Area inertia estimation of power system containing wind power considering dispersion of frequency response based on measured area frequency

Li, Dongdong; Dong, Nan; Yao, Yin; Xu, Bin; Gao, David Wenzhong

doi:10.1049/gtd2.12628

Cited by 8 publications

(2 citation statements)

References 31 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to preliminary assess the proposed method and to select the proper threshold values, several tests have been carried out on synthesized data, representing the frequency measures provided by 12 PMUs. The tests have been designed considering the characteristics of the real measurement data in different operating conditions in terms of noise and oscillations parameters [14], [15], [16]. The sampling rate was set equal to 10 S/s, in agreement with the typical data rate adopted by PMUs of the European network.…”

Section: Methods Assessment With Synthesized Signalsmentioning

confidence: 99%

“…One critical issue revolves around the accuracy of estimation. PMUs need to deliver precise measurements while effectively dealing with distortion levels and rapid frequency variations [14]. Additionally, during transient phenomena characterized by a continuous spectrum, PMUs often assume that the fundamental component can be approximated as a narrowband component.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Robust Spectral Clustering Method Based on PMU Measurements for Coherent Areas Identification

Liccardo,

Lauria,

Bonavolontã

et al. 2023

IEEE Access

View full text Add to dashboard Cite

The paper deals with the separation of power system into coherent areas; this is a relevant issue for managing the network in both normal operating conditions and during anomalous events. In particular, the attention is focused on partitioning the power system in such a way as to group together frequency signals, measured by means of phasor measurement units (PMU), exhibiting similar oscillatory behavior after the occurrence of a fault or disturbance. Unfortunately, the increasingly massive presence of renewable energy sources is undermining the clustering methods defined so far, requiring new solutions to the problem. To overcome the considered drawbacks, the authors propose hereinafter to (i) improve the grouping capabilities of an iterative spectral clustering method thanks to the definition of new parameters for similarity estimation (Modified Bray Curtis index) and cluster thresholding (weighted Fiedler value) as well as (ii) enhance its robustness with respect to both measurement noise and uncertainty affecting the PMUs by means of a deep test procedure. To this aim, particular attention is paid in the design and assessment stage to the definition of both filtering algorithm and measurement parameters (e.g., the length of the analysis window). Once defined these parameters, the method is capable of 100% correctly separating transmission network sections oscillating with similar trends in a number of tests conducted on simulated and actual signals, so highlighting the promising performance of the method highlighting its reliability and efficacy in different test conditions.INDEX TERMS Frequency oscillations, interarea oscillations, PMU measurements, power transmission network, spectral clustering.

show abstract

Section: Methods Assessment With Synthesized Signalsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%