Convolutional neural network-based power system transient stability assessment and instability mode prediction

Shi, Zhongtuo; Yao, Wei; Zeng, Lingkang; Wen, Jianfeng; Fang, Jiakun; Ai, Xiaomeng; Wen, Jinyu

doi:10.1016/j.apenergy.2020.114586

Cited by 148 publications

(66 citation statements)

References 32 publications

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“…Here, we introduce the Jaccard similarity to evaluate the distance between sets of integers. Given any two set s i , s j ∈ N, Jaccard similarity is defined as: (19) where J ∈ [0, 1] and J(s i , s j ) = 1. Here, we consider the sample correct only when J(G c , G c ) = 1.…”

Section: ) Set Similarity Based Metricsmentioning

confidence: 99%

“…In [18], the authors adopt discrete Fourier transform to obtain spectrum from the fault-on generator trajectories and arrange them into 2D images, such that CNN can achieve good performance in refined CCT regressions. Shi et al [19] construct larger images with variables of all buses and verify the effectiveness of CNN on instability mode (e.g., caused by insufficient synchronizing or damping torque) prediction. Aimed at a large scale of contingency screening, Yan et al [20] introduce cascade CNNs in stability probability prediction for early TDS termination without losses of accuracy, based on continuously refreshing themselves with the increase of labeled TDS outputs.…”

Section: Introductionmentioning

confidence: 99%

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Recurrent Graph Convolutional Network-Based Multi-Task Transient Stability Assessment Framework in Power System

Huang

Guan

et al. 2020

IEEE Access

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Reliable online transient stability assessment (TSA) is fundamentally required for power system operation security. Compared with time-costly classical digital simulation methods, data-driven deep learning (DL) methods provide a promising technique to build a TSA model. However, general DL models show poor adaptability to the variation of power system topology. In this paper, we propose a new graphbased framework, which is termed as recurrent graph convolutional network based multi-task TSA (RGCN-MT-TSA). Both the graph convolutional network (GCN) and the long short-term memory (LSTM) unit are aggregated to form the recurrent graph convolutional network (RGCN), where the GCN explicitly integrate the bus (node) states with the topological characteristics while the LSTM subsequently captures the temporal features. We also propose a multi-task learning (MTL) scheme, which provides joint training of stability classification (Task-1) as well as critical generator identification (Task-2) in the framework, and accelerate the process with parallel computing. Test results on IEEE 39 Bus system and IEEE 300 Bus system indicate the superiority of the proposed scheme over existing models, as well as its robustness under various scenarios. INDEX TERMS Deep graph-based learning, transient stability assessment (TSA), graph convolutional network (GCN), recurrent graph convolutional network (RGCN), multi-task learning (MTL).

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Section: ) Set Similarity Based Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recurrent Graph Convolutional Network-Based Multi-Task Transient Stability Assessment Framework in Power System

Huang

Guan

et al. 2020

IEEE Access

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“…(1) ANN related models, including adaptive ANN application for dynamic security assessment [19], convolutional neural networks [20,21], deep imbalanced learning framework [22], and deep belief network and model interpretation method [23] for transient stability assessment; (2) DT related models to predict system vulnerability [24], and transient instability [25]; (3) support vector machines (SVM) related models, such as an improved SVM, were proposed for real-time TSA in power systems in Reference [26]. Rotor speed, rotor angle of generators and voltage amplitude of buses after fault were extracted as the input features to train and test the SVM model in Reference [27].…”

Section: Introductionmentioning

confidence: 99%

An Improved Power System Transient Stability Prediction Model Based on mRMR Feature Selection and WTA Ensemble Learning

Liu

Sun

et al. 2020

Applied Sciences

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Fast online transient stability assessment (TSA) is very important to maintain the stable operation of power systems. However, the existing transient stability assessment methods suffer the drawbacks of unsatisfactory prediction accuracy, difficult applicability, or a heavy computational burden. In light of this, an improved high accuracy power system transient stability prediction model is proposed, based on min-redundancy and max-relevance (mRMR) feature selection and winner take all (WTA) ensemble learning. Firstly, the contributions of four different series of raw sampled data from all of the three-time stages, namely the pre-fault, during-fault and post-fault, to transient stability are compared. The new feature of generator electromagnetic power is introduced and compared with three conventional types of input features, through a support vector machine (SVM) classifier. Furthermore, the two types of most contributive input features are obtained by the mRMR feature selection method. Finally, the prediction results of the electromagnetic power of generators and the voltage amplitude of buses are combined using the WTA ensemble learning method, and an improved transient stability prediction model with higher accuracy for unstable samples is obtained, whose overall prediction accuracy would not decrease either. The real-time data collected by wide area monitoring systems (WAMS) can be fed into this model for fast online transient stability prediction; the results can also provide a basis for the future emergency control decision-making of power systems.

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“…In case of a disturbance driving the power system to transient instability, a fast prediction of its security status could be vital for allowing a sufficient time to take emergency control actions [3,4]. In recent years, artificial intelligence methods, including machine learning techniques, have been broadly applied to realtime transient stability assessment (TSA) of power systems, mainly because of their non-linear modelling capabilities to learn the complex relationship between the stability status of a power system and its time-series measurable quantities of very short time interval following a disturbance [5][6][7][8][9][10][11][12][13][14][15]. Generally, the required time-series measurements are captured synchronously by PMUs located at different points in the power system network, and by arranging these time-series measurements, any instance of features can be built.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the predicted settling times, erroneous measurements can be detected and isolated from the time-series data captured by PMUs. But it is shown that if the time-series data are arranged and transferred to the feature space by the conventional method [8,[13][14][15] of building a dataset to be used by machine learning based classifiers, a significant number of feature vectors within the feature space would include both erroneous and accurate measurements. This high interference between the erroneous and accurate data makes us incapable of removing the corrupted feature vectors (feature vectors that comprise highly erroneous measurements) without losing a significant amount of informative data during the process of feature cleansing.…”

Section: Introductionmentioning

confidence: 99%

A new method of decision tree based transient stability assessment using hybrid simulation for real‐time PMU measurements

Behdadnia

Yaslan

Genç

2020

IET Generation Trans & Dist

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In recent years, machine learning (ML) techniques have gained popularity in facilitating real-time transient stability assessment and prediction for early detection of blackouts in power systems. Conventionally, synchrophasor measurements, as the real-time indicators of system dynamics, are fed into the ML-based models. However, if the quality of the synchrophasors used in the process of developing and application of the ML models is not validated, these models could suffer from unreliability issues due to the unrealistic quantities obtained through simulations and due to the interference of erroneous measurements encountered during their application. In this paper, after investigating the properties of different simulation methods, a new hybrid-type simulator that generates a realistic dataset in a feasible time is proposed. Using this simulator, the distortion of the time-series data due to the dynamics of practical phasor measurement units (PMUs) following a disturbance is analysed and the intervals in which the PMU measurements are significantly erroneous are determined. Moreover, a new method of time-series data arrangement for the dataset to be used in ML models is proposed. With this method, the erroneous parts of the timeseries measurements are effectively removed, while the remaining relevant information is retained to enhance the transient stability prediction accuracy. wileyonlinelibrary.com/iet-gtd

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Convolutional neural network-based power system transient stability assessment and instability mode prediction

Cited by 148 publications

References 32 publications

Recurrent Graph Convolutional Network-Based Multi-Task Transient Stability Assessment Framework in Power System

Recurrent Graph Convolutional Network-Based Multi-Task Transient Stability Assessment Framework in Power System

An Improved Power System Transient Stability Prediction Model Based on mRMR Feature Selection and WTA Ensemble Learning

A new method of decision tree based transient stability assessment using hybrid simulation for real‐time PMU measurements

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