Integrated Reactive Power Optimization Method for Active Distribution Networks Based on a Quantum Krill Herd Algorithm

Li, Yuancheng; Zhao, Xiaoyu

doi:10.1080/15325008.2019.1689443

Cited by 7 publications

(2 citation statements)

References 20 publications

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“…Since the distribution grid voltage is closely related to the reactive power in the system, it is important to study the reactive power optimization of the distribution grid containing DG to improve the power quality and ensure the safe and stable operation of the system [7]. One of the most important parts of the optimization process is the algorithm, the principle of particle swarm algorithm is simple, with easy to implement and other characteristics, in recent years, with the development of computer technology, the algorithm is applied to new research areas such as artificial intelligence [8][9]. However, because the algorithm is limited by the convergence accuracy, easy to "premature" and other problems, many scholars at home and abroad have made a lot of improvements to this problem [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Optimization of reactive power in distribution networks with DG based on improved particle swarm algorithm

Xu,

Long,

Lei

et al. 2024

E3S Web Conf.

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The integration of Distributed Generation (DG) into the distribution network can effectively alleviate the problems of energy shortage and air pollution, but the DG output has the characteristics of uncertainty and randomness, and the integration will lead to the change of distribution network tide distribution, which will further lead to the change of distribution network operation mode with the scale development of DG integration. In this paper, we adopt the improved particle swarm algorithm (PSO) for reactive power optimization calculation of distribution network and establish the optimization model with the system network loss as the objective function and node voltage, reactive power compensation output and transformer tapping position as the main constraints. The inertia weights of PSO are improved to make it adaptive with increasing number of iterations, and the acceleration factor is improved so that the whole particle search capability is achieved. Finally, the reactive power optimization analysis is carried out for the IEEE 33-node distribution system containing distributed power sources. The results show that DG can enhance the stability of grid operation and the proposed algorithm has good optimization performance.

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Section: Introductionmentioning

confidence: 99%

Optimization of reactive power in distribution networks with DG based on improved particle swarm algorithm

Xu,

Long,

Lei

et al. 2024

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…In recent years, with the continuous expansion of PS scale and increasing load changes, the importance of RP OPT in PS has become increasingly prominent [1][2]. RP OPT can improve the stability of PS, reduce voltage deviation and power grid loss, and it is of great significance to the reliability, economy and environmental protection of PS [3][4].…”

Section: Introductionmentioning

confidence: 99%

Reactive Power Optimization of Power System Based on Particle Swarm Optimization Algorithm

Liu,

Wei,

2023

HSET

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With the popularization and development of the Power System (PS), the power factor and stability issues of the PS are becoming increasingly prominent. The Reactive Power (RP) in the PS has a significant impact on system stability and efficiency. Therefore, how to optimize the RP in the PS, improve power factor and stability, has become one of the research hotspots in the field of power. This paper aims to solve the problem of RP Optimization (OPT) in PS, and optimize the size and distribution of RP by using particle swarm OPT, so as to improve the stability and efficiency of PS. Through experimental testing, it was found that the Algorithm (ALG) has good performance in RP OPT problems and has great potential for application in PS OPT. The experimental results show that among these five sets of results, the value range of objective function 1 ranges from 156.75 kW^2 to 162.46 kW^2, indicating that different RP compensation capacities have different effects on the magnitude of RP loss.

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Scientometric analysis of quantum-inspired metaheuristic algorithms

Pooja,

Sood

2024

Artif Intell Rev

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Quantum algorithms, based on the principles of quantum mechanics, offer significant parallel processing capabilities with a wide range of applications. Nature-inspired stochastic optimization algorithms have long been a research hotspot. The fusion of quantum mechanics with optimization methods can potentially address NP-hard problems more efficiently and exponentially faster. The potential advantages provided by the ground-breaking paradigm have expedited the scientific output of quantum-inspired optimization algorithms locale. Consequently, a pertinent investigation is required to explain how ground-breaking scientific advancements have evolved. The scientometric approach utilizes quantitative and qualitative techniques to analyze research publications to evaluate the structure of scientific knowledge. Henceforth, the current research presents a scientometric and systematic analysis of quantum-inspired metaheuristic algorithms (QiMs) literature from the Scopus database since its inception. The scientometric implications of the article offer a detailed exploration of the publication patterns, keyword co-occurrence network analysis, author co-citation analysis and country collaboration analysis corresponding to each opted category of QiMs. The analysis reveals that QiMs solely account to 26.66% of publication share in quantum computing and have experienced an impressive 42.59% growth rate in the past decade. Notably, power management, adiabatic quantum computation, and vehicle routing are prominent emerging application areas. An extensive systematic literature analysis identifies key insights and research gaps in the QiMs knowledge domain. Overall, the findings of the current article provide scientific cues to researchers and the academic fraternity for identifying the intellectual landscape and latest research trends of QiMs, thereby fostering innovation and informed decision-making.

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Integrated Reactive Power Optimization Method for Active Distribution Networks Based on a Quantum Krill Herd Algorithm

Cited by 7 publications

References 20 publications

Optimization of reactive power in distribution networks with DG based on improved particle swarm algorithm

Optimization of reactive power in distribution networks with DG based on improved particle swarm algorithm

Reactive Power Optimization of Power System Based on Particle Swarm Optimization Algorithm

Scientometric analysis of quantum-inspired metaheuristic algorithms

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