A Fully Data-Driven Method Based on Generative Adversarial Networks for Power System Dynamic Security Assessment With Missing Data

Ren, Chao; Xu, Yan

doi:10.1109/tpwrs.2019.2922671

Cited by 183 publications

(76 citation statements)

References 20 publications

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“…It is fast to train, and thus can be easily updated during operation. In the literature, ensembles of ELMs are used mostly for stability classification [19], [29], [32], [35], [56], [75], [76], or combined with random vector functional links [31], [33], [77]. It was also used for regression, to predict load stability margins [78], the fault-induced voltage recovery [30], [79], and maximum frequency deviation and time [80].…”

Section: Learning a Modelmentioning

confidence: 99%

Recent Developments in Machine Learning for Energy Systems Reliability Management

2020

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This paper reviews recent works applying machine learning techniques in the context of energy systems reliability assessment and control. We showcase both the progress achieved to date as well as the important future directions for further research, while providing an adequate background in the fields of reliability management and of machine learning. The objective is to foster the synergy between these two fields and speed up the practical adoption of machine learning techniques for energy systems reliability management. We focus on bulk electric power systems and use them as an example, but we argue that the methods, tools, etc. can be extended to other similar systems, such as distribution systems, micro-grids, and multi-energy systems.

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Section: Learning a Modelmentioning

confidence: 99%

Recent Developments in Machine Learning for Energy Systems Reliability Management

2020

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“…The first effort applying GAN to PMU data generation is presented in [8], which learns the dynamics represented by the training dataset sampled from a single PMU and then produces a single synthetic PMU data stream. Another recent work [9] used GAN to generate missing data of multiple PMUs in order to improve the performance of dynamic security assessment, not showing its potential of more generalized applications though.…”

Section: A Literature Review Of Ganmentioning

confidence: 99%

“…As an alternative, there has been recent effort of introducing machine learning concepts such as Generative Adversarial Network (GAN) for synthetic data generation. GAN can progressively learn the underlying characteristics of the limitedvolume training data set to generate any large amount of credible synthetic data with realistic characteristics and rich diversity [7]- [9]. GAN-based data generation approaches are simulation-free and also free of several common issues like inaccurate generator and load modeling, and low simulation efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…GAN-based data generation approaches are simulation-free and also free of several common issues like inaccurate generator and load modeling, and low simulation efficiency. In recent years, several data-driven GAN-based applications have been proposed, such as renewable energy data generation [7], single PMU data generation [8] and dynamic security assessment [9]. However, these approaches are not capable of creating PMU data streams from multiple locations in a network that possess meaningful electromechanical dynamics and satisfy Kirchhoff's laws.…”

Section: Introductionmentioning

confidence: 99%

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Synthetic Dynamic PMU Data Generation: A Generative Adversarial Network Approach

Zheng¹,

Wang²,

Xie³

2019

2019 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA)

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This paper introduces a machine learning-based approach to synthetically create multiple phasor measurement unit (PMU) data streams at different buses in a power system. In contrast to the existing literature of creating synthetic power grid network and then using simulation software to output synthetic PMU data, we propose a generative adversarial network (GAN) based approach to synthesize multiple PMU measurement streams directly from historical data. The proposed method can simultaneously create multiple PMU measurement streams that reflect practically meaningful electromechanical dynamics which observe the Kirchhoff's laws. We further validate the synthetic data via the statistical resemblance and the modal analysis. The efficacy of this new approach is demonstrated by numerical studies on a 39-bus system and a 200-bus system.

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“…Some researchers proposed a decision tree with surrogate (DTWS) to solve missing features [41]. Others used the feature estimation method to predict the missing data directly [42], or used an emerging deep-learning technique called generative adversarial network (GAN) to address the missing data problem [43].…”

Section: Construction and Incompleteness Of Input Featuresmentioning

confidence: 99%

A Hierarchical Self-Adaptive Method for Post-Disturbance Transient Stability Assessment of Power Systems Using an Integrated CNN-Based Ensemble Classifier

Zhang

et al. 2019

Energies

Self Cite

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Data-driven approaches using synchronous phasor measurements are playing an important role in transient stability assessment (TSA). For post-disturbance TSA, there is not a definite conclusion about how long the response time should be. Furthermore, previous studies seldom considered the confidence level of prediction results and specific stability degree. Since transient stability can develop very fast and cause tremendous economic losses, there is an urgent need for faster response speed, credible accurate prediction results, and specific stability degree. This paper proposed a hierarchical self-adaptive method using an integrated convolutional neural network (CNN)-based ensemble classifier to solve these problems. Firstly, a set of classifiers are sequentially organized at different response times to construct different layers of the proposed method. Secondly, the confidence integrated decision-making rules are defined. Those predicted as credible stable/unstable cases are sent into the stable/unstable regression model which is built at the corresponding decision time. The simulation results show that the proposed method can not only balance the accuracy and rapidity of the transient stability prediction, but also predict the stability degree with very low prediction errors, allowing more time and an instructive guide for emergency controls.

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A Fully Data-Driven Method Based on Generative Adversarial Networks for Power System Dynamic Security Assessment With Missing Data

Cited by 183 publications

References 20 publications

Recent Developments in Machine Learning for Energy Systems Reliability Management

Recent Developments in Machine Learning for Energy Systems Reliability Management

Synthetic Dynamic PMU Data Generation: A Generative Adversarial Network Approach

A Hierarchical Self-Adaptive Method for Post-Disturbance Transient Stability Assessment of Power Systems Using an Integrated CNN-Based Ensemble Classifier

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