Transformer condition assessment using dissolved gas analysis, oil testing and evidential reasoning approach

Power transformer is one of the most important and expensive equipment in a power system. Its reliability directly affects a power system. To ensure the reliable operation of a power transformer, its condition needs to be continuously monitored and evaluated. Over the past two decades, a number of diagnostic techniques have been developed for transformer condition assessment such as dissolved gas analysis (DGA), degree of polymerization (DP) measurement, polarization and depolarization current (PDC) measurement, frequency domain spectroscopy (FDS), frequency response analysis (FRA), and partial discharge (PD) detection. However, the interpretations of measurement results acquired from these diagnostics are usually based upon the empirical models, which are sometimes inaccurate and incomplete especially in abnormal transformer operation scenarios. Therefore, accurate interpreting on the measurement data obtained by the above techniques and subsequently making explicit condition assessment of transformers is still a challenge task. Nowadays, considerable efforts have been made in the field of transformer condition monitoring and assessment. Majority efforts are dedicated in developing accurate transformer models and reliable transformer fault diagnosis systems. After completing a comprehensive literature review on various diagnostic techniques for transformers condition assessment, this thesis focuses on three main aspects of transformer’s health condition, including oil characteristics and dissolved gases in transformers, moisture concentration of oil-cellulose insulation and hot spot temperature of transformer windings. Since there is a lack of common framework for applying pattern recognition algorithms (i.e. data centric approaches) to interpret oil characteristics and DGA data, this thesis firstly provides a critical review on various pattern recognition techniques for power transformer insulation diagnosis using DGA and oil characteristics datasets. A general pattern recognition application framework is then proposed. The important issues for improving the applicability of pattern recognition techniques for transformer insulation diagnosis are also discussed. To improve the data quality of training database and enhance the diagnostic accuracy of pattern recognition algorithms, a hybrid algorithm, SMOTEBoost is proposed. It adopts Synthetic Minority Over-sampling Technique (SMOTE) to handle the class imbalance problem, where samples belonging to different fault types (insulation conditions) are unevenly distributed in the training database. By using the boosting approach for reweighting and grouping data points in the training database, the SMOTEBoost can facilitate pattern recognition algorithms consistently attaining desirable diagnosis accuracies. To solve the intricate difficulties in moisture estimation of transformer oil-cellulose insulation system, this thesis introduces two modelling approaches, i.e., multi-physics finite element modelling (FEM) and particle tracing method, where the temperature dependent moisture dynamics in transformers’ insulation system is taken into account. In multi-physics approach, moisture dynamics is modelled by coupling the electromagnetic, thermal, fluid flow and moisture migration physics simultaneously. In particle tracing method, moisture diffusion is formulated from a microscopic view of water molecules’ motion. Especially, the transmission probability of water molecules (termed as particles in the paper) is employed to correlate the microscopic particles’ motion with the macroscopic moisture distribution. Extensive ageing and moisture diffusion experiments have been conducted on a prototype transformer to verify the proposed modelling approaches for an accurate estimation of moisture in transformers. This thesis also proposes a distributed parameter model to investigate the effect of moisture dynamics on dielectric response of a transformer’s cellulose insulation. The correlation between moisture distribution (under non-equilibrium conditions due to thermal transients) and dielectric response parameters (dielectric losses and permittivity) of transformer cellulose insulation is revealed. The proposed methodology can help the proper interpretation of dielectric response measurement of field transformers under thermal transients. To overcome the inaccuracy in empirical thermal dynamic models, in this thesis a moisture dependent thermal model (MDTM) is developed for estimating transformer’s hot spot temperature. In this model, nonlinear thermal resistance is formulated by considering both oil and cellulose (paper and pressboard) of the transformer. Especially, the effect of moisture concentration and hot spot temperature on the thermal resistance of cellulose is taken into account. The proposed MDTM is verified by using historical data of moisture-in-oil and temperature measurements on an in-service vegetable oil-filled transformer. To integrate every piece of data and information obtained from different transformer diagnostic measurements and subsequently evaluating the overall health condition of a transformer, this thesis proposes a data and information fusion framework based on Bayesian Network (BN). Within the Bayesian Network, Monte Carlo and Bootstrap methods are employed to extract the most informative characteristics regarding transformer condition from different diagnostic measurements. Results of case studies demonstrate that the proposed data and information fusion framework can evaluate the effectiveness of combinations of different diagnostic measurements and subsequently facilitate determining optimal diagnostic strategies involved in transformer condition assessment. It is expected that the data centric diagnostic approaches developed in this thesis can provide an accurate modelling and reliable assessment of transformer’s health condition.

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.

Dissolved gas analysis (DGA) is frequently used for the condition assessment and monitoring of transformers. In the case of free-breathing transformers, gas losses may occur at the phase interface due to the chaotic molecular movement of the dissolved gas molecules. These gas losses and the resulting distortion of the interpretation basis for the DGA can lead to a false assessment of the condition. Furthermore, the relative ratios may shift, which in turn can lead to the misclassification of the present defect. This paper deals with the influences on gas losses and describes a possible consideration for use in the condition assessment. It begins with an explanation of the gas losses problem in more detail based on theoretical considerations and summarised in hypotheses. In addition to the experimental methods used, theoretical model considerations for taking gas losses into account are described below. The results deal on the one hand with the influences on the gas losses (geometry and temperature) and the other hand with the application of the developed model for the consideration of the gas losses. In detail, the results of the gassing behaviour of different complex geometries– simple phase interfaces, a transformer model and a distribution transformer– are described in more detail and it is shown that a correction of the degassing with the model is possible within certain limits. Furthermore, the methodologies for determining the model are discussed in this paper.

This paper presents the developed procedure for condition assessment of power transformer fleet in a utility responsible for supplying electricity in Thailand metropolitan area. The condition assessment is performed by evaluating the quickly-updated test results. The assessment is classified into seven main categories as dissolved gas analysis, oil quality in main tank, oil quality in on-load tap changer, bushing, lightning arrester, visual inspection and thermal scan. The scoring and weighting techniques are applied for condition assessment of each category. The procedure for condition assessment of power transformer components have been further developed as web application software, which displays the results as traffic light indicators. Finally, the developed web application software is now used as a decision support tool for power transformer maintenance in a utility in order to recommend the proper maintenance action.

It is difficult to make decision when the information is diverse and the weight of attribute is unknown.A weight calculating method based on uncertain entropy was proposed in this paper to solve this problem.In this new method,both the complete and incomplete assessments of every attribute were transformed under unified belief framework,and the objective weight of every attribute could be calculated based on its entropy.With the Evidential Reasoning(ER) approach,the new weight calculating method could realize Multiple Attribute Decision Making(MADM) based on uncertain entropy while the weight of attribute is unknown.A case was provided to illustrate the validity and feasibility of this method.

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 (C 2 H 6 ) and Hydrogen (H 2 ) 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.

Power transformers are usually very reliable and durable components. Their performance is often taken for granted and they are overlooked until a problem occurs. Unfortunately, when a fault occurs in a transformer, it can develop catastrophically and failures are usually very expensive if not uneconomic to repair. Clearly, despite the good reliability of transformers, in view of the serious consequences of failures, it is important that effective condition assessment systems are employed so that faults can be detected at an early stage so as to improve the prospects for repairs and minimize the impact of any failures. In order to improve the reliability of electricity supply to customers, CLP power has broken through the application of various condition monitoring and assessment systems for the transmission transformers, and introduced successfully such best practice to MV transformers with promising results.

The power transformer is an important equipment in the power system. Its running state is directly related to the safe and stable operation of the power grid. The volume fraction of the dissolved gas in the oil of the transformer body and its variation law are closely related to the fault mode of the transformer, therefore, the dissolved gas analysis (DGA) technology in transformer oil is widely used. Actually, the relationship between the volume fraction of the dissolved gas in transformer oil and the time is a multi-dimensional time series. The sequences are arranged in the same time interval or in different time intervals, which contains the external environment of the power transformer, the operation conditions and the inherent relationship between the gas content in the transformer oil. Therefore, the operation status of the power transformer can be revealed by the prediction of the time series. Based on this, the fault type of the transformer can be determined. At present, many literatures have been applied to a single time series forecasting method, such as BP neural network and radial basis function neural network. The single prediction method can obtained certain accuracy by adjusting the weights and thresholds of the network. To further improve the prediction accuracy, a combined forecasting model should be proposed. The research result of this paper is to predict the DGA data of transformer, and has a certain guiding significance to determine the transformer fault types. The proposed method for time series prediction can also be used in other time series prediction of the electric power system.

The working environment of transformers in the wind farm is more complex than others, which brings the difference in condition assessment. Moreover, many condition assessment methods based on characteristics or machine learning have difficulty in recognition in cases of multiple transformers, conditions and measuring points. To assess conditions, this paper establishes a condition classification model of the transformer with a modified one-dim residual neural network and uses vibration signal, current and voltage as inputs. The built network mode has faster convergence speed and classification accuracy in transformer condition assessment and is more suitable for transformer condition assessment than the original one.

Expert system for the assessment of power transformer insulation condition based on type-2 fuzzy logic systems

To allow utilities to fulfill self-imposed and regulative performance targets the demand for new optimized tools and techniques to Estimate the performance of modern Transformers has increased. The modern power transformers has subjected to different types of faults, which affect the continuity of power supply which in turn causes serious economic losses. To avoid the interruption of power supply, various fault diagnosis approaches are adopted to detect faults in the power transformer and has to eliminate the impacts of the faults at the initial stage. Among the fault diagnosis methods, the hybrid technique of Particle Swarm Optimization (PSO) with Support Vector Machine (SVM) learning algorithm is simple conceptually derived and its implementation process is faster with better scaling properties for complex problems with non linearity and load variations but performance factor related to accuracy has a declined value in case of correlations implicit . In order to obtain better fault diagnosis to improve the service of the power transformer, SVM is optimized with Improved PSO technique to achieve high interpretation accuracy for Dissolved Gas Analysis (DGA) of power transformer through the extracting positive features from both the techniques. Primary SVM is applied to establish classification features for faults in the transformer through DGA. The features are applied as input data to Autonomous optimized Technique for faults analysis. The proposed methodology obtains the DGA data set from diagnostic gas in oil of 500 KV main transformers of Pingguo Substation in South China Electric Power Company. The simulations are carried out in MATLAB software with an Intel core 3 processor with speed of 3 GHZ and 4 GB RAM PC. The result obtained by Autonomous optimized Technique (IPSO-SVM) is compared against PSO-SVM to estimate the performance of the classifiers in terms of execution time and quality of classification for precision. The test results indicate that the Autonomous optimization of IPSO-SVM approach has significantly improved the classification accuracy and computational time for power transformer fault classification. Keywords: Transformer Fault Analysis, Improved Particle Swarm Optimization, Hybrid Optimization, Dissolved Gas Analysis, Support Vector Machine

This paper proposes a knowledge acquisition method for transformer condition assessment using Synthetic Minority Over-sampling Technique (SMOTE) and decision tree algorithm. SMOTE oversampling algorithm is utilized to supplement the minority samples. The transformer condition assessment is a classification process because its results are divided into four classes: normal condition, attentive condition, abnormal condition, and serious condition. Moreover, a decision tree is constructed using C4.5 algorithm to handle this classification problem. Key indicators and rules of transformer condition assessment are derived for the decision tree. The case study is performed on 110kV oil-immersed transformers using inspection and maintenance records. These records are collected from a power distribution company. The results indicate that the proposed method can automatically acquire the knowledge of condition assessment, which is capable for decision support.

This paper illustrates a subjective safety-cost based design-decision support framework using fuzzy logic and evidential reasoning approaches. The framework is divided into three parts. The first one is for safety estimate and synthesis including fuzzy rule-based safety estimate using fuzzy rule-based evidential reasoning (FRB-ER) approach, as well as the safety synthesis using evidential reasoning approach. Considering the cost incurred for the safety improvement in a design option, the second part focuses on synthesising the safety and cost estimates using evidential reasoning approach to obtain the overall evaluation of the whole system for each design option. The third part is to apply the overall evaluation for the design selection. An illustrative example is used to demonstrate the application of the proposed framework.

In an electric power system, a power transformer is one of the most critical equipment and cannot be separated from possible abnormal conditions due to fault. Dissolved Gas Analysis (DGA) is a reliable technique for detecting the presence of a fault condition that just occurred in oil immersed transformer. Basically DGA is a process to calculate the levels or values of hydrocarbon gases that are formed due to abnormalities. The gas inside the transformer can function as a marker for various types of faults. In this paper, for DGA testing and evaluation of the type of fault in the power transformer using the interpretation of IEEE std C57.104 and IEC 60599. The method used for DGA testing according to the IEEE interpretation is total dissolved combustible gas (TDCG), key gas, doernenburg ratio, and roger ratio. While the method used for DGA testing at IEC 60599 is the duval triangle, the basic gas ratio and CO2/CO ratio. From the results of DGA tests that have been carried out, all of these methods will be used to ascertain the type of fault that occurs in the power transformer.

Power transformers are a central component in the field of energy distribution and transmission. The early recognition of incipient faults in operating transformers is substantially cost effective by lessening impromptu blackouts. A standout amongst the most responsive and dependable strategies utilized for assessing the health of oil filled electrical equipment is dissolved gas analysis (DGA). Nowadays, there is an expanding requirement for better nonintrusive diagnostic and online monitoring tools to survey the internal state of the transformers. Chemical sensors are viewed as a key innovation for condition monitoring of transformer health, coordinating the non-invasiveness with typical sensor features, such as cost, usability, portability, and the integration with the data networks. Low-cost chemical sensors-based DGA techniques are expected to drastically augment the diagnostic abilities empowering the deployment on a broader range of oil filled power assets. The recent development involves both specific sensors designed to detect individual dissolved gas in transformer oil and non-specific sensors, operated in near ambient conditions, with the potential to be applied in a DGA system. In this paper, general background and operating guidelines of DGA are presented to address the origin of the gas formation, methods for their detection and the interpretation of the results by data analytics. The recent significant interest and advancements in chemical sensors to DGA applications are reviewed. Lastly, future research perspectives and challenges for the development of novel DGA chemical sensors are also discussed.