Prediction Method for Power Transformer Running State Based on LSTM_DBN Network

Lin, Jun; Su, Lei; Yan, Yingjie; Sheng, Gehao; Xie, Da; Jiang, Xiuchen

doi:10.3390/en11071880

Cited by 37 publications

(13 citation statements)

References 28 publications

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“…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]. 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].…”

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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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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“…Besides, as one of the essential pieces of equipment in the power system, power and distribution transformers can directly influence the stability and safety of the entire power grid (Badune et al, 2013;Kimment and Matevosyan, 2018). If the transformer fails in operation, it will cause power to turn off and cause damage to the transformer itself and the power system, which may result in more considerable damage (Lin et al, 2018). Lifetime data analysis of power and distribution transformers are essential for a cost-efficient and risk minimized maintenance process (Badune et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Predicting the Remaining Lifetime of Distribution Transformers using Machine Learning

Enoch

George

Aning

2020

American Journal of Engineering and Applied Sciences

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Distribution Transformer is a crucial element in deciding the power flow in large power systems. Their better performance implies high power system efficiency and enhanced power transfer capability. However, various Distribution Transformer failures in the recent past lead to power supply disturbance and have acquired much attention from the electrical intellectuals. It is of considerable significance to accurately get the running state of distribution transformers and timely detect the existence of potential transformer faults. This project work presents a predictive model to predict the potential of a distribution transformer failing before its expected years in service. Using Random Forest machine learning techniques, we examine transformer data from August 2010 to June 2019. Our experimental results reveal that a total of 90 distribution transformers were damaged within nine years. Thus, average the company losses ten (10) transformer in a year, which amount to the US $92300-95770 per year. Also, most of the places that recorded rate of distribution transformer damage were a location that had mini and major factories around. Thus, the Sunyani Municipality recorded the highest transformer damage (12), representing 13%, followed by Mim (10). Again, lighting strike was the significant causes of transformer damage. Thus twenty-one (21) out of the ninety (90) damage transformers was caused by a lightning strike. The results further show that 33.33% of the damage transformers were with 24.75-36.75% of their life expectancy. As low as 3.33% of the damage transformers have been in service for 73% of the life expectancy. From the study results, it can be concluded that a high percentage (68.9%) of the damage transformers in the Bono, Bono East and Ahafo regions of Ghana have been in service less the half of its expected years of service. Rate-offaulty-occurrence, Type-of-faults-sustained and Tap-changer-type are the most significant factors that determine the number of years left for a distribution transformer to fail. We observed that the make of a transformer was of less importance in predicting the years left for a transformer to fail. Finally, the RMSE of 0.001639 and MAPE error of 0.001321 achieved by the proposed model shows that the proposed model fits very well to the dataset.

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“…Devidoà necessidade de se melhorar a segurança operacional no ambiente industrial, diversas abordagens de detecção e identificação de falhas (Fault Detection and Identification -FDI) foram desenvolvidas e aplicadas, com o objetivo de auxiliar os operadores no controle e observação dos processos industriais (Yu et al, 2015;Zhou et al, 2004;Verron et al, 2010). Dentre estas abordagens, destacamse as metodologias com base em técnicas de aprendizagem de máquina (Lin et al, 2018;Chen et al, 2015;Liu et al, 2018). O diagnóstico de falhasé uma importante atividade na prevenção de acidentes, então, um bom sistema de FDI pode ser considerado crítico para o funcionamento desejado do processo.…”

Section: Introductionunclassified

Identificação de Falhas de uma Planta de Nível Piloto Baseada em Rede Neural LSTM

Oliveira

Santos

Nunes

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

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A classificação das falhas industriais utilizando abordagens orientadas a dados é um campo crescente e amplamente discutido na literatura. As Redes Neurais Artificiais, em especial as Redes Neurais Recorrentes se demonstraram fortes aliadas a esse tipo de abordagem, por sua capacidade de transportar informações contidas no histórico das séries temporais ao longo dos instantes de tempo. Aqui será avaliada a capacidade de um modelo Long Short Term Memory previamente proposto em classificar corretamente as falhas em dados gerados obtidos de um processo não simulado. Posteriormente, será testada uma variação dos hiperparâmetros com o objetivo de encontrar um conjunto que apresente resultados otimizados. Através dos testes foi obtido uma acurácia final de 91,49% na classificação de falhas de uma planta de nível piloto.

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Prediction Method for Power Transformer Running State Based on LSTM_DBN Network

Cited by 37 publications

References 28 publications

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

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

Predicting the Remaining Lifetime of Distribution Transformers using Machine Learning

Identificação de Falhas de uma Planta de Nível Piloto Baseada em Rede Neural LSTM

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