Gaussian process surrogate model for an effective life assessment of transformer considering model and measurement uncertainties

Shadab, S.; Hozefa, J.; Sonam, K.; Wagh, Sushama; Singh, Navdeep

doi:10.1016/j.ijepes.2021.107401

Cited by 37 publications

(6 citation statements)

References 33 publications

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“…The dataset comprised a total of 1000 samples, partitioned into 700 training samples and 300 test samples. We considered 10 kernel functions (E, SE, RQ, Matern3/2, Matern5/2, ARDE, ARDSE, ARDRQ, ARDMatern3/2, ARDMatern5/2) and 16 historical points (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), which were combined into 160 distinct schemes for numerical analysis.…”

Section: Impact Of Key Parameters On Resultsmentioning

confidence: 99%

“…Hong H et al expanded the applicability of the Iterative Power and Amplitude Correction (IPAC) algorithm to simulate non-stationary and non-Gaussian processes, considering five transformation pairs 10 .Zhang J et al successfully reduced computational complexity by exploiting the Kronecker structure presented in the state-space model of spatiotemporal Gaussian processes, verifying their findings through the application of weather data prediction 11 .In the realm of 3D surface modeling, Zhao C et al proposed a spherical multi-output Gaussian process method 12 . Shadab S et al offered a systematic method based on the black box model and experimental design to establish an alternative model for predicting and estimating transformer top oil temperature parameters 13 .Gao J et al introduced a residual fatigue life prediction method for metal materials based on Gaussian process regression, which is employed to predict the residual fatigue life of metal materials under two-step loading 14 .Meanwhile, Zeng A et al utilized Gaussian process regression to predict building power consumption 15 . Jo H-S et al proposed a machine learning framework for path loss modeling that is based on multi-dimensional regression of artificial neural networks (ANN), variance analysis via Gaussian processes, and feature selection assisted by Principal Component Analysis (PCA) 16 .Lastly, Rong H et al developed a data-driven nonparametric Bayesian model based on Gaussian processes to describe and predict in real time the uncertainties in ship lateral motion and trajectory 17 .…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Prediction of Bolt Support Drilling Pressure: A Hybrid Algorithm Approach to Screen Gaussian Process Time Series Regression Parameters

Liu

2023

Preprint

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The unpredictability of drilling pressure in bolt support systems has emerged as a significant constraint on support efficiency. Current research gaps exist in the field of machine learning for pre-drilling pressure prediction in bolt support and the selection method for key parameters (kernel function and historical points) in Gaussian processes. This study proposes a novel prediction method for bolt support drilling pressure, leveraging hybrid optimization algorithms to identify the key parameters in Gaussian process time series regression. Initially, the Gaussian process time series regression algorithm is modeled. Through data computation and simulation, it is observed that employing the Gaussian process time series algorithm for predicting the drilling pressure of bolt support results in substantial variation in the outcomes when different combinations of kernel functions and historical points are used. Therefore, it is essential to identify the optimal kernel function and the most suitable number of historical points before utilizing the Gaussian process time series algorithm for predicting drilling pressure. Subsequently, three hybrid optimization algorithms (GA-GPR, PSO-GPR, and ACA-GPR) are employed to iteratively optimize the two key parameters (kernel function and historical points) in Gaussian process time series regression. Among these, the PSO-GPR algorithm proves to be the most effective for identifying the kernel function and historical points of the key parameters in the Gaussian process time series algorithm when applied to the prediction of drilling pressure in bolt support. Remarkably, even with a small sample size and a limited number of iterations, PSO-GPR achieves 80% accuracy while reducing time consumption by 60%. Finally, a prediction system for drilling pressure in underground bolt support is established. The algorithm's generalization capability is verified through the prediction of actual drilling pressure. Thus, this study provides a robust and efficient method for predicting drilling pressure in bolt support systems, potentially enhancing support efficiency significantly.

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Section: Impact Of Key Parameters On Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing Prediction of Bolt Support Drilling Pressure: A Hybrid Algorithm Approach to Screen Gaussian Process Time Series Regression Parameters

Liu

2023

Preprint

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“…ANNs are valuable tools for addressing daunting challenges in the transformer manufacturing industry. Various researchers have employed the ANN technique for predicting many transformer conditions, such as the identification of incipient faults using Dissolved Gas Analysis (DGA) [29][30][31]. Presently, there is no ANN model that has been proposed to predict the remaining DP using 2FAL concentration.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Prediction of the Degree of Polymerization in Transformer Cellulose Insulation Using the Feedforward Backpropagation Artificial Neural Network

Thango

Bokoro

2022

Energies

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The life expectancy of power transformers is primarily determined by the integrity of the insulating oil and cellulose paper between the conductor turns, phases and phase to earth. During the course of their in-service lifetime, the solid insulating system of windings is contingent on long-standing ageing and decomposition. The decomposition of the cellulose paper insulation is strikingly grievous, as it reduces the tensile strength of the cellulose paper and can trigger premature failure. The latter can trigger premature failure, and to realize at which point during the operational life this may occur is a daunting task. Various methods of estimating the DP have been proposed in the literature; however, these methods yield different results, making it difficult to accurately estimate a reliable DP. In this work, a novel approach based on the Feedforward Backpropagation Artificial Neural Network has been proposed to predict the amount of DP in transformer cellulose insulation. Presently, no ANN model has been proposed to predict the remaining DP using 2FAL concentration. A databank comprising 100 data sets—70 for training and 30 for testing—is used to develop the proposed ANN using 2-furaldehyde (2FAL) as an input and DP as an output. The proposed model yields a correlation coefficient of 0.958 for training, 0.915 for validation, 0.996 for testing and an overall correlation of 0.958 for the model.

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“…The estimation performance of unknown term ψ(x 1 , x 3 ) via GP-MRAC and RBFN-MRAC is shown in [22]. The open-loop prediction and its comparison via different machine learning techniques are presented in [41]. The GP model best captures the unknown functions and improves prediction accuracy with predictive variance as depicted by the statistical measures such as RMSE, MAE, and CC in [22], [41].…”

Section: Control Via Proposed Pandi and Comparison Analysismentioning

confidence: 99%

Passivity and Immersion (P&I) Approach With Gaussian Process for Stabilization and Control of Nonlinear Systems

Nayyer¹,

Hozefa

Revati³

et al. 2022

IEEE Access

Self Cite

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The virtual derivatives computation and successive derivations of virtual inputs in an adaptive backstepping controller cause the explosion of complexity. Moreover, the feedback linearization has poor robustness features and necessitates exact estimation of the feedback control law's coefficients. Due to measurement noise, the model-based estimation techniques for identifying uncertainties result in inaccurate gradient and Hessian calculations. Such limitations lead to model and measurement uncertainties that prevent effective stabilization and control of nonlinear systems. Machine learning-based data-driven approaches offer effective tools for identifying dynamical systems and uncertainties with minimal prior knowledge of the model structure. Therefore, the contribution of this research is two-fold: First, the general controller design theory is proposed which utilizes the idea of an invariant target manifold giving rise to a non-degenerate two form, through which the definition of certain passive outputs and storage functions leads to a generation of control law for stabilizing the system. Since the above concepts are linked with the Immersion and Invariance (I&I) design policy and the passivity theory of controller design, the proposed methodology is labeled as the "Passivity and Immersion (P&I) based approach". Second, the proposed P&I approach is integrated with a Bayesian nonparametric approach, particularly the Gaussian Process for stabilization and control of the partially unknown nonlinear systems. The effectiveness of the proposed methodologies has been evaluated on an inverted pendulum using MATLAB in the presence of input-output uncertainties.

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Gaussian process surrogate model for an effective life assessment of transformer considering model and measurement uncertainties

Cited by 37 publications

References 33 publications

Optimizing Prediction of Bolt Support Drilling Pressure: A Hybrid Algorithm Approach to Screen Gaussian Process Time Series Regression Parameters

Optimizing Prediction of Bolt Support Drilling Pressure: A Hybrid Algorithm Approach to Screen Gaussian Process Time Series Regression Parameters

Prediction of the Degree of Polymerization in Transformer Cellulose Insulation Using the Feedforward Backpropagation Artificial Neural Network

Passivity and Immersion (P&I) Approach With Gaussian Process for Stabilization and Control of Nonlinear Systems

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