Risk assessment of power system oscillation instability based on geomagnetic storm events in the 24th solar cycle

Wang, Zezhong; Si, Yuan; Liu, Lianguang

doi:10.1049/gtd2.12283

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…In Fig. 1 , the experiment will select preliminary PN security risk evaluation indicators based on a large amount of existing literature and the principles of indicator construction 24 , 25 . Due to the huge amount of information, to ensure the comprehensiveness of the evaluation indicators, the preliminary selection of evaluation indicators is prone to the problem of huge number, which in turn leads to the distortion of the evaluation results.…”

Section: Construction Of Pnsrem Based On Lsa-svm Algorithmmentioning

confidence: 99%

Construction of power network security risk assessment model based on LSA-SVM algorithm in the background of smart grid

Qi,

Zhu,

et al. 2024

Sci Rep

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Due to theintricate and interdependent nature of the smart grid, it has encountered an increasing number of security threats in recent years. Currently, conventional security measures such as firewalls, intrusion detection, and malicious detection technologies offer specific protection based on their unique perspectives. However, as the types and concealment of attacksincrease, these measures struggle to detect them promptly and respond accordingly. In order to meet the social demand for the accuracy and computation speed of the power network security risk evaluation model, the study develops a fusion power network security risk evaluation algorithm by fusing the flash search algorithm with the support vector machine. This algorithm is then used as the foundation for building an improved power network security risk evaluation model based on the fusion algorithm.The study's improved algorithm's accuracy is 96.2%, which is higher than the accuracy of the other comparative algorithms; its error rate is 3.8%, which is lower than the error rate of the other comparative algorithms; and its loss function curve convergence is quicker than that of the other algorithms.The risk evaluation model's accuracy is 97.8%, which is higher than the accuracy of other comparative models; the error rate is 1.9%, which is lower than the error rate of other comparative models; the computing time of the improved power network security risk evaluation model is 4.4 s, which is lower than the computing time of other comparative models; and its expert score is high. These findings are supported by empirical analysis of the improved power network security risk evaluation model proposed in the study. According to the study's findings, the fusion algorithm and the upgraded power network security risk evaluation model outperform other approaches in terms of accuracy and processing speed. This allows the study's maintenance staff to better meet the needs of the community by assisting them in identifying potential security hazards early on and taking the necessary preventative and remedial action to ensure the power system's continued safe operation.

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Section: Construction Of Pnsrem Based On Lsa-svm Algorithmmentioning

confidence: 99%

Construction of power network security risk assessment model based on LSA-SVM algorithm in the background of smart grid

Qi,

Zhu,

et al. 2024

Sci Rep

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“…There are also scholars who analyze the grid structure at the theoretical level and predict the stability of the grid from the perspective of RLC model through data modeling. However, in the actual power grid operation process, especially the distribution network structure, its topology and load structure can be flexibly changed, so the RLC model cannot adapt to the real time power grid 9 . In the aspect of neural network technology, the risk assessment of oscillation and the intensity of junction points are also important research fields.…”

Section: Introductionmentioning

confidence: 99%

Research on high proportion of clean energy grid-connected oscillation risk prediction technology based on CNN and trend feature analysis

Pu,

Yingnan,

Chongjuan

et al. 2023

Sci Rep

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Oscillations, commonly known as a universal, propagative, and intricate event in the new power system, often give rise to generator tripping and load shedding, not only adversely affecting the power flow limit and the power angle stability but also posing threats to the lines of defense for stability and protection. Traditionally, emphasis has been laid on post-fault oscillation management, an emergency measure to deal with the impact and damage that have already affected the power grid. As such, this paper focuses on an oscillation prediction technique to detect oscillation energy early and intervene proactively to prevent further faults. This technique effectively lessens the damage caused by impacts and disconnects to the power grid. Firstly, this paper proposes the concept of disturbance power density and establishes the correlation between disturbance energy and the time domain, thereby exploring a method for evaluating the pattern of electrical quantities before power system oscillation. Secondly, it speeds up the time it takes to detect faults by catching nuances of voltage-current phase angle and impedance. Lastly, it puts forward a technique to cope with the intricacy and variety of power grid equipment using the convolutional neural network (CNN). This technique incorporates an integrated attention mechanism within a one-dimensional CNN model to capture the implicit mapping between voltage, active power, and reactive power at any time in the power system. This enables the model to self-learn multi-device characteristics and enhances the possibility of using theory in practical ways. Moreover, practical case studies also show that the prediction technique proposed in this paper can effectively issue warnings eight minutes before the occurrence of oscillation.

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“…Although the induced geoelectric field (IGF) intensity in the middle and low latitudes is weaker than that of in high latitude, with the application of large cross-section and multi-split conductors and the development of ultra-high power grid [2], the reduction of line resistance will lead to the increase of GICs, which will lead to the increase of QGIC. It is of practical significance to study the impact of QGIC with large amount and strong volatility on voltage stability [3,4].…”

Section: Introductionmentioning

confidence: 99%

Optimal Blocking Devices Placement for Geomagnetic Disturbance Mitigation Based on Sensitivity of Induced Geoelectric Fields

Shengquan

Sun

et al. 2022

IEEE Access

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Risk assessment of power system oscillation instability based on geomagnetic storm events in the 24th solar cycle

Cited by 5 publications

References 14 publications

Construction of power network security risk assessment model based on LSA-SVM algorithm in the background of smart grid

Construction of power network security risk assessment model based on LSA-SVM algorithm in the background of smart grid

Research on high proportion of clean energy grid-connected oscillation risk prediction technology based on CNN and trend feature analysis

Optimal Blocking Devices Placement for Geomagnetic Disturbance Mitigation Based on Sensitivity of Induced Geoelectric Fields

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