Dissolved gas analysis (DGA) of oil is used to detect the incipient fault of power transformers. This paper presents a new approach for transformer fault diagnosis based on selected gas ratios concentrated in oil and support vector machine (SVM). Firstly, based on IEC TC 10 database, the optimal dissolved gas ratios (ODGR) are obtained by genetic algorithm (GA) that is designed for simultaneous DGA ratios selection and SVM parameters optimization. Three traditional methods, namely, DGA data with SVM and back propagation neural network (BPNN), IEC criteria, and IEC three-key gas ratios with SVM and BPNN are employed for effectiveness comparison. The fault diagnosis results of IEC TC 10 database show that the proposed ODGR with SVM may be used as an alternative tool for transformer fault diagnosis. In addition, the robustness and generalization ability of ODGR is confirmed by the diagnosis accuracy of 87.18% of China DGA samples. The obtained results illustrate that it is preferable to apply the proposed ODGR to transformer fault diagnosis with the assistance of SVM.

Optimal dissolved gas ratios selected by genetic algorithm for power transformer fault diagnosis based on support vector machine

High performance computing for detection of electricity theft

Rolling element bearing fault detection using support vector machine with improved ant colony optimization

This paper presents a novel approach for power transformer incipient fault diagnosis through the analysis of dissolved gas in oil. The proposed approach is implemented for improving the diagnosis accuracy by dissolved gas analysis (DGA) of power transformer based on the combined use of a multi-classification algorithm self-adaptive evolutionary extreme learning machine (SaE-ELM) and a simple arctangent transform (AT). On the one hand, the SaE-ELM algorithm has the ability to approximate any nonlinear functions with its structure parameters, i.e. hidden node biases and output weights, optimized self-sufficiently. On the other hand, the AT can alter the data structure of the experiment data, which will enhance the generalization capability for SaE-ELM as well as other machine learning algorithms. Thus, the combination of SaEELM and AT can complement each other and improve the diagnosis accuracy from the aspect of both algorithm and data structure. The performances of the proposed approach are compared with that derived from ANN, SVM, and ELM methods, respectively. Experimental results with both published and power utility provided data indicate that the developed approach can significantly improve the accuracies for power transformer fault diagnosis.

Interpretation of DGA for transformer fault diagnosis with complementary SaE-ELM and arctangent transform

State-of-the-art of controllers and soft computing techniques for regulated load frequency management of single/multi-area traditional and renewable energy based power systems

Function analysis based rule extraction from artificial neural networks for transformer incipient fault diagnosis

Integrating AI based DGA fault diagnosis using Dempster–Shafer Theory

This paper presents the design of proportional-integral-derivative controller with derivative filter (PIDF) for frequency regulation of a two-area interconnected Solar Photovoltaic (PV)-Thermal power system. A recently developed new metaheuristic algorithm called Mouth-Flame Optimization (MFO) algorithm has been proposed for tuning the gains of PIDF and proportional-integral (PI) controllers. The supremacy of the MFO based PIDF has been proven by contrasting with PI controllers optimized with different well-known competitive algorithms in the proposed power system. The different performance indices have been presented to validate the improved performance of the studied controller. The robustness analysis of the optimal proposed controller have been performed with uncertainty condition made by varying different system parameters.

Frequency Regulation in PV integrated Power System using MFO tuned PIDF controller

Incipient transformer faults usually develop slowly, often in the form of gradual deterioration of insulation due to some cause. Dissolved Gas Analysis (DGA) methods identify faults by considering the ratios of specific dissolved gas concentrations in the transformer mineral oil. This paper compares the two most prevalent methods used for DGA of transformers for incipient faults. The transformer data has been analyzed using Rogers' ratio method, IEC ratio method for DGA using fuzzy logic. The prediction of faults by each of the methods has been compared.

Transformer incipient fault diagnosis based on DGA using fuzzy logic

The gases generated in oil filled transformers can be used for qualitative determination of incipient faults. The Dissolved Gas Analysis has been widely used by utilities throughout the world as the primarily diagnostic tool for transformer maintenance. In this paper, various Artificial Intelligence Techniques that have been used by the researchers in the past have been reviewed, some conclusions have been drawn and a sequential hybrid system has been proposed. The synergy of ANN and FIS can be a good solution for reliable results for predicting faults because one should not rely on a single technology when dealing with real–life applications. Keywords—Dissolved Gas Analysis, Artificial Intelligence Techniques, Incipient Faults, Transformer Fault Diagnosis, and Hybrid Systems.

Raj Kumar Bansal

Papers

Function analysis based rule extraction from artificial neural networks for transformer incipient fault diagnosis

Integrating AI based DGA fault diagnosis using Dempster–Shafer Theory

Frequency Regulation in PV integrated Power System using MFO tuned PIDF controller

Transformer incipient fault diagnosis based on DGA using fuzzy logic

Application of Artificial Intelligence Techniques for Dissolved Gas Analysis of Transformers-A Review