High-performance predictor for critical unstable generators based on scalable parallelized neural networks

IET Generation, Transmission & Distribution

et al. 2017

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In recent years, situation awareness and risk mitigation have become the challenging issues in large-scale power grids. This study presents a novel pair-wise relative energy function for real-time transient stability analysis and emergency control. The proposed energy function is able to accurately identify the clusters of critical and non-critical generators significantly faster when compared with previous methods. Additionally, a new emergency control criterion is proposed in order to stabilise the identified critical generators within a comparatively short interval after fault clearance. The emergency control scheme computes the capacity of the requisite generation curtailment using the pre-calculated relative energy of the equivalent postfault system. Finally, the relative energy oscillation trajectories that occur in the critical cluster are utilised in order to locate the most appropriate generator to launch the emergency control. When compared with the existing methods, it is evident that the novel approach can be applied practically for power systems transient stability analysis and emergency control. The effectiveness of the proposed method is demonstrated and evaluated using the New England 10 machines 39-bus and 16 machines 68-bus systems.

Section: Critical Machines Cluster Identificationmentioning

confidence: 99%

Novel pair‐wise relative energy function for transient stability analysis and real‐time emergency control

Gou¹,

IET Generation, Transmission & Distribution

et al. 2017

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“…CUGs are defined as the first group of the generators whose rotor angle is different from the rest of the generators exceeding a given threshold. Actually, CUGs are the most potential candidates of generator tripping that can be utilized to reduce transient power mismatch in a timely manner [29]. Figure 2 shows the power angle trajectories of different CUGs in the IEEE 68-node testing system.…”

Section: Cug Identificationmentioning

confidence: 99%

A MapReduce Based High Performance Neural Network in Enabling Fast Stability Assessment of Power Systems

Mathematical Problems in Engineering

et al. 2017

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Transient stability assessment is playing a vital role in modern power systems. For this purpose, machine learning techniques have been widely employed to find critical conditions and recognize transient behaviors based on massive data analysis. However, an ever increasing volume of data generated from power systems poses a number of challenges to traditional machine learning techniques, which are computationally intensive running on standalone computers. This paper presents a MapReduce based high performance neural network to enable fast stability assessment of power systems. Hadoop, which is an open-source implementation of the MapReduce model, is first employed to parallelize the neural network. The parallel neural network is further enhanced with HaLoop to reduce the computation overhead incurred in the iteration process of the neural network. In addition, ensemble techniques are employed to accommodate the accuracy loss of the parallelized neural network in classification. The parallelized neural network is evaluated with both the IEEE 68-node system and a real power system from the aspects of computation speedup and stability assessment.

“…4) Artificial neural network (ANN) [11], [12]. It often per- This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).…”

Section: Introductionmentioning

confidence: 99%

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

Journal of Modern Power Systems and Clean Energy

Min

Chen

et al. 2021

Transient stability assessment (TSA) is of great importance in power systems. For a given contingency, one of the most widely used transient stability indices is the critical clearing time (CCT), which is a function of the pre-fault power flow. TSA can be regarded as the fitting of this function with the prefault power flow as the input and the CCT as the output. In this paper, a data-driven TSA model is proposed to estimate the CCT. The model is based on Mahalanobis-kernel regression, which employs the Mahalanobis distance in the kernel regression method to formulate a better regressor. A distance metric learning approach is developed to determine the problem-specific distance for TSA, which describes the dissimilarity between two power flow scenarios. The proposed model is more accurate compared to other data-driven methods, and its accuracy can be further improved by supplementing more training samples. Moreover, the model provides the probability density function of the CCT, and different estimations of CCT at different conservativeness levels. Test results verify the validity and the merits of the method.