Optimizing real power loss and voltage stability limit of a large transmission network using firefly algorithm

Balachennaiah, P.; Suryakalavathi, M.; Nagendra, P.

doi:10.1016/j.jestch.2015.10.008

Cited by 34 publications

(14 citation statements)

References 29 publications

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“…The formulation for C and T is represented by Eqs. (39) and (40), where a is a component that is linearly reduced from 2 to 0 over the complete iterations and r 1 and r 2 represent the random vectors that are uniformly distributed among [0,1].…”

Section: Gw-based Adaptivenessmentioning

confidence: 99%

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Grey Wolf Optimization-Based Second Order Sliding Mode Control for Inchworm Robot

Roy

Ghoshal²

2019

Robotica

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SUMMARY The flexible motion of the inchworm makes the locomotion mechanism as the prominent one than other limbless animals. Recently, the application of engineering greatly assists the inchworm locomotion to be applicable in the robotic mechanism. Due to the outstanding robustness, sliding mode control (SMC) has been validated as a robust control strategy for diverse types of systems. Even though the SMC techniques have made numerous achievements in several fields, some systems cannot be comfortably accepted as the general SMC approaches. Accordingly, this paper develops the Grey Wolf-Second order sliding mode control (GW-SoSMC) to control the manipulator of the inchworm robot. The GW-SoSMC reduces the chattering phenomenon of SMC and improves the controlling ability of SoSMC by weightage function. Subsequently, it compares the performance of the proposed method with several conventional techniques like Grey Wolf-SMC (GW-SMC), FireFly-SoSMC (FF-SoSMC), Artificial Bee Colony-SoSMC (ABC-SoSMC), Group Searching-SoSMC (GS-SoSMC), and Genetic Algorithm-SoSMC (GA-SoSMC). It portrays the valuable comparative analysis by measuring the accomplished joint angles, error, and response of the controller. Thus the proposed method discovers the supervisory controller for the inchworm robot that is immensely better than conventional controllers mentioned earlier.

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Section: Gw-based Adaptivenessmentioning

confidence: 99%

“…In the simulation, the outcome of the GW-SoSMC is compared with the conventional models such as Grey Wolf-SMC (GW-SMC), FireFly-SoSMC (FF-SoSMC), Artificial Bee Colony-SoSMC (ABC-SoSMC), Group Searching-SoSMC (GS-SoSMC), and Genetic Algorithm-SoSMC (GA-SoSMC). [38][39][40]46…”

Section: Simulation Setupmentioning

confidence: 99%

Grey Wolf Optimization-Based Second Order Sliding Mode Control for Inchworm Robot

Roy

Ghoshal²

2019

Robotica

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“…The radial power distribution system is chosen to be the test system to get the optimal International Journal of Intelligent Engineering and Systems, Vol. 14 network reconfiguration with the addition of a distributed generator [8,9]. The enhance water cycle algorithm and grey wolf's algorithm are proposed by [10,11] for power system distribution.…”

Section: Introductionmentioning

confidence: 99%

Optimal Sizing of Supercapacitor Energy Storage Based on Firefly Algorithm for Enhancing Critical Clearing Time

Sari¹,

Sari²,

Sulistyo³

et al. 2021

IJIES

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In the present day, the electrical power demand is increasing and expected to continue growing. The renewable energy sources as the power supply are also increasing but not much as needed. As a result, the power systems operate close to their maximum stability limit. This paper proposed to extend the value of Critical Clearing Time (CCT) to enhance the stability system, especially the transient stability. A Supercapacitor Energy Storage (SCES) is installed at one of the generator bus; however, the sizing of SCES must be optimized due to the economics and power balance constraint. In this paper, the Firefly Algorithm (FA) is used to obtain the optimal sizing of SCES with the objective function is the highest value of CCT. Lagrange interpolation is used to validate the effectiveness of FA, such as computation time and accuracy. According to the simulation result obtained, the proposed method using FA has a faster calculation time than Lagrange interpolation. Moreover, the proposed method is validated on a modified IEEE 9 bus with various cases. By using FA, the best increase of CCT is obtained when the optimal size of SCES is 0.120 p.u. The overall CCT increase is 0.15766s or around 54.61%.

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“…These nondeterministic methods have an advantage over traditional methods while dealing with discrete variables and nonconvexity of the problem . Several methods have been implemented successfully to the TSCOPF problem, such as particle swarm optimization , differential evolution (DE) , genetic algorithm (GA) , Lagrangian duality‐based global optimization algorithm applied to nonconvex problem , artificial bee colony algorithm , and firefly algorithm . Of late, a new EA, biogeography‐based optimization (BBO) technique by D Simon, has shown promising applications for engineering problems.…”

Section: Introductionmentioning

confidence: 99%

An efficient transient stability-constrained optimal power flow using biogeography-based algorithm

Bagde

Umre

Raju

2017

Int Trans Electr Energ Syst

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SummaryWith deregulated regime for electricity market, utilities are being operated close to stability boundaries. Therefore, it has become necessary to secure power systems optimally when subjected to transient stability condition. Transient stability constrained optimal power flow (OPF) is a highly nonlinear, high-dimension optimization problem and requires efficient methods to solve it. In this paper, transient stability constrained OPF problem is implemented by using biogeography-based optimization algorithm, a recent evolutionary algorithm that searches for global optimum through migration and mutation of biological species distributed over geographical space and has shown promising results. The OPF model uses economic dispatch as objective function subjected to various constraints. Single machine equivalent, a hybrid method that combines time domain simulations and direct method, has been used to implement transient stability constraint. Tests on Western System Coordinating Council 3 machine, 9 bus system and New England 10 machine, 39 bus system demonstrate the effectiveness of the approach. The simulation results presented by using MATLAB are compared with recent findings that show that the proposed biogeography-based optimization algorithm is capable of obtaining higher quality solutions.KEYWORDS biogeography-based optimization, optimal power flow, SIME, transient stability | INTRODUCTIONTo establish a secure and optimal state in power system, system operators use optimal power flow (OPF) programs in planning and operating stages. In OPF, the given objective function is optimized subject to operational and physical constraints of the system. Because OPF model can integrate the cost effectiveness and constraints of the power system into a single mathematical formulation, various deterministic approaches have been applied in the past 1 . Deregulation of utilities necessitated transmission of large amounts of electrical energy over long distances with intraday electricity markets. The uncertainty of operating conditions due to distributed generation coupled with complex control hierarchy forced systems to operate at stability boundaries. Against this background, instability has become a major issue, which must be addressed by suitable techniques. This has given rise to a new methodology known as transient stability constrained OPF (TSCOPF) problem, which is a highly nonlinear, nonconvex, and multidimensional optimization problem 2 .Researchers proposed many deterministic approaches of solving TSCOPF problem in the literature. Transient stability constrained optimal power flow problem consists of differential-algebraic equations, which need to be

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Optimizing real power loss and voltage stability limit of a large transmission network using firefly algorithm

Cited by 34 publications

References 29 publications

Grey Wolf Optimization-Based Second Order Sliding Mode Control for Inchworm Robot

Grey Wolf Optimization-Based Second Order Sliding Mode Control for Inchworm Robot

Optimal Sizing of Supercapacitor Energy Storage Based on Firefly Algorithm for Enhancing Critical Clearing Time

An efficient transient stability-constrained optimal power flow using biogeography-based algorithm

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