Synchronous Generator Control Research Articles

Multi-objective parameter design and economic analysis of VSG-controlled hybrid energy systems in islanded grids

Photovoltaic (PV) systems offer cost-effective power solutions for outlying islands but often compromise system stability due to reduced inertia. This study introduces a Virtual Synchronous Generator (VSG) control strategy, integrated with Energy Storage Systems (ESS) and PV, to enhance system inertia. By optimizing coordination between these energy sources, the proposed method mitigates oscillations and improves grid stability. However, PV-VSG systems are generally not favored by energy providers due to the requirement for pre-curtailment of power output. To address this, the paper proposes a parameter design method for VSG control of ESS and PV, utilizing multi-objective genetic algorithm (MOGA) optimization to simultaneously increase the frequency nadir and minimize the settling time after disturbances. Additionally, an adaptive curtailment decision and parameter design method based on artificial neural networks is introduced to enhance the feasibility of PV-VSG systems by reducing PV pre-curtailment and prioritizing PV power release and ESS charging during frequency oscillations. Real data from the Penghu Archipelago in Taiwan are used to build a dynamic model in DIgSILENT, enabling interaction with MOGA. The Value at Risk (VaR) method with dual stochastic variables is employed to assess the allowable PV installed capacity. The results show that when VaR is set at 1%, the proposed PV-VSG method can increase PV penetration by 57.5% compared to scenarios without VSG. Furthermore, compared to traditional PV-VSG methods, the proposed approach achieves a 16.8% increase in PV penetration and reduces annual PV curtailment by 25 MWh. This study also evaluates the economic impact of planners choosing different risk levels, offering valuable insights for grid development in remote or island regions.

Advancing wind energy: adaptive neural network control for optimized permanent magnet synchronous generators

Our contribution in this paper is to design an adaptive neural network (NN) based control of a permanent magnet synchronous generator in order to overcome the effect of structured and unstructured uncertainties and external disturbances, in addition to ensuring the stability of the system, the proposed robust control strategy compensates adaptively for significant uncertainties present in uncertain nonlinear systems. First, we present the vector control of PMSG model applying two tests: with and without constrains, and then, the neural network control with PI controllers. However, conventional controllers suffer from limitations due to the presence of these high uncertainties. Therefore, we aim to improve the efficiency of artificial neural control by using dynamic compensators instead of PI controllers. The estimated states are used as inputs to the neural network (NN). Simulations of the proposed control algorithm are conducted then compared to the previous controllers: vector control and neural network control with PI controllers. Furthermore, using dynamic compensators in artificial neural control achieved both efficiency and optimality. Results have been successfully confirmed via computer simulations using MATLAB.

A Novel VSG with Adaptive Virtual Inertia and Adaptive Damping Coefficient to Improve Transient Frequency Response of Microgrids

This paper proposes a combined adaptive virtual Inertia and adaptive damping control of a virtual synchronous generator (AID-VSG) to improve the dynamic frequency response of microgrids. In the proposed control scheme, the VSG’s virtual inertia and damping coefficients adapt themselves during the transients to, respectively, reduce frequency deviations and increase the oscillations’ damping. In addition, as an important feature, the proposed AID-VSG is suitable for distributed control scheme applications and is designed to not rely on phase-locked loop (PLL) measurements, which avoids PLL stability issues on weak grids. The control parameters of the proposed AID-VSG are tuned by the particle swarm optimization (PSO) algorithm to minimize the overshoot and settling time of the microgrid’s frequency during an islanding event. The AID-VSG is validated by a comparative analysis with three existing VSG control schemes, also tuned by the stated optimization algorithm. The performance of each compared VSG strategy is evaluated through the simulation of a set of 10,000 initial conditions, using the islanded microgrid’s nonlinear model. The best response among the VSG strategies was achieved by the proposed AID-VSG control for both the optimization problem and the set of initial conditions’ simulations.

A Virtual Synchronous Generator-Based Control Strategy and Pre-Synchronization Method for a Four-Leg Inverter under Unbalanced Loads

Virtual synchronous generator (VSG) control has positive effects on the stability of microgrids. In practical power systems, both single-phase loads and three-phase unbalanced loads are present. The four-leg inverter is an alternative solution for the power supply of unbalanced loads and grid connections. The traditional VSG control strategy still faces challenges when using a four-leg inverter to provide a symmetrical voltage and stable frequency in the load power supply and pre-synchronization. This paper proposes a VSG-based control strategy along with a pre-synchronization method for four-leg inverters. An improved VSG control strategy is put forward for four-leg inverters. The improved virtual impedance control and power calculation methods are integrated into the control loop to suppress the voltage asymmetry and frequency ripples. Building on the improved VSG control strategy, a pre-synchronization control approach is proposed to minimize the amplitude and phase angle discrepancies between the inverter output voltage and the power grid voltage. In addition, an optimized design method for control parameters is presented to improve VSG dynamic performance. A hardware prototype of the four-leg inverter is built; the results show that the voltage unbalance ratio can be reduced by 89%, and the response time can be shortened by 50%.

A heuristic-fuzzy improved virtual synchronous generator control strategy for charging station frequency regulation

To make use of charging station (CS) frequency regulation (FR) capability to get a better FR effect, a CS virtual synchronous generator (VSG) frequency control strategy based on the heuristic-fuzzy logic controller (FLC) is proposed in this paper. First, a CS VSG control architecture with heuristic-FLC is designed. Second, considering electric vehicle (EV) users' charging demands, a CS FR capability evaluation model based on the constant current-constant voltage (CC-CV) charging process and a V2G power response model based on frequency up/down-regulation participation factor are designed. Furthermore, a heuristic-FLC optimized by genetic algorithm (GA) is designed to improve the FR effect of the VSG control strategy. Simulation results show that the proposed strategy can make full use of the CS FR capability and get a better FR effect. In addition, the V2G power response model proposed in this paper can achieve the orderly responses of EVs in a CS under different scenarios.

Macroscopic state‐based reactive voltage control of virtual synchronous generator in AC microgrid

AbstractIn building a smarter and more flexible low‐carbon smart grid system, alternating current (AC) microgrids using virtual synchronous generator (VSG) technology are viewed as a key link in integrating distributed renewable energy access into the main grid. Given that renewable energy sources (such as solar, hydroenergy, and wind) do not have sufficient capacity for reactive power when not available, AC microgrids face challenges in maintaining stable operation. In order to overcome this difficulty, it is hoped that digging deeper and applying more system information can significantly improve the overall performance of the microgrid. This paper proposes a novel method based on macroscopic state dynamic modeling. This method expands the understanding of the inherent rational control mechanism within the microgrid, enabling the overall control objective of the microgrid to be expressed in a more abstract and direct manner. Additionally, by implementing additional convergence constraint conditions on the macroscopic state dynamics, such as based on some optimality criteria, a set of macroscopic state controllers can be obtained to meet specific performance indicators. Theoretical analysis combined with simulation validation demonstrate the effectiveness of this macroscopic state based control strategy. It proves that when meeting the predefined design requirements, the designed controller can enhance the transient response of microgrids in practical applications, thus supporting higher rate of renewable energy access and promoting the development of the smart grid.

Fault detection and synchronization control in hybrid DC/AC microgrids using grid-forming inverter DC-link controller

This paper introduces a DC-link fault detection and synchronization control strategy for grid-forming inverters in hybrid DC/AC microgrids, aiming to bolster system stability and reliability. The proposed control scheme seamlessly integrates DC-link fault detection with virtual synchronous generator (VSG) control, facilitating inertia emulation and synchronization without the need for a phase-locked loop (PLL). The DC bus controller is designed to swiftly identify faults triggered by power imbalances, such as load fluctuations or generator integration, and dynamically adjust the power reference for the VSG controller to ensure inertia emulation. The Ziegler-Nichols method is utilized for meticulous tuning of the DC-link controller gains, while the modified swing equation governs VSG inertia and synchronization. MATLAB/Simulink demonstrate the superior performance of the proposed method compared to traditional GFM control strategies. Specifically, the proposed controller improved the frequency nadir to 59.98 Hz compared to 59.75 Hz and showed an enhanced power step response of 7.9×10^5 W compared to 6.9×10^5 W for conventional control method. The proposed GFM control also eliminated the 10×10^6 W switching spike observed with conventional controllers. These findings highlight the potential of this fault-controller approach to advance the reliability and efficiency of hybrid DC/AC microgrids.

Sliding mode control strategy of grid-forming energy storage converter with fast active support of frequency and voltage

The random fluctuation of renewable power generation output makes the frequency and voltage of distribution network fluctuate frequently. And the stable operation performance of the system is decreased. Therefore, the sliding mode control (SMC) strategy of grid-forming (GFM) energy storage converter with fast active support of frequency and voltage is proposed in this paper. Firstly, the virtual synchronous generator (VSG) control possessing the superior GFM performance is applied to single-stage neutral point clamped (NPC) converter of energy storage system. Meantime, the improved comprehensive equivalent circuit model of lithium iron phosphate battery and equivalent model of converter are constructed by means of ampere-hour method and Kirchhoff’s law. Then, the SMC with fast response and strong robustness is utilized into the current inner-loop controller. Combined with VSG control, the SMC strategy of GFM energy storage converter is proposed, so that the converter could play an active supporting role by quickly adjusting the output power while the frequency and voltage are reduced. Finally, the simulation model of GFM energy storage converter SMC system is established. Through the simulation analyses, it can be seen that the response time of the proposed strategy to complete the active support is about 0.65 s.

Virtual synchronous generator control with parameters optimization based on new WaOA algorithm for grid-feeding inverters

The widespread integration of renewable energy sources (RESs) within traditional power systems causes a considerable impact, such as a decrease in total inertia, damping properties, and large frequency deviation. To tackle this issue, numerous research have been proposed to introduce the virtual synchronous generator (VSG) control strategy as an effective solution applied to power electronics inverters operating in grid-forming or grid-feeding control modes. However, the proper tunning of the inertia and damping parameters is considered as a significant challenge to ensure the system stability and reliability. In this paper, an efficient meta-heuristic Walrus Optimization Algorithm (WaOA) -based VSG control intended for grid-feeding inverters within a weak grid is proposed. The objective of this work is to offer a proper tuning of the VSG parameters, thereby improving the dynamic characteristics of the microgrid (MG) and guaranteeing a stable operation. In addition, it highlights the adopted optimization technique which is introduced on VSG control while ensuring minimum objective function value. Through offline simulations in MATLAB/Simulink, the superiority of the suggested WaOA method is first confirmed, in which it offers the lowest Integral Square Error (ISE) fitness function value of 0.026, compared to the Mountain Gazelle Optimizer (MGO) and Tunicate Swarm Algorithm (TSA) with 0.027 9 and 0.0281, respectively. Second, the performance of the WaOA-based VSG control is compared to the ones based on MGO and TSA techniques under active and reactive power variations. The obtained results demonstrate that the proposed VSG control ensures a better transient performance, especially in terms of under and overshoots.

Enhanced grid integration through advanced predictive control of a permanent magnet synchronous generator - Superconducting magnetic energy storage wind energy system

In this study, the use of an Unscented Kalman Filter as an indicator in predictive current control (PCC) for a wind energy conversion system (WECS) that employs a permanent magnetic synchronous generator (PMSG) and a superconducting magnetic energy storage (SMES) system connected to the main power grid is presented. The suggested UKF indication in the hybrid WECS-SMES arrangement is in charge of estimating vital metrics such as stator currents, electromagnetic torque, rotor angle, and rotor angular speed. To optimize control strategies, PCCs use these projected properties rather than direct observations. To control the unpredictable wind energy's nature, SMES must be regulated to minimize fluctuations in the DC-link voltage and power output to the main grid. Fractional order-PI (FOPI) controllers are used in a novel control structure for the SMES system to regulate the output power and DC-link voltage. An artificial bee colony optimization approach is employed to optimize the FOPI controllers. Three commonly utilized indicators, including sliding-mode, EKF, and Luenberger, were evaluated using “MATLAB" to evaluate the performance of the UKF estimate. Assessment criteria such as mean absolute percentage error and root mean squared error were used to gauge the accuracy of the estimates. Simulation findings showed the efficiency of fractional order-PI controllers for SMES and the proposed UKF indication for predictive current control, especially in the presence of measurement noise and over a variety of wind speeds. An improvement in estimation accuracy of up to 99.9 % was demonstrated by the UKF indicator. Moreover, the stability of the suggested UKF-based PCC control for the hybrid WECS-SMES combination was confirmed using Lyapunov stability criteria."

Finite Speed-Set Model Reference Adaptive System Based on Sensorless Control of Permanent Magnet Synchronous Generators for Wind Turbines

This paper proposes a novel finite speed-set model reference adaptive system (FSS-MRAS) based on the current predictive control (CPC) of a permanent magnet synchronous generator (PMSG) in wind energy turbine systems (WETSs). The mathematical models of wind energy systems (WESs) coupled with a permanent magnet synchronous generator (PMSG) are presented in addition to the implementation of the CPC of PMSGs. The proposed FSS-MRAS is based on eliminating the tuning burden of the conventional MRAS by using a limited set of speeds of the PMSG rotor that are employed to predict the rotor speed of the generator. Consequently, the optimal speed of the rotor is the one resulting from the optimization of a proposed new cost function. Accordingly, the conventional MRAS controller is eliminated and the main disadvantage represented in the tuning burden of the constant-gain proportional-integral (PI) controller has been overcome. The proposed FSS-MRAS observer is validated using MATLAB/Simulink (R2023b) at different operating conditions. The results of the proposed FSS-MRAS have been compared with those of the conventional MRAS, which proved the high robustness and reliability of the proposed observer.

Robust model predictive control of virtual synchronous generators without weighting factors

Robust model predictive control of virtual synchronous generators without weighting factors

A Stabilization Control Strategy for Wind Energy Storage Microgrid Based on Improved Virtual Synchronous Generator

In high-penetration renewable-energy grid systems, conventional virtual synchronous generator (VSG) control faces a number of challenges, especially the difficulty of maintaining synchronization during grid voltage drops. This difficulty may lead to current overloads and equipment disconnections, and it has an impact on the security and reliability of the system, as well as limiting the dynamic reactive power support capability of the system. To solve this problem, in this study, a wind–solar hybrid power generation system is designed with a battery energy storage device connected on the DC side, and proposes a low voltage ride-through (LVRT) control strategy for the grid-connected inverter based on an improved VSG. The control strategy employs an integrated current limiting technique combining virtual impedance and vector current limiting to ensure that the VSG exhibits good dynamic power support characteristics during symmetrical faults by adjusting the setpoint value of reactive power. At the same time, it maintains the synchronization and power angle stability of the VSG itself to achieve the goal of LVRT. Simulation results show that the proposed control strategy can effectively suppress the renewable power fluctuations (about 30% reduction in fluctuations compared to the conventional strategy) and ensure the safe and reliable operation of the renewable energy sources and VSGs during grid-side faults. In addition, it provides a given reactive power support and stable grid voltage control (voltage dips reduced by about 20%), which significantly enhances the LVRT capability of the hybrid wind–solar-storage generation system.

Seamless Switching Control Strategy for a Power Conversion System in a Microgrid Based on Extended State Observer and Super-Twisting Algorithm

Microgrids can operate stably in both islanded and grid-connected modes, and the transition between these modes enhances system reliability and flexibility, enabling microgrids to adapt to diverse operational requirements and environmental conditions. The switching process, however, may introduce transient voltage and frequency fluctuations, causing voltage and current shocks to the grid and potentially damaging devices and systems connected to the microgrid. To address this issue, this study introduces a novel approach based on the Extended State Observer (ESO) and the Super-Twisting Algorithm (STA). Power conversion systems use Virtual Synchronous Generator (VSG) control and Power-Quality (PQ) control when they are connected to the grid or when the microgrid is not connected to the grid. VSG and PQ share a current loop. Transitioning the reference current generated by the outer loop achieves the switching of control strategies. A real-time observer is designed to estimate and compensate for current fluctuations, disturbances, and variations in id, iq, and system parameters during the switching process to facilitate a smooth transition of control strategies. Furthermore, to enhance the dynamic response and robustness of the system, the Proportional–Integral (PI) controller in the ESO is replaced with a novel super-twisting sliding mode controller based on a boundary layer. The Lyapunov stability principle is applied to ensure asymptotic stability under disturbances. The proposed control strategy is validated through simulation using a seamless switching model of the power conversion system developed on the Matlab/Simulink (R2021b) platform. Simulation results demonstrate that the optimized control strategy enables smooth microgrid transitions, thereby improving the overall reliability of grid operations.

Optimizing Power Control for Dual Excited Synchronous Generators in Wind Turbine Systems

This study presents a MATLAB Simulink-based approach for optimizing power control in wind turbine systems with dual excited synchronous generators (DESGs), implementing an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller. DESGs offer advantages in efficiency and reliability, necessitating effective power control strategies. The proposed methodology integrates Simulink models of wind turbine dynamics, DESG behavior, and an ANFIS controller to optimize power output while ensuring stability and minimizing losses. Various factors such as wind speed variations and grid conditions are incorporated into the simulation environment. The ANFIS controller dynamically adjusts DESG control parameters based on real-time inputs, enhancing system adaptability and performance. Simulation graphs validate the efficiency of proposed approach in improving DESG performance and overall wind turbine system efficiency. This research contributes to advancing renewable energy technologies by leveraging MATLAB Simulink and ANFIS controllers for optimized power control in DESG-based wind turbine systems. Key Words: Wind power generation, Grid, DESG, MSC, GSC, and ANFIS.

An Algorithm for Calculating the Parameter Selection Area of a Doubly-Fed Induction Generator Based on the Guardian Map Method

Large-scale wind farms incorporating doubly-fed induction generators (DFIGs) are considered a promising direction for modern energy supply systems due to their role in reducing dependence on fossil energy sources. However, the dynamic interactions between DFIGs and AC grids sometimes lead to sub-synchronous oscillation (SSO) that threatens the safe and stable operation of wind power systems. Therefore, it is essential to develop a mathematical model and design an algorithm to quantitatively design the control parameters. Such algorithms are helpful in preventing or mitigating system stability problems coming from wind power connected to the grid and reducing damage to power equipment. The traditional state-space model is mainly established to determine the stable operating point and analyze the influence of parameters on the system operating mode. However, this method does not provide the selection area for the system parameters. To address this shortcoming, this paper introduces a modular state-space model for DFIGs containing series compensation lines and proposes an algorithm for calculating the parameter selection area based on the Guardian map method. First, a detailed modular state-space model based on the virtual synchronous generator (VSG) control is established. The modular model helps to reflect the relationship between state variables and focuses on describing the operating state of DFIGs in wind farms. Second, this paper focuses on the influence of VSG control parameters and compensation capacitance on SSO. It aims to clarify the role of the series compensation level and control parameters on SSO based on VSG control. Then, an algorithm for the parameter selection area based on the Guardian map is proposed and the area of the VSG-controlled DFIG is obtained. Finally, the accuracy and validity of the algorithm are verified by time domain simulation in MATLAB/Simulink and HIL experiment.

Stability Region of Grid-Forming Wind Turbine with Variable Parameters Using Bialternate Sum Matrix Approach

Although the stability regions of wind turbines in the islanding mode have been widely researched, small-signal modeling of grid-forming wind turbines (GFWTs) in the islanding mode has yet to be explored. In addition, the state-space matrix of the wind turbine system has yet to be fully represented. Therefore, this paper proposes a small-signal modeling of GFWT and a method for identifying the stabilization region of a system with variable parameters. First, small-signal modeling of a GFWT based on virtual synchronous generator control is developed. Second, the bialternate sum matrix approach is used to determine the system stabilization region. The system matrix with multiple variable parameters is first decomposed into the sum of several matrices in this paper. Furthermore, the rotor-side generator control is simplified. It can reduce the dimensionality of the system matrix model. Finally, the simulation shows that the proposed method for determining the stabilization region of the variable system is accurate.

Consensus-based secondary frequency control for parallel virtual synchronous generators

In off-grid operating modes, microgrids with Virtual Synchronous Generators (VSG) controlled inverters are limited to primary frequency control capabilities. When operating under the primary frequency control, if VSG's output frequency exceeds the safe operational range, it can adversely affect stable operation. Consequently, there is a need to implement a secondary frequency control strategy. This paper presents an analysis of the conventional secondary frequency regulation in VSGs. It reveals that when multiple VSGs in parallel to the Point of Common Coupling (PCC) with varied line impedances, the proportionate distribution of each VSG's active power output, post frequency control, does not align with predetermined ratios. In response to this challenge, the paper introduces a quasi-distributed secondary frequency control approach, tailored for systems with multiple feed-in points. This approach modifies the traditional method of compensating for frequency deviation through average ratio integral compensation, replacing it with a compensation based on a consensus algorithm. This negates the need for global communication and additional impedance measuring equipment. By facilitating information exchange between neighboring converters, it enables global control, effectively resolving the issue of line impedance parameters impacting the distribution of VSG's active power output. This decouples the previously strong correlation between line impedance and VSG's active power. The proposed method is characterized by its strong adaptability, scalability, and swift dynamic adjustments, significantly enhancing the fault tolerance of information exchange and the stability of microgrid operations. Finally, the correctness and effectiveness of the proposed improved control strategy are verified by simulation and HIL-based platform.

An improved design for virtual synchronous generator control loop based on synergy theory

Virtual synchronous generator (VSG) technology has achieved some results in distributed generation networks and enhanced system stability. For problems that may occur in the system due to ignoring parameter linkages, this paper designs a synergistic controller that establishes the connection between the active frequency and the reactive voltage control loop based on synergy theory. As the output of the synergetic controller, the derived control law u is added to the power frequency control in the form of negative feedback. Two disturbance forms, a step disturbance, and a three-phase short-circuit fault, are set up. The conventional droop control and synergetic droop control results are compared using a MATLAB simulation system. Then, the system parameter variations were studied and analyzed, and the sensitivity of the control system with different values of parameters K1, K2, and K3 in the case of a three-phase short circuit was analyzed, which provides a reference guide for selecting parameters in later control optimization. Finally, the control effectiveness of a multi-machine VSG system was tested. The simulation results show that the proposed method has a pronounced effect on making the system stabilize quickly, which illustrates the effectiveness of this technique.

A novel reinforcement learning policy optimization based adaptive VSG control technique for improved frequency stabilization in AC microgrids

In this paper, an improved Reinforcement Learning (RL) based approach for adaptive Virtual Synchronous Generator (VSG) control of grid-forming inverters is proposed. High penetration of inverter-based resources causes recurrent frequency stabilization issues. For this matter, a novel model-free Actor–Critic policy optimization technique is proposed to improve frequency transients metrics such as frequency nadir and Rate of Change of Frequency (RoCoF). Stability analysis of the VSG controlled system is conducted through small signal modeling to determine the stability margins of both the virtual inertia parameter J and damping coefficient Dp. The control problem is formulated in RL terms using a new technique for state, action and reward construction that improves the learning process of the agents. The proposed approach’s efficiency is tested for three RL algorithms, namely, Deep Deterministic Policy Gradient (DDPG), Soft Actor–Critic (SAC), and Twin-Delayed DDPG (TD3) with special attention given to the latter. Various performance metrics such as training time and maximum cumulative episodic reward are used to compare the three algorithms. The resulting agent based VSG adaptive controllers are simulated and then compared for power quality and frequency stabilization capabilities. Finally, a comparison between non-adaptive VSG and TD3 based VSG is presented to conclude on the efficiency improvements achieved by the proposed approach.