Fast and Reliable Estimation of Composite Load Model Parameters Using Analytical Similarity of Parameter Sensitivity

Kim, Jae-Kyeong; An, Kyungsung; Jin, Ma; Shin, Jung Won; Song, Kyung‐Bin; Park, Jung-Do; Park, Jung–Wook; Hur, Kyeong

doi:10.1109/tpwrs.2015.2409116

Cited by 39 publications

(45 citation statements)

References 24 publications

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“…As shown in [4][5][6][7], dynamic load modeling is generally challenged by the unidentifiability of parameters that describe the model structure. The unidentifiability has two primary causes: the insensitivity of some parameters and the dependency among several parameters [6].…”

Section: Background and Objectivementioning

confidence: 99%

“…Nonetheless, the modeling has long been a challenging task due to the inherent uncertainty, complexity, and diversity of power system loads [3]. With the development of digital fault recorders, the measurement-based load modeling approach has become increasingly popular [4][5][6][7][8][9]. In this approach, the parameters describing a given model structure need to be determined by fitting field measurements during system disturbances.…”

Section: Introductionmentioning

confidence: 99%

“…In this approach, the parameters describing a given model structure need to be determined by fitting field measurements during system disturbances. However, not all the parameters are identifiable using only field measurements according to some previous work [5][6][7][8], especially for complex model structures with large numbers of parameters. Hence the analysis of parameter sensitivity serves as the base for measurement-based load modeling.…”

Section: Introductionmentioning

confidence: 99%

“…One popular choice consists of a static constant impedance-current-power (ZIP) component and a dynamic induction motor (IM) component. This ZIP+IM model has been widely used due to its simple structure [4][5][6][7]. In addition, the complex load model (CLOD) developed by PSS/E, which has been preliminarily investigated in [8], has several more components including two IMs with different torque-speed and current-speed curves.…”

Section: Introductionmentioning

confidence: 99%

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Parameter Sensitivity and Dependency Analysis for the WECC Dynamic Composite Load Model

Zhang

Guo

Zhu

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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An accurate dynamic load model plays a crucial role in the analysis of power system transient stability. The WECC dynamic composite load model (CMPLDW) has been developed recently to better represent faultinduced delayed-voltage-recovery (FIDVR) events, which are of increasing concern to electric utilities. To facilitate the understanding of the CMPLDW, it is worth studying the effect of parameters that describe the model structure on its dynamic response. In this paper, we show that 1) some parameters have very minimal sensitivities under certain FIDVR events; 2) sensitivities of certain parameters are strongly dependent on the temporal profile of given fault, such as its minimum fault voltage or recovery time; and 3) some parameters share similar sensitivity patterns and thus the change of their values may complement each other. These observations are essential for further developing enhanced measurement-based dynamic load modeling approaches by tackling the parameter identifiability issues pointed out in the present work.

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Section: Background and Objectivementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Parameter Sensitivity and Dependency Analysis for the WECC Dynamic Composite Load Model

Zhang

Guo

Zhu

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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“…They include signature identification of loads [85], dynamic modelling of loads for studying power system damping [86], linear, polynomial and exponential representations of loads [87], composite models via measurement approach [88][89][90], characterisation and profiling of load patterns [91] and Markov models [92]. It is to be mentioned that the above papers mainly focus on the voltage dependency of loads.…”

Section: Load Frequency Relief (Lfr) and Contingency Reserve Requirementmentioning

confidence: 99%

Security assessment due to increased wind generation in a complex power grid

Masood¹

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Wind energy is becoming a major source of generation in many countries because of its zero fuel cost and no air pollution. Due to the integration of large-scale wind power in conventional grids, synchronous generators are being economically replaced. Modern wind turbine generators (WTGs) are based on power electronic interfaces. Hence, unlike synchronous generators, they do not usually provide inertia and governor response. Therefore, from a power system security point of view, wind penetration can be limited by frequency response criteria. Up to now, several methodologies have been proposed to estimate the maximum wind penetration level. However, none of them suggest how a system operator could be immediately informed about a secured wind penetration limit as soon as a generation profile is known. Therefore, a tool to estimate the maximum wind penetration level by maintaining an adequate frequency response is proposed in this thesis. The proposed tool is able to instantly provide the highest margin of wind power that could be dispatched at a particular load condition without violating frequency response constraints.In some power systems, the combined generation capacity of wind and distributed photovoltaic (PV) sources is close to reaching the level of average demands (e.g. South Australia). Under high availability of wind and PV, a few synchronous machines may be required to be committed for frequency regulation and hence the system can be viewed as a low inertia grid. Such a grid could be potentially at risk of experiencing an excessive rate of change of frequency (ROCOF) after a contingency. A high ROCOF may initiate tripping of other synchronous generators. As a result, network frequency response may become more vulnerable and the system may be subject to significant under frequency load shedding (UFLS) or even at risk of a blackout. Furthermore, some existing distributed PV units may have a default under frequency protection setting, which is higher than the UFLS threshold. Hence, a contingency may cause a subsequent trip of those PV units. It may result in an unacceptable low frequency, triggering further load shedding. To explore the above issues, the effects of cascading contingencies triggered by high ROCOF and PV tripping on the frequency response of a low inertia grid are investigated in this thesis. Remedial measures using synchronous condenser and WTG frequency support to prevent the cascading contingencies are also suggested.Any mismatch between load and generation is intended to be balanced by contingency reserve, which is also known as contingency Frequency Control Ancillary Services (FCAS) requirement.Load frequency relief (LFR), which represents the effect of frequency dependent loads on power system frequency excursion, is crucial for correctly evaluating contingency FCAS requirement during a generation dispatch to ensure an adequate frequency response. Conventionally, the LFR is iii considered as a fixed quantity during the estimation of frequency response and FCAS requirement.How...

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Dynamic Equivalent Model of Active Distribution Network Based on Improved Reinforcement Learning Algorithm

Wei

Kang

Guo

et al. 2023

Lecture Notes in Electrical Engineering

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Fast and Reliable Estimation of Composite Load Model Parameters Using Analytical Similarity of Parameter Sensitivity

Cited by 39 publications

References 24 publications

Parameter Sensitivity and Dependency Analysis for the WECC Dynamic Composite Load Model

Parameter Sensitivity and Dependency Analysis for the WECC Dynamic Composite Load Model

Security assessment due to increased wind generation in a complex power grid

Dynamic Equivalent Model of Active Distribution Network Based on Improved Reinforcement Learning Algorithm

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