Combined Forecasting Method of Dissolved Gases Concentration and Its Application in Condition-Based Maintenance

Liu, Chang; Zhang, Hongzhi; Xie, Zhicheng; Wang, Zhixun; Chen, Le; Lin, Xin; Ding, Hongfa; Li, Zhengtian; Xiong, Weihong

doi:10.1109/tpwrd.2018.2881747

Cited by 24 publications

(9 citation statements)

References 16 publications

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“…The prediction results show that W-LSSVM has a good learning ability for actual limited samples based on the mutation particle swarm algorithm, and the prediction ability is more stable [ 17 ]. Then, a combined prediction model based on root function neural network, backpropagation neural network, two different kernel functions of least square support vector machine, and gray model was studied in the literature [ 18 ], which can accurately predict dissolved gas in oil. Moreover, Ghunem et al [ 19 ] proposed a prediction model based on transformer oil parameters (breakdown voltage, moisture content, and acidity) and dissolved gas in oil as an input to predict furan content in transformer oil.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Wang

et al. 2022

Polymers

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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.

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Section: Introductionmentioning

confidence: 99%

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

Zhang

Wang

et al. 2022

Polymers

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“…Its empirical results illustrated that the advanced intelligent algorithm could effectively improve prediction accuracy. Liu et al [18] presented a hybrid forecasting model with the weight coefficient calculated by the cross-entropy, based on radial basis function neural network, backpropagation neural network (BPNN), LSSVM and grey model for oil dissolved gas prediction. The experimental results indicated that the combined forecasting method could guarantee better performance and guide the maintenance of devices in the actual operation.…”

Section: Introductionmentioning

confidence: 99%

Forecasting thermal parameters for ultra‐high voltage transformers using long‐ and short‐term time‐series network with conditional mutual information

Lin

Miao

Chen

et al. 2022

IET Electric Power Appl

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Precise forecasting of the thermal parameters is a critical factor for the safe operation and fault incipient warning of the ultra-high voltage (UHV) transformers. In this work, a novel multi-step forecasting method based on the long-and short-term time-series network (LSTNet) with the conditional mutual information (CMI) is proposed for the UHV transformer. To improve the computational efficiency and eliminate the redundancy, the CMI-based feature selection algorithm is applied to analyse the correlation between the original monitoring parameters and construct the optimal feature subset. LSTNet, which is composed of a convolutional layer, recurrent layer and recurrent-skip layer, is utilized to capture both the short-term nonlinear characteristics and the longterm periodic characteristics. The LSTNet model is established to forecast the variation tendency of the oil and winding temperatures for different locations in the UHV transformer. The results show that the proposed method significantly enhances the accuracy in both one-step and multi-step thermal parameters forecasting and achieves better performance in terms of the RMSE and MAE compared with other existing methods. K E Y W O R D Sconditional mutual information (CMI), forecasting method, long-and short-term time-series network (LSTNet), top oil temperature, UHV transformers, winding temperatureThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…Lin et al proposed a transformer operation state prediction method based on long short-term memory and deep belief network (LSTM_DBN), which predicted the dissolved gas concentration by developing a long short term memory (LSTM) model [12]. On the basis of radial basis function neural network (RBFNN), back propagation neural network (BPNN), LSSVM of two different kernel functions and grey model, Liu et al proposed a combined prediction model based on cross entropy, in which the weight coefficient of each algorithm is determined by cross entropy theory, and analyzed its application [13]. Peimankar, et al proposed an integrated time series prediction algorithm based on evolutionary multi-objective optimization algorithm for predicting dissolved gas concentration in power transformers, which has higher accuracy and reliability [14].…”

Section: Introductionmentioning

confidence: 99%

Prediction Model for Dissolved Gas Concentration in Transformer Oil Based on Modified Grey Wolf Optimizer and LSSVM with Grey Relational Analysis and Empirical Mode Decomposition

et al. 2020

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Oil-immersed transformer is one of the most important components in the power system. The dissolved gas concentration prediction in oil is vital for early incipient fault detection of transformer. In this paper, a model for predicting the dissolved gas concentration in power transformer based on the modified grey wolf optimizer and least squares support vector machine (MGWO-LSSVM) with grey relational analysis (GRA) and empirical mode decomposition (EMD) is proposed, in which the influence of transformer load, oil temperature and ambient temperature on gas concentration is taken into consideration. Firstly, GRA is used to analyze the correlation between dissolved gas concentration and transformer load, oil temperature and ambient temperature, and the optimal feature set affecting gas concentration is extracted and selected as the input of the prediction model. Then, EMD is used to decompose the non-stationary series data of dissolved gas concentration into stationary subsequences with different scales. Finally, the MGWO-LSSVM is used to predict each subsequence, and the prediction values of all subsequences are combined to get the final result. DGA samples from two transformers are used to verify the proposed method, which shows high prediction accuracy, stronger generalization ability and robustness by comparing with LSSVM, particle swarm optimization (PSO)-LSSVM, GWO-LSSVM, MGWO-LSSVM, EMD-PSO-LSSVM, EMD-GWO-LSSVM, EMD-MGWO-LSSVM, GRA-EMD-PSO-LSSVM and GRA-EMD-GWO-LSSVM.

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Combined Forecasting Method of Dissolved Gases Concentration and Its Application in Condition-Based Maintenance

Cited by 24 publications

References 16 publications

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

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

Forecasting thermal parameters for ultra‐high voltage transformers using long‐ and short‐term time‐series network with conditional mutual information

Prediction Model for Dissolved Gas Concentration in Transformer Oil Based on Modified Grey Wolf Optimizer and LSSVM with Grey Relational Analysis and Empirical Mode Decomposition

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