Robust frequency control in a renewable penetrated power system: an adaptive fractional order-fuzzy approach

Annamraju, Anil; Nandiraju, Srikanth

doi:10.1186/s41601-019-0130-8

Cited by 48 publications

(37 citation statements)

References 32 publications

(40 reference statements)

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“…Besides this, the PID controller based on the Hammerstein type neural network is utilized to regulate the LFC of the multi-area power system [10]. Additionally, the fuzzy logic controller [31], distributed model predictive control [32], and hierarchical distributed model predictive control [33] are also implemented to investigate the frequency regulation in MG. Various optimization methods like a genetic algorithm (GA), jay algorithm, and particle swarm optimization (PSO) are used to optimize the conventional controllers PI/PID and fuzzy PI/PID controller.…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, their impact is also studied for stabilizing and guarantee their robustness of the load frequency control [34]. However, the above authors didn't consider either one or more parameters, for instance in [31]- [34], the EVs participation is not studied, in [34] RTS is not considered, and in [32] only WTS are investigated. Furthermore, the above-mentioned controllers PI/PID controller's approach has good frequency performances, but their performances are still not optimum, and their parameters were not well-tuned.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive and Fuzzy PI Controllers Design for Frequency Regulation of Isolated Microgrid Integrated With Electric Vehicles

et al. 2020

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The number of electric vehicles and renewable energy resources integrated into the power system is increasing day by day. The objective behind the development of electric vehicles and renewable energy sources is to build a sustainable and green power system. The renewables either don't possess system inertia or have less system inertia, therefore, they don't effectively respond to the load variations. The battery storage system of electric vehicles is used as the first line of defense to counter the effect of load/frequency variations and make the system stable. As active power is inversely proportional to the system frequency, for this purpose electric vehicles are included in the microgrid environment. In this paper, an isolated microgrid having a reheat turbine system, wind turbine system, photovoltaic system, and electric vehicles is studied. The output of the renewables is not controlled to utilize its maximum output power. Therefore, adaptive droop control and fuzzy PI control mechanisms are implemented to cater to the frequency variations of the isolated microgrid; the former regulates the power of electric vehicles while maintaining the energy needs of each EV and the later controls the output power of reheat turbine system according to the frequency variation. Furthermore, the genetic algorithm optimization toolbox is utilized to optimize the parameters of the adaptive and fuzzy PI controllers. The proposed model is developed in MATLAB/Simulink which shows that these control techniques effectively sustained the system frequency of isolated microgrid in the desired limits. INDEX TERMS Adaptive droop control, electric vehicles, frequency regulation, fuzzy PI control, GA optimization technique, renewable energy sources, reheat turbine system.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive and Fuzzy PI Controllers Design for Frequency Regulation of Isolated Microgrid Integrated With Electric Vehicles

et al. 2020

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“…In order to ensure secure and stable operation of EGIES, it is very important for operators to quickly and accurately evaluate the operational risk of the system. At present, research on integrated energy systems has focused on energy flow analysis [7,8], optimized operation [9][10][11], and collaborative planning [12]. Most of the researches on system risk assessment have stayed in a single energy system, and there are few studies on risk assessment of multi-energy systems.…”

Section: Introductionmentioning

confidence: 99%

Operational Risk Assessment of Electric-Gas Integrated Energy Systems Considering N-1 Accidents

Liu

Cao

et al. 2020

Energies

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The reliability analysis method and risk assessment model for the traditional single network no longer meet the requirements of the risk analysis of coupled systems. This paper establishes a risk assessment system of electric-gas integrated energy system (EGIES) considering the risk security of components. According to the mathematical model of each component, the EGIES steady state analysis model considering the operation constraints is established to analyze the operation status of each component. Then the EGIES component accident set is established to simulate the accident consequences caused by the failure of each component to EGIES. Furthermore, EGIES risk assessment system is constructed to identify the vulnerability of EGIES components. Finally, the risk assessment of IEEE14-NG15 system is carried out. The simulation results verify the effectiveness of the proposed method.

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“…To control a power system against frequency variation in response to changes in demand, load frequency control (LFC) is a well acknowledged strategy. LFC has been addressed in the literature in great detail [1][2][3][4][5][6][7]. By and large, all the investigations extensively utilize optimal control theory to develop LFC.…”

Section: Introductionmentioning

confidence: 99%

Optimal load frequency control through combined state and control gain estimation for noisy measurements

Pillai

Samuel

Unnikrishnan

2020

Prot Control Mod Power Syst

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Combined estimation of state and feed-back gain for optimal load frequency control is proposed. Load frequency control (LFC) addresses the problem of controlling system frequency in response to disturbance, and is one of main research areas in power system operation. A well acknowledged solution to this problem is feedback stabilization, where the Linear Quadratic Regulator (LQR) based controller computes the feedback gain K from the known system parameters and implements the control, assuming the availability of all the state variables. However, this approach restricts control to cases where the state variables are readily available and the system parameters are steady. Alternatively, by estimating the states continuously from available measurements of some of the states, it can accommodate dynamic changes in the system parameters. The paper proposes the technique of augmenting the state variables with controller gains. This introduces a non-linearity to the augmented system and thereby the estimation is performed using an Extended Kalman Filter. This results in producing controller gains that are capable of controlling the system in response to changes in load demand, system parameter variation and measurement noise.

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Robust frequency control in a renewable penetrated power system: an adaptive fractional order-fuzzy approach

Cited by 48 publications

References 32 publications

Adaptive and Fuzzy PI Controllers Design for Frequency Regulation of Isolated Microgrid Integrated With Electric Vehicles

Adaptive and Fuzzy PI Controllers Design for Frequency Regulation of Isolated Microgrid Integrated With Electric Vehicles

Operational Risk Assessment of Electric-Gas Integrated Energy Systems Considering N-1 Accidents

Optimal load frequency control through combined state and control gain estimation for noisy measurements

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