Andrew Lapthorn

HTS Transformer: Construction Details, Test Results, and Noted Failure Mechanisms

An experimental high temperature superconducting transformer has been designed and built using Bi2223 HTS tape. The transformer is unique in that the magnetic circuit is comprised of air and a silicon steel partial core. Electrical tests were performed on the transformer and it was found to be 98.6% efficient at full load. The transformer failed during a full load endurance run and an investigation was carried out to determine the cause of the failure. The cause was believed to be from operating the HTS windings close to critical conditions. Presentation of the failure details will be of use to other researchers who are building HTS transformers.

A 15-kVA High-Temperature Superconducting Partial-Core Transformer—Part 1: Transformer Modeling

A new 15-kVA, 230-230 V, high-temperature superconducting (HTS), partial-core transformer has been designed, built, and tested. The transformer utilizes a unique core design termed partial core, consisting of a central laminated slug of core steel only. The windings are layer wound with first-generation Bi2223 HTS. A model was developed to predict the performance of the transformer as well as the ac losses of the HTS and is presented in this paper. Part 2 of this paper presents the construction details and experimental results.

Ensemble classifier selection using multi-objective PSO for fault diagnosis of power transformers

This paper presents a binary version of the Multi-Objective Particle Swarm Optimization (bi-MOPSO) algorithm to classify the faults of power transformers. The proposed method selects the most accurate and diverse classifiers, simultaneously. Then, the selected classifiers are combined to diagnose the actual faults of power transformers using dissolved gas analysis (DGA) performed on the oil of power transformers. The obtained results are compared to other scenarios such as combining the outputs of all classifiers or using only the most accurate classifier to diagnose the faults. The comparison reveals that the proposed method is highly reliable and useful for diagnosing the faults of power transformers.

A 15-kVA High-Temperature Superconducting Partial-Core Transformer—Part II: Construction Details and Experimental Testing

A new 15-kVA, 230-230 V, high-temperature superconducting, partial-core transformer has been designed, built, and tested. The transformer utilizes a unique core design called partial core, consisting of a central laminated slug of core steel only. The windings are layer wound with first-generation Bi2223 HTS. In part 1 of this paper, a model is used to predict the performance of the transformer as well as the ac losses of the HTS. In this part, a series of electrical tests was performed on the transformer, including open circuit, short circuit, resistive load, overload, ac withstand voltage, and fault ridethrough tests. The test results are compared with the model. The transformer was found to be 98.2% efficient at rated power with 2.86% voltage regulation.

Evolutionary multi-objective fault diagnosis of power transformers

Abstract This paper introduces a two step algorithm for fault diagnosis of power transformers (2-ADOPT) using a binary version of the multi-objective particle swarm optimization (MOPSO) algorithm. Feature subset selection and ensemble classifier selection are implemented to improve the diagnosing accuracy for dissolved gas analysis (DGA) of power transformers. First, the proposed method selects the most effective features in a multi objective framework and the optimum number of features, simultaneously, which are used as inputs to train classifiers in the next step. The input features are composed of DGA performed on the oil of power transformers along with the various ratios of these gases. In the second step, the most accurate and diverse classifiers are selected to create a classifier ensemble. Finally, the outputs of selected classifiers are combined using the Dempster-Shafer combination rule in order to determine the actual faults of power transformers. In addition, the obtained results of the proposed method are compared to three other scenarios: 1) multi-objective ensemble classifier selection without any feature selection step which takes all the features to train classifiers and then applies MOPSO algorithm to find the best ensemble of classifiers, 2) a well-known classifier ensemble technique called random forests, and 3) another powerful decision tree ensemble which is called oblique random forests. The comparison results were favourable to the proposed method and showed the high reliability of this method for power transformers fault classification.

Multiobjective Design Optimization Using Dual-Level Response Surface Methodology and Booth's Algorithm for Permanent Magnet Synchronous Generators

This paper studies a dual-level response surface methodology (DRSM) coupled with Booths algorithm using a simulated annealing (BA-SA) method as a multiobjective technique for parametric modeling and machine design optimization for the first time. The aim of the research is for power maximization and cost of manufacture minimization resulting in a highly optimized wind generator to improve small power generation performance. The DRSM is employed to determine the best set of design parameters for power maximization in a surface-mounted permanent magnet synchronous generator with an exterior-rotor topology. Additionally, the BA-SA method is investigated to minimize material cost while keeping the volume constant. DRSM by different design functions including mixed resolution robust design, full factorial design, central composite design, and box-behnken design are applied to optimize the power performance resulting in very small errors. An analysis of the variance via multilevel RSM plots is used to check the adequacy of fit in the design region and determines the parameter settings to manufacture a high-quality wind generator. The analytical and numerical calculations have been experimentally verified and have successfully validated the theoretical and multiobjective optimization design methods presented.

Multi-objective ensemble forecasting with an application to power transformers

Abstract In this paper we present an ensemble time series forecasting algorithm using evolutionary multi-objective optimization algorithms to predict dissolved gas contents in power transformers. In this method, the correlation between each individual dissolved gas and other transformers’ features such as temperature characteristics and loading history is first determined. Then, a non-linear principal component analysis (NLPCA) technique is applied to extract the most effective time series from the highly correlated features. Afterwards, the forecasting algorithms are trained using a cross validation technique. In addition, evolutionary multi-objective optimization algorithms are used to select the most accurate and diverse group of forecasting algorithms to construct an ensemble. Finally, the selected ensemble is examined to predict the value of the dissolved gases on the testing set. The results of one day, two day, three day, and four day ahead forecasting are presented which show higher accuracy and reliability of the proposed method compared with other statistical methods.

The Dielectric Strength of Nomex 410 Paper in Liquid Nitrogen Under Boiling Situations

It is crucial for the successful application of high-temperature superconducting (HTS) power transformers that they are able to survive under abnormal operations, such as a short-circuit fault. Modeling, based on the design of a four-winding three-phase HTS power transformer, suggests severe boiling will occur on the surface of the winding during a short-circuit fault and will jeopardize the turn-to-turn insulation of the HTS power transformer. In this paper, a turn-to-turn insulation breakdown experiment platform involving the introduction of boiling on the surface of electrode has been built. Nomex 410 paper, one of the candidates for turn-to-turn insulation in the design of the HTS power transformer, was tested. The characteristics of dielectric strength of Nomex 410 in three different thicknesses (0.13, 0.18, and 0.25 mm) have been investigated, without boiling, as well as under different boiling situations, in an open bath of liquid nitrogen. The voltage application methods applied during both experiments test the electrical breakdown of Nomex 410 under a dynamic situation. The experiment results show that when under film boiling, compared to the test results for nucleate boiling, the dielectric strength of Nomex 410 paper decreases by approximately 38% of the value when no boiling is involved. Moreover, it is verified that the presence of an electric field accelerates the heat transfer between the liquid nitrogen and the heated electrode.

Evaluation and optimization of network capacity utilization for electric distribution

The optimization of network capacity utilization is becoming increasingly important in an electric distribution network (EDN) with the variation in load demand due to seasonal changes and uptake of new technologies such as photovoltaics, batteries, electric vehicles and distributed generation. The proliferation in the use of these technologies introduces uncertainties in future load demands and increases the risk of asset stranding. In this paper, the thermal model of key distribution assets are summarized. Based on the thermal model, a network evaluation and optimization system were developed and discussed. Benefits in the application of dynamic rating in an EDN to optimize and evaluate the utilization of the network assets are then highlighted.

Equivalent circuit model of cascade connected partial core resonant transformers

This paper presents a proof of concept and equivalent circuit analysis of a cascade arrangement of tunable HV testing transformers intended for field use. The transformers use a partial core with air completing the flux path and are tuned to resonate with insulation capacitance. This minimises the power drawn from the supply and the size and weight of the transformer. A prototype set of transformers were built to validate the model. Each transformer was modelled as a set of coupled inductors to determine the input impedance frequency response. Good agreement is shown between the modelled and measured input impedance. The inclusion of core loss resistance was shown to significantly increase the accuracy of the cascade model.