A Data-driven Approach to Multi-event Analytics in Large-scale Power Systems Using Factor Model

12/24/2017 ∙ by Fan Yang, et al. ∙ 0

Multi-event detection and recognition in real time is of challenge for a modern grid as its feature is usually non-identifiable. Based on factor model, this paper porposes a data-driven method as an alternative solution under the framework of random matrix theory. This method maps the raw data into a high-dimensional space with two parts: 1) the principal components (factors, mapping event signals); and 2) time series residuals (bulk, mapping white/non-Gaussian noises). The spatial information is extracted form factors, and the termporal infromation from residuals. Taking both spatial-tempral correlation into account, this method is able to reveal the multi-event: its components and their respective details, e.g., occurring time. Case studies based on the standard IEEE 118-bus system validate the proposed method.

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I Introduction

For a large-scale power system, multiple events can hardly be identified properly as it is difficult to distinguish the features of multi-event from the ones of single-event. The multi-event poses a more serious threat to the systems: it can hardly be identified, and thus be addressed, which may lead to a wide-spread blackout.

This paper proposes a statistical, data-driven solution, rather than its deterministic, empirical or model-based counterpart, to solve the problem given above. The study is built upon our previous work in the last several years. See Section I-B for details.

I-a Contribution

This paper, based on random matrix theory (RMT), proposes a high statistical tool, namely, factor model, for multi-event detection and recognition in a modern grid. This paper extracts the spatial and temporal information from the massive raw data, respectively, in the form of principal components (factors) and residuals (bulk). The factors map event signals, and the residuals map white/non-Gaussian noises.

The proposed method can be used for multi-event analytics effectively. To the factors, we experimentally obtain that there is a linear relationship between the number of factors and the event number of the multi-event. To the residuals, on the other hand, we extract their information rather than simply assuming it to be identically independent “pure white noise” as

[1]. Time series information contained in noise, together with the spatial information in factors, reveals the multi-event status. The proposed method is practical for real-time analysis.

Besides, the proposed solution is model-free, requiring no knowledge of topologies or parameters of the power system [2, 3, 4], and able to handle non-Gaussian noises. To the best of our knowledge, it is the first time to propose an algorithm aiming at multi-event detection based on random matrix theory in the field of power systems.

I-B Related Work

In our previous work, a universal architecture with big data analytics is proposed [5]

and is applied for anomaly detection

[6, 7]. Little work, howerve, has been done to multi-event analytics in a complex situation. Current researches on event analysis are mainly model-based, aiming at single-event analytics. They may not be suitable for real-time analytics in a complex situation [8]. Some other methods adopt graph theory [9, 10]; the methods strongly depend on the structure of the power system.

Some data-driven methods for event analysis are proposed recently [11] and applied for multi-event analytics [12].

Rafferty utilizes principal component analysis (PCA) for real-time multi-event detection and classification in

[12]

. In his approach, a threshold of cumulative percentage of total variation is selected in advance. Then, the number of principal components is determined according to the threshold mentioned above. The threshold is selected empirically and subjectively. Besides, for other supervised tools, like deep learning

[13] and kernel-based algorithms [14], which are hot-spot to data-driven approaches, the same problem is inevitable. The deep learning algorithms automatically select the features from the massive datasets. This is one big advantage of deep learning over our paradigm. Our paradigm, however, has the advantage of transparency in that our results are provably. Also, our paradigm is deeply rooted in random matrix theory.

Nowadays, high-dimensional factor model has been actively studied and already successfully applied in statistics [15] , econometrics [16] and biology [17].

Ii Theory Foundation and Data Processing

The frequently used notations are given in Table 1.

Ii-a Random Matrix Theory and Spectral Analytics

Random matrices have been an important issue in multi-variate statistical analysis since the landmark work of Wigner and Wishart [18]

, motivated by problems in quantum physics. Factor model, on the other side, can be used to identify non-random properties (in the form of spikes/outliers) which are deviations from the universal predictions (in the form of bulk)

[19, 20]

. To be specific, the eigenvalues of covariance matrix (spectrum) can usually be divided into two parts: a bulk and several spikes. The bulk represents the noises, while the spikes represent the signals, namely, factors.

In previous work, noises are usually assumed to be identically independent in power systems, namely, white noises. However, ”no information” or ”pure noise” assumption is invalid in practice. For instance, for a certain PMU, there exits time correlation between the measured voltage magnitude data of adjacent sampling points [21, 22]. The time correlation is non-ignorable, especially for a large inter-connected system! This paper formulars the noises using time series analytics.

Notations Means


a matrix, a vector, an entry of a matrix

mean, variance for

raw data source
dimensional complex space
the row and column size of moving split-window
the number of measurable status variables
the sampling time
a raw data matrix
a standard non-Hermitian matrix
the number of factors
the covariance structure of residualss
a complex eigenvalue,

the estimated value of

,
TABLE I: Some Frequently Used Notations

Reference [23] provides a fundamental theory to estimate noises, and formulates them as:

(1)

where is an matrix with i.i.d (identically independent distribution) Gaussian entries, and and are and symmetric non-negative definite matrices, representing cross- and auto- covariances, respectively. For more details about the model, please refer to Appendix A. On the other side, the spikes (deviating eigenvalues) of the spectrum map the event signals. They represent dominant information for system operating status.

Ii-B Data Processing

Massive raw data can be represented by matrix naturally [24]. In a power system, assume that there are kinds of measurable variables. At sampling time , we arrange the measured data of these variables in the form of a column vector [25]. Then, arranging the column vectors in chronological order (), we obtain raw data source .

For the raw data source , we can cut off any arbitrary part, e.g., size of , at any time, e.g., samping time , forming as

(2)

where is measured data at sampling time (). It is worth noting that is the length of the moving split-window. If we keep the last sampling time as the current time, with the moving split-window, the real-time analytics is conducted.

Then, we convert the raw data matrix obtained at each sampling time into a standard non-Hermitian matrix with the following algorithm.

(3)

where , , , and .

In the following section, is used to analyze the factors and noises at sampling time .

Iii Factor Model Algorithm

For a certain window, e.g. the one obtained at sampling time , we aim to decompose the standard non-Hermitian matrix , as given in (3), into factors and residuals as follows:

(4)

where is the number of factors, is the th factor, is the corresponding loading, is the residual. Usually, only is available, while , and need to be estimated.

Factor model aims to simultaneously provide estimators of factor number and correlation structures in residuals. We turn the parameter-estimation problem into a minimum-distance problem. Specifically, we consider a minimum distance between the experimental spectral distribuion and the theoretical spectral distribuion . The experimental one , depending on the sampling data, is obtained as empirical eigenvalue density (EED) of in (7), and the theoretical one , based on Sokhotskys formlua, is given as in (8). As a result, we turn the factor model estimation into a classical optimziation as

(5)

where

is a spectral distance measure or loss function. The solution of this minimization problem gives the number of factors, in the form of

, and the parameters for the correlation structure of the residuals, in the form of .

Iii-a Principal Component Estimation :

The first step is to generate -level empirical residuals, by substracting largest principal components according to (4).

(6)

where is a matrix of factors, each row of which is a -th principal component from , is an matrix of factor loadings, estimated by multivariate least squares regression of on .

Then the covariance matrix from -level residuals is obtained as

(7)

The subscript “real” indicates that is obtained from real data. The steps can be summarized as follows:

Steps of Calculating
1.Calculate : each row of which is a -th principal component from correlation matrix of , i.e. ; denote as: .
2.Conduct least squares regression of on : .
3.Calculate -level residual: .
4.Calculate covariance matrix from -level residual:
.
5.Calculate the empirical eigenvalue density of .

Iii-B Modeling Covariance of Residuals:

In section , we consider residuals as time series, which is represented by (1). The , however, is difficult to be obtained, since the limiting distribution of general and cost too much calculation resource via Stieltjes transform in [23] Fortunately, a recent work by [26]

provides an analytic derivation of limiting spectral density using free random variable techniques. This paper uses the results of

[26] to calculate . If we assume that the cross-correlations [23], i.e. , are effectively removed by the factors, then, the cross-correlations among the normalized residuals are negligible: . Under this assumption, only the auto-correlations, i.e. , left. The is in the forms of exponential decays with respect to time lags, as: . As a result, the is replaced by .

This enables us to calculate the modeled spectral density, , much more easily. It can be done through the free random variable techniques proposed in [26] (Refer to Appendix A for analytic derivation). The steps can be summarized as follows:

Steps of Calculating
1.Get the mean spectral density from Green Function G(z) by using Sokhotsky’s formula:
(8)
2. Green Function

can be obtained from the Moments Generating Function

(9)
3. Solve the polynomial equation for and (a 6th-order polynomial equations for ):

(10)

With the above procedure, we can rewrite (5) as

(11)

Iii-C Distance Measure

Since the empirical spectrum contains spikes, a distance measure which is sensitive to the presence of spikes should be given. This paper uses Jensen-Shannon divergence, which is a symmetrized version of Kullback-Leibler divergence.

(12)

where and

are probability densities,

, and is the Kullback-Leibler divergence defined by . Note that the Kullback-Leibler distance becomes larger if one density has a spike at a point while the other is almost zero at the same point. Refer to Appendix B for more details.

Fig. 1: Topology of the Standard IEEE 118-bus System.

Iv Case Studies

Fig. 2: Case Stusies: (a) Case 1 (b) Case 2 (c) in Case 3.

The proposed method is tested with simulated data in the standard IEEE 118-bus system (the topology is shown in Fig. 1 on the Matpower platform [27]. In this simulations, we regard a sudden power consumption (active power, ) change on some node as an event.

Three cases are designed to validate the proposed method. In case 1, case 2, case 3, we set different numbers of events on node 52, 117, 75, and observe the number of factors, respectively. To make a comparison, we illustrate the results of the three cases in the same picture as Fig. 2.

The raw data source, , is in size of =118, =2500. The size of the moving split-window is set to be =118, =250, i.e. .

Then, (11) is used to estimate the parameters and as and . The for the number of the assumed events, and the for the correlation structure of the noises. It is noted that we implement the simulated system model for dozens of times to collect data, the noise of each time follows the same distribution. The dozens times simulation is reasonable, as that we can obtain dozens observations for a real physical system through its sampling data, which have noises following a certain distribtuion.

Then, in the -th simulation, is generated. With (11), the estimation result and are obtained. For these and , their mean value and is calculated, which may appear in decimal form. We need to point out that the estimation of and begins at =250 due to the length of the split-window. In Fig.2, we amplify the value of for twenty times to make it obvious, i.e. .

The events in Case 1, Case 2, and Case 3 are given as Tab.II, Tab.III, and Tab.IV, respectively. The corresponding estimation resuts, i.e., the number of factors (i.e., ) and the correlation structure of the residuals (i.e., ), are obtained as Fig. 2.

Iv-a Case 1: Single Event Detection

Single Step Signal on Node 52:

Node Sampling Time Power Consumption (MW)
52 0+
100+
others

* is a constant of node ,

*, are noises following AR(1) model, where

TABLE II: Events Assumed in Case 1

Fig. 2(a) shows that:

  • During the sampling time 111(The beginning of Sigal)(Length of Split-Window). and remain steady around 3 and 0.28, respectively.

  • At =500, starts to decline to around 2. Also, declines slightly.

Actually, as can be seen from Tab. II: no event occurs in the system when keeps steady. Right at =500, the changes from 0 to 100MW. Therefore, we can conduct event detection with the proposed method.

Moreover, in this case, for the split-window , there exist a single-event (i.e., step signal on Node 52, , at =500) and 2 factors, i.e., 1 event, during to . A linear relationship between the number of events and the number of factors will be revealed afterwards.

Iv-B Case 2: Multiple Event Detection (Two Events)

Multiple Step Signal on Node 52 and Node 117:

Node Sampling Time Active Load (MW)
52 0+
100+
117 0+
150+
0+
others
TABLE III: Events Assumed in Case 2

Fig. 2(b) shows that:

  • During the sampling time 222(The beginning of Sigal)(Length of Split-Window), and remain steady. Thus, we deduce that no event occurs in the system, which meets Tab. III.

  • At =1300, starts to decline (from 3.023 to 2.603) and then keeps around 2 till =1399. For the split-window to , there exist a single-event (i.e., at =1300) and 2 factors, i.e., 1 event, .

  • At =1400, starts to raise (from 2.333 to 3.047) and then keeps around 3. For the split-window to , there exist two multi-event (i.e., at =1300, at =1400) and 3 factors, i.e., 2 event, .

Iv-C Case 3: Multiple Event Detection (Three Events)

Multiple Step Signal on Node 52, Node 117 and Node 75:

Node Sampling Time Active Load (MW)
52 0+
100+
117 0+
150+
75 0+
400+
others
TABLE IV: Events Assumed in Case 3

Fig. 2(c) shows that:

  • During the sampling time 333(The beginning of Sigal)(Length of Split-Window), and remain steady. Thus, we deduce that no event occurs in the system, which meets Tab. IV.

  • At =2250, starts to decline (from 2.873 to 2.603) and then keeps around 2 till =2300. For the split-window to , there exist a single-event (i.e., at =2250) and 2 factors, i.e., 1 event, .

  • At =2300, starts to raise (from 2.307 to 3.173) and then keeps around 3 till =2400. For the split-window to , there exist two multi-event (i.e., at =2250, at =2300) and 3 factors, i.e., 2 event, .

  • At =2400, starts to raise (from 3.433 to 3.547) and then keeps around 4. For the split-window to , there exist three multi-event (i.e., at =2250, at =2300, at =2400) and 4 factors, i.e., 3 event, .

Iv-D Further Discussions about the Cases

Through the above three cases, the relationship between the number of events (i.e., ) and the number of factors (i.e., ) is revealed.

The results of the three cases are summarized in Tab. V:

Case Split Window
1 1 2
2 1 2
2 3
3 1 2
2 3
3 4
TABLE V: Relationship between Event Number () and Factor Number ()

, estimated by factor model, is approximately equal to plus one, i.e. . There exists a linear relationship between them. Therefore, we can deduce the number of the multi-event for a certain split-window.

Besides, every time an event occurs, drops. It indicates that the correlation in the residuals decreases when there exit events.

V Conclusion

This paper proposes a data-driven method, namely, factor model, to conduct multi-event detection and recognition in a large power system. In the analysis procedure, we estimate the number of factors and the parameter for the correlation structure of residuals by minimizing the distance between two spectrums. Then, we conduct real-time analysis of the two parameters, and , using moving split-window. The proposed method is direct and practical for multi-event analytics in a complex situation. Following conclusions are obtained: First, the number of factors estimated by factor model has an approximately linear relationship with the number of events that occur in the system. Second, taking non-Gaussian noises into account, time series analytics is implemented to extract the information from noises. The decrease of parameter is related to the occurrence of events. It is noted that, the number of factors reveal the spatial information (events on different nodes) in the system; while the correlation structure in noises contains temporal information. The proposed method considers space-time correlation in a large power system. Finally, case studies verify the effectiveness of the method.

Along this direction, following work can be done. For example, we can employ more general modeling for noises. If we consider vector ARMA (1, 1) processes, we have up to 6th-order polynomial equations [26]. Furthermore, the relationship betweeen the number of factors and events can be further studied with physical model.

Appendix A An Overview of Free Random Variable Techniques

We summarize the main concepts and key results in free random variables techniques that we employ to derive . We follow the notations and derivations from [26, 28]. First, consider a simple decomposition of covariance structures:

(13)

where is an cross-covariance matrix and is a auto-covariance matrix, , . Suppose is an Gaussian random matrix. Then a correlated Gaussian random matrix ( time series) can be written as . Its sample (empirical) covariance matrix is

(14)

Consider a real symmetric random matrix .
Definition 1 Mean Spectral Density

(15)

where the expectation is taken w.r.t. the rotationally invariant probability measure, is a Dirac delta function, and is a unit matrix.
Definition 2 The Green’s Function (or Stieltjes Transform)

(16)

The relationship between and is:

(17)

The Green’s function generates moments of a probability distribution, where the

moment is defined by:
Definition 3 Moment

(18)

Definition 4 Moment Generating Function

(19)

The relationship between and is

(20)

Blue’s function and N-transform are the inverse transform of the Green’s function and moment generating function, respectively.
Definition 5 Blue’s function and N-transform

(21)

Then, return to . The N-transform of can be derived as:

(22)

Using the moments’ generating function and its inverse relation to N-transform, can be written as:

(23)

Now, we consider the simplified model with . In such case,

is a time-series (AR(1)) following the autoregressive model:

(24)

where , , . We calculate the eigenvalue distribution of correlation matrix based on the following strategy.
Step 1: Find , from the equation for N-transform.
Step 2: Find , by .
Step 3: Find , by .

For Step 1, consider . Because , so . Therefore, can be rewritten as:

(25)

To find , note that the auto-covariance matrix of AR(1) process has a simple form:

(26)

Using Fourier-transform of the matrix B, it can be shown that the moment generating function of B is

(27)

Therefore, we obtain for Step 1. The other steps are followed straightforwardly as and .

Appendix B Kullback-Leibler divergence

The Kullback-Leibler divergence is defined as follows:

(28)

where and are probability densities. To deal with zero elements of , we use:

(29)

where is a small enough positive number. Denote the number of zero elements in as , . Probability density is dealt with in the same way.

References

  • [1] X. Xu, X. He, Q. Ai, and R. C. Qiu, “A correlation analysis method for power systems based on random matrix theory,” IEEE Transactions on Smart Grid, vol. PP, no. 99, pp. 1–10, 2015.
  • [2] X. Miao and D. Zhang, “The opportunity and challenge of big data’s application in distribution grids,” in China International Conference on Electricity Distribution, 2014, pp. 962–964.
  • [3] J. Zhang and M. L. Huang, “5ws model for big data analysis and visualization,” in IEEE International Conference on Computational Science and Engineering, 2013, pp. 1021–1028.
  • [4] M. Mayilvaganan and M. Sabitha, A cloud-based architecture for Big-Data analytics in smart grid: A proposal, 2013.
  • [5] X. He, Q. Ai, R. C. Qiu, W. Huang, L. Piao, and H. Liu, “A big data architecture design for smart grids based on random matrix theory,” IEEE Transactions on Smart Grid, vol. 8, no. 2, pp. 674–686, 2017.
  • [6]

    X. He, R. C. Qiu, Q. Ai, and X. Xu, “An unsupervised learning method for early event detection in smart grid with big data,”

    Computer Science, 2015.
  • [7] L. Chu, R. C. Qiu, X. He, Z. Ling, and Y. Liu, “Massive streaming pmu data modeling and analytics in smart grid state evaluation based on multiple high-dimensional covariance tests,” IEEE Transactions on Big Data, vol. PP, no. 99, pp. 1–1, 2016.
  • [8] M. J. Smith and K. Wedeward, “Event detection and location in electric power systems using constrained optimization,” in IEEE Power and Energy Society General Meeting, 2009, pp. 1–6.
  • [9] S. Soltan, D. Mazauric, and G. Zussman, “Cascading failures in power grids:analysis and algorithms,” in International Conference on Future Energy Systems, 2014, pp. 195–206.
  • [10] M. He and J. Zhang, “A dependency graph approach for fault detection and localization towards secure smart grid,” IEEE Transactions on Smart Grid, vol. 2, no. 2, pp. 342–351, 2011.
  • [11] L. Xie, Y. Chen, and P. R. Kumar, “Dimensionality reduction of synchrophasor data for early event detection: Linearized analysis,” IEEE Transactions on Power Systems, vol. 29, no. 6, pp. 2784–2794, 2014.
  • [12] M. Rafferty, X. Liu, D. M. Laverty, and S. Mcloone, “Real-time multiple event detection and classification using moving window pca,” IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2537–2548, 2016.
  • [13]

    Y. Wang, M. Liu, and Z. Bao, “Deep learning neural network for power system fault diagnosis,” in

    Control Conference, 2016, pp. 6678–6683.
  • [14]

    S. Naderian and A. Salemnia, “An implementation of type‐2 fuzzy kernel based support vector machine algorithm for power quality events classification,”

    International Transactions on Electrical Energy Systems, vol. 27, no. 5, pp. –, 2016.
  • [15] M. Forni, A. Giovannelli, M. Lippi, and S. Soccorsi, “Dynamic factor model with infinite dimensional factor space: Forecasting,” Center for Economic Research, 2016.
  • [16] J. Yeo and G. Papanicolaou, “Random matrix approach to estimation of high-dimensional factor models,” Papers, 2016.
  • [17] Z. Sun, X. Liu, and L. Wang, “A hybrid segmentation method for multivariate time series based on the dynamic factor model,” Stochastic Environmental Research and Risk Assessment, pp. 1–14, 2016.
  • [18] E. P. Wigner, “On a class of analytic functions from the quantum theory of collisions,” Annals of Mathematics, vol. 53, no. 1, pp. 36–67, 1951.
  • [19] V. Plerou, P. Gopikrishnan, B. Rosenow, L. A. N. Amaral, and H. E. Stanley, “Universal and non-universal properties of cross-correlations in financial time series,” Papers, vol. 83, no. 7, pp. 1471–1474, 2012.
  • [20] L. LALOUX, P. CIZEAU, M. POTTERS, and J.-P. BOUCHAUD, “Random matrix theory and financial correlations,” International Journal of Theoretical and Applied Finance, vol. 3, no. 03, pp. 391–397, 2000.
  • [21] S. C. Chevalier and P. D. H. Hines, “Identifying system-wide early warning signs of instability in stochastic power systems,” in Power and Energy Society General Meeting, 2016, pp. 1–5.
  • [22] C. Xu, J. Liang, Z. Yun, and L. Zhang, “The small-disturbance voltage stability analysis through adaptive ar model based on pmu,” in Transmission and Distribution Conference and Exhibition: Asia and Pacific, 2005 IEEE/PES, 2005, pp. 1–5.
  • [23] L. Zhang, “Spectral analysis of large dimentional random matrices,” Ph D, 2007.
  • [24] R. Qiu and M. Wicks, Cognitive Networked Sensing and Big Data.   Springer Publishing Company, Incorporated, 2013.
  • [25]

    D. L. Donoho, “High-dimensional data analysis: The curses and blessings of dimensionality,” 2000.

  • [26] Z. Burda, A. Jarosz, M. A. Nowak, and M. Snarska, “A random matrix approach to varma processes,” vol. 12, no. 1002.0934, pp. 1653–1655, 2010.
  • [27] R. D. Zimmerman and C. E. Murillo-Sánchez, “Matpower 4.1 user’s manual,” Power Systems Engineering Research Center, 2011.
  • [28] B. Z, J. J, and W. B, “Spectral moments of correlated wishart matrices.” Phys.rev.e, vol. 71, no. 2 Pt 2, p. 026111, 2005.