuracy, and it is improved by competitive adaptive reweighted sampling (CARS) that selects the optimal wavelength combinations. Prediction precisions given by BPNN and CARS-BPNN models are shown to be less satisfactory than that of CARS-PLS. We process the original spectra with principal component analysis (PCA) as the input of BPNN and the PCA-BPNN model realizes high prediction precision for three types of paper (RMSE ⩽ 24, r = 0.99). With the identification of paper type by the k-nearest neighbors (KNN) method before prediction, the KNN-PCA-BPNN model solves the problem of the low prediction precision for mixed (unknown) paper samples (RMSE = 36, r = 0.98), which facilitates future field tests as well as related applications in practice.

Insulation Condition Assessment of Power Transformers Employing Fused Information in Time and Space Dimensions

Based on the association rules and variable weight synthesizing theory, a new insulation condition assessment (ICA) method for power transformers is presented, employing fused information in both time and space dimensions. With the uncertainty and fuzziness of available data from the power transformer concerned taken into account, a multilateral transformer ICA model based on fuzzy and evidence reasoning is developed. The ICA model contains three layers: target layer, factor layer, and index layer. The association rules and variable weight synthesizing theory are applied to determine the variable weight coefficients of factors and its indicators in the assessment model. A fuzzy membership function is formulated to describe the factor layer on assessment model. The evidence reasoning and the multilateral assessment scheme are adopted to merge the information of each factor in time and space dimensions, and then the assessing result of the transformer is obtained. Numerical results based on practical scenarios demonstrate that the proposed method is feasible and efficient.

Power transformer condition assessment based on online monitor with SOFC chromatographic detector

Power transformer is vital for energy conversion in power system, the condition assessment of which based on dissolved gas analysis (DGA) is used worldwide. In this paper, an online monitor based on solid oxide fuel cell (SOFC) detector is designed to address the poor stability, low sensitivity of other online-monitoring system. The system is composed of a gas measurement subsystem, a central controlling unit, and an intelligent fault diagnosis subsystem. A portable gas chromatography module developed using SOFC detector. The quantificational algorithm is derived, on basis of which calibration can be skipped. Due to the essential role of the chromatography column as well as gas the detector’s temperature, proportional-integral-differential (PID) controller is implemented in the controlling unit. To evaluate the effectiveness of the developed online monitor, the quantitative analysis and engineering application have been performed; then, based on the measured results, the RVM-ANFIS algorithm is adopted to diagnosis the latent transformers faults. Using the developed online monitor, the results indicate that five characteristic gases, H2, CH4, C2H4, C2H6, and C2H2 can be measured accurately, and incipient faults can be detected, validate the effectiveness of this system.

Condition Assessment of Power Transformers Based on Health Index Value

In electric power systems, health index algorithms are mostly used for evaluation of the transformer population. In this method, some assessment criteria are insensitive when it comes to judging the technical state of the edges of the age spectrum. This paper presents a new health index calculation method that aims to improve the overall effectiveness of the assessment. The proposed algorithm is based on regularly conducted oil diagnostics and easily available maintenance data to enable estimation and updating of the device’s health status in short intervals from an operational point of view. This method is compared to another health index algorithm built from the same parameters, but with different weights and an alternative result assessment philosophy. The two health index calculation methods are tested on a population of 96 power transformers and then compared to results obtained with an expert system, which is based on much more advanced diagnostic tests to determine the technical condition of the unit. The results of the experiment show that proper selection of weighting factors of the transformer’s technical condition parameters during health index calculation may help in simplifying its assessment while maintaining satisfactory accuracy in comparison to a highly advanced expert method.

Condition assessment of power transformers based on dissolved gas analysis

DGA is more sensitive and reliable technique for detecting and evaluating the incipient fault conditions in oil-immersed transformers. The most frequently interpretation technique that used in dissolved gas analysis is Duval triangle due to its simplicity and accuracy. Unfortunately the impacts of Ethane (C2H6) and Hydrogen (H2) were not included in Duval triangle in spite of their importance in diagnosing certain faults types. In addition, the problem of no decision associated with some cases that lie out of the specified codes appeared in Doernenburg, Roger's and IEC techniques. Accordingly, this paper presented a new proposed graphical triangle technique using new three gas concentration ratios. These ratios were converted to new three ratio percentages of DGA based on the consideration of all five combustible gases. The aim of that was to overcome the conflict takes place in Duval triangle and other traditional techniques. The accuracy of the proposed triangle technique was evaluated using practical DGA database obtained from different transformers of different ratings life spans reported by Egyptian Electricity Holding Company, IEC TC 10 and related databases surveyed from actual incipient cases. The comparison with Duval and other traditional techniques showed good diagnostic accuracy of the new proposed triangle technique.

Vibro-Acoustic Methods in the Condition Assessment of Power Transformers: A Survey

It has been over two decades since the publication of pioneer works about the power transformer diagnostics based on monitoring of their acoustic fingerprints. Since then, there has been great progress in this field and the methods used are as complex as ever. Any unnecessary intervention on a power transformer implies its temporary disconnection from the power grid. The inability to supply electricity to the customer means not only financial loss for the utility but also generates a non-material loss, e.g., the loss of reputation to the customer. Faster, more accurate, more reliable, and less invasive diagnosis is the main reason behind development and improvement in this field. The main goal of this paper is to categorize and review state-of-the art of vibro-acoustic diagnostic methods for power transformers. This paper opens with a brief note about continuous condition monitoring, after which we overview the causes of transformer vibrations as well as the collection and preprocessing of diagnostic data. Then, we review and categorize works related to the acoustic condition assessment of power transformers considering both: feature extraction in the time, frequency, time-frequency domain, and mathematical modeling and system identification of dynamic systems.