Improved Consistent Interpretation Approach of Fault Type within Power Transformers Using Dissolved Gas Analysis and Gene Expression Programming

Abstract

Dissolved gas analysis (DGA) of transformer oil is considered to be the utmost reliable condition monitoring technique currently used to detect incipient faults within power transformers. While the measurement accuracy has become relatively high since the development of various off-line and on-line measuring sensors, interpretation techniques of DGA results still depend on the level of personnel expertise more than analytical formulation. Therefore, various interpretation techniques may lead to different conclusions for the same oil sample. Moreover, ratio-based interpretation techniques may fail in interpreting DGA data in case of multiple fault conditions and when the oil sample comprises insignificant amount of the gases used in the specified ratios. This paper introduces an improved approach to overcome the limitations of conventional DGA interpretation techniques, automate and standardize the DGA interpretation process. The approach is built based on incorporating all conventional DGA interpretation techniques in one expert system to identify the fault type in a more consistent and reliable way. Gene Expression Programming is employed to establish this expert system. Results show that the proposed approach provides more reliable results than using individual conventional methods that are currently adopted by industry practice worldwide.

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

“…Energies 2019, 12, 4170 2 of 18 DGA interpretation methods [1], including key gas method [2,3], IEC three-ratio method [4,5], Duval triangle method [6], Rogers ratio method [7] and Dornenburg ratio method [8], Duval pentagon [9], Mansour pentagon method [10,11], etc., are available to identify the different types of faults occurring in operating transformers. Although the commonly used methods are simple and effective in transformer fault diagnosis, they suffer from defects such as coding deficiencies, excessive coding boundaries and critical value criterion defects, which will affect the reliability of fault analysis [12].With the development of artificial intelligence (AI), machine learning and pattern recognition methods have been widely used in power transformer fault diagnosis, including artificial neural network (ANN) [13][14][15], support vector machine (SVM) [16][17][18][19][20][21][22][23][24], probabilistic neural network [25,26], Bayesian neural network [27], fuzzy logic [28][29][30], deep belief network [31], expert system [32,33], which make up for the shortcomings of the traditional DGA methods, directly or indirectly improve the accuracy of transformer fault diagnosis, and provide a new idea for high-precision transformer fault diagnosis. Although these methods have achieved good results, there are also some shortcomings.…”

Gene expression technique‐based approach to improve the accuracy of estimating the total generated power by neighbouring photovoltaic systems

“…This technique has been inspired by two early genetic techniques of GA and GP. Similar to these two techniques, the GEP technique selects the solutions (individuals or chromosomes) based on several metrics and presents solutions at each stage based on a number of genetic operators [34]. However, while the solutions in the GA are of fixed length and linear strings and in the GP approach are of nonlinear shapes and sizes, the solutions in the GEP are of fixed‐length, nonlinear entities, different sizes and shapes which combines the benefits of GA and GP approaches [35, 36].…”

“…The main characteristic of the GEP technique is the capability of expressing a mathematical model between dependent and independent parameters with high accuracy [37]. GEP is a powerful evolutionary technique that can be used to construct a mathematical model by training non‐linear data to find the correlation between the considered input and output parameters [34, 38]. This technique achieves a practice of analysing, estimating, forecasting and optimising the provided data set [39].…”

“…The employed GEP algorithm identifies a precise correlation between the VRI and a set of input variables. At the end of the training stage on properly collected data, the GEP results in a new model that can identify the relationship between the input and output data [34, 35, 37]. The proposed technique consists of two stages; training and testing.…”

Towards Precise Interpretation of Oil Transformers via Novel Combined Techniques Based on DGA and Partial Discharge Sensors

“…In [ 25 ], a deep parallel detection technique for dissolved gas analysis of the transformer is used. In [ 26 ], modified diagnosis techniques of fault types within oil-immersed power transformers using DGA and genetic algorithm software are presented.…”