Transmitting electric power system dynamics in SCADA using polynomial fitting

Duan, Xianzhong; Su, Sheng; Mei, Nian

doi:10.1007/s11431-008-0227-1

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…The basic idea of this approach is to use the residual data as input to reconstruct the missing data. A review of the literature indicates that numerous methods, including polynomial fitting (Duan et al ,2009; Goodin et al , 2010), probabilistic method (Nurulkamal, 2015), neural network model (Qu et al , 2020) and interpolation reconstruction method (Zheng et al , 2017; Wang et al , 2022) have been proposed in this regard. It was shown that the modeling method has good inference and estimation functions and can be applied to accurately estimate the missing values based on historical data.…”

Section: Preprocessing Of Supervisory Control and Data Acquisition Datamentioning

confidence: 99%

Application of SCADA data in wind turbine fault detection – a review

Yuan

2022

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Purpose With the rapid increase in the number of installed wind turbines (WTs) worldwide, requirements and expenses of maintenance have also increased significantly. The condition monitoring (CM) of WT provides a strong “soft guarantee” for preventive maintenance. The supervisory control and data acquisition (SCADA) system records a huge amount of condition data, which has become an effective means of CM. The main objective of the present study is to summarize the application of SCADA data to fault detection in wind turbines, analyze its advantages and disadvantages and predict the potential of future investigations on the use of SCADA data for fault detection. Design/methodology/approach The authors first review the means of WT CM and summarize the characteristics of CM based on SCADA data. To ensure the quality of SCADA data, data preprocessing methods are analyzed and compared. Then, the failure modes of the key components are discussed and the SCADA data used for fault detection of each component are compared. Moreover, the fault detection methods for WT are classified and a general framework for fault detection is proposed. Finally, the issues in the WT fault detection method based on SCADA data are reviewed. Findings Based on the performed analyses, it is found that although the fault detection accuracy based on SCADA data is relatively poor, it has low capital expenses and low computational cost. More specifically, when there is scarce fault data, the normal SCADA data can be used to detect the fault time. However, the specific fault type cannot be identified in this way. When a large amount of fault data are accumulated in the SCADA system, it can not only detect the occurrence time of the fault but also identify the specific fault type. Originality/value The main contribution of the present study is to summarize the pre-processing methods for SCADA data, the data required for fault detection of key components and the characteristics of the fault detection model. Then we propose a general fault detection framework for wind turbines based on SCADA data, where the maintenance workers can choose the appropriate fault detection method according to different fault detection requirements and data resources. This article is expected to provide guidance for fault detection based on time-series sensor signals and be of interest to researchers, maintenance workers and managers.

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Section: Preprocessing Of Supervisory Control and Data Acquisition Datamentioning

confidence: 99%

Application of SCADA data in wind turbine fault detection – a review

Yuan

2022

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“…Nevertheless, the information captured by WAMS requires advanced techniques to extract and estimate the LFO oscillatory parameters and approximate mode shapes (AMS). At present, many methods have been used to analyze the LFO off-line and on-line based on the measured data [6,7], including discrete Fourier transform (DFT) [8], Kalman filter [9], Prony method [10], Wavelet transform (WT) [11], stochastic subspace identification (SSI) [12] and Hilbert-Huang transform (HHT) [13,14]. Each of them has its own characteristics and application areas.…”

Section: Introductionmentioning

confidence: 99%

A novel method for analyzing dominant oscillation mode based on improved EMD and signal energy algorithm

Yang

Rehtanz

et al. 2011

Sci. China Technol. Sci.

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In this paper, a novel method that integrates the improved empirical mode decomposition (EMD) and signal energy algorithm is proposed to estimate the dominant oscillation parameters and corresponding mode shape. Firstly, the EMD with symmetrical extrema extension (SEE) is utilized to decompose the measured data from wide area measurement system (WAMS) into a finite set of intrinsic mode functions (IMFs). Then, the signal energy algorithm is used to calculate the approximate oscillation parameters of the IMFs. The nodes involved the dominant oscillation mode are classified based on the calculated frequency and reasonable threshold. Furthermore, for the dominant oscillation mode, the IMF with maximum mean amplitude is defined as the reference. Next, the relative phases (RPs) between the reference IMF and other IMFs are calculated in order to identify the negative and positive oscillation groups. According to the values of RPs, the coherent group and corresponding node contribution factor (NCF) can be identified, and the dominant approximate mode shape (AMS) can also be determined. The efficiency of the proposed approach is tested by applying it to synthetic signal and measured data from the simulation model. low frequency oscillation, wide area measurement system, symmetrical extrema extension, signal energy algorithm, total oscillation energy, node oscillation energy, node contribution factor Citation:Yang D C, Rehtanz C, Li Y, et al. A novel method for analyzing dominant oscillation mode based on improved EMD and signal energy algorithm.

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“…In FRP, a feasibility restoring model was presented, and a priority-listing strategy of restoring actions was proposed to restore the unsolvable OPF problems. Duan et al [22] proposed an approach to transmit electric power system dynamics to compress dynamics data with curve fitting in the remote terminal units (RTUs) and reconstruct the dynamics for reducing communication demand. The approach has extensive application potential.…”

mentioning

confidence: 99%

Some new results of electric power science

2011

Sci. China Technol. Sci.

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Transmitting electric power system dynamics in SCADA using polynomial fitting

Cited by 6 publications

References 7 publications

Application of SCADA data in wind turbine fault detection – a review

Application of SCADA data in wind turbine fault detection – a review

A novel method for analyzing dominant oscillation mode based on improved EMD and signal energy algorithm

Some new results of electric power science

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