A primal–dual penalty-interior-point method for solving the reactive optimal power flow problem with discrete control variables

Delgado, Jéssica Antonio; Baptista, Edméa Cássia; Balbo, Antônio Roberto; Soler, Edilaine Martins; Silva, Diego N.; Martins, André; Nepomuceno, Leonardo

doi:10.1016/j.ijepes.2021.107917

Cited by 13 publications

(2 citation statements)

References 39 publications

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“…For noisy problems, the authors in [41] use piecewise polynomial interpolation and box reformulations, along with an interior-point (IP) method. Authors in [42] tackle similar problems with integrated penalty techniques. Overall, gradient methods are effective at solving NOPT tasks, but they have a challenging time convergent and are expensive to run in noisy and nonlinear situations [43].…”

Section: Related Workmentioning

confidence: 99%

A Regularized Physics-Informed Neural Network to support Data-Driven Nonlinear Constrained Optimization

Perez-Rosero,

Álvarez-Meza,

Castellanos-Dominguez

2024

Preprint

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Nonlinear optimization (NOPT) is a meaningful tool for solving complex tasks in fields like engineering, economics, and operations research, among others. However, NOPT has problems when it comes to dealing with data variability and noisy input measurements that lead to incorrect solutions. Furthermore, nonlinear constraints may result in outcomes that are either infeasible or suboptimal, such as non-convex optimization. This paper introduces a novel regularized physics-informed neural network (RPINN) framework as a new NOPT tool for both supervised and unsupervised data-driven scenarios. By using custom activation functions and regularization penalties in an artificial neural network (ANN), RPINN can handle data variability and noisy inputs. Besides, it uses physics principles to build the network architecture, computing the optimization variables based on network weights and learned features. In addition, it uses automatic differentiation training to make the system scalable and cut down on computation time through batch-based back-propagation. The test results for both supervised and unsupervised NOPT tasks show that our RPINN can provide solutions that are competitive compared to state-of-the-art solvers. In turn, RPINN’s robustness against noisy input measurements makes it particularly valuable in environments with fluctuating information. Additionally, RPINN’s ANN-based foundation offers significant flexibility and scalability.

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Section: Related Workmentioning

confidence: 99%

A Regularized Physics-Informed Neural Network to support Data-Driven Nonlinear Constrained Optimization

Perez-Rosero,

Álvarez-Meza,

Castellanos-Dominguez

2024

Preprint

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“…These best global PSO weights are applied as an initial input. In recent years, IPP has been applied in numerous applications, e.g., riveting simulation in aircraft parts [64], complementarity monotone systems [65], viscoplastic fluidics system [66], dispatch system of the financial load [67], identification of the nonlinear stable system [68], non-smooth interaction dynamics [69], reactive optimal power flow problem with discrete control variables [70] and flow constraints in a pressure-dependent water distribution system [71]. This study is related presenting the hybrid form of the PSOIPP, which is pragmatic to compute the variables for the TON-DD-EFM.…”

Section: Interior-point Programming (Ipp)mentioning

confidence: 99%

Swarming Computational Efficiency to Solve a Novel Third-Order Delay Differential Emden-Fowler System

Weera¹,

Sabir²,

Raja³

et al. 2022

Computers, Materials &Amp; Continua

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The purpose of this research is to construct an integrated neuro swarming scheme using the procedures of the artificial neural networks (ANNs) with the use of global search particle swarm optimization (PSO) along with the competent local search interior-point programming (IPP) called as ANN-PSOIPP. The proposed computational scheme is implemented for the numerical simulations of the third order nonlinear delay differential Emden-Fowler model (TON-DD-EFM). The TON-DD-EFM is based on two types along with the particulars of shape factor, delayed terms, and singular points. A merit function is performed using the optimization of PSOIPP to find the solutions to the TON-DD-EFM. The effectiveness of the ANN-PSOIPP is certified through the comparison with the exact results for solving four examples of the TON-DD-EFM. The scheme's efficiency is observed by performing the absolute error in suitable measures found around 10 −04 to 10 −07 . Furthermore, the statistical-based assessments for 100 trials are provided to compute the accuracy, stability, and constancy of the ANN-PSOIPP for solving the TON-DD-EFM.

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NSGA-III integrating eliminating strategy and dynamic constraint relaxation mechanism to solve many-objective optimal power flow problem

Zhang

Cai

Zhang

et al. 2023

Applied Soft Computing

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A primal–dual penalty-interior-point method for solving the reactive optimal power flow problem with discrete control variables

Cited by 13 publications

References 39 publications

A Regularized Physics-Informed Neural Network to support Data-Driven Nonlinear Constrained Optimization

A Regularized Physics-Informed Neural Network to support Data-Driven Nonlinear Constrained Optimization

Swarming Computational Efficiency to Solve a Novel Third-Order Delay Differential Emden-Fowler System

NSGA-III integrating eliminating strategy and dynamic constraint relaxation mechanism to solve many-objective optimal power flow problem

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