MOEA based design of decentralized controllers for LFC of interconnected power systems with nonlinearities, AC–DC parallel tie-lines and SMES units

Ganapathy, S.; Velusami, S.

doi:10.1016/j.enconman.2009.11.023

Cited by 23 publications

(9 citation statements)

References 8 publications

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“…The two-area interconnected deregulated power system with one reheat and one non-reheat unit in each area as shown in Figure 1 is used to reveal the effectiveness of the projected ANFIS approach. The system parameters for the proposed power system model are given in (Donde et al, 2001;Ganapathy & Velusami, 2010) respectively. The following parameter variations, shown in Table 2 are considered for simulation and the corresponding optimal integral gain values obtained by ACS algorithm and ACS tuned ANFIS are given below.…”

Section: Resultsmentioning

confidence: 99%

ACS algorithm tuned ANFIS-based controller for LFC in deregulated environment

Selvaraju

Ganapathy

2017

Journal of Applied Research and Technology

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In this paper, an artificial cooperative search (ACS) algorithm tuned adaptive network-based fuzzy inference system (ANFIS) controller for optimal gain tuning of load frequency control (LFC) operation in deregulated scenario has been offered. The conventional controllers for load frequency control operation are having fixed gain values intended for nominal operating conditions of the power system and they do not afford effective and efficient performance over a large range of operating scenarios in the deregulated environment. To progress the system performance to its near optimum for all probable operating circumstances of the power system, the controller gains have to be computed for the equivalent operating conditions by using the restructured parameters. For this intention, a controller based on an adaptive network-based fuzzy inference system seems to be the most excellent and valuable preference. The ANFIS is trained by off-line data obtained using a new optimization technique, artificial cooperative search optimization algorithm and the corresponding gains are updated in real-time as per the changing operating conditions. ACS is a swarm intelligence algorithm developed for solving numerical optimization problems. The swarm intelligence philosophy behind ACS algorithm is based on the migration of two artificial superorganisms as they biologically interact to achieve the global minimum value pertaining to the problem. To exhibit the competence and robustness of the projected ACS algorithm tuned ANFIS controller, the controller has been implemented on a two-area two-unit interconnected deregulated power system having one reheat unit and one non-reheat unit in each area. The simulation results exhibit the ability of the designed ACS algorithm tuned ANFIS controller for online LFC operation in deregulated environment.

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Section: Resultsmentioning

confidence: 99%

ACS algorithm tuned ANFIS-based controller for LFC in deregulated environment

Selvaraju

Ganapathy

2017

Journal of Applied Research and Technology

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“…In order to get a precise realization of the AGC issue, it is important to take into account the main intrinsic requirements such as physical constraints of generation rate constraint (GRC) and governor dead-band (GDB) nonlinearities [22,23]. The review of literature on AGC in the presence of FACTS devices reveals that in most of papers [5][6][7][8][9][10][11], GRC and GDB nonlinearities of hydro and thermal units are not considered.…”

Section: Review On Realistic Considerations In Agc Performance Evaluamentioning

confidence: 99%

Effective oscillation damping of an interconnected multi-source power system with automatic generation control and TCSC

Zare

Hagh

Morsali

2015

International Journal of Electrical Power & Energy Systems

107

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“…As the governor and other control mechanisms start working to set the power system to the new equilibrium condition, the coil current changes back to its initial value and are similar for sudden release of load. On other hand, when loads are suddenly released the superconducting coil immediately gets charged to its full value, thus absorbing some portion of the excess energy in the system (Ganapathy and Velusami, 2010).…”

Section: Superconducting Magnetic Energy Storagementioning

confidence: 99%

Improving the dynamic frequency regulation of a multisource power system considering GRC and dead band with TCSC and SMES

Deepak

Abraham

2019

IJPEC

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Coordinated operation of superconducting magnetic energy storage (SMES) and thyristor controlled series compensator (TCSC) for dynamic frequency regulation on a multisource power system is presented in this paper. The optimal integral gains of the control areas are obtained by tuning a quadratic performance index consisting of frequency deviations and tie-line power error using integral squared error technique. The effects of generation rate constraint and governor dead band nonlinearities are also considered. Time domain simulations carried out in MATLAB on a sample two area multi-unit power system comprising hydro, thermal and gas power plants reveal that the SMES-TCSC combination can effectively damp out deviations in area frequencies and tie-line power flow following a sudden load perturbation with better transient performance and reduced settling time.

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MOEA based design of decentralized controllers for LFC of interconnected power systems with nonlinearities, AC–DC parallel tie-lines and SMES units

Cited by 23 publications

References 8 publications

ACS algorithm tuned ANFIS-based controller for LFC in deregulated environment

ACS algorithm tuned ANFIS-based controller for LFC in deregulated environment

Effective oscillation damping of an interconnected multi-source power system with automatic generation control and TCSC

Improving the dynamic frequency regulation of a multisource power system considering GRC and dead band with TCSC and SMES

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