Small-signal stability analysis for two-area interconnected power system with load frequency controller in coordination with FACTS and energy storage device

Shankar, Ravi; Bhushan, Ravi; Chatterjee, Kalyan

doi:10.1016/j.asej.2015.06.009

Cited by 42 publications

(17 citation statements)

References 20 publications

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“…In this paper FACTS devices TCPS and SSSC and Energy Storage device SMES are considered. The linearized model of TCPS, SSSC and SMES are briefed in this section [9][10][11].…”

Section: Linearized Model Of Sssc Tcps and Smes For Load Frequementioning

confidence: 99%

LFC of DFIG based Two Area System using TCPS, SSSC and SMES

Stephen D*

2019

IJITEE

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The frequency and tie-line power in a system varies as the load changes. Therefore in order to damp the frequency, Load Frequency Control (LFC) is used. The function of the LFC is to minimizes the transient deviation and to make the steady state change in frequency, zero. This paper presents a Load frequency control (LFC) of Doubly Fed Induction Generator (DFIG) based two area system using Thyristor Controlled Phase Shifter (TCPS), Static Synchronous Series Compensator (SSSC) and Superconducting Magnetic Energy storage (SMES). Flexible AC Transmission Systems (FACTS) devices and SMES are used to damp out the frequency oscillations effectively in a power system. In order to maintain the system frequency at nominal value and the oscillations to settle down quickly a fast acting controller is desirable. In this paper a MATLAB based PID controller has been proposed in order to minimize the damping oscillation of the two-area interconnected power system. The results obtained with various combinations are presented and the results were encouraging.

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“…In this paper FACTS devices TCPS and SSSC and Energy Storage device SMES are considered. The linearized model of TCPS, SSSC and SMES are briefed in this section [9][10][11].…”

Section: Linearized Model Of Sssc Tcps and Smes For Load Frequementioning

confidence: 99%

LFC of DFIG based Two Area System using TCPS, SSSC and SMES

Stephen D*

2019

IJITEE

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“…Therefore, in order to ensure the active power balance of the power system and eliminate a series of negative effects brought by fluctuating power, it is necessary to adopt distributed hybrid energy storage equipment to adjust the fluctuating power. Several types of advanced energy storage technologies can be considered for the energy storage of a multi-area interconnected power system, e.g., lithium battery [15], electrochemical energy storage [16], superconducting magnetic energy storage [11][12][13], super capacitor energy storage [12,17], pumped-energy storage [18], flywheel energy storage [19]. Developing the electric load microforecasting modules in the energy management system provides a chance to optimally control the energy distribution in the power system [20].…”

Section: Introductionmentioning

confidence: 99%

Observer-Based Sliding Mode FTC for Multi-Area Interconnected Power Systems against Hybrid Energy Storage Faults

Yang

Dong

et al. 2019

Energies

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An observer-based sliding mode fault-tolerant controller is developed in this paper, which is applied to an interconnected power system with a hybrid energy storage system (HESS). The model of the interconnected power system with HESS is introduced first. An observer is then proposed to estimate the unknown but bounded load disturbances and the actuator fault. The sliding mode fault-tolerant controller is further designed based on the observer ensuring that the area control error of the interconnected power system asymptotically converges to zero. The stability and the convergence of the whole system are proven based on the Lyapunov stability theory. Finally, the effectiveness of the proposed fault-control scheme is demonstrated through simulations.

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“…Although other techniques such as pole placement technique and utilizing asymptotic modal properties are used by many researchers in determining the weight matrices, they face similar difficulties and do not guarantee the expected performance. To overcome these difficulties, genetic algorithm (GA) approach was also used for designing the Q and R matrices in various systems, such as multimachine power system, inverted pendulum system, and low‐voltage ride‐through capability of the DFIG‐based wind turbine system.…”

Section: Introductionmentioning

confidence: 99%

Mathematical modeling and control of DFIG-based wind energy system by using optimized linear quadratic regulator weight matrices

Bhushan

Chatterjee

2017

Int Trans Electr Energ Syst

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Summary This paper deals with a genetic algorithm (GA)–based linear quadratic regulator (LQR) controller to improve the dynamic response, stability, and robustness of the doubly fed induction generator (DFIG) system at various stator voltage disturbances. The complete model has been represented by a state‐space model. This helps to optimally control all the states through the full‐state feedback LQR controller. GA is employed in the LQR algorithm for optimal tuning of the Q and R matrices. For finding out the effectiveness of the proposed controller, its dynamic response has been compared with proportional integral (PI) and LQR controllers. The simulation results indicate that the proposed controller has better performance in comparison to the PI and LQR controllers in terms of peak value, settling time, rise time, and steady‐state values of the DFIG outputs. The stability and robustness of the study system have also been investigated by the eigenvalue and participation factor analyses.

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Small-signal stability analysis for two-area interconnected power system with load frequency controller in coordination with FACTS and energy storage device

Cited by 42 publications

References 20 publications

LFC of DFIG based Two Area System using TCPS, SSSC and SMES

LFC of DFIG based Two Area System using TCPS, SSSC and SMES

Observer-Based Sliding Mode FTC for Multi-Area Interconnected Power Systems against Hybrid Energy Storage Faults

Mathematical modeling and control of DFIG-based wind energy system by using optimized linear quadratic regulator weight matrices

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