Optimization of Overcurrent Relays in Microgrid Using Interior Point Method and Active Set Method

Swathika, O.V. Gnana; Das, Arka; Gupta, Yatharth; Mukhopadhyay, Santanab; Hemamalini, S.

doi:10.1007/978-981-10-3156-4_9

Cited by 15 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ASA is used to execute various stiff, complex and nonlinear systems. Recently, ASA is executed to pricing the American option [120], the actual control through optimization [121], pressure-dependent system of water distribution [122], embedded model predictive control [123], overcurrent relays in microgram optimization [124] and frictional contact models based on electrodynamic [125].…”

Section: B Optimization Process: Ga-asamentioning

confidence: 99%

IoT Technology Enabled Heuristic Model With Morlet Wavelet Neural Network for Numerical Treatment of Heterogeneous Mosquito Release Ecosystem

Sabir

Nisar²,

Raja

et al. 2021

IEEE Access

View full text Add to dashboard Cite

The utmost advancements of artificial neural networks (ANNs), software-defined networks (SDNs) and internet of things (IoT) technologies find beneficial in different applications of the smart healthcare sector. Aiming at modern technology's use in the future development of healthcare, this paper presents an advanced heuristic based on Morlet wavelet neural network for solving the mosquito release ecosystem in a heterogeneous atmosphere. The mosquito release ecosystem is dependent of six classes, eggs density, larvae density, pupae density, mosquitoes searching for hosts density, resting mosquito's density and mosquitoes searching for ovipositional site density. An artificial neural network with the layer structure of Morlet wavelet (MWNN) kernel is presented using the global and local search optimization schemes of genetic algorithm (GA) and active-set algorithm (ASA), i.e., MWNN-GA-ASA. The accurateness, reliability and constancy of the proposed MWNN-GA-ASA is established through comparative examinations with Adams method based numerical results to solve the proposed nonlinear system with matching of order 10 -06 to 10 -09 . The accuracy and convergence of the proposed MWNN-GA-ASA is certified using the statistical operators based on root mean square error (RMSE), Theil's inequality coefficient (T.I.C) and mean absolute deviation (MAD) operators.INDEX TERMS Mosquito release ecosystem, IoT, SDN, artificial neural networks, heuristic algorithm, Adams's method.

show abstract

Section: B Optimization Process: Ga-asamentioning

confidence: 99%

IoT Technology Enabled Heuristic Model With Morlet Wavelet Neural Network for Numerical Treatment of Heterogeneous Mosquito Release Ecosystem

Sabir

Nisar²,

Raja

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…ASM is executed in numerous optimizations models of numerous complex and non-stiff systems. Recently, ASM is applied to execute the real-time optimal control [29], the pricing of American better-of option on two assets [30], the pressure-dependent model of water distribution systems [31], overcurrent relays in microgrid optimization [32], embedded model predictive controls [33] and elastodynamic frictional contact problems [34]. To control the slowness of GA, the process of hybridization into GA-ASM is provided in Table 1 for training or learning of the decision variables, i.e., the unknown weights of ANNs, while the parameter settings of GA and ASM are handled using the 'optimset' routine of the Matlab optimization toolbox.…”

Section: Optimization Performances: Ga-asmmentioning

confidence: 99%

Soft Computing Paradigms to Find the Numerical Solutions of a Nonlinear Influenza Disease Model

et al. 2021

View full text Add to dashboard Cite

The aim of this work is to present the numerical results of the influenza disease nonlinear system using the feed forward artificial neural networks (ANNs) along with the optimization of the combination of global and local search schemes. The genetic algorithm (GA) and active-set method (ASM), i.e., GA-ASM, are implemented as global and local search schemes. The mathematical nonlinear influenza disease system is dependent of four classes, susceptible S(u), infected I(u), recovered R(u) and cross-immune individuals C(u). For the solutions of these classes based on influenza disease system, the design of an objective function is presented using these differential system equations and its corresponding initial conditions. The optimization of this objective function is using the hybrid computing combination of GA-ASM for solving all classes of the influenza disease nonlinear system. The obtained numerical results will be compared by the Adams numerical results to check the authenticity of the designed ANN-GA-ASM. In addition, the designed approach through statistical based operators shows the consistency and stability for solving the influenza disease nonlinear system.

show abstract

Optimal Coordination of Overcurrent Relays Using Simulated Annealing and Brute Force Algorithms

Verma

Mahaptara

Anwaya

et al. 2018

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Optimization of Overcurrent Relays in Microgrid Using Interior Point Method and Active Set Method

Cited by 15 publications

References 8 publications

IoT Technology Enabled Heuristic Model With Morlet Wavelet Neural Network for Numerical Treatment of Heterogeneous Mosquito Release Ecosystem

IoT Technology Enabled Heuristic Model With Morlet Wavelet Neural Network for Numerical Treatment of Heterogeneous Mosquito Release Ecosystem

Soft Computing Paradigms to Find the Numerical Solutions of a Nonlinear Influenza Disease Model

Optimal Coordination of Overcurrent Relays Using Simulated Annealing and Brute Force Algorithms

Contact Info

Product

Resources

About