Optimal power flow study using conventional and neural networks methods

Imen, Labed; Djamel, Labed; Hassiba, Serghine; Abdellah, Draidi; Selwa, Fetissi

doi:10.1109/icrera.2015.7418642

Cited by 12 publications

(7 citation statements)

References 7 publications

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“…A more recent work [7] trains a neural network for the task of voltage-secured OPF prediction. Additional efforts on tackling economic dispatch and OPF prediction can be found in [8], [9], [10]. Common to all attempts listed is the multi-dimensional target output of generation values of each generator.…”

Section: B Related Workmentioning

confidence: 99%

Supervised learning for optimal power flow as a real-time proxy

Canyasse

Dalal

Mannor

2017

2017 IEEE Power &Amp; Energy Society Innovative Smart Grid Technologies Conference (ISGT)

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Abstract-In this work we design and compare different supervised learning algorithms to compute the cost of Alternating Current Optimal Power Flow (ACOPF). The motivation for quick calculation of OPF cost outcomes stems from the growing need of algorithmic-based long-term and medium-term planning methodologies in power networks. Integrated in a multiple time-horizon coordination framework, we refer to this approximation module as a proxy for predicting short-term decision outcomes without the need of actual simulation and optimization of them. Our method enables fast approximate calculation of OPF cost with less than 1% error on average, achieved in run-times that are several orders of magnitude lower than of exact computation. Several test-cases such as IEEE-RTS96 are used to demonstrate the efficiency of our approach.

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Section: B Related Workmentioning

confidence: 99%

Supervised learning for optimal power flow as a real-time proxy

Canyasse

Dalal

Mannor

2017

2017 IEEE Power &Amp; Energy Society Innovative Smart Grid Technologies Conference (ISGT)

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“…In reference [6], the paper discussed the use of ANN in solving load flow problem. Other paper of reference [7], treated the study of optimal power flow using ANN. In [8], the authors used the ANFIS for power flow analysis.…”

Section: Introductionmentioning

confidence: 99%

Calculating voltage magnitudes and voltage phase angles of real electrical networks using artificial intelligence techniques

Fikri¹,

Sabri²,

Cheddadi³

2020

IJECE

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In the field of electrical network, it is necessary, under different conditions, to learn about the behavior of the system. Power Flow Analysis is the tool per excellent that allow as to make a deep study and define all quantities of each bus of the system. To determine power flow analysis there is a lot of methods, we have either numerical or intelligent techniques. Lately, researchers always work on finding intelligent methods that allow them to solve their complex problems. The goal of this article is to compare two intelligent methods that are capable of predicting quantities; Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System using real electrical networks. To do that we used few significant discrepancies. These methods are characterized by giving results in real time. To make this comparison successful, we implemented these two methods, to predict the voltage magnitudes and the voltage phase angles, on two Moroccan electrical networks. The results of the comparison show that the method of Adaptive Neuro-Fuzzy Inference System have more advantages than the method of Artificial Neural Network.

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“…The authors perturbed the real and reactive powers to produce larger dataset, used Newton-Raphson method to calculate the voltages and reactive powers, and used ANN to estimate voltages and reactive powers. Quite similarly, the authors of [ 54 ] have presented an optimal power flow study using two methods, Newton-Raphson based iterative method and Back-Propagation ANN. The outputs to be estimated include voltages amplitudes, phases, and other parameters.…”

Section: Introductionmentioning

confidence: 99%

STATCOM Estimation Using Back-Propagation, PSO, Shuffled Frog Leap Algorithm, and Genetic Algorithm Based Neural Networks

Soodi

Vural

2018

Computational Intelligence and Neuroscience

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Different optimization techniques are used for the training and fine-tuning of feed forward neural networks, for the estimation of STATCOM voltages and reactive powers. In the first part, the paper presents the voltage regulation in IEEE buses using the Static Compensator (STATIC) and discusses efficient ways to solve the power systems featuring STATCOM by load flow equations. The load flow equations are solved using iterative algorithms such as Newton-Raphson method. In the second part, the paper focuses on the use of estimation techniques based on Artificial Neural Networks as an alternative to the iterative methods. Different training algorithms have been used for training the weights of Artificial Neural Networks; these methods include Back-Propagation, Particle Swarm Optimization, Shuffled Frog Leap Algorithm, and Genetic Algorithm. A performance analysis of each of these methods is done on the IEEE bus data to examine the efficiency of each algorithm. The results show that SFLA outperforms other techniques in training of ANN, seconded by PSO.

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Optimal power flow study using conventional and neural networks methods

Cited by 12 publications

References 7 publications

Supervised learning for optimal power flow as a real-time proxy

Supervised learning for optimal power flow as a real-time proxy

Calculating voltage magnitudes and voltage phase angles of real electrical networks using artificial intelligence techniques

STATCOM Estimation Using Back-Propagation, PSO, Shuffled Frog Leap Algorithm, and Genetic Algorithm Based Neural Networks

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