A Reliable Intelligent System for Real-Time Dynamic Security Assessment of Power Systems

Xu, Yan; Zhang, Dong; Zhao, Jun; Zhang, Pei; Wong, Kit Po

doi:10.1109/tpwrs.2012.2183899

Cited by 199 publications

(134 citation statements)

References 28 publications

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“…In the proposed method, ELM-based ensemble model is used as the intelligent model to provide diversified stability prediction outputs, and the credible decision-making rule in [9] is employed as the credibility check mechanism. This section introduces the existing methodologies used in the proposed method.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…ELM avoids issues, such as learning rate setting, local minima, and stopping criteria, which are commonly encountered on the traditional learning algorithms. Meanwhile, ELM retains high computation accuracy on many benchmark problems [9,15,16].…”

Section: Extreme Learning Machinementioning

confidence: 99%

“…In the literature, ensemble learning has been widely used in power system dynamic security assessment [9,10,12,16,23]. For ensemble learning, a set of single learning units are individually trained and combined together to make the final decision.…”

Section: Elm Ensemble Learningmentioning

confidence: 99%

“…To achieve fast real-time TSA, intelligent system IS-based methods have been identified as a promising solution [7][8][9][10][11][12][13][14][15][16]. In IS-based TSA, the conventional T-D simulation data constructs the database to train the intelligent models at offline stage, and then the trained models can perform fast online TSA with the minimal computation effort.…”

Section: Introductionmentioning

confidence: 99%

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Post-disturbance transient stability assessment of power systems towards optimal accuracy-speed tradeoff

Ren

Zhang

2018

Prot Control Mod Power Syst

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The recent development of phasor measurement technique opens the way for real-time post-disturbance transient stability assessment (TSA). Following a disturbance, since the transient instability can occur very fast, there is an urgent need for fast TSA with sufficient accuracy. This paper first identifies the tradeoff relationship between the accuracy and speed in post-disturbance TSA, and then proposes an optimal self-adaptive TSA method to optimally balance such tradeoff. It uses ensemble learning and credible decision-making rule to progressively predict the post-disturbance transient stability status, and models a multi-objective optimization problem to search for the optimal balance between TSA accuracy and speed. With such optimally balanced TSA performance, the TSA decision can be made as fast as possible while maintaining an acceptable level of accuracy. The proposed method is tested on New England 10-machine 39-bus system, and the simulation results verify its high efficacy.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Extreme Learning Machinementioning

confidence: 99%

Section: Elm Ensemble Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Post-disturbance transient stability assessment of power systems towards optimal accuracy-speed tradeoff

Ren

Zhang

2018

Prot Control Mod Power Syst

Self Cite

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“…Most of the existing works are focused on the binary state prediction for global stability using clustering and classification. For example, support vector machine, decision tree and artificial neural network (ANN) are widely used to detect instability of power systems by using post-fault dynamic data during a few cycles [11][12][13]. Guo and Milanović presented a probabilistic framework to evaluate the accuracy of data mining tools applied for online prediction of transient stability [14], enabling the comprehensive analysis of performance of different implementations.…”

Section: Introductionmentioning

confidence: 99%

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

Liu¹,

Yang²,

et al. 2016

J. Mod. Power Syst. Clean Energy

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A high-performance predictor for critical unstable generators (CUGs) of power systems is presented in this paper. The predictor is driven by the MapReduce based parallelized neural networks. Specifically, a group of back propagation neural networks (BPNNs), fed by massive response trajectories data, are efficiently organized and concurrently trained in Hadoop to identify dynamic behavior of individual generator. Rather than simply classifying global stability of power systems, the presented approach is able to distinguish unstable generators accurately with a few cycles of synchronized trajectories after fault clearing, enabling more in-depth emergency awareness based on wide-area implementation. In addition, the technique is of rich scalability due to Hadoop framework, which can be deployed in the control centers as a high-performance computing infrastructure for real-time instability alert. Numerical examples are studied using NPCC 48-machines test system and a realistic power system of China.

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Static security assessment of power systems: A review

Gholami

Sanjari

Safari

et al. 2020

Int Trans Electr Energ Syst

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Summary The security assessment, based on which determinant decisions should be made for power system design, control and operation, is a challenging issue for utility engineers and network designers, especially in large‐scale power systems. Numerous methods have been proposed and implemented for this purpose, and a variety of indices have been suggested to address the static security condition of power networks. Large‐scale datasets of measurements in continually expanding power systems necessitate advanced knowledge in big data analytics. In this review paper, numerical techniques and machine learning‐based methods are reviewed as two main categories for static security assessment in power systems based on principal features of static security status classification such as type of classifier, the static security index, and feature selection and extraction methods. This paper can be used as a useful reference for static security assessment of power systems.

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A Reliable Intelligent System for Real-Time Dynamic Security Assessment of Power Systems

Cited by 199 publications

References 28 publications

Post-disturbance transient stability assessment of power systems towards optimal accuracy-speed tradeoff

Post-disturbance transient stability assessment of power systems towards optimal accuracy-speed tradeoff

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

Static security assessment of power systems: A review

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