Data-driven Transient Stability Assessment Based on Kernel Regression and Distance Metric Learning

Liu, Xianzhuang; Min, Yong; Chen, Lei; Zhang, Xiaohua; Feng, Changyou

doi:10.35833/mpce.2019.000581

Cited by 20 publications

(21 citation statements)

References 23 publications

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“…For example, Li et al used extreme gradient boosting machine [46]. Liu et al employed kernel regression [47], while Pannell et al proposed using the naive Bayes approach [35].…”

Section: Machine Learningmentioning

confidence: 99%

Artificial Intelligence Techniques for Power System Transient Stability Assessment

et al. 2022

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The high penetration of renewable energy sources, coupled with decommissioning of conventional power plants, leads to the reduction of power system inertia. This has negative repercussions on the transient stability of power systems. The purpose of this paper is to review the state-of-the-art regarding the application of artificial intelligence to the power system transient stability assessment, with a focus on different machine, deep, and reinforcement learning techniques. The review covers data generation processes (from measurements and simulations), data processing pipelines (features engineering, splitting strategy, dimensionality reduction), model building and training (including ensembles and hyperparameter optimization techniques), deployment, and management (with monitoring for detecting bias and drift). The review focuses, in particular, on different deep learning models that show promising results on standard benchmark test cases. The final aim of the review is to point out the advantages and disadvantages of different approaches, present current challenges with existing models, and offer a view of the possible future research opportunities.

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“…For example, Li et al used extreme gradient boosting machine [46]. Liu et al employed kernel regression [47], while Pannell et al proposed using the naive Bayes approach [35].…”

Section: Machine Learningmentioning

confidence: 99%

Artificial Intelligence Techniques for Power System Transient Stability Assessment

et al. 2022

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“…An interesting work discussing a problem of embedding the Nadaraya-Watson regression into the neural network as a novel trainable CNN layer was presented in [85]. Applied machine learning problems solved by using the Nadaraya-Watson regression were considered in [86]. A method of approximation using the kernel functions made from only the sample points in the neighborhood of input values to simplify the Nadaraya-Watson estimator is proposed in [87].…”

Section: Related Workmentioning

confidence: 99%

Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regression

Konstantinov¹,

R.²,

Utkin³

2022

Preprint

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A new method for estimating the conditional average treatment effect is proposed in the paper. It is called TNW-CATE (the Trainable Nadaraya-Watson regression for CATE) and based on the assumption that the number of controls is rather large whereas the number of treatments is small. TNW-CATE uses the Nadaraya-Watson regression for predicting outcomes of patients from the control and treatment groups. The main idea behind TNW-CATE is to train kernels of the Nadaraya-Watson regression by using a weight sharing neural network of a specific form. The network is trained on controls, and it replaces standard kernels with a set of neural subnetworks with shared parameters such that every subnetwork implements the trainable kernel, but the whole network implements the Nadaraya-Watson estimator. The network memorizes how the feature vectors are located in the feature space. The proposed approach is similar to the transfer learning when domains of source and target data are similar, but tasks are different. Various numerical simulation experiments illustrate TNW-CATE and compare it with the well-known T-learner, S-learner and X-learner for several types of the control and treatment outcome functions. The code of proposed algorithms implementing TNW-CATE is available in https://github.com/Stasychbr/TNW-CATE.

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“…In this paper, CCT is taken as the TSI, and the MD-kernel regression method proposed in [42], [45] which combines the Nadaraya-Watson kernel regression and the Mahalanobis distance, is adopted to construct the TSA model. The target of this method is to determine the mapping as:…”

Section: A Data-driven Tsa Model Based On Kernel Regressionmentioning

confidence: 99%

“…The premise of realizing the analytical representation of stability constraints is to choose the appropriate data-driven TSA method. The data-driven methods generally fall into two categories [42]: parametric regression and non-parametric regression. The general form of non-parametric model is a regression expression of the labels of all training samples.…”

Section: Introductionmentioning

confidence: 99%

Analytical Representation of Data-driven Transient Stability Constraint and Its Application in Preventive Control

Zhang

Chen

et al. 2022

Journal of Modern Power Systems and Clean Energy

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Accurate transient stability assessment (TSA) and effective preventive control are important for the stable operation of power systems. With the superiorities in precision and efficiency, data-driven methods are widely used in TSA nowadays. Data-driven TSA model can be adopted in the stability constraints of preventive control optimization, but existing methods are mostly iteration-based ones, which may result in low efficiency, sometimes even non-convergence. In this paper, an analytical representation method of data-driven transient stability constraint is proposed based on a non-parametric regression model built for TSA. Key feature extraction and dominant sample selection are proposed to reduce the scale of the TSA model, and bi-level linearization is applied to further modify it. Optimal preventive control model is then formulated as a mixed-integer linear program (MILP) problem with the linearized analytical data-driven transient stability constraint, which can be solved without iterations. An overall procedure of datadriven TSA and preventive control is finally developed. Case studies show that the proposed method has high accuracy in TSA and can achieve effective preventive control of power system with high efficiency.

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Data-driven Transient Stability Assessment Based on Kernel Regression and Distance Metric Learning

Cited by 20 publications

References 23 publications

Artificial Intelligence Techniques for Power System Transient Stability Assessment

Artificial Intelligence Techniques for Power System Transient Stability Assessment

Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regression

Analytical Representation of Data-driven Transient Stability Constraint and Its Application in Preventive Control

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