A New Support Vector Machine Model Based on Improved Imperialist Competitive Algorithm for Fault Diagnosis of Oil-immersed Transformers

Abstract

-Support vector machine (SVM) is introduced as an effective fault diagnosis technique based on dissolved gases analysis (DGA) for oil-immersed transformers with maximum generalization ability; however, the applicability of the SVM is highly affected due to the difficulty of selecting the SVM parameters appropriately. Therefore, a novel approach combing SVM with improved imperialist competitive algorithm (IICA) for fault diagnosis of oil-immersed transformers was proposed in the paper. The improved ICA, which is proved to be an effective optimization approach, is employed to optimize the parameters of SVM. Cross validation and normalizations were applied in the training processes of SVM and the trained SVM model with the optimized parameters was established for fault diagnosis of oil-immersed transformers. Three classification benchmark sets were studied based on particle swarm optimization SVM (PSOSVM) and IICASVM with four multiple classification schemes to select the best scheme for transformer fault diagnosis. The results show that the proposed model can obtain higher diagnosis accuracy than other methods. The comparisons confirm that the proposed model is an effective approach for classification problems.

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

“…In the two investigations, the rough set theory has been integrated into SVM to obtain the rough faulty point of the power transformer with a satisfactory accuracy. In addition, Zhang et al [188] developed a new SVM model for fault diagnosis of oil-immersed transformers based on an improved imperialist competitive algorithm (IICA), in which SVM is introduced as an effective fault diagnosis technique based on DGA for transformers with maximum generalization ability, and the IICA is employed to optimize the SVM parameters appropriately. Three classification benchmark sets are investigated in [188] based on PSO-SVM and IICA-SVM with four multiple classification schemes to select the best scheme for transformer fault diagnosis.…”

“…In addition, Zhang et al [188] developed a new SVM model for fault diagnosis of oil-immersed transformers based on an improved imperialist competitive algorithm (IICA), in which SVM is introduced as an effective fault diagnosis technique based on DGA for transformers with maximum generalization ability, and the IICA is employed to optimize the SVM parameters appropriately. Three classification benchmark sets are investigated in [188] based on PSO-SVM and IICA-SVM with four multiple classification schemes to select the best scheme for transformer fault diagnosis. Meanwhile, Chao et al [199] thoroughly investigated the combined improved artificial fish swarm and SVM applied in transformer fault diagnosis; and Wang et al [263] made use of the distinctive strength of SVM algorithm in solving small sample size problems and applied the SVM in DGA-based transformer fault diagnosis, by employing the cross-validation based grid search method to determine the parameters of SVM, so as to construct the power transformer fault diagnosis model, which is better used in practice.…”

“…The basic idea of GRA is to determine the degree of correlation between the factors according to the similarity degree of the geometric shape of their variation curves. Through quantitative analysis of the development trend of dynamic processes, this method can achieve the comparison of geometric relations of statistical data related to time series, so as to find out the grey relational degree among all factors [154,188]. As elaborated in [154], the grey relational degree is introduced to measure the affinity among the factors, in order to obtain the main factors affecting the concentration of each kind of characteristic gas.…”

“…In addition to the five main categories of research approaches and techniques summarized above, there are some other intelligent algorithms, such as artificial immune algorithm (AIA) [72,163,176], GA [67,68,196], improved artificial fish swarm optimizer (IAFSO) [197][198][199], PSO [69,77,80], dynamic clustering (DC) [79,81], WA [83,[124][125][126][127], SVM [5,68,72,77,80,85,154,169,170,188,199], BN [87,[166][167][168], information fusion technology [200][201][202], extreme learning machine (ELM) [203][204][205], DL [70,71,105,206,207], optimized neural network [208,209], and evident...…”

“…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…”