Application of gray relational analysis to k-means clustering for dynamic equivalent modeling of wind farm

Fang, Ruiming; Shang, Rong; Minling, Wu; Peng, Changqing; Guo, Xinhua

doi:10.1016/j.ijhydene.2017.06.023

Cited by 38 publications

(23 citation statements)

References 16 publications

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“…Thus, thev i,k andμ ij,k are calculated based on the source domain samples using the traditional MV-FCM. The parameters setting of MV-FCM is shown in TABLE 4, then thev i,k andμ ij,k can be computed using (4) and (5), iteratively.…”

Section: B the Test Wf And Its Equivalent Resultsmentioning

confidence: 99%

“…However, a WF usually contains dozens or even hundreds of WTs, and it is not feasible to model each WT in detail due to the huge model size and simulation time [3]. Thus, it is critical to develop the WF equivalent model for LVRT analysis on the basis of reasonable reduction [4]. In general, the WF equivalent methods can be categorized as two types: single-machine equivalence and multi-machines equivalence (MME).…”

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confidence: 99%

“…The operation characteristics are different among the WTs in a WF, so it is important to select the proper CI to assess these differences. Until now, wind speed [3], pitch angle [4], rotor speed [5], and active power [6] have already been fully investigated to be assigned as CIs. These CIs are selected at specific time spot, and with time passes, these CIs might be changeable and the clustering results might not be valid in the whole time span.…”

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confidence: 99%

“…Each group of WTs is called as one equivalent WT, and the parameters of the equivalent WTs need to be calculated. Equal power loss method [10] and equal voltage loss method [4] are the mostly used for collector network equivalence, where capacity weighted method is commonly used in the parameter estimation of equivalent transformer and equivalent WT [4].…”

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confidence: 99%

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A Wind Farm Equivalent Method Based on Multi-View Transfer Clustering and Stack Sparse Auto Encoder

Han

Miao

et al. 2020

IEEE Access

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Large-scale wind farm (WF) constitutes dozens or even hundreds of wind turbines (WTs), making it complex and even impractical to develop each individual WT in detail when building WF model. Thus, the equivalent model of WF, with a reasonable reduction of the detailed model, is essential to be developed. In this paper, we propose a multi-view transfer clustering and stack sparse auto encoder (SSAE) based WF equivalent method, which can be used in the low voltage ride through (LVRT) analysis of WF. First, to obtain distinguishable deep-level and multi-view representation of wind turbine (WT), stack sparse auto encoder (SSAE) is used to extract features from the time series of several WT physical quantities, and these features are used as the clustering indicator (CI). Then, a multi-view transfer FCM (MVT-FCM) clustering algorithm, which combines transfer learning with multi-view FCM (MV-FCM), is put forward for WTs clustering. Two transfer rules are designed in this algorithm, and the clustering center and membership degree in the source domain are transferred to guide the clustering process of target domain samples. Finally, the calculation method of equivalent parameters is presented. To verify the effectiveness of the proposed method, a modified actual system in East Inner Mongolia of China is utilized for case study, and the performance of the proposed model is compared with several state-of-the-art models. Simulation results show that the equivalent errors of the proposed model decrease at least 3% when comparing with other models. Also, the error fluctuations are within 6% under different simulation conditions, which illustrates the well-performed robustness of the proposed model. INDEX TERMS Wind farm equivalence, multi-view, transfer learning, deep learning, MVT-FCM, SSAE.

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Section: B the Test Wf And Its Equivalent Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A Wind Farm Equivalent Method Based on Multi-View Transfer Clustering and Stack Sparse Auto Encoder

Han

Miao

et al. 2020

IEEE Access

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“…Most of the wind farms are irregularly arranged, thus different wind turbines have different operating conditions. To increase the efficiency of simulation of a wind farm, in multi-machine models, wind turbines have been grouped by wind speed using various clustering methods [60][61][62][63][64].…”

Section: Equivalent Model For a Wind Farmmentioning

confidence: 99%

Key Issues on the Design of an Offshore Wind Farm Layout and Its Equivalent Model

Zeng

2019

Applied Sciences

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Offshore wind farms will have larger capacities in the future than they do today. Thus, the costs that are associated with the installation of wind turbines and the connection of power grids will be much higher, thus the location of wind turbines and the design of internal cable connections will be even more important. A large wind farm comprises of hundreds of wind turbines. Modeling each using a complex model leads to long simulation times—especially in transient response analyses. Therefore, in the future, simulations of power systems with a high wind power penetration must apply the equivalent wind-farm model to reduce the burden of calculation. This investigation examines significant issues around the optimal design of a modern offshore wind farm layout and its equivalent model. According to a review of the literature, the wake effect and its modeling, layout optimization technologies, cable connection design, and wind farm reliability, are significant issues in offshore wind farm design. This investigation will summarize these important issues and present a list of factors that strongly influence the design of an offshore wind farm.

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A Deep Learning Spatiotemporal Prediction Framework for Mobile Crowdsourced Services

et al. 2018

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This papers presents a deep learning-based framework to predict crowdsourced service availability spatially and temporally. A novel two-stage prediction model is introduced based on historical spatio-temporal traces of mobile crowdsourced services. The prediction model first clusters mobile crowdsourced services into regions. The availability prediction of a mobile crowdsourced service at a certain location and time is then formulated as a classification problem. To determine the availability duration of predicted mobile crowdsourced services, we formulate a forecasting task of time series using the Gramian Angular Field. We validated the effectiveness of the proposed framework through multiple experiments.

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Application of gray relational analysis to k-means clustering for dynamic equivalent modeling of wind farm

Cited by 38 publications

References 16 publications

A Wind Farm Equivalent Method Based on Multi-View Transfer Clustering and Stack Sparse Auto Encoder

A Wind Farm Equivalent Method Based on Multi-View Transfer Clustering and Stack Sparse Auto Encoder

Key Issues on the Design of an Offshore Wind Farm Layout and Its Equivalent Model

A Deep Learning Spatiotemporal Prediction Framework for Mobile Crowdsourced Services

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