The Online Frequency Security Assessment of a Power System Considering the Time-Varying Characteristics of Renewable Energy Inertia

Zhang, Peng; Lu, Yunfeng; Zhang, Yingmin; Deng, Wenjun; Zeng, Qi

doi:10.3390/electronics12102205

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…In addition, the frequency security assessment is affected by the randomness of the output of renewable generations such as wind turbines, which puts forward higher requirements for real-time performance [31]. References [32,33] find that online monitoring and management methods can effectively provide data support for frequency security assessments based on a simplified frequency response model.…”

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

“…However, in addition to inevitable data noise, these real-time measurements frequently contain bad data. Such data not only distorts the system's true state and adversely affects the accuracy and convergence of state estimation, but also poses significant challenges to the performance of subsequent advanced applications [2,3]. For instance, voltage amplitude errors in line parameter identification can be amplified a thousandfold in resistance identification [4].…”

Section: Introductionmentioning

confidence: 99%

Bad Data Repair for New Energy Stations in Power System Based on Multi-Model Parallel Integration Approach

Li,

Liu,

Gao

et al. 2024

Electronics

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The accurate and reliable acquisition of measurement information is very important for the stable operation of power systems, especially the operation status information of new energy stations. With the increasing proportion of new energy stations in power systems, the quality issues of data from these stations, caused by communication congestion, interference, and network attacks, become more pronounced. In this paper, to deal with the issue of low accuracy and poor performance of bad data restoration in new energy stations, a novel deep learning approach by combining the modified long short-term memory (LSTM) neural network and Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is proposed. The proposed method can be implemented in a parallel ensemble way. First, the normal data set acquired from multiple sections of new energy stations is utilized to train the modified LSTM and WGAN-GP model. Secondly, according to the data characteristics and rules captured by each model, the two models are systematically integrated and the bad data repair model pool is constructed. Subsequently, the results of model repair are screened and merged twice by the parallel integration framework to obtain the final repair result. Finally, the extensive experiments are carried out to verify the proposed method. The simulative results of energy stations in a real provincial power grid demonstrate that the proposed method can effectively repair bad data, thereby enhancing the data quality of new energy stations.

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Inertia identification of power system based on energy function method

Ma,

Su,

Wang

et al. 2024

Energy Reports

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The Online Frequency Security Assessment of a Power System Considering the Time-Varying Characteristics of Renewable Energy Inertia

Cited by 5 publications

References 28 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

Bad Data Repair for New Energy Stations in Power System Based on Multi-Model Parallel Integration Approach

Inertia identification of power system based on energy function method

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