Review of Physicochemical-Based Diagnostic Techniques for Assessing Insulation Condition in Aged Transformers

This article presents evolving neural nets (ENNs) for protection and condition monitoring of power transformer. Based on the proposed evolutionary algorithm, the ENNs automatically tune the network parameters (connection weights and bias terms) of the neural nets to achieve the best model. The ENNs can identify, classify and detect the fault and issue the trip signal in the case of internal fault only, using the global search capabilities of the evolutionary algorithm and the highly nonlinear mapping nature of the neural nets. The proposed protection scheme has been realized through two different structures using ENNs algorithm. This scheme has been evaluated using simulated data obtained through EMTP/ATP package. The results amply demonstrate the capabilities of fault detector (FD) and condition monitoring (CM) in terms of accuracy and speed with respect to detection of fault, classification and pattern recognition of different event of power transformer.

The power transformer plays a vital role in the power system network. Nowadays, the condition monitoring of oil-filled power transformer is greatest requirement for its reliable operations and quality power supply to the utility. The condition of such transformer can be indeed determined by knowing the ageing status of the dielectric liquid used for insulation and cooling. Though Dissolved Gas Analysis (DGA) has been well-acclaimed in the field of the transformer fault diagnosis, it has certain drawbacks like need of carrier gas, regular calibration. To overcome these drawbacks, in this proposed work, optical methods such as Ultra Violet-Visible (UV-vis) spectroscopy and Nuclear Magnetic Resonance (NMR) have been used. The obtain results of UV-vis spectroscopy demonstrate that all the characteristic peaks are covered and qualitatively analyzed within the wavelength region of 200–400 nm. Further, NMR spectroscopy results precisely identified the molecular groups present in the oil such as the methyl group, methylene group and the aromatic hydrocarbon groups. Moreover, this method is fast and reliable for determining the moisture content in the transformer oil. Overall, the obtained data confirms that UV-visible and NMR spectroscopy methods are highly efficient for analyzing the degraded transformer oils.

The secure and reliable operation of power transformer is of significance to the construction of new power system and the national target of "dual carbon". Acoustic signals are important for the condition monitoring of transformer. In view of the difficulties to obtain abundant data samples of acoustic signals for the recognition of different failures of transformer, this paper proposes an online condition monitoring method for power transformer by the deep Q-network. With the input of the gammatone frequency cepstral coefficients (GFCCs) and the interaction of Q-learning strategy with the environment, the deep Q-network (DQN) model is built to recognize the operation condition of transformer by properly selecting the hyper-parameters of the framework. The acoustic signals of a 500kV power transformer during the running process are measured and analyzed. It is seen that the designed DQN model converges fast with high learning performance and exploring ability and high recognition rate.

In order to take advantage of the merits of WPT and HHT in feature extraction from vibration signals of power transformer, a time-scale-frequency analysis method is developed based on the combination of these two techniques. This method consists of two steps. First, the desirable wavelet packet nodes corresponding to characteristic frequency bands of power transformer are selected through a Correlation Degree Threshold Screening (CDTS) technique for reconstructing a time-domain signal that contains useful information of power transformer. Second, the HHT is then conducted on the reconstructed signal to track the instantaneous frequencies corresponding to natural characteristics of power transformer. Experimental results are provided by analyzing a real power transformer vibration signal. Compared with the features extracted by directly using HHT, the features obtained by the proposed method reveal clearer condition pattern of the transformer, which shows the potential of this method in condition monitoring of power transformer.

This paper provides the information on the diagnostics techniques for condition monitoring of power transformer (PT). This paper deals with the practical importance of the transformer diagnostic in the Electrical Engineering field. A review has been made on the modern development of this practical technology.

A series of accelerated aging tests of high voltage transformer oil from Karamay of China were completed. All the oil samples were processed by dehydration and degasification firstly, and added with different contents of oxygen and copper, then aged under 120degC in different periods up to 675 hours, which is equal to 30 years on service of transformer oil. Improved mini-static tester was adopted to measure the electrostatic charging tendency (ECT), meanwhile, other traditional parameters, such as dissipation factor, volume resistivity, acid number, were also measured according to standard methods. The results indicate that ECT increases with aging time obviously, addition of copper advances the process of accelerated aging and leads to saturated period occurred in the late part of the aging (450 h-675 h). It can be found from mathematic analysis that correlation between ECT and dissipation factor is positive and the highest. These results are valuable for the life evaluation and condition monitoring of large power transformers.

Condition monitoring of power transformers is a significant issue for electrical utilities. Research has demonstrated the efficacy of employing ultra high frequency measurement of partial discharge in the monitoring of transformers. A condition monitoring architecture is described that supports the capture and interpretation of diagnostic data, and provides engineers with meaningful diagnostic advice using intelligent system technologies.

Acoustic emission has been applied in identifying evolving failures of electrical, mechanical or thermal nature in power transformers and reactors. Different kinds of failures can be identified by the characteristics of the signals received and also give the location of the source of the signals. Also, operating sequence and contact timing of on-load tap changers can be evaluated with the aid of acoustic emission.The technique has the advantage of being utilized with in-service, energized equipments, avoiding downtime to perform tests. Case studies of acoustic emission tests performed to condition monitoring of several high-voltage power transformers and reactors, rated 230 and 500 kV are presented. Results have demonstrated the effectiveness of the acoustic emission technique as a tool for the condition diagnostic of electric power equipments.KeywordsAcoustic emissionPartial dischargesPower transformersShunt reactors

Oil-paper insulation has been widely used in power equipments such as bushing, high voltage cable and power transformer. As a result, effective methods to diagnose equipment's insulation condition that in high reliability are urgently needed. In recent year, with advantages of accurate results and strong anti-interference ability, frequency domain spectroscopy(FDS) has been widely used for insulation condition diagnosis of oil-paper insulating equipment all over the world. In this paper, a new modeling method using lumped element circuit was introduced to study the effect of temperature factor on the overall broadband frequency domain spectroscopy curves of transformer, and results from XY model, finite element model (FEM) and lumped element circuit (LEC) were compared to discuss their different characteristics and the application scope. Results show that there are peaks and valleys at about 105Hz of the FDS curves got from lumped element circuit, and temperature mainly affects the amplitude of peaks and valleys. Data got from XY model and finite element model have a high consistency, but are still different in certain frequency bands. The curves of dielectric loss tangent got from lumped element circuit are quite different with the former two models. Lumped element circuit should be firstly considered when the insulation condition of transformers are assessed from multiple perspectives or with broadband curves.

Frequency Response Analysis (FRA) is one of the most effective tools for the condition monitoring and mechanical fault detection of power transformers. Accurate interpretation of FRA is critical while evaluating the condition of a transformer. The analysis of FRA responses is done by comparing the measured parameters with the reference measurements that could be previous measurements on the same or identical units. For the accurate interpretation of FRA results, it is important to follow the recommendations given in IEEE guide, IEC standards. This paper presents the basic guidelines for developing a benchmark for the Interpretation and test procedures of the transformer Frequency Response Analysis based upon the analysis of standards, reports, guides and case studies published by IEEE, IEC and CIGRÉ etc.

One application for wireless sensor networks (WSN) is the condition monitoring of high voltage power transformers to actively monitor, predict and identify causes of failures. This paper details a prototype design and testing of a hybrid power management system for a wireless sensor mote. The sensor mote, mounted on the outside of a high voltage transformer, utilizes a piezoelectric cantilever to generate electricity from the transformer tank vibration. Together with solar energy harvesting, the system allows for a battery-less self-sustained wireless sensor mote capable of autonomously monitoring its surroundings.

The condition monitoring and fault diagnosis of power transformers plays a significant role in the safe, stable and reliable operation of the whole power system. Dissolved gas analysis (DGA) methods are widely used for fault diagnosis, however, their accuracy is limited by the selection of DGA features and the performance of fault diagnosis models, for example, the classical support vector machine (SVM), is easily affected by unbalanced training samples. This paper presents a transformer fault diagnosis model based on chemical reaction optimization and a twin support vector machine. Twin support vector machines (TWSVMs) are used as classifiers for solving problems involving unbalanced and insufficient samples. Restricted Boltzmann machines (RBMs) are used for data preprocessing to ensure the effective identification of feature parameters and improve the efficiency and accuracy of fault diagnosis. The chemical reaction optimization (CRO) algorithm is used to optimize TWSVM parameters to select the optimal training parameters. The cross-validation (CV) method is used to ensure the reliability and generalization ability of the diagnostic model. Finally, the validity of the model is verified using real fault samples and random testing.

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.

Aging assessment of transformer solid insulation: A review

Transformers is one of the key equipment of the power system. Power rating of the transformers varies from KVA to several hundreds of MVA. The transformer is expensive equipment of the power system. Failures of transformer usually lead to substantial profit loss to the utility, potential environmental damage, explosion and fire hazards and expensive repairing or replacement costs, thus, it is desirable that the maximum service life of transformer is required. Condition monitoring of transformer can help to increase the life of the transformer and reduce the maintenance cost. Online monitoring is the record of significant data of a transformer and analysis of data and including the history of the transformer. In this paper percent the different techniques adopted for condition monitoring of transformer.