A Novel Approach Based on Crow Search Algorithm for Solving Reactive Power Dispatch Problem

Meddeb, Asma; Amor, Nahla Ben; Abbès, Mohamed Slim; Chebbi, Souad

doi:10.3390/en11123321

Cited by 28 publications

(22 citation statements)

References 33 publications

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“…Table compares the obtained results by the proposed algorithm QPSODM for Case (Deterministic approach for 14 bus test) with some previously published results on MO‐ORPD problem. According to this table, the proposed algorithm can achieve better and logical results comparing with other methods reported in . In fact, the comparison of the results depends not only on the algorithms used but also on the introduced limit values (generator voltage, OLTC and shunt compensator value), the constraints handling method.…”

Section: Case Study Simulation Results and Discussionmentioning

confidence: 86%

“…A trade‐offs between the minimization of active power losses and the number of control adjustments, the MO‐ORPD is formulated as MINLP and solved by different GAMS solvers with, the different solutions are compared. In,17,18 proposes the crow search algorithm (CSA) algorithm to solve the ORPD problem, the algorithm is performed on two standards test systems and the Tunisian 86‐bus system. In a modified differential evolution (DE) algorithm is proposed to solve the ORPD problem considering HVDC lines, simulations are performed using practical Algerian's power system.…”

Section: Introductionmentioning

confidence: 99%

“…In order to compare our results obtained with the test system (IEEE 14‐bus) used in simulation with those of other references, some recent algorithms published in the literature were employed in this context, namely: PSO, Whale optimisation algorithm (WOA), ALO and CSA, PSO, DE, Interior point method (IPM), PSO with cauchy mutation (PSO‐CM) and adaptive mutation (PSO‐AM), Modified DE (MDE), Artificial bee colony (ABC) and (DE–ABC), Ant colony optimization (ACO), Modified Gaussian barebones teaching learning‐based optimization (MGBTLBO), Gravitational search algorithm (GSA), GSA with conditional selection strategies (GSA‐CSS), improved GSA‐CSS (IGSA‐CSS), GAMS, WOA . To the best of the authors' knowledge, no literature has carried out on stochastic ORPD using quantum‐behaved particle swarm optimization differential mutation (QPSODM) algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Stochastic multi‐objective optimal reactive power dispatch considering load and renewable energy sources uncertainties: a case study of the Adrar isolated power system

Naidji

Boudour

2020

Int Trans Electr Energ Syst

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Optimal reactive power dispatch (ORPD) is a particular case of the optimal power flow (OPF) which consists in determining the state of an electric power system by optimizing a specific objective function and satisfying a set of some operating constraints. In this paper, the purpose is to solve deterministic and stochastic multi‐objective ORPD (MO‐ORPD) problem under load and renewable energy sources (RES) uncertainties. The uncertainty is modelled using stochastic scenario‐based approach (SSBA). The objectives to be minimized are active power loss and cumulative voltage deviation from their corresponding nominal values. The MO‐ORPD is solved using sum weighed method, and fuzzy satisfying method is used to select the best compromise solution among Pareto front of non‐dominated solutions. In this paper, quantum‐behaved particle swarm optimization differential mutation (QPSODM) algorithm is proposed to solve the ORPD problem. The proposed methodology has been examined and confirmed on the IEEE 14‐bus and the practical Adrar's isolated power system. The performance of the proposed methodology is compared with recent algorithms. Simulation results show that the proposed methodology can solve the MO‐ORPD including RES effectively and can give best and logic results. Furthermore, a sensitivity analysis is carried out to show the performance of the proposed algorithm comparing to own developed algorithms particle swarm optimization (PSO) and quantum PSO (QPSO).

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Section: Case Study Simulation Results and Discussionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Stochastic multi‐objective optimal reactive power dispatch considering load and renewable energy sources uncertainties: a case study of the Adrar isolated power system

Naidji

Boudour

2020

Int Trans Electr Energ Syst

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“…Sensitivity analysis (SA) is a statistical method that provides an idea of how sensitive is the best solution chosen to any changes in input values from one or more parameters 39 . ANOVA is an independent SA method that assesses if there is any statistically significant association between one or more inputs and output 40 – 43 .…”

Section: Methodsmentioning

confidence: 99%

Prediction of functional properties of nano $$\hbox {TiO}_2$$ coated cotton composites by artificial neural network

Amor

Noman

Petrů

2021

Sci Rep

Self Cite

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This paper represents the efficiency of machine learning tool, i.e., artificial neural network (ANN), for the prediction of functional properties of nano titanium dioxide coated cotton composites. A comparative analysis was performed between the predicted results of ANN, multiple linear regression (MLR) and experimental results. ANN was applied to map out the complex input-output conditions to predict the optimal results. A backpropagation ANN model called a multilayer perceptron (MLP), trained with Bayesian regularization were used in this study. The amount of chemicals and reaction time were selected as input variables and the amount of titanium dioxide coated on cotton, self-cleaning efficiency, antimicrobial efficiency and ultraviolet protection factor were analysed as output results. The accuracy of the proposed algorithm was evaluated and compared with MLR results. The obtained results reveal that MLP provides efficient results that are statistically significant in the prediction of functional properties ($$p<0.1 e^{-10} $$ p < 0.1 e - 10 ) compared to MLR. The correlation coefficient of MLP model ($$>95\%$$ > 95 % ) indicates that there is a strong correlation between the measured and predicted functional properties with a trivial mean absolute error and root mean square errors values. MLP model is suitable for the functional properties and can be used for the investigation of other properties of nano coated fabrics.

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“…Thus, crow i generates its new position at iteration t+1 based on the previous states, which is expressed as follows [49][50][51]:…”

Section: Cs Combined With Pso Algorithmmentioning

confidence: 99%

A novel sizing inherits allocation strategy of renewable distributed generations using crow search combined with particle swarm optimization algorithm

Farh

Eltamaly

Al-Shaalan

et al. 2021

IET Renewable Power Gen

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In this study, a new distributed generation sizing that inherits the allocation strategy for the IEEE 30-bus benchmark system is proposed. The hybrid crow search-particle swarm optimization technique would select the optimal buses through the automatic cancelation of the unfeasible load buses. Additionally, it performs sizing that inherits allocation for minimum costs and transmission losses. Also, it selects the best distributed generation technology among wind turbines and photovoltaic energy systems based on the minimum total capital cost. The proposed strategy outperformed others in terms of total cost alleviation, power losses reduction, voltage profile improvement, and lines loading mitigation.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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A Novel Approach Based on Crow Search Algorithm for Solving Reactive Power Dispatch Problem

Cited by 28 publications

References 33 publications

Stochastic multi‐objective optimal reactive power dispatch considering load and renewable energy sources uncertainties: a case study of the Adrar isolated power system

Stochastic multi‐objective optimal reactive power dispatch considering load and renewable energy sources uncertainties: a case study of the Adrar isolated power system

Prediction of functional properties of nano $$\hbox {TiO}_2$$ coated cotton composites by artificial neural network

A novel sizing inherits allocation strategy of renewable distributed generations using crow search combined with particle swarm optimization algorithm

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