A novel hybrid GWO-PSO optimization technique for optimal reactive power dispatch problem solution

Shaheen, Mohamed A. M.; Hasanien, Hany M.; Alkuhayli, Abdulaziz

doi:10.1016/j.asej.2020.07.011

Cited by 132 publications

(51 citation statements)

References 40 publications

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“…Owing to the responsive impact of such parameter, tuning the PI controller faces great difficulties. Therefore, many metaheuristic algorithms have been improved in order to overcome those difficulties, such as particle swarm optimization (PSO) [8], sunflower optimization algorithm (SFO) [9][10], hybrid GWO-PSO optimization technique [11], genetic algorithm (GA) [12], hybrid firefly and particle swarm optimization technique [13], Harris hawks optimization Method [14], marine predators algorithm [15], hierarchical model predictive control [16], Tabu search [17], quasi-oppositional selfish herd optimization (QSHO) [18], Cuttlefish optimization algorithm (CFA) [19], and teaching-learning based optimization [20]. Each of those techniques has its benefits and drawbacks [21].…”

Section: A Literature Reviewmentioning

confidence: 99%

LMSRE-Based Adaptive PI Controller for Enhancing the Performance of an Autonomous Operation of Microgrids

et al. 2021

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Microgrids take a large part in power networks thanks to their operational and economic benefits. This research introduces a novel implementation of an adaptive proportional plus integral (PI) controller to boost the autonomous microgrid operation efficiency. The least mean and square roots of the exponential algorithm are utilized in the adaptive PI control strategy. The multi-objective function for both sunflower optimization (SFO) and particle swarm optimization (PSO) algorithms is obtained by The Response Surface Methodology. The system is evaluated under different environments, which are stated as follows: 1) disconnect the system from the grid (islanding), 2) autonomous system exposure to load variability, and 3) autonomous system exposure to a symmetrical fault. The proposed practicality of the control plan is shown by the data of the simulation, which is extracted from PSCAD/EMTDC software. The strength of the suggested adaptive control is confirmed through matching its results with those obtained using the SFO and PSO based optimal PI controllers. INDEX TERMS microgrid; optimization; power systems; renewable energy. Ahmed Al-Durra (S'07-M'10-SM'14) received his PhD in ECE from Ohio State University in 2010.He is a Professor in the EECS Department at Khalifa University, UAE. His research interests are applications of control and estimation theory on power systems stability, micro and smart grids, renewable energy systems and integration, and process control. He has one US patent, one edited book, 12 book chapters, and over 210 scientific articles in top-tier journals and refereed international conference proceedings. He has supervised/co-supervised

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Section: A Literature Reviewmentioning

confidence: 99%

LMSRE-Based Adaptive PI Controller for Enhancing the Performance of an Autonomous Operation of Microgrids

et al. 2021

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“…The results indicated a high ability of this algorithm to resolve this kind of problem. The same hybrid approach that uses GWO and PSO was introduced in [55] to reach optimal reactive power dispatch problem solution in the field of electric power networks. A hybrid algorithm was presented in [54] called HPSOGWO.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Swarm Intelligence Based Feature Selection Algorithm for High Dimensional Datasets

Yousef¹,

Youssef

Keshk

2021

IJCI. International Journal of Computers and Information

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High dimensional datasets expose a critical obstacle in machine learning. Feature selection overcomes this obstacle by eliminating duplicated and unimportant features from the dataset to increase the robustness of learning algorithms. This paper introduces a binary version of a hybrid swarm intelligence approach as a wrapper method for feature selection that gathers between the strengths of both the grey wolf and particle swarm optimizers. This approach is named Improved Binary Grey Wolf Optimization (IBGWO). The original version of this hybrid approach was proposed in the literature with a continuous search space as a high-level hybrid form, which runs the optimizers one after the other. Two different types of transfer functions, named S-Shaped and V-Shaped, are applied in this work to turn continuous data into binary. Nine of high-dimensional small-instance medical datasets are employed to assess the proposed approach. The experimental results demonstrate that IBGWO based on S-Shaped (IBGWO-S) outperforms the binary particle swarm and the binary grey wolf optimizers on six out of nine datasets according to the classification accuracy and fitness values. IBGWO-S selects the fewest features on 100% of the datasets. The results show IBGWO based on V-Shaped (IBGWO-V) outperforms the binary particle swarm and binary grey wolf optimizers on five datasets based on the classification accuracy and fitness values. The results indicate that IBGWO-V outperforms IBGWO-S in terms of all studied evaluation metrics. The results also show that IBGWO-S and IBGWO-V outperform eight metaheuristics known in the literature in selecting the relevant features with acceptable classification accuracy.

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“…The author in [5] solves the ORPD problem using meta-heuristic based Crow Search Algorithm (CSA) and finds the optimal control variables settings. An enhanced and efficient differential evolution (DE) with new mutation approach is described in [6,7] to solve ORPD problem with HVDC transmission link. A methodology based on entropy evolution approach is proposed in [8] using Gravitational Search technique and fractional particle swarm optimization (PSO) for the solution of ORPD problem.…”

Section: A Literature Reviewmentioning

confidence: 99%

Solving Reactive Power Scheduling Problem using Multi-objective Crow Search Algorithm

Salkuti¹

2021

IJACSA

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This paper presents the solution of multi-objective based optimal reactive power dispatch (MO-ORPD) problem by optimizing the system power losses and voltage stability enhancement index (VSEI)/L-index objectives. ORPD problem is considered as an important issue from system security and operational point of view for optimal steady-state operation of power system. Here, single-objective based ORPD problem is solved using Crow Search Algorithm (CSA) and multi-objective based ORPD problem is solved using multi-objective CSA (MO-CSA). The CSA is considered as an efficient and robust algorithm which determines the global optimal solution for solving the non-linear and discontinuous objective functions. Two standard test systems, i.e., IEEE 30 and 57 bus systems are considered to show the effectiveness, suitability and robustness of CSA and MO-CSA for solving the ORPD problem.

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A novel hybrid GWO-PSO optimization technique for optimal reactive power dispatch problem solution

Cited by 132 publications

References 40 publications

LMSRE-Based Adaptive PI Controller for Enhancing the Performance of an Autonomous Operation of Microgrids

LMSRE-Based Adaptive PI Controller for Enhancing the Performance of an Autonomous Operation of Microgrids

A Hybrid Swarm Intelligence Based Feature Selection Algorithm for High Dimensional Datasets

Solving Reactive Power Scheduling Problem using Multi-objective Crow Search Algorithm

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