Enhanced ambient signals based load model parameter identification with ensemble learning initialisation

Zhang, Xinran; Hill, David; Zhu, Lipeng

doi:10.1049/iet-gtd.2020.0612

Cited by 4 publications

(2 citation statements)

References 29 publications

(36 reference statements)

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“…where B and ω are the estimated vectors of the node total power, and proportional coefficient, respectively. A is the load unit model parameter matrix, as shown in Equation (8).…”

Section: Principle Of Load Component Decomposition and Online Modellingmentioning

confidence: 99%

“…Subsequently, the model parameters are solved using the optimization method. The ensemble learning method is combined with sequential quadratic programming (SQP) algorithm [8]. First, an ensemble learning method is used to find the initial feasible solution of the model parameters, and then SQP algorithm is used to optimize the objective function based on this solution to obtain the global optimal solution.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Load component decomposition and online modelling based on small disturbance response characteristic matching

Zeng

Meng

Liu

et al. 2021

IET Generation Trans & Dist

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Accurate load model significantly influences the numerical simulations of power system. The load model obtained by the component-based method does not adequately reflect the load time-varying characteristics, and the measurement-based method relies on fault data, but system faults rarely occur. Inspired by the theory of non-intrusive load monitoring, the load component decomposition and online modelling method based on the small disturbance response characteristic matching is proposed. An optimization model for the load component decomposition based on the curve fitting of small disturbance response characteristics is established. The construction method of a typical load model library is given and a neural network-based solution is designed. The proposed method can determine the load components that constitute the synthesis load model and their proportions by only analysing the small disturbance real-time measurement data. To verify the effectiveness of the proposed method, the distribution of the decomposition errors was analysed by the Monte Carlo simulation. Furthermore, the accuracy of the synthesis load model was verified by a fault simulation using the CEPRI 36-node system. Experimental results show that the proposed method can provide accurate load compositions and dynamically update the synthesis load model for the online power system simulation, planning and operation.

show abstract

“…where B and ω are the estimated vectors of the node total power, and proportional coefficient, respectively. A is the load unit model parameter matrix, as shown in Equation (8).…”

Section: Principle Of Load Component Decomposition and Online Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Load component decomposition and online modelling based on small disturbance response characteristic matching

Zeng

Meng

Liu

et al. 2021

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

Bagging-based neural network ensemble for load identification with parameter sensitivity considered

Zeng

Qin

et al. 2022

Energy Reports

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Modeling of active and reactive power steady state load in optimal reactive power planning

Karmakar,

Bhattacharyya

2022

e-Prime - Advances in Electrical Engineering, Electronics and E

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Enhanced ambient signals based load model parameter identification with ensemble learning initialisation

Cited by 4 publications

References 29 publications

Load component decomposition and online modelling based on small disturbance response characteristic matching

Load component decomposition and online modelling based on small disturbance response characteristic matching

Bagging-based neural network ensemble for load identification with parameter sensitivity considered

Modeling of active and reactive power steady state load in optimal reactive power planning

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