Concentration Prediction of Polymer Insulation Aging Indicator-Alcohols in Oil Based on Genetic Algorithm-Optimized Support Vector Machines

Abstract

The predictive model of aging indicator based on intelligent algorithms has become an auxiliary method for the aging condition of transformer polymer insulation. However, most of the current research on the concentration prediction of aging products focuses on dissolved gases in oil, and the concentration prediction of alcohols in oil is ignored. As new types of aging indicators, alcohols (methanol, ethanol) are becoming prevalent in the aging evaluation of transformer polymer insulation. To address this, this study proposes a prediction model for the concentration of alcohols based on a genetic-algorithm-optimized support vector machine (GA-SVM). Firstly, accelerated thermal aging experiments on oil-paper insulation are conducted, and the concentration of alcohols is measured. Then, the data of the past 4 days of aging are used as the input feature of SVM, and the GA algorithm is utilized to optimize the kernel function parameter and penalty factor of SVM. Moreover, the concentrations of methanol and ethanol are predicted, after which the prediction accuracy of other algorithms and GA-SVM are compared. Finally, an industrial software program for predicting the concentration of methanol and ethanol is established. The results show that the mean square errors (MSE) of methanol and ethanol concentration predictions of the model proposed in this paper are 0.008 and 0.003, respectively. The prediction model proposed in this paper can track changes in methanol and ethanol concentrations well, providing a theoretical basis for the field of alcohol concentration prediction in transformer oil.

A novel artificial neural network approach for residual life estimation of paper insulation in oil‐immersed power transformers

“…The developed models have been trained using the prepared experimental dataset and establishing the correlation between failure indices taken one at a time, moisture and temperature to estimate the useful life of transformer insulation. The correlation established resembles the modified Arrhenius equation as given in Equation (1). Each training set consists of three input vectors, that is, a failure index, a moisture level and a constant temperature, and a single output vector which gives the time to failure of CSKP insulation for the four NN models.…”

“…It is a significant asset to which everyone relies. Its failure will cause unwanted interruption in the power supply leading to serious financial consequences [1,2]. So, it is pertinent to spot the severe faults of an LFEPT at earlier stages for its rectification to enjoy an uninterrupted supply and get rid of costly outages.…”

Application of back propagation neural network in complex diagnostics and forecasting loss of life of cellulose paper insulation in oil-immersed transformers

“… Years Proposed technique Contribution 42 2021 Regression modeling The study estimated DP utilizing the furfural marker at different oil-to-pressboard ratios and oil change statuses 43 2022 Artificial Neural Network (ANN) The study estimated furans by analyzing temperature, carbon dioxide, carbon monoxide, and moisture to estimate DP 44 2022 Empirical modeling The study estimated DP utilizing methanol concentrations obtained at low temperatures. The relative error was 7% 45 2023 ANFIS, Roger’s ratio approach A hybrid Rogers ratio technique-based ANFIS was proposed to detect transformer faults. The training was carried out by employing the gas ratios presented by the IEEE C57-104 and IEC 60599 standards 46 2024 Multi-classification model The study analyzes DGA by using machine learning (ML) techniques, adherence to IEC 60599:2022, and Eskom (Specification—Ref: 240-75,661,431) standards Current study 2024 Back Propagation Neural Network (BPNN) Presented in section “ Introduction ” (Paper contribution) …”

Effective Electrical Properties and Fault Diagnosis of Insulating Oil Using the 2D Cell Method and NSGA-II Genetic Algorithm

“…GAs have been used previously to detect incipient transformer oil faults [ 17 ], while GA-based predictive models have been used as an auxiliary indicator method to determine the aging condition of transformer polymer insulation [ 18 ]. In [ 19 ], a GA was used for accurate measurements of partial discharge.…”