Power System Frequency Security Index Considering All Aspects of Frequency Profile

Delkhosh, Hamed; Seifi, Hossein

doi:10.1109/tpwrs.2020.3047510

Cited by 30 publications

(9 citation statements)

References 14 publications

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“…With the more renewable generations connecting to AC power systems, finding the relationship between frequency security boundaries and dominant frequency stability problems is a key topic for power system planning and operation [20]. Dynamic frequency security assessments are concentrated on three indicators: the rate of the change of frequency (f RoCoF ), the frequency nadir (f N ) and the steady-state frequency (f ss ) [21]. When the over-response of wind turbines causes a SFD problem, the frequency recovery process deteriorates further, and there is no specific principle for assessing the SFD [22].…”

Section: The Research On System Frequency Security Assessmentmentioning

confidence: 99%

Quantitative Frequency Security Assessment of Modern Power System Considering All the Three Indicators in Primary Frequency Response

Tang,

Qi,

Liu

et al. 2023

Sustainability

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The primary frequency response scale is deteriorating in the modern power system due to the high penetration of different power devices. Frequency security assessments are essential for the operation or stability-checking of the power system. Firstly, this paper establishes the Unified Transfer Function Structure (UTFS) of power systems with highly penetrated wind turbines. Based on the UTFS, this paper analyzes the three indicators of the primary frequency responses. Secondly, to better assess the security of the frequency, the secondary frequency drop (SFD) is avoided, with the frequency response parameters of the wind turbines calculated. Moreover, considering all three indicators of the primary frequency response, this paper proposes a frequency security margin index (FSMI). The FSMI divides the system stability margin into three levels, quantitively and linearly representing the frequency response capability of different power devices. Finally, to show the effectiveness and practicability of the FSMI, this paper establishes a simulation model with high wind energy penetration, including four machines and four zones in DigSILENT. Based on the FSMI, the frequency stability margins in different typical operating scenarios are divided into three zones: “Absolut secure”, “Secure” and “Relative secure”. The FSMI also shows the dominant frequency stability problem and the risk of system frequency instability for each zone. Considering the checking principles, the frequency stability margin is equivalently expanded by calculating the energy storage’s minimum frequency response capacity.

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Section: The Research On System Frequency Security Assessmentmentioning

confidence: 99%

Quantitative Frequency Security Assessment of Modern Power System Considering All the Three Indicators in Primary Frequency Response

Tang,

Qi,

Liu

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…Besides, as illustrated in Table 3, three transient frequency characteristics (Delkhosh and Seifi, 2021;Xiong et al, 2021) (i.e., FFD, SFD, and tertiary frequency drop (TFD)) are used here to further concretely quantify and compare the control performance of three designed approaches. Among which the optimal value is highlighted in bold.…”

Section: Case Studiesmentioning

confidence: 99%

Optimal Adaptive Inertial Droop Control–Based Power System Frequency Regulation via Wind Farms

et al. 2022

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Wind farm–based frequency regulation of the power system has progressively attracted more attention owing to its higher power generation capacity. Among which the step start-up and adaptive inertial droop control of wind turbines with a maximum power point efficiently regulate the system frequency via fully utilizing the rotational kinetic energy. Besides, the coefficients of adaptive droop control and virtual inertial control will facilitate a significant influence on the frequency support performance and operation status of wind turbines for the power grid. To obtain optimal control parameters, a parameter optimization framework of a step start-up adaptive inertial and droop controller combined with the CGO algorithm is proposed, whose effectiveness is verified by a three-area four-terminal VSC-MT-HVDC–based WFs and AC system in MATLAB/Simulink. As a result, a set of parameters with a satisfactory and comprehensive dynamic control performance can be acquired by the proposed method under both 200 MW and 300 WM load increases compared with the trial-and-error approach.

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“…None of the previous studies paid attention to safety margins. Delkhosh and Seifi [ 48 ] proposed an FSI considering all frequency key characteristics. Due to the decline in power system inertia, this paper combines the center-of-inertia frequency (COIF) and FSI to divide the system frequency responses into three categories, i.e., insecurity, relative security, and absolute security.…”

Section: Related Workmentioning

confidence: 99%

Frequency Stability Prediction of Power Systems Using Vision Transformer and Copula Entropy

Liu

Han

et al. 2022

Entropy

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This paper addresses the problem of frequency stability prediction (FSP) following active power disturbances in power systems by proposing a vision transformer (ViT) method that predicts frequency stability in real time. The core idea of the FSP approach employing the ViT is to use the time-series data of power system operations as ViT inputs to perform FSP accurately and quickly so that operators can decide frequency control actions, minimizing the losses caused by incidents. Additionally, due to the high-dimensional and redundant input data of the power system and the O(N2) computational complexity of the transformer, feature selection based on copula entropy (CE) is used to construct image-like data with fixed dimensions from power system operation data and remove redundant information. Moreover, no previous FSP study has taken safety margins into consideration, which may threaten the secure operation of power systems. Therefore, a frequency security index (FSI) is used to form the sample labels, which are categorized as “insecurity”, “relative security”, and “absolute security”. Finally, various case studies are carried out on a modified New England 39-bus system and a modified ACTIVSg500 system for projected 0% to 40% nonsynchronous system penetration levels. The simulation results demonstrate that the proposed method achieves state-of-the-art (SOTA) performance on normal, noisy, and incomplete datasets in comparison with eight machine-learning methods.

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Power System Frequency Security Index Considering All Aspects of Frequency Profile

Cited by 30 publications

References 14 publications

Quantitative Frequency Security Assessment of Modern Power System Considering All the Three Indicators in Primary Frequency Response

Quantitative Frequency Security Assessment of Modern Power System Considering All the Three Indicators in Primary Frequency Response

Optimal Adaptive Inertial Droop Control–Based Power System Frequency Regulation via Wind Farms

Frequency Stability Prediction of Power Systems Using Vision Transformer and Copula Entropy

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