Optimal Placement of FACTS Devices Using Filter Feeding Allogenic Engineering Algorithm

Mutegi, Mbae Ariel; Nnamdi, Nwulu I.

doi:10.1007/s40866-022-00132-3

Cited by 9 publications

(9 citation statements)

References 41 publications

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“…Based on the observed result for the three regular IEEE 14-, 30-, and 57-bus test networks, it can be recommended that the CRC sensitivity approach provides more promising results than the LMP difference sensitivity technique. Mutegi et al [200] projected a voltage stability index based on the FVSI and the LSI for the proper allotment of FACTS to improve the voltage stability network. In this work, an IEEE 39-bus was considered as a test network to justify the ability and robustness of the employed methods.…”

Section: Summary Of Sensitive Index Methods Related To Facts Device O...mentioning

confidence: 99%

Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

et al. 2023

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The use of FACTS devices in power systems has become increasingly popular in recent years, as they offer a number of benefits, including improved voltage profile, reduced power losses, and increased system reliability and safety. However, determining the optimal type, location, and size of FACTS devices can be a challenging optimization problem, as it involves mixed integer, nonlinear, and nonconvex constraints. To address this issue, researchers have applied various optimization techniques to determine the optimal configuration of FACTS devices in power systems. The paper provides an in-depth and comprehensive review of the various optimization techniques that have been used in published works in this field. The review classifies the optimization techniques into four main groups: classical optimization techniques, metaheuristic methods, analytic methods, and mixed or hybrid methods. Classical optimization techniques are conventional optimization approaches that are widely used in optimization problems. Metaheuristic methods are stochastic search algorithms that can be effective for nonconvex constraints. Analytic methods involve sensitivity analysis and gradient-based optimization techniques. Mixed or hybrid methods combine different optimization techniques to improve the solution quality. The paper also provides a performance comparison of these different optimization techniques, which can be useful in selecting an appropriate method for a specific problem. Finally, the paper offers some advice for future research in this field, such as developing new optimization techniques that can handle the complexity of the optimization problem and incorporating uncertainties into the optimization model. Overall, the paper provides a valuable resource for researchers and practitioners in the field of power systems optimization, as it summarizes the various optimization techniques that have been used to solve the FACTS optimization problem and provides insights into their performance and applicability.

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Section: Summary Of Sensitive Index Methods Related To Facts Device O...mentioning

confidence: 99%

Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

et al. 2023

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“…Some of the commonly used Heuristic and Metaheuristic techniques are Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Bacterial Foraging Algorithm (BFA), Grey Wolf Optimizer (GWO), Fuzzy logic (FL), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Bat Algorithm (BA), Firefly Optimization Algorithm (FOA), Cuckoo Search (CS), etc. Despite their superior fast computational advantage and ability to handle more complex optimization problem, metaheuristic methods have different drawbacks; hence, a continuous and sustained search by researchers for a better algorithm for optimal placement and sizing of FACTS devices [54]. The drawbacks of some of metaheuristic approach include undefined convergence by ACO; low convergence rate; parameter adjustment, as in the case of BA; and many more for each individual algorithm [54].…”

Section: A Survey On Optimization Methodsmentioning

confidence: 99%

“…Despite their superior fast computational advantage and ability to handle more complex optimization problem, metaheuristic methods have different drawbacks; hence, a continuous and sustained search by researchers for a better algorithm for optimal placement and sizing of FACTS devices [54]. The drawbacks of some of metaheuristic approach include undefined convergence by ACO; low convergence rate; parameter adjustment, as in the case of BA; and many more for each individual algorithm [54].…”

Section: A Survey On Optimization Methodsmentioning

confidence: 99%

Optimal Placement and Operation of FACTS Technologies in a Cyber-Physical Power System: Critical Review and Future Outlook

2022

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With the current transitioning and increasing complexity of power systems owing to the continuous integration of distributed generators (DGs) and Flexible AC Transmission Systems (FACTS), power system quality and security studies have extended to incorporate the impacts of these technologies. This paper presents a review of the operation and reliability impacts of FACTS technologies in improving power quality and security in modern Cyber-Physical Power Systems (CPPS). While introducing DG to the power system helps to decentralize the network for easy accessibility and enhances clean energy system, it creates new challenges such as harmonics, voltage instability, and frequency distortion. These challenges can be tackled with FACTS devices which are flexible and dynamic smart electronic controllers used to stabilize power system parameters to improve power quality and reliability. This paper examines the current state-of-the-art optimization techniques and artificial intelligence and/or computational techniques for optimal placement and operation of FACTS devices. This review highlights the generational advancement of FACTS technologies and the different objectives of optimal placement and operation of these devices. Moreover, the concept of CPPS is discussed with the potential utilization of distribution-FACTS (D-FACTS) devices for network security. Furthermore, a bibliometric analysis was carried out to show research trend of FACTS utilization. The result presents future trajectories for power utility industries and researchers interested in power system optimization and the application of FACTS technologies in smart power system networks. Some of the significant findings leads to proposed demand-side management for placement of DGs and FACTS technologies as a more strategic optimal system sizing to minimize cost. It was also concluded that future design of FACTS/D-FACTS devices must consider and appreciate interactions with the automated systems of CPPS to enhance effective integration. To this end, design modification of the operational configuration of these devices with sensors for real-time synchronized control and interaction with other CPPS technologies is an area that requires more research attention in the future.

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“…e development of water swimming in swimming pools has been developing for more than ten years, and it has begun to take shape, cultivating many swimming enthusiasts in swimming pools. However, due to the poor management of swimming participants in swimming places in sports management departments, most of the swimming participants in such swimming places are hovering between indoor swimming and outdoor swimming activities, and there is no specific classification of a department to manage.. [23].…”

Section: Analysis Of Intelligent Management Model Of Swimming Place W...mentioning

confidence: 99%

Nonlinear Random Matrix-Based Intelligent Management Model for Swimming Place Waters

Yuan

Han

2022

Mathematical Problems in Engineering

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In this paper, a nonlinear random matrix approach is used to analyze the management of swimming place waters, and in this way, an intelligent management model is designed and applied to the actual swimming place waters management. Firstly, some basic small deviation results of the random matrix are presented. Then, several types of small deviation inequalities are obtained for the maximum eigenvalues of independent stochastic semi-positive definite (PSD) matrices. These small deviation inequalities are independent of the matrix dimension, and the results apply to high-dimensional and even infinite-dimensional matrices. For the inverse eigenvalue problem of higher-order doubly random matrices, the conclusion that two symmetric doubly random matrices are combined into a higher-order symmetric doubly random matrix is proved. In other words, the method of constructing new symmetric random matrices using smaller matrices with known spectra is applied to the inverse eigenvalue problem of higher-order symmetric random matrices, and sufficient conditions for the existence of the solution of the inverse eigenvalue problem of higher-order random matrices are derived, avoiding the discussion of the parity of the number of eigenvalues. This paper focuses on realistic management, to explore the standardization of safety management of swimming venues, and is based on modern management theory, risk management theory and methods, using research integration method, fieldwork method, questionnaire survey method, expert interview method, and other research methods, detailing the methods and conclusions of risk identification in safety management of swimming venues, and further exploring the safety management standards of swimming venues on this basis. And, combined with risk management theory, depending on the probability of occurrence and the degree of harm of risk, four basic disposal methods such as risk avoidance, risk control, risk transfer, and risk self-retaining are proposed.

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Optimal Placement of FACTS Devices Using Filter Feeding Allogenic Engineering Algorithm

Cited by 9 publications

References 41 publications

Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

Optimal Placement and Operation of FACTS Technologies in a Cyber-Physical Power System: Critical Review and Future Outlook

Nonlinear Random Matrix-Based Intelligent Management Model for Swimming Place Waters

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