An advanced virtual synchronous generator control technique for frequency regulation of grid-connected PV system

Rehman, Haseeb Ur; Yan, Xiangwu; Abdelbaky, Mohamed Abdelkarim; Jan, Mishkat Ullah; Iqbal, Sheeraz

doi:10.1016/j.ijepes.2020.106440

Cited by 71 publications

(34 citation statements)

References 44 publications

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“…Due to its short reaction time and organizational versatility, the energy storage system (ESS) is among the most desirable candidates that provide frequency regulation services in isolated MG. ESS technology can insert a huge amount of electricity into the grid in a limited period due to quick reaction time that can be utilized as virtual inertia [1]. The ESS offers upward / downward control by considering the need to inject/absorb power according to the grid requirements, as comparing to the fast-traditional generation units that are held on-line to provide primary frequency regulation by controlling the power output in case of any frequency deviation event occurs [37].…”

Section: Prosumersmentioning

confidence: 99%

“…degree relaxed by the power system's inertia. The system's inertia is defined by a revolving mass of synchronous generating units primarily used in traditional power systems [1], [2]. For instance, if a system encounters failure in one of its generating units by any accident, an imbalance of power is observed due to a substantial fall in the generation.…”

Section: Introductionmentioning

confidence: 99%

“…The proportional, integral and derivative or commonly known as PID controllers were used due to their simple structure and easy deployment [12]. However, with the passage of time fuzzy controller [13], co-operative control [14], robust [15] and adaptive virtual control [16], virtual droop based synchronous generator [1], and neural networkbased [17] techniques were developed for the frequency control of the system. Moreover, the optimization algorithms including ant colony optimization, bat algorithm, particle swarm optimization (PSO), artificial neural network (ANN), and genetic algorithm (GA) were developed to improve the performance of the of these controllers [18]- [20].…”

Section: Introductionmentioning

confidence: 99%

“…The ESS provides partial power support due to its limited capacity, therefore a cooperative control schemes between the generating units and ESS is essential for smooth operation [14], [22]. ESS is implemented in power system to support RES [4], [23], PV [1], wind [24], [25] to alleviate the frequency irregularities. Different controllers are deployed in MG to regulate the RES and ESS according to system frequency.…”

Section: Introductionmentioning

confidence: 99%

“…Different controllers are deployed in MG to regulate the RES and ESS according to system frequency. Furthermore, droop control [1], fuzzy logic [23], co-operative control [14], and MPC [4], [25] are implemented on ESS to provide frequency support to MG. For the deployment of the EVs in MG and their active participation, a lot of research is carried out on designing appropriate controller or control schemes that can effectively control/regulate the EVs storage [14], [15]. The authors proposed droop and modified droop control techniques to regulate the EVs battery according to system frequency deviations [26], [27].…”

Section: Introductionmentioning

confidence: 99%

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Frequency Regulation of an Isolated Microgrid With Electric Vehicles and Energy Storage System Integration Using Adaptive and Model Predictive Controllers

et al. 2021

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Energy storage system (ESS) possesses tremendous potential to counter both the rapid growth of intermittent renewable energy resources (RESs) and provide frequency support to the microgrid (MG). Since the deployment of ESS has overcome the imbalance between generation and consumption, however, their massive cost, as well as degradation tendency, are the restricting considerations that demand alternative solutions to provide stable microgrid operation. To assist ESS, the electric vehicles (EVs) are incorporated into the system. EVs have been gradually commercially viable and considerable focus has been paid to vehicle-to-grid technologies. Appropriate collaboration between ESS and EVs has good capability to manage the frequency irregularities to ensure the efficient operation of the MG. This article presents a novel combination of two control techniques i.e., model predictive control (MPC) and adaptive droop control (ADC), to tackle the frequency regulation issue in the isolated MG, by effectively controlling the ESS and EVs during the large-scale integration of RESs or huge change in load demand. Firstly, the MPC regulates the ESS according to the system frequency deviation, and secondly, the ADC manages the power of EVs according to system specifications by retaining the least possible power for potential usage of EVs. Moreover, an advanced genetic algorithm is applied to tune the MPC and ADC parameters in order to achieve optimized performance. An isolated MG is modeled and verified in MATLAB/Simulink using the above-mentioned control techniques. Further, different case studies are taken into account to validate the combination of ADC and MPC for frequency regulation of an isolated MG. Additionally, the proposed MPC controller is compared with fuzzy logic proportional-integral (FPI) controller and proportional-integral (PI) controller, the MPC provides better performance results as compared with FPI and PI controllers. INDEX TERMS Electric vehicles, adaptive droop control, energy storage system, model predictive control, frequency regulation, GA optimization technique.

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Section: Prosumersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Frequency Regulation of an Isolated Microgrid With Electric Vehicles and Energy Storage System Integration Using Adaptive and Model Predictive Controllers

et al. 2021

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Early warning of the abnormal response of the generator set electrical control based on SIP concept

Li¹

2023

Adv Control Appl

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An early warning method based on Session Initiation Protocol (SIP) concept for generator set electrical control abnormal response is proposed. The first is to clean the electrical data of the generator set by SIP. Using Supervisory Control and Data Acquisition (SCADA) and Generalized Linear Models (GLM) algorithm, a linear model is constructed to analyze the electrical control of generating units. Using the Autoregressive Integrated Moving Average Model (ARIMA), an abnormal response early warning model for electrical control of generating units is established. BP neural network is used to train the abnormal response data of the generator set electrical control. According to the current response data, the model prediction is realized, and the early warning of the abnormal response of the generator set electrical control is effectively realized. The simulation results show that the proposed method can effectively reduce the early‐warning error, false alarm rate, and early‐warning delay of generator electrical control abnormal response.

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Optimal fractional‐order adaptive neuro fuzzy inference system‐based droop controller for DFIG‐wind turbine to control load frequency of hybrid microgrid

Asgharpour-Alamdari

2021

Int J Numerical Modelling

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This paper aims to ameliorate the contribution capability of doubly fed induction generator to damp the frequency and output power deviation of stand‐alone hybrid microgrid system. Skyrocketing progress in converter‐interfaced renewable energy sources leads to weakness of islanded microgrids' inertia which imposes some problems in the frequency control and hybrid system stability. This problem can be effectively solved by utilizing renewable resources with high harvestable energy such as doubly fed induction generator‐wind turbine. Furthermore, the alternative energy resources have been taken into account to participate in strength of weak inertia of the microgrid. Since the conventional controller like proportional integral derivative droop controller cannot present efficient damping term because of their slow controlling action in order to diminish the frequency and output power deviation. In this regard, a fractional‐order adaptive neuro fuzzy inference system droop is here introduced to stabilize the frequency within a satisfactory secure range with low fluctuation over the conventional droop controllers. To attain an accurate control action, multi‐objective sine cosine algorithm is used to optimize the parameters of based on multi‐criterion objective functions. To verify the performance of proposed droop controller, it has been tested under different scenarios. At last, the simulation results reveal that sine cosine algorithm‐based proposed droop controller can significantly enhance the microgrids' inertia and diminish the frequency and output power deviation.

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An advanced virtual synchronous generator control technique for frequency regulation of grid-connected PV system

Cited by 71 publications

References 44 publications

Frequency Regulation of an Isolated Microgrid With Electric Vehicles and Energy Storage System Integration Using Adaptive and Model Predictive Controllers

Frequency Regulation of an Isolated Microgrid With Electric Vehicles and Energy Storage System Integration Using Adaptive and Model Predictive Controllers

Early warning of the abnormal response of the generator set electrical control based on SIP concept

Optimal fractional‐order adaptive neuro fuzzy inference system‐based droop controller for DFIG‐wind turbine to control load frequency of hybrid microgrid

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