Improved control of parallel virtual synchronous generator based on adaptive voltage compensation

Yang, Yu; Hu, Xiangqian

doi:10.1007/s40435-021-00843-y

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…For that, scientific researchers have performed extensive research on the parallel powertrains by splitting each single device in the powertrain system, such as the gearbox, alternator, and power converter, into several parallel devices [23][24][25][26][27]. On the one hand, the parallel generators can increase power generation, optimize efficiency of generators by running in the optimal 70-75% of rated power and ensure the continuity of power conversion during faults in generators or maintenance [28][29][30]. On the other hand, the parallel power converter can improves the power factor in AC/DC converter and the quality of sine-wave in DC/AC converter, also ensure the continuity of system during faults in power converter [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear optimal control with effective wind speed estimation for maximum power extraction based on adaptive fuzzy logic controller and extended Kalman Filter

Benmahdjoub

Mezouar

Ibrahim

et al. 2023

Int. J. Dynam. Control

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The present paper aims to improve the effectiveness of an isolated water pumping system based on parallel wind turbine powertrains by using a nonlinear Integral Backstepping Controller (IBSC). In fact, the powertrain system with parallel Permanent Magnet Synchronous Generators (PMSG) is used to ensure the continuity of the power conversion system and to optimize the efficiency of generators. A three-phase PWM rectifier is implemented in the power system to convert the AC voltage obtained from parallel PMSG generators to a DC voltage. In fact, the regulated DC voltage will be supplied to a DC motor-driven water pump to control the tank level in the wind turbine pumping system. In this paper, the effective wind speed of a wind turbine is estimated by using an Extended Kalman Filter (EKF) instead of using anemometers. In addition, the Tip Tip Speed Ratio (TSR) method based on an Adaptive Fuzzy Logic Controller (AFLC) is performed to apply the Maximum Power Point Tracking (MPPT) algorithm for maximum power extraction from the available energy in the wind. Moreover, the Voltage Oriented Control (VOC) strategy is handled to control the currents of the three-phase rectifier. To evaluate the efficiency of the EKF estimator, the covariance and correlation coefficient between the estimated and measured wind speed are computed for each s 1 , while the AFLC and nonlinear IBSC controllers are evaluated by comparing their results with those obtained by the PI controller. For the wind speed estimation, the covariance and correlation coefficient results demonstrate that the EKF estimator has high accuracy with a 98% similarity between the estimated and measured wind speed. For the power extraction, the TSR based on adaptive FLC controller can extract slightly more wind energy than the PI controller. For tank level control, the nonlinear IBSC controller improves the efficiency of the wind turbine water pumping system by reducing the DC voltage surge and the overshoot of water level obtained by PI controller.

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Section: Introductionmentioning

confidence: 99%

Nonlinear optimal control with effective wind speed estimation for maximum power extraction based on adaptive fuzzy logic controller and extended Kalman Filter

Benmahdjoub

Mezouar

Ibrahim

et al. 2023

Int. J. Dynam. Control

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“…References [14,15] proposed a DC system based on a virtual synchronous generator to participate in an AC frequency modulation control strategy, but its inertia was fixed, and there was still some room for improvement. References [16,17] adapted the adaptive inertia control strategy and successfully applied it to the microgrid, which can enhance the system stability, but the application in VSC-MTDC systems needs further research.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Droop Control of VSC-MTDC System Based on Virtual Inertia

Zhang

et al. 2023

Electronics

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In order to solve the problem that the voltage source converter based multi-terminal direct current (VSC-MTDC) system cannot provide inertia and participate in frequency modulation after connecting to the AC power grid under the traditional control strategy, an adaptive control strategy based on virtual inertia is proposed. First, the relationship between AC frequency and DC voltage was established by a virtual inertia control, allowing the VSC-MTDC system to provide inertia to the AC side. Second, to address the limited inertia coefficient selection due to DC voltage deviation, an adaptive control was adopted. When the DC voltage deviation is small, the inertia coefficient is increased to obtain a better inertial response; on the contrary, the inertia coefficient is reduced to prevent the DC voltage from exceeding the limit. Finally, to solve the problem of insufficient flexibility of the fixed droop coefficient, this paper introduces the power margin of a VSC-station into the droop coefficient to dynamically adjust the distribution ratio of unbalanced power and reduce the DC voltage deviation. The three-terminal VSC-MTDC system was modelled on the PSCAD/EMTDC simulation platform, and the superiority of the control strategy was highlighted in this paper by comparing it with conventional droop control and a fixed virtual inertia coefficient.

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“…e main problem of the conventional Q-V and P-V droops is the inaccurate power sharing, which leads to an inequality in the output voltage of DGs caused by unequal line voltage drop. In the literature, many researchers have focused on this issue and suggested various control strategies to solve it [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55]. An inclusive solution is that the DG output impedance mismatch is removed [41,42].…”

Section: Introductionmentioning

confidence: 99%

“…In [43], an approach based on the compensation of line voltage drop is presented. Also, other methods such as Q-dV/dt droop control [44], adaptive voltage droop [45], and virtual capacitor control [46] are suggested. However, these methods cannot completely eliminate errors of reactive power sharing as shown in the experimental results.…”

Section: Introductionmentioning

confidence: 99%

Microgrid Stability Improvement Using a Deep Neural Network Controller Based VSG

Mohammadreza

Tavakoli

Samanfar

2022

International Transactions on Electrical Energy Systems

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In order to support the inertia of a microgrid, virtual synchronous generator control is a suitable control method. However, the use of the virtual synchronous generator control leads to unacceptable transient active power sharing, active power oscillations, and the inverter output power oscillation in the event of a disturbance. This study aims to propose a deep neural network controller which combines the features of a restricted Boltzmann machine and a multilayer neural network. To initialize a multilayer neural network in the unsupervised pretraining method, the restricted Boltzmann machine is applied as a very important part of the deep learning controller. The Lyapunov stability method is used to update the weight of the deep neural network controller. The proposed method performs power oscillation damping and frequency stabilization. The experimental and simulation results are presented to assess the usefulness of the suggested method in damping oscillations and frequency stabilization.

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Improved control of parallel virtual synchronous generator based on adaptive voltage compensation

Cited by 5 publications

References 17 publications

Nonlinear optimal control with effective wind speed estimation for maximum power extraction based on adaptive fuzzy logic controller and extended Kalman Filter

Nonlinear optimal control with effective wind speed estimation for maximum power extraction based on adaptive fuzzy logic controller and extended Kalman Filter

Adaptive Droop Control of VSC-MTDC System Based on Virtual Inertia

Microgrid Stability Improvement Using a Deep Neural Network Controller Based VSG

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