On-line transient stability assessment of large-scale power systems by using ball vector machines

Mohammadi, Mohammad; Gharehpetian, Gevork B.

doi:10.1016/j.enconman.2009.10.021

Cited by 14 publications

(7 citation statements)

References 26 publications

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“…); Refs. [5,6,11,16,34,37]. Features that are used for the ML are extracted from these time-domain signals at two distinct time instants: (a) pickup time (pre-fault value) and (b) trip time (post-fault value) of the associated distance protection relays.…”

Section: Features Engineering and Statistical Processingmentioning

confidence: 99%

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Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble

et al. 2021

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Increased integration of renewable energy sources brings new challenges to the secure and stable power system operation. Operational challenges emanating from the reduced system inertia, in particular, will have important repercussions on the power system transient stability assessment (TSA). At the same time, a rise of the “big data” in the power system, from the development of wide area monitoring systems, introduces new paradigms for dealing with these challenges. Transient stability concerns are drawing attention of various stakeholders as they can be the leading causes of major outages. The aim of this paper is to address the power system TSA problem from the perspective of data mining and machine learning (ML). A novel 3.8 GB open dataset of time-domain phasor measurements signals is built from dynamic simulations of the IEEE New England 39-bus test case power system. A data processing pipeline is developed for features engineering and statistical post-processing. A complete ML model is proposed for the TSA analysis, built from a denoising stacked autoencoder and a voting ensemble classifier. Ensemble consist of pooling predictions from a support vector machine and a random forest. Results from the classifier application on the test case power system are reported and discussed. The ML application to the TSA problem is promising, since it is able to ingest huge amounts of data while retaining the ability to generalize and support real-time decisions.

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Section: Features Engineering and Statistical Processingmentioning

confidence: 99%

“…Support vector machine (SVM) is another popular ML method for the TSA, which was used in [13,14] and, with certain improvements, in [15]. Several adaptations of the original SVM have also been proposed for the TSA: core vector machine [5] and ball vector machine [16]. Additionally, a neuro-fuzzy SVM approach was proposed in [17].…”

Section: Introductionmentioning

confidence: 99%

Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble

et al. 2021

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“…In hybrid methods, by performing a time-domain simulation after fault clearance, a transient energy margin (TEM) is attained using the implication of potential energy boundary surface (PEBS) [16], [17]. It worth mentioning that the variation of hybrid TEM around its critical value against variation in some parameters of the system is non-linear and unpredictable [18]. To solve this problem, the concepts of corrected transient kinetic energy (CTKE) and transient potential energy (TPE) were presented.…”

Section: Implication Of Corrected Transient Energy Margin (Ctem)mentioning

confidence: 99%

“…and social cost [18]. In this paper, only the first three types of abovementioned costs have been considered.…”

Section: Creating a System Instability Probability Matrix (B)mentioning

confidence: 99%

“…Nowadays, as a result of indisputable progress in superconducting technology, SFCL has become a crucial and practicable device for suppressing fault currents and improving transient behavior [17]. Some of the effective advantages of this economical device include enhancing the power balance of the system, increasing both voltage and frequency stabilities [18], as well as distribution reliability. Considering the modern power systems requirements to be quick and on-line transient stability assessment (TSA), a fundamental tool for evaluating first-swing transient stability has been developed in [19], and it is the cornerstone of the TSA proposed in this paper.…”

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confidence: 99%

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An Evaluation of Flux-Coupling Type SFCL Placement in Hybrid Grid System Based on Power Quality Risk Index

et al. 2020

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Due to the expansion of restructured electricity markets and consequently power network operation approaching dynamic and static margins, the probability of transient instability has remarkably been increased. To reduce the transient instability risk or probabilistic cost of power quality, the present study focuses on determining the optimum location of a flux-coupling type SFCL. To this end, the candidate locations with lower related transient instability risk are selected as the best locations for installing fluxcoupling type SFCLs. Nowadays, the type of SFCL employed in this study, which enjoys the benefits of both resistive and inductive type SFCLs, is a suitable means for enhancing the transient stability of electrical grids. Despite being another useful approach in transient stability evaluation, the equal-area criterion is merely restricted to the analysis of the stability of a single-machine power system connected to an infinite bus and also a two-machine power system. The online approach employed in this study is based on the corrected transient energy margin (CTEM), some probabilistic factors, and the cost of transient instability. Furthermore, CTEM index is a useful tool for calculating the critical clearing time (CCT). Therefore, in recent years, this index has become more and more useful. The studies carried out on the IEEE New England 39-bus test system ascertain the high applicability of the proposed method.INDEX TERMS Flux-coupling type SFCL, power quality stability assessment, power quality instability probability, transient instability risk, corrected transient energy margin, critical clearing time.

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Power system transient security assessment based on deep learning considering partial observability

Pournabi

Mohammadi

Afrasiabi

et al. 2022

Electric Power Systems Research

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On-line transient stability assessment of large-scale power systems by using ball vector machines

Cited by 14 publications

References 26 publications

Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble

Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble

An Evaluation of Flux-Coupling Type SFCL Placement in Hybrid Grid System Based on Power Quality Risk Index

Power system transient security assessment based on deep learning considering partial observability

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