Real-time dynamic security assessment of power grids

Kerin, Uros; Heyde, Chris; Krebs, R.; Lerch, E.

doi:10.1140/epjst/e2014-02272-1

Cited by 13 publications

(4 citation statements)

References 10 publications

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“…Focussing on real-time operational decisions in real-world power grids, [20] presents a set of security indices regarding rotor angle, voltage, and frequency stability, damping of power swings, and frequency or voltage excursions across specified thresholds, which are useful for evaluating the severity of several possible contingencies in a power system. Rotor angle and voltage stability are studied more theoretically in [21] by augmenting the widely used simple swing-equation with dynamical voltage equations and assessing basin stability in the resulting model.…”

Section: Resilient Power Grids and Extreme Events 2385mentioning

confidence: 99%

“…These papers can be roughly grouped into five categories, namely, surveys [1][2][3][4] prediction and volatility of renewable infeed [5][6][7] derivation of requirements for a resilient power grid infrastructure [8][9][10][11][12][13] relationships between grid stability and complex network topology [14][15][16][17] and the analysis of special issues in dynamic stability [20][21][22]. Because of the various overlaps, we still chose to assemble these works in alphabetical order in this volume.…”

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confidence: 99%

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Interdisciplinary challenges in the study of power grid resilience and stability and their relation to extreme weather events

Heitzig

Fujiwara

Aihara

et al. 2014

Eur. Phys. J. Spec. Top.

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Abstract. This topical issue collects contributions to the interdisciplinary study of power grid stability in face of increasing volatility of energy production and consumption due to increasing renewable energy infeed and changing climatic conditions. The individual papers focus on different aspects of this field and bring together modern achievements from various disciplines, in particular complex systems science, nonlinear data analysis, control theory, electrical engineering, and climatology. Main topics considered here are prediction and volatility of renewable infeed, modelling and theoretical analysis of power grid topology, dynamics and stability, relationships between stability and complex network topology, and improvements via topological changes or control. Impacts for the design of smart power grids are discussed in detail.Many countries face the problem that reducing greenhouse gas emissions while phasing out nuclear energy requires a transformation of the energy system that includes large amounts of renewable energy production. The latter, however, is highly influenced by extreme weather events, which are likely to get more frequent and more severe due to climate change. Extreme events influence not only the availability of energy, but also and of more fundamental importance, the stability of the whole power grid, which is not yet understood sufficiently. Grid operation and control will profit a

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Section: Resilient Power Grids and Extreme Events 2385mentioning

confidence: 99%

mentioning

confidence: 99%

Interdisciplinary challenges in the study of power grid resilience and stability and their relation to extreme weather events

Heitzig

Fujiwara

Aihara

et al. 2014

Eur. Phys. J. Spec. Top.

View full text Add to dashboard Cite

show abstract

“…However, the application of the online DSA is limited. It is not suitable for real-time protection and remedial control action [7][8][9] following a disturbance in the system. This is due to the fact that the power system is characterized by a large number of dynamic and nonlinear algebraic equations [10].…”

Section: Introductionmentioning

confidence: 99%

Real-time power system dynamic security assessment based on advanced feature selection for decision tree classifiers

Al-Gubri¹,

Ariff²

2018

Turk J Elec Eng & Comp Sci

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This paper proposes a novel algorithm based on an advanced feature selection technique for the decision tree (DT) classifier to assess the dynamic security in a power system. The proposed methodology utilizes symmetrical uncertainty (SU) to reduce the data redundancy in a dataset for DT classifier-based dynamic security assessment (DSA) tools. The results show that SU reduces the dimension of the dataset used for DSA significantly. Subsequently, the approach improves the performance of the DT classifier. The effectiveness of the proposed technique is demonstrated on the modified IEEE 30-bus test system model. The results show that the DT classifier with SU outperforms the DT classifier without SU. The performance of the proposed algorithm indicates that the DT classifier with SU is able to assess the dynamic security of the system in near real time. Therefore, it is able to provide vital information for protection and control applications in power system operation.

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“…as a significant part of modern control system [125]. Ideally the electrical power systems are required to be stable, but instability will always affect the operation of the systems, mainly because of the uncertain assumptions made in system designs [126]. Since the integrated resource for power systems is quite complex, and the state of the system will rapidly change with quantities of unstable factors, pre-fault Dynamic Security Assessment (DSA) was carried out to process real-time PMU measurements.…”

Section: Organization Of the Thesismentioning

confidence: 99%

Advanced data-analytics for power system security enhancement

Chen

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Security in power systems is of utmost importance to ensure the reliable and safe operation of electricity infrastructure. With the increasing integration of digital technologies and the advent of the smart grid, modern power systems infrastructure have become more interconnected and reliant on data communication networks. Nevertheless, they are also exposed to various cybersecurity threats and vulnerabilities at the same time.To solve the security problems mentioned above, this Ph.D. thesis aims to develop data-analytics methods for security-relevant problems in power systems. This thesis includes 2 aspects, namely dynamic security assessment and nonintrusive loading monitoring, where the common thread is to identify the abnormal situations and raise potential solutions. With a vast amount of real-time data collected and utilized in modern power systems, data-driven methods can be a powerful tool to deal with these problems for their computational efficiency. The techniques employed in this thesis involve anomaly detection (to detect cyberattacks, e.g., Long Short-Term Memory, Local Outlier Factor, etc.), interpretation analysis (to quantify the contributions of input features against the machine learning models, e.g., SHapley Additive exPlanations and Shapley Additive Global importancE), adversarial attacks and mitigation (to measure the model vulnerability), and ensemble learning (to enhance the reliability of the predictions).All the proposed methods have been well verified on simulations over the corresponding data sets recorded from power system operations. Throughout the data experiments, it is shown that proper employment of ML algorithms could efficiently solve the security-relevant problems in power systems, and interpretation analysis will help to better understand the internal operation modes of the trained ML models as well as identify these crucial data features that dominate the com-

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Real-time dynamic security assessment of power grids

Cited by 13 publications

References 10 publications

Interdisciplinary challenges in the study of power grid resilience and stability and their relation to extreme weather events

Interdisciplinary challenges in the study of power grid resilience and stability and their relation to extreme weather events

Real-time power system dynamic security assessment based on advanced feature selection for decision tree classifiers

Advanced data-analytics for power system security enhancement

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