Measurement-Based Hybrid Approach for Ringdown Analysis of Power Systems

Papadopoulos, Theofilos A.; Chrysochos, Andreas I.; Kontis, Eleftherios O.; Papadopoulos, Panagiotis; Papagiannis, Grigoris K.

doi:10.1109/tpwrs.2016.2514536

Cited by 27 publications

(19 citation statements)

References 33 publications

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“…The first five methods apply to the TD, while FFT operates on the FD. Additionally, SIEMToolbox supports the use of the Vector Fitting (VF) algorithm [22], which operates on the FD for the identification of power system modes [23], as well as a hybrid FD/TD method [24], which has been proved very robust and accurate for the mode identification of active distribution networks (ADNs) [25]. Moreover, SIEMToolbox is equipped with an algorithmic procedure aiming to identify automatically the dominant modes contained in power system responses using data acquired either from individual or multiple signals.…”

Section: Functionalities Of System Identification and Equivalent Modementioning

confidence: 99%

“…It should be noted, that dashed lines in Figure 1 refer to the optional procedures "Initialization" and "Optimal order determination", available in SIEMToolbox. More specifically, the initialization procedure is required for PEM [19] and hybrid FD/TD [24] methods, in order to determine an initial estimate of the mode parameters. Additionally, in order to develop models of the minimum order, i.e., described only by dominant modes, excluding any possible artificial modes, the optional "Optimal order determination" procedure is also available.…”

Section: Mode Identification Proceduresmentioning

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: Functionalities Of System Identification and Equivalent Modementioning

confidence: 99%

Section: Mode Identification Proceduresmentioning

confidence: 99%

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

et al. 2019

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“…More specifically, raw data of positive-sequence voltage magnitude, recorded from PMUs #1, #6, and #9, are used. In all cases, the identification procedure is performed successively using the sliding window technique [29]. The first window starts at t=755 s. Every other window starts after 0.01 s (relatively to the previous one).…”

Section: A Identification Of Oscillatory Modesmentioning

confidence: 99%

Power Hardware-in-the-Loop Setup for Developing, Analyzing and Testing Mode Identification Techniques and Dynamic Equivalent Models

Kontis

Nousdilis

Papagiannis

et al. 2019

2019 IEEE Milan PowerTech

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During the last decades, a significant number of mode identification techniques and dynamic equivalent models have been proposed in the literature to analyze the dynamic properties of transmission grids and active distribution networks (ADNs). The majority of these methods are developed using the measurement-based approach, i.e., by exploiting dynamic responses acquired from phasor measurement units (PMUs). However, there is lack of a common framework in the literature for the performance evaluation of such methods under real field conditions. Aiming to address this gap, in this paper, a power hardware-in-the-loop setup is introduced to generate dynamic responses, suitable for the testing and validation of measurementbased mode identification techniques and dynamic equivalent models. The setup consists of a high voltage transmission grid, two medium voltage distribution grids as well as a low voltage ADN. Using this setup, several disturbances are emulated and the resulting dynamic responses are recorded using PMUs. The measurements are made available to other researchers through a public repository to act as benchmark responses for the evaluation of measurement-based methods.

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“…They include Prony method [14], the Eigenvalue Realization Algorithm (ERA) [15] and the Matrix Pencil (MP) method [16]. Alternatively, there are also methods that derive the dominant modes using frequency domain (FD) responses [17] or a combination of the above as in the recently proposed hybrid FD/TD approach [18]. Such methods extract the dominant modes of measured responses with relatively high accuracy but do not provide any information on the participation of individual generators in these oscillatory modes.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, it deals with practical application aspects when applied to a large number of cases for varying operating conditions imposed by RES intermittent behaviour. The distinct contributions of the proposed method are: 1) Mode identification is applied to TD responses of individual generators to identify both stable and unstable modes contrary to a single point of measurement usually used in the literature [14]- [18]. 2) Two post-processing techniques are incorporated, i.e.…”

Section: Introductionmentioning

confidence: 99%

Measurement Based Method for Online Characterization of Generator Dynamic Behaviour in Systems With Renewable Generation

Papadopoulos

Chrysochos³

et al. 2018

IEEE Trans. Power Syst.

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This paper introduces a two-step methodology for online identification of the participation of generators in power system oscillatory modes, based on measured responses. The dominant modes in generator measured responses are initially identified using a mode identification technique and then introduced, in the next step, as input into a clustering algorithm. Critical groups of generators that exhibit poorly or negatively damped oscillations are identified, in order to enable corrective control actions and stabilize the system. The uncertainties associated with the operation of modern power systems with Renewable Energy Sources (RES) are investigated as well as the impact of the dynamic behavior of power electronic interfaced RES.

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Measurement-Based Hybrid Approach for Ringdown Analysis of Power Systems

Cited by 27 publications

References 33 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

Power Hardware-in-the-Loop Setup for Developing, Analyzing and Testing Mode Identification Techniques and Dynamic Equivalent Models

Measurement Based Method for Online Characterization of Generator Dynamic Behaviour in Systems With Renewable Generation

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