Nonlinear Autoregressive Neural Network Models for Prediction of Transformer Oil-Dissolved Gas Concentrations

Pereira, Fábio Henrique; Bezerra, Francisco Elânio; Júnior, S. Toledo Piza; Santos, Josemir Coelho; Chabu, Ivan Eduardo; Souza, Gilberto Francisco Martha de; Micerino, Fábio; Nabeta, Sílvio Ikuyo

doi:10.3390/en11071691

Cited by 30 publications

(19 citation statements)

References 23 publications

(27 reference statements)

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“…The authors of [35] proposed a deep belief network (DBN) approach to forecast transformer dissolved gas content. Pereira et al [36] proposed a nonlinear autoregressive neural network model combined with discrete wavelet transform to forecast the content of dissolved gas in transformer oil, which showed better results compared with the current prediction model and commonly used time series technique.…”

Section: Related Workmentioning

confidence: 99%

An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil

et al. 2020

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Transformer state forecasting and fault forecasting are important for the stable operation of power equipment and the normal operation of power systems. Forecasting of the dissolved gas content in oil is widely conducted for transformer faults, but its accuracy is affected by data scale and data characteristics. Based on phase space reconstruction (PSR) and weighted least squares support vector machine (WLSSVM), a forecasting model of time series of dissolved gas content in transformer oil is proposed in this paper. The phase spaces of time series of the dissolved gas content sequence are reconstructed by chaos theory, and the delay time and dimension are obtained by the C-C method. The WLSSVM model is used to forecast time series of dissolved gas content, the chemical reaction optimization (CRO) algorithm is used to optimize training parameters, the bootstrap method is used to build forecasting intervals. Finally, the accuracy and generalization ability of the forecasting model are verified by the analysis of actual case and the comparison of different models.

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Section: Related Workmentioning

confidence: 99%

An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil

et al. 2020

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“…Pereira, F.H. et al proposed a nonlinear autoregressive neural network model combined with discrete wavelet transform to predict the concentration of dissolved gas, which shows better prediction results compared with the current prediction models and the commonly used time series techniques [11]. 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].…”

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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“…Many approaches based on artificial intelligence (AI) have been proposed and applied for forecasting the concentration of dissolved gases in power transformers, such as grey model (GM) [10], artificial neural networks (ANN) [11][12][13][14][15], least squares support vector machine (LSSVM) [16][17][18][19] and support vector regression (SVR) [20,21], etc. Each approach has its own advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

“…Mixed-kernel functions (MKF) have recently attracted great attention since they are able to achieve better classification and regression performance [23,24]. According to literature reviews [10][11][12][13][14][15][16][17][18][19][20][21][22], previously proposed forecasting models are implemented by a single kernel, and MKF-SVR model for dissolved gas content forecasting is rarely investigated. Therefore, in this paper we intend to propose a novel forecasting model based on MKF-SVR to improve prediction performance.…”

Section: Introductionmentioning

confidence: 99%

Mixed Kernel Function Support Vector Regression with Genetic Algorithm for Forecasting Dissolved Gas Content in Power Transformers

et al. 2018

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Forecasting dissolved gas content in power transformers plays a significant role in detecting incipient faults and maintaining the safety of the power system. Though various forecasting models have been developed, there is still room to further improve prediction performance. In this paper, a new forecasting model is proposed by combining mixed kernel function-based support vector regression (MKF-SVR) and genetic algorithm (GA). First, forecasting performance of SVR models constructed with a single kernel are compared, and then Gaussian kernel and polynomial kernel are retained due to better learning and prediction ability. Next, a mixed kernel, which integrates a Gaussian kernel with a polynomial kernel, is used to establish a SVR-based forecasting model. Genetic algorithm (GA) and leave-one-out cross validation are employed to determine the free parameters of MKF-SVR, while mean absolute percentage error (MAPE) and squared correlation coefficient (r2) are applied to assess the quality of the parameters. The proposed model is implemented on a practical dissolved gas dataset and promising results are obtained. Finally, the forecasting performance of the proposed model is compared with three other approaches, including RBFNN, GRNN and GM. The experimental and comparison results demonstrate that the proposed model outperforms other popular models in terms of forecasting accuracy and fitting capability.

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Nonlinear Autoregressive Neural Network Models for Prediction of Transformer Oil-Dissolved Gas Concentrations

Cited by 30 publications

References 23 publications

An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil

An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil

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

Mixed Kernel Function Support Vector Regression with Genetic Algorithm for Forecasting Dissolved Gas Content in Power Transformers

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