Bootstrap-based hypothesis test for detecting sustained oscillations

Ghorbaniparvar, Mohammadreza; Zhou, N.

doi:10.1109/pesgm.2015.7286196

Cited by 15 publications

(6 citation statements)

References 20 publications

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“…1-Used White Gaussian Noise 2-State vector contains only |V|, δ 3-The setback is an uncertain number of equations when just PMU measurements are available due to insufficient number of PMUs. This could be addressed through regularization which is basically using the distributed Compressive Sensing [42].…”

Section: Fig 1 Algorithm Of Proposed Methods In [27]mentioning

confidence: 99%

Application of PMU on Fault Detection and Hybrid State Estimations

Lockie¹,

Dowd²

2020

IJSCE

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It is feasible to record and monitor the power systems’ dynamic behavior with the extensive deployment of phasor measurement units (PMU). PMU provides high-precision data in which has shown applications in many fields. This study gives a comprehensive prospective of the application of PMU in fault detection. Basically, this paper evaluates the application of this data for fault detection. In which, it is crucial to detect the faults at their early stage so remedial reaction can be taken to reduce the potential damage. Second, the incorporation of the PMU data into state estimation algorithms to achieve more stable and accurate estimation algorithm. It is demonstrated, incorporating the PMU data will increase the observability of the system, however more comprehensive algorithm to monitor the system and detect the faults is needed.

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Section: Fig 1 Algorithm Of Proposed Methods In [27]mentioning

confidence: 99%

Application of PMU on Fault Detection and Hybrid State Estimations

Lockie¹,

Dowd²

2020

IJSCE

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“…In addition, this method does not specifically detect forced oscillations, rather it provides a magnitude of coherence between 0 to 1. Furthermore, a bootstrap method was applied to set up appropriate threshold on self-coherence for detecting forced oscillations in [30]. Based on the bootstrap method, a threshold can be determined to detect the sustained oscillations with pre-selected probability of false alarm.…”

Section: Oscillation Detection Methodsmentioning

confidence: 99%

Survey on forced oscillations in power system

Ghorbaniparvar

2017

J. Mod. Power Syst. Clean Energy

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The oscillations in a power system can be categorized into free oscillations and forced oscillations. Many algorithms have been developed to estimate the modes of free oscillations in a power system. Recently, forced oscillations have caught many researchers' attentions. Techniques are proposed to detect forced oscillations and locate their sources. In addition, forced oscillations may have a negative impact on the estimation of mode and mode-shape if they are not properly accounted for. To improve the power system reliability and dynamic properties, it is important to first distinguish forced oscillations from free oscillations and then locate the sources of forced oscillations in a timely manner. The negative impact of forced oscillation can be mitigated when they are detected and located. This paper provides an overview of the analysis technique of forced oscillations in power systems. In addition, some future opportunities are discussed in forced oscillation studies.

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“…where N is the number of observations, y i is the actual target value at time instant i, the symbol x i is the input vector, and f is the forecasting model. In addition, to estimate the error statistics, the bootstrapping method is applied [19]. The bootstrapping is an efficient numerical approach for estimating some statistical parameters like mean and standard deviation of population from a sample.…”

Section: Weather Data Analysismentioning

confidence: 99%

“…In addition, to estimate the error statistics, the bootstrapping method is applied [19] . The bootstrapping is an efficient numerical approach for estimating some statistical parameters like mean and standard deviation of population from a sample.…”

Section: Mape = ( ) ×100mentioning

confidence: 99%

Weather forecasting error in solar energy forecasting

Sangrody

Sarailoo

Zhou

et al. 2017

IET Renewable Power Generation

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Abstract:As renewable distributed energy resources (DERs) penetrate the power grid at an accelerating speed, it is essential for operators to have accurate solar photovoltaic (PV) energy forecasting for efficient operations and planning. Generally, observed weather data are applied in the solar PV generation forecasting model while in practice the energy forecasting is based on forecasted weather data. In this paper, a study on the uncertainty in weather forecasting for the most commonly used weather variables is presented. The forecasted weather data for six days ahead is compared with the observed data and the results of analysis are quantified by statistical metrics. In addition, the most influential weather predictors in energy forecasting model are selected. The performance of historical and observed weather data errors is assessed using a solar PV generation forecasting model. Finally, a sensitivity test is performed to identify the influential weather variables whose accurate values can significantly improve the results of energy forecasting.

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Bootstrap-based hypothesis test for detecting sustained oscillations

Cited by 15 publications

References 20 publications

Application of PMU on Fault Detection and Hybrid State Estimations

Application of PMU on Fault Detection and Hybrid State Estimations

Survey on forced oscillations in power system

Weather forecasting error in solar energy forecasting

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