An interval Taylor-based method for transient stability assessment of power systems with uncertainties

Liao, Xiaobing; Liu, Kaipei; Niu, Huanhuan; Jun, Luo; Li, Yuye; Qin, Liang

doi:10.1016/j.ijepes.2017.11.043

Cited by 19 publications

(8 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where ϕ i (F i ) is the regression equation of F i against F k , and Y corresponds to the target F k in (16). Taking into account the non-linear properties of TTC, it is challenging to implement linear regression; hence, this paper uses the cubic B-spline function to perform regression to reflect the TTC nonlinearity.…”

Section: B Nonparametric Independence Screeningmentioning

confidence: 99%

Ensemble Learning for Power Systems TTC Prediction With Wind Farms

Qiu

Liu

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Being aware of the reliable margin of vital tie-lines, acting on the connection of power exporting area and power importing area, is significant to power systems. However, the high penetration of wind power causes fast variation of boundary limit parameters such as the available amount of power that can be transferred on the tie-lines, namely, total transfer capability (TTC), which may result in the inaccurate security assessment. Unfortunately, the traditional optimal power flow-based TTC model has computation burden for online applications. To address this problem, computational efficiency is improved via a datadriven TTC predictor based on an ensemble learning architecture in this paper. In the first stage, a daily profiles-based method including probabilistic sampling is proposed to simulate plenty of operation scenarios as data samples for ensemble training. Then, a hybrid feature selection approach, which is composed of the maximal information coefficient and nonparametric independence screening, is applied to determine the most correlative features to the objective variable. To enable the TTC predictor with high accuracy and generalization ability, a novel ensemble learning scheme for TTC predictor is constituted through clustering few adaptive hierarchical GA-based neural networks (AHGA-NNs predictor). At last, a modified New England test system is used to validate the proposed methodology. The results illustrate that combining with the appropriate feature selection, the presented ensemble learning has high performance on creating the accurate TTC predictor, which enables online secure margin monitoring for the vital tie-lines. INDEX TERMS Artificial neural networks, ensemble learning, feature selection, total transfer capability, wind power.

show abstract

Section: B Nonparametric Independence Screeningmentioning

confidence: 99%

Ensemble Learning for Power Systems TTC Prediction With Wind Farms

Qiu

Liu

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…By solving a power flow equation and two linear programming problems, the upper and lower bounds of the power flow solutions that restrain interval extension were obtained. In [18], Taylor expansion of intervals was carried out according to affine ideas, and interval power flow equations in the Cartesian coordinate system were expanded into three deterministic power flow equations for solution, which avoids interval iteration process. However, as with [18], the complexity of the calculation is increased by Taylor expansion.…”

Section: Introductionmentioning

confidence: 99%

“…In [18], Taylor expansion of intervals was carried out according to affine ideas, and interval power flow equations in the Cartesian coordinate system were expanded into three deterministic power flow equations for solution, which avoids interval iteration process. However, as with [18], the complexity of the calculation is increased by Taylor expansion. In [19], an interval optimization framework was proposed.…”

Section: Introductionmentioning

confidence: 99%

“…However, in [17,18], Taylor expansion was adopted, which undoubtedly increased the amount of calculation and made the calculation process too tedious. When using the affine optimization method, the objective function and constraint conditions need to be constructed.…”

Section: Introductionmentioning

confidence: 99%

“…When the output of PV cells within the cluster fluctuate greatly, non-convergence may occur. In [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22], only the uncertainty of power was studied, and there was no specific research object. In [23][24][25], the centralized PV cluster was taken as the research object, but only to study their external characteristics, namely the fluctuation of output, without considering the huge internal network structure, which ignores the operation fluctuation of each PV generation unit within the cluster.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Mixed Uncertainty Power Flow Algorithm-Based Centralized Photovoltaic (PV) Cluster

Zhou

Wan

et al. 2019

Energies

View full text Add to dashboard Cite

With the large-scale centralized PV clusters connected to grid, the grid power flow has certain randomness. Considering the fluctuation of PV output, an improved Krawczyk-Moore algorithm in a mixed coordinate system is proposed to solve the uncertain power flow problem. Firstly, aiming at the special structure of a centralized PV cluster with only load node and no generator node, this paper proposes a power flow calculation in the mixed power flow coordinate, and then the Krawczyk-Moore operator is used to combine interval and affine arithmetic to overcome the shortcoming of over-conservative interval algorithm. Finally, the voltage operating condition under different volatility and different partial shading conditions is studied through the simulation of a practical example, and the out-of-limit voltage problem inside the centralized PV cluster is analyzed. Meanwhile, the effectiveness of the proposed algorithm is verified.

show abstract