Black‐box dynamic equivalent model for microgrids using measurement data

Papadopoulos, Panagiotis; Papadopoulos, Theofilos A.; Crolla, Paul; Roscoe, Andrew; Papagiannis, Grigoris K.; Burt, Graeme

doi:10.1049/iet-gtd.2013.0524

Cited by 42 publications

(26 citation statements)

References 31 publications

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“…Additionally, the application of system identification techniques to different types of dynamic responses in a given MG topology can be used as historical data to create databases for offline simulations [29].…”

Section: Black-box Modelling Methodologymentioning

confidence: 99%

“…Therefore, nonlinear least square optimization is additionally applied in discrete time form on the measured responses to identify with higher accuracy the unknown parameters of (1) [23], [29]. The proposed procedure is a two-step approach, where first the initial values of the set of parameters , , , ]…”

mentioning

confidence: 99%

“…Results from Prony analysis are also used to determine boundaries values of each parameter, following the empirical rules of [29]. Next, nonlinear least square optimization is applied and the parameter set  is adjusted successively in order to match the estimated to the measured response by minimizing the objective function J of (6).…”

mentioning

confidence: 99%

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Measurement‐based analysis of the dynamic performance of microgrids using system identification techniques

Papadopoulos

Crolla³

et al. 2015

IET Generation, Transmission & Distribution

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The dynamic performance of microgrids is of crucial importance, due to the increased complexity introduced by the combined effect of inverter interfaced and rotating distributed generation. This paper presents a methodology for the investigation of the dynamic behavior of microgrids based on measurements using Prony analysis and state-space black-box modeling techniques. Both methods are compared and evaluated using real operating conditions data obtained by a laboratory microgrid system. The recorded responses and the calculated system eigenvalues are used to analyze the system dynamics and interactions among the distributed generation units. The proposed methodology can be applied to any real-world microgrid configuration, taking advantage of the future smart grid technologies and features.

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Section: Black-box Modelling Methodologymentioning

confidence: 99%

mentioning

confidence: 99%

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Measurement‐based analysis of the dynamic performance of microgrids using system identification techniques

Papadopoulos

Crolla³

et al. 2015

IET Generation, Transmission & Distribution

Self Cite

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“…Non-mechanism model is also used in the dynamic equivalent of power system. In the equivalent modeling of microgrid, a dynamic equivalent black-box model based on prony analysis is presented in [38], the proposed equivalent model is compared with the black-box equivalent model. Figure 10 shows the comparison the proposed model and the black-box model; it can be seen that the two models are in good agreement with the detailed model, and the proposed model is more accurate.…”

Section: Comparing To Other Modelsmentioning

confidence: 99%

General Dynamic Equivalent Modeling of Microgrid Based on Physical Background

Jiang

Deng

2015

Energies

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Microgrid is a new power system concept consisting of small-scale distributed energy resources; storage devices and loads. It is necessary to employ a simplified model of microgrid in the simulation of a distribution network integrating large-scale microgrids. Based on the detailed model of the components, an equivalent model of microgrid is proposed in this paper. The equivalent model comprises two parts: namely, equivalent machine component and equivalent static component. Equivalent machine component describes the dynamics of synchronous generator, asynchronous wind turbine and induction motor, equivalent static component describes the dynamics of photovoltaic, storage and static load. The trajectory sensitivities of the equivalent model parameters with respect to the output variables are analyzed. The key parameters that play important roles in the dynamics of the output variables of the equivalent model are identified and included in further parameter estimation. Particle Swarm Optimization (PSO) is improved for the parameter estimation of the equivalent model. Simulations are performed in different microgrid operation conditions to evaluate the effectiveness of the equivalent model of microgrid.

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“…An artificial neural network (ANN) method has been used for training the dynamic behaviors of Active Distribution Networks, giving an equivalent model of the system based on the measurement data [22]. In [23,24], the black-box model based on Prony analysis and the equivalent model composed of different Prony items, and the change of the topology of microgrid is presented by the introducing or subtracting the corresponding Prony items, and the parameters of the models are identified from the measurement data. Multi-agent-oriented methodologies for modeling the real-time operation of microgrids are discussed in [25], whereby each agent has its own function and represents a different component in the microgrid.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Equivalent Modeling of a Grid-Tied Microgrid Based on Characteristic Model and Measurement Data

et al. 2017

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Abstract:Microgrids can significantly improve the utilization of distributed generation (DG) and the reliability of the power supply. However, in the grid-tied operational mode, the interaction between the microgrid and the distribution network cannot be ignored. The paper proposes an equivalent modeling method for the microgrid under grid-tied mode based on a characteristic model. It can simplify the microgrid model in the numerical simulation of the distribution network. The proposed equivalent model can present the dynamic response of a microgrid but not miss any of its primary characteristics. The characteristic model is represented by a low-order time-varying differential equation with the same characteristics of the original microgrid system. During the modeling process, the voltage and the power exchanged between the microgrid and distribution network are collected as the training data for the identification of model parameters. A recursive damped least squares algorithm (RDLS) is used for the parameter identification. A microgrid system containing different DGs is built to test the proposed modeling method in DIgSILENT, and the results show that the proposed dynamic equivalent modeling method is effective and the characteristic model can present the dynamic behaviors of the detailed model of a microgrid.

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Black‐box dynamic equivalent model for microgrids using measurement data

Abstract: A dynamic equivalent black-box model, based on Prony analysis is presented in this

Cited by 42 publications

References 31 publications

Measurement‐based analysis of the dynamic performance of microgrids using system identification techniques

Measurement‐based analysis of the dynamic performance of microgrids using system identification techniques

General Dynamic Equivalent Modeling of Microgrid Based on Physical Background

Dynamic Equivalent Modeling of a Grid-Tied Microgrid Based on Characteristic Model and Measurement Data

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