Evolutionary multi-objective fault diagnosis of power transformers

Peimankar, Abdolrahman; Weddell, Stephen J.; Jalal, Thahirah Syed; Lapthorn, Andrew

doi:10.1016/j.swevo.2017.03.005

Cited by 67 publications

(23 citation statements)

References 84 publications

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“…Ensemble learning methods have been applied in different forecasting applications and decision making in various applications [64,65]. It has been shown in that efficiency of ensemble models is greater than single model in terms of accuracy [66,67].…”

Section: Ensemble Learning Applicationsmentioning

confidence: 99%

On the Ensemble of Recurrent Neural Network for Air Pollution Forecasting: Issues and Challenges

Surakhi¹,

Serhan²,

Salah³

2020

Adv. sci. technol. eng. syst. j.

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Time-series is a sequence of observations that are taken sequentially over time. Modelling a system that generates a future value from past observations is considered as time-series forecasting system. Recurrent neural network is a machine learning method that is widely used in the prediction of future values. Due to variant improvements on recurrent neural networks, choosing of the best model for better prediction generation is dependent on problem domain and model design characteristics. Ensemble forecasting is more accurate than single model due to the combination of more than one model for forecasting. Designing an ensemble model of recurrent neural network for time-series forecasting applications would enhance prediction accuracy and improve performance. This paper highlights some of the challenges that are faced by the design of the ensemble model of different recurrent neural network versions, and surveys some of the most relevant works in order to give a direction of how to conduct ensemble learning research in the future. Based on the reviewed literature, we propose a framework for time-series forecasting based on the using of ensemble technique.

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Section: Ensemble Learning Applicationsmentioning

confidence: 99%

On the Ensemble of Recurrent Neural Network for Air Pollution Forecasting: Issues and Challenges

Surakhi¹,

Serhan²,

Salah³

2020

Adv. sci. technol. eng. syst. j.

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“…Also, the accuracy of the proposed technique has not been widely confirmed due to the lack of data it was verified against. A particle swarm optimization technique is presented in [27] to classify various faults within power transformers based on DGA results. While the technique revealed good accuracy, its practical application may not be an easy task, in particular for online DGA sensors.…”

Section: Introductionmentioning

confidence: 99%

Improved Consistent Interpretation Approach of Fault Type within Power Transformers Using Dissolved Gas Analysis and Gene Expression Programming

Abu-Siada

2019

Energies

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Dissolved gas analysis (DGA) of transformer oil is considered to be the utmost reliable condition monitoring technique currently used to detect incipient faults within power transformers. While the measurement accuracy has become relatively high since the development of various off-line and on-line measuring sensors, interpretation techniques of DGA results still depend on the level of personnel expertise more than analytical formulation. Therefore, various interpretation techniques may lead to different conclusions for the same oil sample. Moreover, ratio-based interpretation techniques may fail in interpreting DGA data in case of multiple fault conditions and when the oil sample comprises insignificant amount of the gases used in the specified ratios. This paper introduces an improved approach to overcome the limitations of conventional DGA interpretation techniques, automate and standardize the DGA interpretation process. The approach is built based on incorporating all conventional DGA interpretation techniques in one expert system to identify the fault type in a more consistent and reliable way. Gene Expression Programming is employed to establish this expert system. Results show that the proposed approach provides more reliable results than using individual conventional methods that are currently adopted by industry practice worldwide.

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“…However, most of these traditional diagnosis methods only make a limited contribution to a transformer's fault diagnosis, which cannot accurately reflect its real fault type [18]. Particularly, it is more difficult to accurately judge the fault state with few dissolved gases; high probability of misdiagnosis will happen when the measured and calculated gas ratio is close to the critical value [19]. In addition, the more detailed the classifications of fault types are, the lower the accuracy rate of fault diagnosis is, and vice versa.…”

Section: Introductionmentioning

confidence: 99%

A Novel Expertise-Guided Machine Learning Model for Internal Fault State Diagnosis of Power Transformers

Zhang

2019

Sustainability

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The fault diagnosis of power transformers is of great significance to improve the reliability of power systems. This paper proposes a novel fault diagnosis method called the expertise-guided machine learning (EGML) model where a genetic algorithm (GA) and a mind evolutionary algorithm (MEA) are used as optimization algorithms. Thereby, two types of EGML models are generated, that is, the GA-EGML model and the MEA-EGML model. In the EGML model, knowledge function replaces the cost function of traditional artificial intelligence algorithms, which can provide additional information for each individual and bring some corrections to the prediction results. To investigate the application potentials of the proposed models in power transformer fault diagnosis, real dissolved gases data are utilized to evaluate the diagnosis performance of the proposed models. Results indicate that the performance of the EGML model outperforms the traditional back propagation neural network (BPNN) model and all other models participating in the comparison. Both the GA-EGML model and MEA-EGML model can be used to diagnose the faults of a power transformer, and the latter is better. In addition, to further investigate the robustness of the proposed models for different data, four scenarios are simulated. Empirical results show that the accuracies of all models decrease in the other three scenarios compared to the baseline scenario, especially in scenario 2. However, the proposed models decline less than the traditional models in scenario 2 and scenario 4, and obtain satisfactory accuracy in all scenarios.

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Evolutionary multi-objective fault diagnosis of power transformers

Cited by 67 publications

References 84 publications

On the Ensemble of Recurrent Neural Network for Air Pollution Forecasting: Issues and Challenges

On the Ensemble of Recurrent Neural Network for Air Pollution Forecasting: Issues and Challenges

Improved Consistent Interpretation Approach of Fault Type within Power Transformers Using Dissolved Gas Analysis and Gene Expression Programming

A Novel Expertise-Guided Machine Learning Model for Internal Fault State Diagnosis of Power Transformers

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