mixed‐sensitivity robust control design for damping low‐frequency oscillations with DFIG wind power generation

Isbeih, Younes J.; Moursi, Mohamed Shawky El; Xiao, Weidong; El-Saadany, Ehab F.

doi:10.1049/iet-gtd.2018.6433

Cited by 25 publications

(9 citation statements)

References 21 publications

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“…The number of kernels in the first convolutional layer is equal to double the number of input channels for a system (K (1) = 2 × 2B). The second convolution layer has double the number of kernels in the first convolution layer with a cap of 512 kernels (K (2) = max(2K (1) , 512)) to minimize trainable parameters. Our proposed CNN deploys padded and strided convolutions, as opposed to pooling layers, to quicken the convolution operation step while maintaining performance [31].…”

Section: Cnn Architectures: Gcnn and Ginnmentioning

confidence: 99%

“…The dynamic response of a power system is governed by a set of highly nonlinear differential and algebraic equations (DAE) which describe the behavior of the synchronous generators and its associated control systems, loads, renewable power generation, flexible AC transmission devices (FACTs) in addition to the transmission network [2], [3]. When a power system is subjected to small changes, the DAE model can be linearized about the equilibrium point.…”

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confidence: 99%

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A Unified Online Deep Learning Prediction Model for Small Signal and Transient Stability

Azman

Isbeih

Moursi

et al. 2020

IEEE Trans. Power Syst.

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This paper proposes a novel unified prediction approach for both small-signal and transient rotor angle stability as opposed to other studies that have only addressed transient rotor angle stability. Deep learning techniques are employed in this paper to train an online prediction model for rotor angle stability (RAS) using the voltage phasor measurements which are collected across the entire system. As a result, the trained model provides a fast yet accurate prediction of the transient stability status when a power system is subjected to a disturbance. Also, if the system is transiently stable, the prediction model updates the power system operator concerning the damping of low-frequency local and inter-area modes of oscillations. Therefore, the presented approach provides information concerning the transient stability and oscillatory dynamic response of the system such that proper control actions are taken. To achieve these objectives, advanced deep learning techniques are employed to train the online prediction model using a dataset which is generated through extensive time domain simulations for wide range of operating conditions. A convolutional neural network (CNN) transient stability classifier is trained to operate on the transient response of the phasor voltages across the entire system and provide a binary stability label. In tandem, a long-short term memory (LSTM) network is trained to learn the oscillatory response of a predicted stable system to capture the step-by-step dynamic evolution of the critical poorly damped low-frequency oscillations. The superior performance of the proposed model is tested using the New-England 10-machine, 39-bus, IEEE 16-machine, 68-bus, 5-area and IEEE 50-machine, 145-bus test systems and is verified with time domain simulation.

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Section: Cnn Architectures: Gcnn and Ginnmentioning

confidence: 99%

mentioning

confidence: 99%

A Unified Online Deep Learning Prediction Model for Small Signal and Transient Stability

Azman

Isbeih

Moursi

et al. 2020

IEEE Trans. Power Syst.

Self Cite

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“…After applying demand changes, the voltage and frequency of the system are adjusted, and it is proven that the steady-state system has its stability after fault clearance. An H ∞ controller is designed to mitigate the small-frequency oscillations of the power grid with a DFIG [20].…”

Section: Introductionmentioning

confidence: 99%

“…e MH-DRPC strategy achieve sinusoidal forms of generated AC current with a constant frequency with minimal current total harmonic distortion and minimum output voltage ripple irrespective of the wind speed fluctuations and the robustness against parametric variations, but it has not been investigated about the measurement uncertainties. In contrast to [18][19][20][21][22][23][24][25][26] improving the performance of DFIG by using the H ∞ controller and other controllers, this paper intends to improve both the performance and the stability of the DFIG in the presence of both model and measurement uncertainties by proposing a new controller based on the combination of H ∞ controller method with the Kalman filter compared to the classical vector control. Table 1 shows the advantages and limitations of other control methods and the proposed method.…”

Section: Introductionmentioning

confidence: 99%

Power Control of a Grid-Connected Doubly Fed Induction Generator Using H∞ Control and Kalman Filter

Nezhad

Tousi

Sabahi

2022

International Transactions on Electrical Energy Systems

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A new control method for a grid-connected doubly fed induction generator (DFIG) is proposed in this paper, which is robust against parametric uncertainty and measurement noise. In general, the DFIG controllers can be divided into two main groups: the rotor side converter (RSC) and the grid side converter (GSC) controllers. The parameters of a DFIG may deviate from their rated values due to the operating conditions. For this parametric uncertainty, a robust H∞ vector control (VC) is employed using the complex sensitivity approach. The design of the RSC controller has been carried out using the vector control strategy, and instead of proportional-integral (PI) controllers, a designed robust controller is used. One of the steps in vector control is the extraction of the measured currents to be used in the control equations. If the currents are polluted with noise, the system control will be impaired. Thus, using a Kalman filter is suggested to solve this problem. The effectiveness of the proposed method is then investigated using extensive simulations under various conditions. The obtained results confirm the efficient performance and robustness of the presented controller with model and measurement uncertainties.

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“…The LFO modes of power systems may be damped by the power system stabilizer (PSS) installed at the SGs, or recently, the power oscillation damper (POD) at the DFIGs [8,9]. With the signals from the transmission lines and the SGs inputted to the POD [10,11], the LFO modes may be damped by the POD whose control effects are affected by the parameters setting. The heuristic algorithms, e.g.…”

Section: Introductionmentioning

confidence: 99%

Optimization to POD parameters of DFIGs based on the 2nd order eigenvalue sensitivity of power systems

Zhang

2020

IET Generation Trans & Dist

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Here, the analytical model of the 2nd order eigenvalue sensitivity is improved, and applied to optimize the parameters of the power oscillation dampers of the doubly‐fed induction generators. To solve the 2nd order eigenvalue sensitivity, the eigenvector sensitivity is required whose difficulty lies in insufficient constraints, for which two constraints about the magnitude and the angle of eigenvector elements are newly introduced, and the normalization conditions are derived to solve the eigenvector sensitivity. The 2nd order eigenvalue sensitivity is applied to analyse the change of the low‐frequency oscillation modes with the varying of parameters in the power systems with the doubly‐fed induction generators. To improve the low‐frequency oscillation modes, the optimization model of the power oscillation damper parameters is newly formulated based on the 1st and the 2nd order eigenvalue sensitivities and solved by the interior point method. The numerical results are provided to verify the effectiveness of the constraints proposed; the advantages of the 2nd order eigenvalue sensitivity in the eigen‐analysis and parameters optimization, and the robustness against wind fluctuation.

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mixed‐sensitivity robust control design for damping low‐frequency oscillations with DFIG wind power generation

Cited by 25 publications

References 21 publications

A Unified Online Deep Learning Prediction Model for Small Signal and Transient Stability

A Unified Online Deep Learning Prediction Model for Small Signal and Transient Stability

Power Control of a Grid-Connected Doubly Fed Induction Generator Using H∞ Control and Kalman Filter

Optimization to POD parameters of DFIGs based on the 2nd order eigenvalue sensitivity of power systems

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