- Open Access
New algorithm for mode shape estimation based on ambient signals considering model order selection
© Wu et al; licensee Springer. 2013
- Received: 29 November 2011
- Accepted: 23 December 2012
- Published: 26 January 2013
Using time-synchronized phasor measurements, a new signal processing approach for estimating the electromechanical mode shape properties from ambient signals is proposed. In this method, Bayesian information criterion and the ARMA(2n,2n – 1) modeling procedure are first used to automatically select the optimal model order, and the auto regressive moving averaging models are built based on ambient data, then the low-frequency oscillation modal frequency and damping ratio are identified. Next, Prony models of ambient signals are presented, and the mode shape information of multiple dominant interarea oscillation modes are simultaneously estimated. The advantages of the new ARMA-P method are demonstrated by its applications in both a simulation system and measured data from China Southern Power Grid.
- Electromechanical dynamics
- Mode shape
- Ambient signal
- Model order selection
- ARMA-P method
Modal frequency, modal damping ratio, mode shape magnitude, and mode shape angle are the key parameters describing the electromechanical modal properties of a power system . Similar to former two, the mode shape properties that describe the participation of state variables in a particular mode are of vital importance for the safety and reliable operation of the system. Near real-time knowledge of mode shape characteristics provide critical information for the optimization of generators and/or load shedding, in order to improve the damping of the dangerously low-damped modes of power systems.
In general, the analysis of mode shape properties can be accomplished using two basic approaches: the eigenanalysis of a small signal model , or as shown in this article, signal processing of the time-synchronized measurements. One important advantage of the signal-based method is that the identification is dependent on the large, complex system model. In this category, the Prony method [2, 3] and the Eigensystem Realization Algorithm [4, 5] are widely used. However, their applications are generally limited only for ringdown signals, which are relatively few in actual power grids. Ambient signals, caused by low-level stochastic disturbances, are more frequently and easily collected in real systems. Some publications have offered algorithms for identifying the modal frequency and damping ratio from ambient data [6–9], which fully demonstrate that this kind of signal includes abundant information about the system. Only recently the mode shape has been considered [10–14]. Based on the relationship between the cross- and power spectral densities and mode shapes, an approach for estimating the mode shapes using spectral method is presented in . Liu and Venkarasubramanian  applied the frequency domain decomposition method to the mode shape identification. Then, the channel matching method was introduced in  and refined in , in which a narrowband bandpass filter must firstly be used to extract one single mode from multiple modes. In this case, the changes of operation modes in actual power system would inevitably cause the ineffectiveness of the prefixed filter, and influence the estimation accuracy. In , the transfer function method was proposed which showed that mode shape could be calculated by evaluating a transfer function, constructed between a pair of system outputs, at the mode of interest. However, as the key factors of transfer function, the selection of model order are not taken into account in these articles, this will directly affect the accuracy of the mode shape analysis results.
In this article, in order to identify the low-frequency oscillation mode shape properties based on ambient signals, a new multiple modes estimation method called the auto regressive moving averaging-Prony (ARMA-P) is proposed. In addition, the problem of optimal model order selection in ARMA modeling is considered, which would improve the efficiency and accuracy of the new ARMA-P method.
The remainder of this article is organized as follows. Section 2 describes the feasibility and the theoretical basis of using the ARMA and Prony models together to extract the mode shapes from ambient data. In Section 3, different model order selection criteria are comparatively discussed, and ARMA(2n,2n – 1) modeling procedures are adopted to optimize the order selection path. To estimate the mode shape characteristics based on ambient signals, the equations of ARMA-P method are derived in Section 4. Sections 5 and 6 provide the simulation and actual system examples, respectively. The results demonstrate that the new ARMA-P method can effectively estimate the mode shape properties from ambient data. Conclusions are provided in Section 7.
where x is the n × 1 system state vector, including machine rotor angles and velocities. Input vector q (order m × 1) is a hypothetical random-noise source vector perturbing the system. Under ambient conditions, input q is typically conceptualized as noises produced by random loads switching in power systems. A (order n × n) is the state matrix and B (order n × m) is the input matrix. Measurement-based electromechanical mode estimation assumes that the power system is in the steady-state condition described by (1).
It has been well established that the eigensolution of the state matrix A in (1) provides all the required information to completely describe the modal properties of power system. The reader can refer to  for more detail. A brief review of these properties is described here.
where λ k is the k th eigenvalue (k = 1…n), u k = [u 1,k , u 2,k , … u n,k ]T is the k th right eigenvector, v k = [v k,1, v k,2, …v k,n ] is the k th left eigenvector.
where b l,j is the l th row j th column element in the input matrix B, z l (0) is the initial value of z k .
Equation (4) provides information on how the modes are combined to create the system states. The element u i,k (the i th element of u k ) provides critical information on how the i th state (generators and other dynamic devices) participates in the k th oscillation mode. The magnitude of u i,k determines the intensity level for the state variable x i to participate the k th oscillation mode, and the angle of u i,k determines the oscillation phase of the state variable x i in the k th oscillation mode.
where κ = 0,1…N – 1, N is the data length, n is the number of oscillation modes.
Each term in (9) has four elements: the damping factor α k , the frequency f k , the magnitude A i,k , and the angle θ i,k . Each exponential component with a different frequency is viewed as one unique mode of the original signal.
It can easily be found that Ψ i,k 0 is time-invarying, and it is proportional to the right eigenvectors u i,k with a constant z k (0), whereas ΔΨ i,k (κ) is time-varying due to the change of the input vector q .
Assuming for the moment that all signals are time-synchronized samples, and a reference state or signal is chosen as the state having the high observability in the k th oscillation mode, the theoretical feasibility of ARMA-P method estimating mode shape properties of multiple modes based on ambient signals is certified.
In the ARMA modeling of ambient signal, the model order selection is an important step. The applicability of model order will influence the accuracy and efficiency of oscillation modal frequency and damping ratio analysis, and further affect the mode shapes identification. In addition, the mode shape characteristics are relevant to multiple nodes in power grids, that is to say, we have to build the ARMA models of multiple signals. Obviously, it is time-consuming. And for the online application of ARMA-P method, it is best to automatically select the model order. Therefore, the model order selection is studied in this section. Different model order selection criteria are comparatively discussed, and the modeling procedure is considered to improve the calculation efficiency.
Model order selection criteria
Model order is a key factor in the ARMA model of ambient signal. A model with too high an order will include too much irrelevant oscillation information, and a model with too low an order may not include enough essential information about the system. Only, the ARMA model, with an optimal model order, can precisely describe the dynamic characteristics of power grids.
where f(x i |θ k ), i = 1,…,N describes the conditional probability density of the observations x 1,…,x N , C(N) is an increasing function of the observations number N, is the estimator for the unknown model parameter based on the observations, and the optimal order choice is such that = argmin IC(p).
Obviously that the second term grows as the parameters becomes complex, while the first term has the opposite variation, so the minimization of IC realizes a compromise between the data fitting and the complexity of the chosen parameter.
Theoretical research in the AIC has helped to specify the asymptotic behavior of AIC. This criterion is unsatisfactory since it asymptotically leads to a strictly positive over parameterization probability of the model order .
Theoretical research has found that BIC and MDL criteria are almost surely convergent in that they help in finding the appropriate model order when the observations number N → ∞ (strong consistency) and penalized the term of likelihood more than AIC . Because of the similarity of the two criteria, we choose to discuss the performance of BIC in this article.
In this article, we proposed to apply AIC, BIC, and φ β , to the estimation of the order of ARMA models shown in (21).
where a(κ) is the stochastic disturbance input, ς h (h = 1…n) and ϕ g (g = 1…m) are the coefficients of AR and MA parts, N is the observations number, κ = 1…N.
The selected model order verifies = argmin IC(p).
ARMA(2n,2n– 1) modeling procedure
One immediate disadvantage of using the model order selection criterion is that since it is aimed at finding the optimal order among optional items. That is to say, a great many of ARMA models with different orders have to be built first. Obviously, it is time consuming and not good for the online application of the new ARMA-P method. In order to improve the calculation efficiency, the modeling procedure that specifies the search path of the optimal model order is considered in this article.
Based on the theoretical deduction of the ARMA-P method in Section 2, the equations of this new approach for extracting the mode shape information from ambient signals are derived in this section.
Estimating the oscillation modal frequency and damping by ARMA model
With the assumption that the input is approximately white over the frequency band of interest, the ARMA model is proposed to estimate the oscillation mode characteristics from ambient data. As we know, small fluctuations associated with power system operation are the results of low-level stochastic disturbances inherent in power grids. This kind of disturbance is assumed to relatively be statistically stationary for a block of data over the frequencies of interest [22, 24].
The ARMA model of ambient signal x i is shown in (21).
Equation (23) is called the Modified Yule-Walker equation. The solution of (23) is the estimated coefficient vector of AR part.
where ϕ(B) is the polynomial of MA part, B is the backward operator, and T is the sample time.
Obviously when B = 1/η i,j , (26) equals to zero.
where R yi,k is the autocorrelation function of y i .
By comparing the homogenous exponential coefficients of operator in (29), the coefficients of MA part are obtained. Thus, the ARMA model of ambient signal is built up.
where k = 1…n d , n d is the number of dominant oscillation modes.
Estimating the mode shape magnitude and angle by Prony model
The approach of Prony model to estimate the electromechanical properties of power grids can be broken down into two parts [2, 3]: first, calculating the eigenvalues of discrete model for estimating the modal frequency and damping ratio; second, computing the weights or coefficients to further extract the mode shapes properties.
where k = 1…n d , n d is the number of dominant oscillation modes.
Then the coefficients a l (l = 1…p, p = 2×n d ) are obtained from (32).
Considering (13b), the item ε i is a weighted sum of disturbance inputs. In this article, the inputs are assumed to relatively be statistically stationary over the frequencies of interest, so the item ε i can also be termed as relatively statistically stationary.
Dominant low-frequency oscillation modes of 36-node benchmark system
Damping ratio (%)
−0.0549 + j 4.885
−0.270 + j 6.152
Mode shape information of mode I in 36-node benchmark system
ARMA-P method is applied to estimate the mode shape properties from the frequency signals of the eight generators in the simulation system. First, these ambient signals are preprocessed. Considering the frequency range of electromechanical mode (generally [0.1 Hz, 2.5 Hz]) in power systems, the signals are low pass filtered with a cutoff frequency of 3 Hz, then decimated from 50 samples per second to 5 samples per second, and finally high pass filtered to remove any low-frequency trends.
Then according to the procedure of ARMA-P method shown in Figure 2, the model order must be determined first. In order to fully compare the performances of the three typical criteria in power grids, AIC, BIC, and φ β are, respectively, applied to select the optimal order of ARMA model based on the simulative ambient data. And the ARMA (2n,2n – 1) modeling procedure is employed to specify the search path.
In this part, the frequency signal of Gen8, which includes rich information about the dynamic characteristic of system, is chosen to be the analysis object. The number of observations is N = 3000. According to (20), the bounds on the value β in φ β criterion are calculated, β min = 0.2598, β max = 0.7402. So in this case, the φ β criterion has been calculated for values of β equal to 0.3, 0.4, and 0.5.
Oscillation modes results of three typical model order selection criteria
Damping ratio (%)
Damping ratio (%)
We can notice that AIC leads to over-parameterization while φ β (β = 0.4, 0.5) under-parameterize because of the high penalty. The order results of BIC and φ β (β = 0.3) are similar. Considering the relatively limited system dynamic information that include in ambient data, a small change in model order would lead to a large variation in estimated modal parameters and influence the identification accuracy. So, the oscillation mode characteristic results corresponding to BIC and φ β (β = 0.3) are further compared, in order to discuss the applicability of these criteria in power systems. Comparing with the eigenanalysis results shown in Table 1, the accuracy of the modal information corresponding to BIC are much better. Considering Occam’s Razor , BIC is appropriate to select the optimal order of ARMA model, and its feasibility is testified.
Analysis results of Mode I based on frequency signals of eight generators
Damping ratio (%)
Results of variables vector of Mode I
−0.611 + j 0.206
−0.099 + j 0.041
0.518 – j 0.011
0.439 – j 0.021
0.620 – j 0.108
0.461 – j 0.031
0.993 + j 0.015
1.000 + j 0.000
Mode shape results of Mode I in 36-node benchmark system
The ARMA-P method results closely approximate the eigenanalysis results shown in Table 2. The relative errors are mostly no more than 10%, the accuracy of mode shape angle is somewhat better than that of magnitude, and the effectiveness of model order selection is verified again. The mode shape properties can be estimated accurately using the ARMA-P method based on ambient data.
Mode shape results of Mode I from ambient signals with different SNR values
It can be seen from the table that the relative errors of angle results are all less than 11%, which means that the mode shape angle can be identified accurately from ambient data; the relative errors of magnitude results are a little bigger, yet still less than 15%, which also meets the accuracy requirements of engineering.
Moreover, to test the statistical accuracy of the ARMA-P method, 100 Monte Carlo simulations are run, the mean and root mean square error (RMSE) are calculated. Monte Carlo trials work as to take several independent measurements on the power system in order to get a sense of the method’s statistical performance.
Mode shape with mean and RMSE value of Mode I based on ambient signals
For demonstration purposes, five locations spread across the power grid are selected, including Anshun substation, Gaopo converting plant, and Xingren converting plant in Guizhou province, and Luoping substation in Yunnan Province, Luodong substation in Guangdong Province.
Results of mode shape angle in China Southern Power Grid
From Table 9 , it can be seen that the system includes two dominant interarea oscillation modes, with the frequency of mode I at about 0.47 Hz, and the frequency of mode II at about 0.68 Hz. Anshun, Gaopo, Xingren, Luoping all participate in the two modes, whereas Luodong only participates in mode I. The plots in Figure 9 indicate that in mode I Anshun, Gaopo, and Xingren swing together against Luoping, which conforms to the angle relationship shown in Figure 7, and in mode II Anshun, Gaopo, Xingren, and Luoping swing together against Luodong. It can be deduced that mode I is the Yunnan-Guizhou mode, and mode II is the Yunnan&Guizhou-Guangdong mode. The analysis results conform to the mode shape characteristics information that analyzed in advance. The ARMA-P method performs well in estimating the mode shape of multiple modes simultaneously based on actual ambient signals in China Southern Power Grid.
A methodology considering the model order selection called ARMA-P, used for estimating the mode shape properties from time-synchronized phasor measurements, is presented. Based on its theoretical analysis basis, the approach is applied to a simulation system and measured data from China Southern Power Grid. The results demonstrate that the optimal model order can be selected automatically and efficiently using BIC and the ARMA(2n,2n – 1) modeling procedure. This method works well in estimating the mode shape information of multiple oscillation modes simultaneously based on ambient signals with different SNR value. And further based on Monte Carlo studies, it is shown that the ARMA-P method can estimate mode shapes with reasonably good accuracy.
The algorithm proposed in this article shows great promise for estimating the mode shape properties of power systems. Future work will more rigorously investigate its performance including the data length required and the calculation speed.
This study was supported in part by the National Natural Science Foundation of China 51207093 and 51037002, the Natural Science Foundation of Guangdong Province, China S2011040000995, the Foundation for Distinguished Young Talents in Higher Education of Guangdong, China LYM11108, and the Shenzhen Technology Research and Development Foundation JC201105130407A and GJHS20120621154628775.
- Kunder P: Power System Stability and Control. New York: McGrawHill, Inc.; 1994.Google Scholar
- Hauer JF, Demeure CJ, Scharf LL: Initial results in Prony analysis of power response signals. IEEE Trans. Power Syst. 1990, 5(1):80-89. 10.1109/59.49090View ArticleGoogle Scholar
- Trudnowski DJ, Pierre JW: Signal processing methods for estimating small-signal dynamic properties from measured responses, in Analysis of Nonlinear and Non-stationary Inter-area Oscillations: A Time Frequency Perspective. New York: Springer; 2009.Google Scholar
- Kamwa I, Trudel G, Gerin-Lajoie L: Low-order black-box models for control system design in large power systems, in IEEE Proceeding of Power Industry Computer Application Conference. Salt Lake City; 1995:190-198.Google Scholar
- Sanchez-Gasca JJ, Chow JH: Performance comparison of three identification methods for the analysis of electromechanical oscillations. IEEE Trans. Power Syst. 1999, 14(3):995-1002. 10.1109/59.780912View ArticleGoogle Scholar
- Pierre JW, Trudnowski DJ, Donnelly MK: Initial results in electromechanical mode identification from ambient data. IEEE Trans. Power Syst. 1997, 12(3):1245-1251. 10.1109/59.630467View ArticleGoogle Scholar
- IEEE Task Force Report: Identification of electromechanical modes in power systems. The Institute of Electrical and Electronics Engineers, Inc; 2012.Google Scholar
- Vanfretti L, Dosiek L, Pierre JW, Trudnowski DJ, Chow JH, García-Valle R, Aliyu U: Application of ambient analysis techniques for the estimation of electromechanical oscillations from measured PMU data in four different power systems. Eur. Trans. Electr. Power 2011, 21(4):1640-1656. 10.1002/etep.507View ArticleGoogle Scholar
- Wu C, Lu C, Han YD: Closed-loop identification of power system based on ambient data. Math. Probl. Eng. 2012. Article ID 632897, 16 (2012)Google Scholar
- Trudnowski DJ: Estimating electromechanical mode shape from synchrophasor measurements. IEEE Trans. Power Syst. 2008, 23(3):1188-1195.View ArticleGoogle Scholar
- Liu G, Venkatasubramanian V: Oscillation monitoring from ambient PMU measurements by frequency domain decomposition, in IEEE Proceeding of Symposium Circuits and Systems. Seattle; 2008:2821-2824.Google Scholar
- Dosiek L, Trudnowski DJ, Pierre JW: New algorithm for mode shape estimation using measured data, in IEEE Proceeding of PES General Meeting. Pittsburgh; 2008:1-8.Google Scholar
- Dosiek L, Pierre JW, Trudnowski DJ, Zhou N: A channel matching approach for estimating mode shape, in IEEE Proceedings of PES General Meeting. Calgary; 2009:1-8.Google Scholar
- Zhou N, Huang ZY, Dosiek L, Trudnowski DJ, Pierre JW: Electromechanical mode shape estimation based on transfer function identification using PMU measurements, in IEEE Proceedings of PES General Meeting. Calgary; 2009:1-7.Google Scholar
- Patrick S: Optimization in Signal and Image Processing. New Jersey: Wiley; 2009.MATHGoogle Scholar
- Akaike H: Information theory and an extension of the maximum likelihood principle, in Proceeding of 2nd International Symposium on Information Theory. Budapest; 1973:267-281.Google Scholar
- Shibata R: Selection of the order of an AR model by AIC. Biometrika 1976, 63: 117-126. 10.1093/biomet/63.1.117MATHMathSciNetView ArticleGoogle Scholar
- Schwarz G: Estimating the dimension of a model. Ann. Stat. 1978, 6: 461-464. 10.1214/aos/1176344136MATHView ArticleGoogle Scholar
- Rissanen J: Modeling by shortest data description. Automatica 1978, 14: 465-471. 10.1016/0005-1098(78)90005-5MATHView ArticleGoogle Scholar
- El M, Hallin M: Order selection, stochastic complexity and Kullback–Leibler information, in Athens Conference on Applied Probability and Time Series Analysis, vol. II (1995), volume 115 of Lecture Notes in Statist. New York: Springer; 1996:291-299.Google Scholar
- Rissanen J: Stochastic Complexity in Statistical Inquiry. New Jersey: World Scientific; 1989.MATHGoogle Scholar
- George BEP, Jenkins GM, Reinsel GC: Time Series-Forecasting and Controls. New Jersey: Wiley; 2008.Google Scholar
- Pandit SM, Wu SM: Time-Series and System Analysis with Applications. FL: Krieger Publishing Company; 2006.Google Scholar
- James Douglas H: Time Series Analysis. Princeton: Princeton University Press; 1994.Google Scholar
- Blumer A, Ehrenfeucht A, Haussler D, Warmuth M: Occam’s Razor. Inf. Process. Lett. 1987, 24: 377-380. 10.1016/0020-0190(87)90114-1MATHMathSciNetView ArticleGoogle Scholar
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