Plants growing in nature often experience fluctuating irradiance. However, in the laboratory, the dynamics of photosynthesis are usually explored by instantaneously exposing dark-adapted plants to ...constant light and examining the dark-to-light transition, which is a poor approximation of natural phenomena. With the aim creating a better approximation, we exposed leaves of pea (Pisum sativum) to oscillating light and measured changes in the functioning of PSI and PSII, and of the proton motive force at the thylakoid membrane. We found that the dynamics depended on the oscillation period, revealing information about the underlying regulatory networks. As demonstrated for a selected oscillation period of 60 s, the regulation tries to keep the reaction centers of PSI and PSII open. We present an evaluation of the data obtained, and discuss the involvement of particular processes in the regulation of photosynthesis. The forced oscillations provided an information-rich fingerprint of complex regulatory networks. We expect future progress in understanding these networks from experiments involving chemical interventions and plant mutants, and by using mathematical modeling and systems identification and control tools.
Location of non-stationary forced oscillation (FO) sources can be a challenging task, especially under resonance condition with natural system modes. In this case, the magnitudes of the oscillations ...could be greater in places distant from the source and the oscillation spreads over a large region of the power system. Detection, frequency identification and filtering of FO oscillatory components constitutes an initial and critical step for the application of oscillation source location (OSL) methods. Specifically, this step has a major impact on the performance of the OSL method, such as the Dissipating Energy Flow (DEF) method. In this paper we develop a systematic methodology for detection, identification and filtering of non-stationary FO based on multi-channel time-frequency (TF) representation (TFR). We compare three TF approaches applied together with the DEF method: short-time Fourier transform (STFT), STFT-based synchrosqueezing transform (FSST) and second order FSST (FSST2). We have used simulated signals and real world PMU data to show that the proposed method provides a systematic framework for the identification and filtering of power systems non-stationary forced oscillations.
Forced oscillations in power systems can lead to disruptive large oscillations across the grid when resonating with inter-area system modes. This letter proposes a rigorous method for estimating the ...overall impact of a forced oscillation when the forced oscillation frequency is close to the frequencies of multiple system modes. Apart from providing analytical insight, the method is shown to be useful in offline analysis of forced oscillations in large-scale power system models and in measurement-based analysis.
This article presents a machine learning based time-series classification method for using synchrophasor measurements to locate the source of forced oscillation (FO) for fast disturbance removal. ...First, multivariate time series (MTS) matrices are constructed by the most informative measurements selected by sequential feature selection from each power plant. Then, the Mahalanobis matrix is trained such that the Mahalanobis distance between the MTSs from the same class (i.e., with the same FO source location) are minimized and from different classes (i.e., with different FO source locations) are maximized. This allows MTSs to be classified by classifiers with class membership corresponding to the location of each FO source. To meet the runtime requirements of online matching, class templates are constructed to reduce data size and improve matching efficiency. To account for uncertainty in identifying the exact beginning of an FO event, dynamic time warping is used to align the out-of-sync MTSs. IEEE 39bus and WECC 179bus systems are used for algorithm development and validation. Simulation results demonstrate that the algorithm meets online operation runtime requirement with high accuracy using misaligned data sets.
Since forced oscillations are exogenous to dynamic power system models, the models by themselves cannot predict when or where a forced oscillation will occur. Locating the sources of these ...oscillations, therefore, is a challenging problem which requires analytical methods capable of using real time power system data to trace an observed oscillation back to its source. The difficulty of this problem is exacerbated by the fact that the parameters associated with a given power system model can range from slightly uncertain to entirely unknown. In this paper, a Bayesian framework, via a two-stage maximum a posteriori optimization routine, is employed in order to locate the most probable source of a forced oscillation given an uncertain prior model. The approach leverages an equivalent circuit representation of the system in the frequency domain and employs a numerical procedure, which makes the problem suitable for real time application. The derived framework lends itself to successful performance in the presence of phasor measurement unit measurement noise, high generator parameter uncertainty, and multiple forced oscillations occurring simultaneously. The approach is tested on a four-bus system with a single forced oscillation source and on the WECC 179-bus system with multiple oscillation sources.
This paper develops a systematic framework for analyzing how low frequency forced oscillations propagate in electric power systems. Using this framework, the paper shows how to mathematically justify ...the so-called Dissipating Energy Flow (DEF) forced oscillation source location technique. The DEF's specific deficiencies are pinpointed, and its underlying energy function is analyzed via incremental passivity theory. This analysis is then used to prove that there exists no passivity transformation (i.e. quadratic energy function) which can simultaneously render all components of a lossy classical power system passive. The paper goes on to develop a simulation-free algorithm for predicting the performance of the DEF method in a generalized power system, and it analyzes the passivity of three non-classical load and generation components. The proposed propagation framework and performance algorithm are both tested and illustrated on the IEEE 39-bus New England system and the WECC 179-bus system.
Here, this article presents a machine learning based time-series classification method for using synchrophasor measurements to locate the source of forced oscillation (FO) for fast disturbance ...removal. First, multivariate time series (MTS) matrices are constructed by the most informative measurements selected by sequential feature selection from each power plant. Then, the Mahalanobis matrix is trained such that the Mahalanobis distance between the MTSs from the same class (i.e., with the same FO source location) are minimized and from different classes (i.e., with different FO source locations) are maximized. This allows MTSs to be classified by classifiers with class membership corresponding to the location of each FO source. To meet the runtime requirements of online matching, class templates are constructed to reduce data size and improve matching efficiency. To account for uncertainty in identifying the exact beginning of an FO event, dynamic time warping is used to align the out-of-sync MTSs. IEEE 39bus and WECC 179bus systems are used for algorithm development and validation. Simulation results demonstrate that the algorithm meets online operation runtime requirement with high accuracy using misaligned data sets.
How to Mitigate Sloshing Ockendon, H.; Ockendon, J. R.
SIAM review,
12/2017, Letnik:
59, Številka:
4
Journal Article
Recenzirano
Odprti dostop
Walking across a room carrying a mug of coffee can often lead to spillage. Everyday experience tells us that it is better to walk slowly or not have the mug too full, but it is also well known that ...carrying coffee in a bucket with a pivoted handle is much less dangerous. Here we show how to construct a mathematical model for sloshing in the very similar problem of a mug on a smooth horizontal table forced to oscillate in one dimension via a spring connection. We find that analyzing this problem using quite simple ideas of mathematical modeling and analysis gives good physical understanding of how to reduce everyday sloshing.
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