Grid impedance estimation for islanding detection and adaptive control of converters

Ghanem, Abdelhady; Rashed, Mohamed; Sumner, Mark; El-Sayes, Mohamed Adel Rateb Mohamed; Mansy, Ibrahim Ibrahim Ibrahim

doi:10.1049/iet-pel.2016.0780

Cited by 51 publications

(31 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Various methods have been proposed for estimating the grid impedance: nonparametric frequency-response based methods [10]- [17] and parametric model-based methods [18]- [27]. The nonparametric methods estimate the frequency response while the parametric methods estimate the parameters of a predefined model, such as an LR or LCL circuit.…”

Section: Introductionmentioning

confidence: 99%

“…The nonparametric methods estimate the frequency response while the parametric methods estimate the parameters of a predefined model, such as an LR or LCL circuit. Some methods aim to estimate the grid impedance behind a known L filter [18]- [21], [26] or behind a known LCL filter [11]- [17], [22]- [27]. In [16], [19], model parameters are estimated without a grid connection.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Plug-In Identification Method for an <italic>LCL</italic> Filter of a Grid Converter

Koppinen

Kukkola

Hinkkanen

2018

IEEE Trans. Ind. Electron.

View full text Add to dashboard Cite

This paper proposes a method for estimating the two inductances and the capacitance of an LCL filter, connected between the converter and the grid. Only the DC-bus voltage and converter phase currents need to be measured. An excitation signal is fed into the converter voltage reference. The fundamental and selected harmonic components are removed from the stored identification data to prevent biases in the parameter estimates. The parameters of the hold-equivalent discrete-time model are estimated recursively. The inductance and capacitance estimates are calculated from the estimated discrete-time parameters using the corresponding closed-form model. The proposed method can be added to existing converter control algorithms. It can provide the parameter estimates for the converter control. Further, it can be run occasionally during the normal operation in order to obtain an updated grid inductance estimate. Experimental results show that the proposed method yields very good parameter estimates and that it can also detect the changes in the grid inductance.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Plug-In Identification Method for an <italic>LCL</italic> Filter of a Grid Converter

Koppinen

Kukkola

Hinkkanen

2018

IEEE Trans. Ind. Electron.

View full text Add to dashboard Cite

show abstract

“…Various methods are proposed to estimate impedance of power network, though all of the methods can be classified into passive and active methods [1,2,7,[9][10][11][12][13][14][15] or combination of these two [16]. The passive methods are known to be 'noninvasive' and active methods are 'invasive'.…”

Section: Introductionmentioning

confidence: 99%

“…Then the signal processing techniques are used to estimate the impedance of the grid. However, passive methods do not need any disturbance injection, and the available information of the noncharacteristic current and voltage at the PCC is used to estimate the grid impedance [1,[13][14][15][16][17]. Hence, the performance of the power system is not degraded using passive methods.…”

Section: Introductionmentioning

confidence: 99%

Machine learning based impedance estimation in power system

Givaki

Seyedzadeh²,

Givaki³

2019

8th Renewable Power Generation Conference (RPG 2019)

View full text Add to dashboard Cite

A passive machine learning based technique to estimate the impedance of the power grid at the point of common coupling of a converter interfaced distributed generation source is proposed. The proposed method is based on supervised learning and provides a fast and accurate estimation of the grid impedance without adversely impacting the power quality of the system. This method does not need an injection of additional signals to the grid and provides an accurate estimation of the grid impedance. Multi-objective NSGA-II algorithm is used for optimisation and tuning the random forest model for accurate estimation of both R and X The resistive and inductive reactance of grid is estimated using Random Forest model due to its capability in the prediction of multiple output values simultaneously.

show abstract

“…In additional, the grid-side online impedance estimation methods based on the power relationship and voltage vector relationship were presented respectively in Reference [22][23][24][25][26], these methods only consider the ground grid connected converter situation, the parameters of grid network changed largely and the control methods with parameter estimation is not limited to the MPC control. The Uncertainty of parameters of PWM rectifiers in ACVF electrical power system cannot be investigated, the characteristics of ac side impedance parameters changed with main AC generator frequency with the load profile.…”

Section: Introductionmentioning

confidence: 99%

A Novel Robust Model Predictive Controller for Aerospace Three-Phase PWM Rectifiers

et al. 2018

View full text Add to dashboard Cite

This paper presents a novel Model Predictive Direct Power Control (MPDPC) approach for the pulse width modulation (PWM) rectifiers in the Aircraft Alternating Current Variable Frequency (ACVF) power system. The control performance of rectifiers may be largely affected by variations in the AC side impedance, especially for systems with limited power volume system. A novel idea for estimating the impedance variation based on the Bayesian estimation, using an algorithm embedded in MPDPC is presented in this paper. The input filter inductance and its equivalent series resistance (ESR) of PWM rectifiers are estimated in this algorithm by measuring the input current and input voltage in each cycle with the probability Bayesian estimation theory. This novel estimation method can overcome the shortcomings of traditional data based estimation methods such as least square estimation (LSE), which achieves poor estimation results with the small samples data set. In ACVF systems, the effect on the parameters estimation accuracy caused by the number of sampling points in one cycle is also analyzed in detail by simulation. The validity of this method is verified by the digital and Hard-in-loop simulation compared with other estimation methods such as the least square estimation method. The experimental testing results show that the proposed estimation algorithm can improve the robustness and the control performance of the MPDPC under the condition of the uncertainty of the AC side parameters of the three-phase PWM rectifiers in aircraft electrical power system.

show abstract

Grid impedance estimation for islanding detection and adaptive control of converters

Cited by 51 publications

References 22 publications

Plug-In Identification Method for an <italic>LCL</italic> Filter of a Grid Converter

Plug-In Identification Method for an <italic>LCL</italic> Filter of a Grid Converter

Machine learning based impedance estimation in power system

A Novel Robust Model Predictive Controller for Aerospace Three-Phase PWM Rectifiers

Contact Info

Product

Resources

About