Dynamic equivalencing of an active distribution network for large‐scale power system frequency stability studies

Golpîra, Hêmin; Seifi, Hossein; Haghifam, Mahmoud Reza

doi:10.1049/iet-gtd.2015.0484

Cited by 42 publications

(24 citation statements)

References 25 publications

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“…Dynamic equivalents generally use difference or differential equations [6]. Static equivalents are mostly used for power flow analysis [6], while dynamic equivalents are used for small signal and transient stability analysis [12], frequency stability analysis [13], and long-and short-term voltage stability studies [14].…”

Section: Motivation Literature Review and Scope Of The Papermentioning

confidence: 99%

“…The derived modal estimates are grouped based on the mode frequency. For each group of modes, a final value of the mode frequency i f and damping factor σ i is determined as the average value of the corresponding mode estimate as follows [1,37] To further evaluate the accuracy of the estimated response the root mean square error (RMSE), the mean absolute percentage error (MAPE) and the coefficient of determination (R 2 ) indexes defined in (11) and (13) are employed.…”

Section: Multiple Signal Analysismentioning

confidence: 99%

“…Step 2: The selected mode identification method is applied sequentially for each case in the range from M min to M max ; the ringdown response is simulated and the corresponding MAE is calculated. To further evaluate the accuracy of the estimated response the root mean square error (RMSE), the mean absolute percentage error (MAPE) and the coefficient of determination (R 2 ) indexes defined in (11) and (13) are employed. (13) Note that y is the mean value of the original ringdown signal.…”

mentioning

confidence: 99%

“…To further evaluate the accuracy of the estimated response the root mean square error (RMSE), the mean absolute percentage error (MAPE) and the coefficient of determination (R 2 ) indexes defined in (11) and (13) are employed. (13) Note that y is the mean value of the original ringdown signal.…”

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confidence: 99%

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A Toolbox for Analyzing and Testing Mode Identification Techniques and Network Equivalent Models

et al. 2019

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During the last decade the dynamic properties of power systems have been altered drastically, due to the emerge of new non-conventional types of loads as well as to the increasing penetration of distributed generation. To analyze the power system dynamics and develop accurate models, measurement-based techniques are usually employed by academia and power system operators. In this regard, in this paper an identification toolbox is developed for the derivation of measurement-based equivalent models and the analysis of dynamic responses. The toolbox incorporates eight of the most widely used mode identification techniques as well as several static and dynamic network equivalencing models. First, the theoretical background of the mode identification techniques as well as the mathematical formulation of the examined equivalent models is presented and analyzed. Additionally, multi-signal analysis methods are incorporated in the toolbox to facilitate the development of robust equivalent models. Additionally, an iterative procedure is adopted to automatically determine the optimal order of the derived models. The capabilities of the toolbox are demonstrated using simulation responses, acquired from large-scale benchmark power systems, as well as using measurements recorded at a laboratory-scale active distribution network.

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Section: Motivation Literature Review and Scope Of The Papermentioning

confidence: 99%

Section: Multiple Signal Analysismentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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A Toolbox for Analyzing and Testing Mode Identification Techniques and Network Equivalent Models

et al. 2019

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“…The Prony method has been used to obtain a black‐box model for an MG in Papadopoulos et al The MG of these studies contains synchronous generators and voltage source converters (VSCs). In Golpîra et al, a Prony‐based equivalencing method has been proposed for an ADS for frequency stability studies. Artificial neural network (ANN) has been used to represent an EM for generation units of an ADS, where DGs are microturbines and FCs .…”

Section: Introductionmentioning

confidence: 99%

Equivalent model parameter estimation of grid-connected fuel cell-based microgrid

Zaker

Gharehpetian

Karrari

2018

Int Trans Electr Energ Syst

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Simulation of the power grid including numerous microgrids (MGs) with complex and detailed model (DM) is very time-consuming. Nevertheless, these kinds of simulations are vital for power engineers and utilities to analyze the impact of dynamic interconnections among MGs and the power grid. In this paper, an electrical equivalent model (EM) including electrical components, such as an equivalent solid oxide fuel cell (SOFC) model, voltage source converter (VSC), and static load, is proposed for a fuel cell-based MG. The parameters of the EM are estimated to behave similar to the DM of the MG in different scenarios and operating points. The parameter estimation procedure is conducted using genetic algorithm. The IEEE 9-Bus transmission system is considered as the main grid, while the IEEE 33-Bus distribution system is considered as the grid-connected MG. Three SOFCs of different size and dynamic parameters are connected through VSCs to the MG. Different scenarios of active and reactive power changes are simulated, and results of the EM and DM are compared, to demonstrate the accuracy of the determined EM.KEYWORDS equivalent model, microgrid, parameter estimation, SOFC | INTRODUCTIONRecently, the need for high quality and reliable power along with emission concerns has drawn the attentions to the renewable distributed generations (DGs). 1 At first, the conventional passive distribution systems were transformed to active ones with integration of different types of DGs. In the next step, the concept of the microgrid (MG) was formed and defined as a low voltage distribution system containing DGs, electrical and thermal loads, which all operate together under a common control system. This control system provides the ability of operating in grid-connected and islanded modes compared to active distribution systems (ADSs). 2 Fuel cells (FCs) have always been a promising DC source for MGs in literature. Many researches have been conducted on MGs considering different types of FCs as the source of DC side. For instance, for stability assessment and real-time applications, a simplified dynamic model for a gridconnected FC power plant has been presented in Hatziadoniu et al 3 and Resrepo et al. 4 Authors have presented the control and modeling of a proton exchange membrane FC as a DG in Wang et al 5 and Karami et al. 6 Fuel cell-based DGs have been considered as DGs in islanded MGs for frequency regulation in previous studies. 7-9 Among different types of FCs, solid oxide fuel cell (SOFC) is one of the most attractive ones in MGs due to its low emissions and high energy efficiency. 10 In Sedighisigarchi and Feliachi, 11,12 a comprehensive nonlinear dynamic model of SOFC has been developed for transient stability analysis and enhancement. Mathematical modeling of a grid-connected SOFC power plant for small-signal stability studies has been presented in Wenjuan et al. 13 The mathematical modeling of SOFC will be briefly explained in Section 2.Besides the well-known advantages of MGs, there are some concerns that should be analy...

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Advanced Virtual Inertia Control and Optimal Placement

Golpîra¹,

Roman-Messina²,

Bevrani³

2021

Renewable Integrated Power System Stability and Control

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Dynamic equivalencing of an active distribution network for large‐scale power system frequency stability studies

Cited by 42 publications

References 25 publications

A Toolbox for Analyzing and Testing Mode Identification Techniques and Network Equivalent Models

A Toolbox for Analyzing and Testing Mode Identification Techniques and Network Equivalent Models

Equivalent model parameter estimation of grid-connected fuel cell-based microgrid

Advanced Virtual Inertia Control and Optimal Placement

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