Dissolved gases forecast to enhance oil-immersed transformer fault diagnosis with grey prediction-clustering analysis

Abstract

A method is proposed for dissolved gases forecast and fault diagnosis in oil-immersed transformers using grey prediction-clustering analysis. Incipient faults can produce hydrocarbon molecules and carbon oxides due to the thermal decomposition of mineral oil, cellulose and other solid insulation. Dissolved gas analysis is employed to detect and monitor abnormal conditions in oil-immersed power transformers. However, the procedure takes a long time to decompose overall key gases and monitor conditions. The grey prediction GM(1, 2) model uses the variant information of hydrogen to forecast the further trends of both combustible and non-combustible gases. Grey clustering analysis is applied to diagnose internal faults including thermal faults, electrical faults and faults involving cellulose degradation. Numerical tests with field gas records were conducted to show the effectiveness of the proposed model, and are easy to implement with the help of portable devices.

Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey

“…The diagnostic results indicate that the fault samples are effectively classified using this proposed method and the fault diagnosis precision is improved, by comparing it with the results obtained by BPNN. Lin et al [63] combined the grey prediction with clustering analysis, and developed a relevant model to enhance oil-immersed transformer fault diagnosis using dissolved gases forecasting. Aiming at the problem that power transformer fault reasons are very complicated owing to the fuzziness and uncertainty between the failure phenomenon and failure mechanisms, Zheng et al [81] proposed an iterative self-organizing data analysis technique algorithm, called ISODATA, based on DGA, which can largely overcome the dependence on initial cluster centre and can be easily applied to oil-immersed transformer fault diagnosis.…”

“…Analogously, Chang et al [62] proposed a fault diagnosis method for transformer based on the DGA and grey relational theory, which is available for the transformer fault diagnosis and has fault classified ability. Lin et al [63] proposed a method for dissolved-gases prediction and fault diagnosis in oil-immersed transformers using grey prediction-clustering analysis. In this model, DGA is employed to detect and monitor abnormal conditions in transformer, the grey prediction GM(1, 2) model is used to forecast the further trends of both combustible and non-combustible gases by using the variant information of hydrogen, and the grey clustering analysis is applied for internal faults diagnosis.…”

“…Among them, the most important theory in the grey decision model is the weighted grey target theory. Recently, the weighted grey target theory and GRA model have been extensively adopted in fault diagnosis of the transformer [59][60][61][62][63][64][65][66][185][186][187]. The two are briefly introduced as follows:…”

“…Procedures of GRA Primary Means needs less data for fault diagnosis high precision without prior information good at dealing with the small samples with some information known and some information unknown, as well as the uncertain systems with poor information first construct a comparative sequence then use GRA to calculate grey correlation between comparative sequence and reference sequence lastly determine the actual fault mode according to the calculated grey correlation weighted grey target theory [59][60][61][62][63][64][65][66][185][186][187]190] GRA model [154,188] combined with hot spot temperature [59] combined with fuzzy SVM [154] grey relation entropy [61] grey prediction-clustering analysis [63] improved grey correlation analysis model [191] combined with feedback cloud entropy model [192] combined with improved SVM, cloud entropy model, BN, ML and data mining techniques…”

“…In other words, it is essential to explore the principles, methods and means from various disciplines that are helpful to the fault diagnosis of transformers, so as to make the fault diagnosis technology interdisciplinary. Aiming at the limitations of traditional methods above, with the rapid development of computer technology and artificial intelligence (AI) theory, multiple intelligence techniques, including artificial neural network (ANN) [37][38][39][40][41][42][43][44][45][46], expert system (EPS) [47][48][49][50][51], fuzzy theory [52][53][54][55][56][57][58], rough sets theory (RST) [36], grey system theory (GST) [59][60][61][62][63][64][65][66], and other intelligent diagnosis tools [5, such as swarm intelligence (SI) algorithm, data mining technology, machine learning (ML), mathematical statistics method, wavelet analysis (WA), optimized neural network, Bayesian network (BN), and evidential reasoning approach, have been introduced to the research field of transformer fault diagnosis based on the DGA approach. These intelligent methods make up for the deficiencies of the mentioned traditional DGA methods, and directly or indirectly improve the accuracy of transformer fault diagnosis, and provide a new train of thought for high-precision transformer fault diagnosis.…”