Frequency domain modelling and identification of nonlinear loads using a least error squares algorithm

Soliman, S. A.; Abbasy, Nabil H.; El-Hawary, M.E.

doi:10.1016/s0378-7796(96)01123-6

Cited by 11 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mather (2012) established a distributed photovoltaic cell model with grid-connected inverters to simulate the characteristics of distributed photovoltaics similar to a constant power load. Soliman et al (1997) proposed an AND dynamic equivalent model, which considers static load, dynamic load, and distributed generation. In this paper, we comprehensively analyze the applicability of a load model and adopt the composite load model with distributed photovoltaic power as the research object.…”

Section: Introductionmentioning

confidence: 99%

Online correction of the transient voltage security region boundary based on load parameter variations in power systems

Zhang,

Zeng,

et al. 2023

Front. Energy Res.

View full text Add to dashboard Cite

The transient voltage security region (TVSR) is an essential part of the dynamic safety region of a power system, which represents the safe operating region of the transient voltage where there is no loss in stability. The power system load significantly affects the transient voltage stability. However, the load model in the existing power systems, which considers the security region of the dynamic process, is too simple to comprehensively characterize the load characteristics of the dynamic process. Furthermore, it neglects the influence of the load model parameters on the dynamic process security region. In this study, a composite load model with distributed photovoltaic power is used as the research object. The main load parameters affecting the voltage stability and the generator nodes that are sensitive to the load parameters are calculated using the trajectory sensitivity method. The TVSR of the system is constructed based on the load and generator’s active powers. The correlations between different load-leading parameter combination scenarios and TVSR boundary points are mined using the CatBoost learning framework. Thus, the TVSR boundary can be promptly corrected online based on the changes in the load parameters, and the system security boundary can be described more accurately. The proposed method is verified using the IEEE39 and IEEE118 node systems. It is observed that the proposed method can correct the TVSR boundary online with high precision corresponding to real-time changes in the load model parameters. This provides a more accurate TVSR boundary for the power system operation scheduler, which helps in guiding it to control the system more accurately.

show abstract

Section: Introductionmentioning

confidence: 99%