Numerical simulation of Stochastic Differential Algebraic Equations for power system transient stability with random loads

Wang, Keyou; Crow, M.L.

doi:10.1109/pes.2011.6039188

Cited by 56 publications

(22 citation statements)

References 20 publications

(24 reference statements)

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“…Their use in power system analysis, however, has thus far been limited. Recent studies have demonstrated the suitability of stochastic calculus for power system analysis, e.g., [5], [6] and [7]. However, the models proposed in these references are not based on real-world measurements and data.…”

Section: A Review Of Stochastic Load Modelingmentioning

confidence: 99%

Validation of the Ornstein-Uhlenbeck process for load modeling based on µPMU measurements

Roberts

Stewart

Milano

2016

2016 Power Systems Computation Conference (PSCC)

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This paper investigates the suitability of the Ornstein-Uhlenbeck process, driven by various Lévy processes, for load modeling at the distribution network level. An indepth description outlining the procedure for estimating the required parameters is given. Both the statistical properties of the simulated processes and its auto-correlation is compared to that of the field measured data to demonstrate the suitability of the proposed methodology. The development of such stochastic models is facilitated by measures obtained from microsynchrophasors (µPMU's). The data from these devices serves to demonstrate the need to model the volatility along with validating a model attempting to model said volatility.

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Section: A Review Of Stochastic Load Modelingmentioning

confidence: 99%

Validation of the Ornstein-Uhlenbeck process for load modeling based on µPMU measurements

Roberts

Stewart

Milano

2016

2016 Power Systems Computation Conference (PSCC)

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“…B u,i = hB u,i , and B ω,i = hB ω,i . In addition, since the electrical system is subject to various sources of uncertainties including for example frequency measurement uncertainties [17], renewable generation uncertainties [18], and transmission line noise [19], a noise term is added to the discretised equation (8) to give,…”

Section: Mathematical Model Of Multi-area Powermentioning

confidence: 99%

“…and H represnts the control horizon. Also, note that sincex i k enters control area i as external signal, its corresponding ideal pdf is taken to be equal to its actual pdf given in Equation (18). Following the same procedure in [21] the minimum cost function resulting from the minimisation of Equation (16) with respect to the randomised controller c(u i k−1 z i k−1 ) can then be shown to be given by the following recurrence equation,…”

Section: A Randomised Controller Designmentioning

confidence: 99%

Probabilistic Decentralised Control and Message Passing Framework for Future Grid

Herzallah¹

2020

Preprint

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<pre>In this paper, we propose a unified probabilistic decentralised control and message passing framework for real time control of the electrical grid which enables the development of the future smart grid. The key elements of the proposed framework are the design of local randomised controllers and probabilistic message passing methodology which enables the coordination between the designed local controllers to account for optimisation considerations on the system operation. Within the proposed framework, the electric grid is decomposed into a number of control areas. Additionally, since the frequency is the ubiquitous grid state variable, representing the balancing of active power generation and consumption, the proposed framework is demonstrated based on local Load Frequency Control (LFC). Simulation studies involving a six-area power system and three interconnection schemes are carried out to illustrate the applicability and effectiveness of the proposed approach.</pre>

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“…The second stage consists in associating SDE to the residuals of the Fourier adjustment, which os the Ornstein-Uhlenbeck process. In general, SDE can be used to simulate the continuous behavior of a variable, which is why in engineering topics it is common to find applications to power systems for short-term studies and real time studies [25][26][27], such as the case of small signal stability [28]. However, as an application to the wave generation area, it is not a highly exploited resource [29].…”

Section: Numerical Modelmentioning

confidence: 99%

A Dynamic Stochastic Hybrid Model to Represent Significant Wave Height and Wave Period for Marine Energy Representation

et al. 2019

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This paper presents a methodology to represent ocean wave power generation based on real data observation for significant wave height (SWH or H s ) and wave period (WP or T). This technique is based on a hybrid model, which considers Fourier series and stochastic differential equations, allowing a continuous time representation of the random changes in the parameters associated with wave power generation ( H s and T). The methodology is explained, including estimation methods and a validation procedure. The data series generated by the models erre used to create simulated wave power output applying a transformed matrix and a theoretical model. The results validate the utilization of this technique, when the objective is to obtain a robust dynamic representation of a random process, oriented to linear studies.

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Numerical simulation of Stochastic Differential Algebraic Equations for power system transient stability with random loads

Cited by 56 publications

References 20 publications

Validation of the Ornstein-Uhlenbeck process for load modeling based on µPMU measurements

Validation of the Ornstein-Uhlenbeck process for load modeling based on µPMU measurements

Probabilistic Decentralised Control and Message Passing Framework for Future Grid

A Dynamic Stochastic Hybrid Model to Represent Significant Wave Height and Wave Period for Marine Energy Representation

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