Oscillation mode identification based on wide-area ambient measurements using multivariate empirical mode decomposition
Introduction
Power system oscillation is a common phenomenon in interconnected power grids. Insufficient damping of inter-area oscillation may increase system risks and even cause failures [1]. Therefore, fast detection and analysis on inter-area oscillations are critical to activate proper oscillation damping controls to increase system reliability [2], [3], [4]. Moreover, as the conditions of modern power girds vary more constantly and significantly with the increase of renewables, energy storage and other distributed resources, updating the oscillation information is becoming more important but also challenging [5].
Approaches to analyze the oscillation modes of power grids fall into two categories: model-based methods and measurement-based methods. Model-based methods analyze system oscillation based on detailed system dynamic models using eigenvalue analysis approaches. It is becoming difficult for system operators to use model-based approaches due to many factors, such as the computation burden of large-scale dynamic simulations, changing environments, information privacy issues and parameter inaccuracy (e.g. inadequate modeling of loads) [6]. Applying synchrophasor measurement technology, Wide-Area Measurement Systems (WAMSs) provide a powerful tool to monitor and analyze the dynamics of interconnected power grids [7], [8], [9]. Since large disturbances are rare and usually destructive in modern power grids, system event data are far from enough for real-time estimation of system oscillation modes. It is necessary to provide oscillation information to system operators under normal operation conditions. Studies on ambient synchrophasor measurements show that there is a constant level of noise caused by load variations or other environmental disturbances at the transmission level [10] and the distribution level [7]. These ambient sychrophasor measurements have been recently used as a data source to extract real-time inter-area oscillation information [8], [10], [11], [12], [13], [14], [15], [16], [17], [18].
Since ambient measurements were first used in Ref. [11] to analyze the electromechanical oscillation, multiple approaches have been developed based on various signal processing techniques. There are two main categories of methods for oscillation analysis based on ambient and event data: transfer function based methods and subspace methods. Transfer-function-based methods directly estimate mode shape through treating measurements as system outputs. Typical transfer function based methods include Fourier transform [12], [13], [14], the Prony's method [15], the Matrix-Pencil method [16], Empirical Mode Decomposition (EMD) [17], the Yule–Walker method [18], and the singular value decomposition method [19], etc. To increase analysis efficiency, Ref. [13] adopted a FFT-based distributed optimization method to select the dominant measurement channels for estimating each oscillation mode based on ambient data. Ref. [20] proposed a two-step method which comprised of independent component analysis and random decrement to estimate the oscillation mode. Different from transfer-function-based methods, subspace methods obtain the oscillation mode information through identifying the system state space model using the measurements [11]. Typical subspace methods include the Canonical Variate Algorithm [21], the N4SID algorithm [22], and the autoregressive moving average block-processing method [23]. Recently, Ref. [24] proposed the robust recursive least square algorithm to analyze measurement data. Ref. [25] improved this method by proposing a regularized robust recursive least square method.
Existing transfer function based methods have limitations in one or two of the following aspects. (a) The capability to analyze drifting, non-stationary signals and provide localized results. Oscillation may drift frequently in ambient measurements [26]. For example, a high damping local oscillation mode may stimulate an inter-area oscillation mode with a pseudo negative damping ratio within a short duration. Existing methods may not be able to find the appropriate time window for analyzing this inter-area oscillation due to this drifting [27]. In addition, some measurements are mixed with fluctuations unrelated to oscillations, such as the frequency fluctuations caused by normal system operation and regulations [28]. These non-stationary measurements usually lead to difficulties to accurately analyze the subtle oscillation modes [29]. For example, the Prony's method requires the signal to be zero-mean and stationary, which may result to difficulties to analyze signals with high amplitude trends [23]. (b) The capability to analyze multi-channel signals. Most existing methods can only process one signal so they have to analyze multiple signals in a separate way. they usually require measurements from critical devices, which has good observability (e.g., branch flow and bus frequency) on certain inter-area oscillation modes [8], [11], [17], [19], [23], [24], [25], [29], [30], [31], [32], [33], [34], [35], [36]. If the measurement that has good oscillation observability is not available in some areas, single-channel methods may not be able to provide oscillation information in these areas. Therefore, there is a need of developing multi-channel methodology to extract oscillation information utilizing high-noise measurements, which individually have less observability on oscillation.
The aim of this study is to introduce multi-channel EMD based methods: Bivariate EMD (BEMD), Trivariate EMD (TEMD), and Multivariate EMD (MEMD), to facilitate identifying inter-area oscillation mode using multi-channel ambient synchrophasor measurements. Particularly, MEMD is investigated in details using real-world ambient measurements. The test results show that MEMD has better performance than typical oscillation identification methods. For convenience, wide-area frequency measurements are adopted for illustration and analysis in the following sections. The introduced methods and descriptions are also applicable to other wide-area measurement data such as instantaneous real power, voltage angle, and current.
The rest of this paper is organized as follows. Section 2 describes the original form of the EMD method. Section 3 expands it to bivariate, trivariate, and multivariate empirical mode decomposition to identify oscillation. Section 4 presents two test cases to verify the proposed methods. Conclusions are provided in Section 5.
Section snippets
Background—Empirical mode decomposition based oscillation identification
EMD was developed for analyzing non-stationary signals that commonly exist in many science and engineering fields [1]. Due to its data-driven nature and the strong capability in analyzing non-stationary signals to provide information on localized amplitudes and frequencies, EMD has been proved to be effective for time-frequency analysis in various areas, such as power quality assessment [37], biomedical signals [38], mechanical signals [39], and geographical signals [40]. For oscillation
Bivariate/trivariate empirical mode decomposition for oscillation identification
The bivariate (complex) EMD (BEMD) [43] and the trivariate EMD (TEMD) [44] enhanced the capability of identifying synchronous behaviors of bivariate and trivariate signals. In many fields, BEMD and TEMD has been proved to be able to determine common frequency components through simultaneous decomposition of two or three signals, such as equipment condition monitoring [45] and biomedical signal analysis [46]. Since inter-area oscillations typically happen in two or three areas, if the
Case studies based on FNET/GridEye measurements
This section shows two cases to investigate the features of TEMD and MEMD for oscillation identification based on FNET/GridEye ambient measurements. As a WAMS at the distribution level, the FNET/GridEye system is capable of monitoring the power grid with high dynamic accuracy [51], [52]. A frequency disturbance recorder (FDR) can measure power grid voltage, angle, and frequency at the 120 V outlets. These highly accurate synchrophasor measurements at transmitted across the Internet at a 10 Hz
Conclusions
This paper introduced BEMD, TEMD, and MEMD as multi-channel data-driven approaches for inter-area oscillation identification based on wide-area ambient synchrophasor measurements. The proposed methods have the capability to identify inter-area oscillation modes using highly noisy ambient measurements. Moreover, they are robust to drifting, non-linear and non-stationary measurement signals. Test results based on real-world frequency measurements and comparison with existing methods show that the
Acknowledgments
This work made use of the Engineering Research Center Shared Facilities supported by the Engineering Research Center Program of the U.S. National Science Foundation and DOE under NSF Award Number EEC-1041877 and the CURENT Industry Partnership Program. The authors would like to express gratitude to all the past and current FNET/GridEye sponsors and hosts. Also, the authors would like to thank reviewers of this paper for their constructive comments.
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