This paper explores the use of changepoint detection (CPD) for an improved time-localization of forced oscillations (FOs) in measured power system data. In order for the autoregressive moving average plus sinusoids (ARMA+S) class of electromechanical mode meters to successfully estimate modal frequency and damping from data that contains a FO, accurate estimates of where the FO exists in time series are needed. Compared to the existing correlation-based method, the proposed CPD method is based on upon a maximum likelihood estimator (MLE) for the detection of an unknown number changes in signal mean to unknown levels at unknown times. Using the pruned exact linear time (PELT) dynamic programming algorithm along with a novel refinement technique, the proposed approach is shown to provide a dramatic improvement in FO start/stop time estimation accuracy while being robust to intermittent FOs. These findings were supported though simulations with the minniWECC model.
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A Complex-LASSO Approach for Localizing Forced Oscillations in Power Systems
We study the problem of localizing multiple sources of forced oscillations (FOs) and estimating their characteristics, such as frequency, phase, and amplitude, using noisy PMU measurements. For each source location, we model the input oscillation as a sum of unknown sinusoidal terms. This allows us to obtain a linear relationship between measurements and the inputs at the unknown sinusoids’ frequencies in the frequency domain. We determine these frequencies by thresholding the em- pirical spectrum of the noisy measurements. Assuming sparsity in the number of FOs’ locations and the number of sinusoids at each location, we cast the location recovery problem as an 1-norm regularized least squares problem in the complex domain—i.e., complex-LASSO (linear shrinkage and selection operator). We numerically solve this optimization problem using the complex- valued coordinate descent method, and show its efficiency on the IEEE 68-bus, 16 machine and WECC 179-bus, 29-machine systems.
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- Award ID(s):
- 1934766
- NSF-PAR ID:
- 10353956
- Date Published:
- Journal Name:
- 2022 IEEE Power & Energy Society General Meeting (PESGM)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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