Stability improvement of the PSS-connected power system network with ensemble machine learning tool

Shahriar, Mohammad Shoaib; Shafiullah, Md; Pathan, M Ilius; Sha’aban, Yusuf A.; Bouchekara, Houssem R. E. H.; Ramli, Makbul A.M.; Rahman, Mohammad Mominur

doi:10.1016/j.egyr.2022.08.225

Cited by 19 publications

(8 citation statements)

References 43 publications

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“…Simulation results for responses with SGGP, MGGP and fixed values of PSS-UPFC parameters indicated that MGGP tuned model exhibits quicker response compared to the conventional and the SGGP tuned models. Even more, an ensemble method has been proposed in [108] incorporating MGGP with two other approaches such as neurogenetic (NG) system and extreme learning machine (ELM) for estimating the PSS parameters in real-time to damp out the unwanted oscillations of electrical power system stability. Two single-machine power system models, one with PSS only and one with PSS-UPFC to verify the robustness of this selected ensemble method.…”

Section: Summary Of Meta Heuristic Methods Related To Facts Devices O...mentioning

confidence: 99%

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

Ismail¹,

Guesmi²,

Alshammari³

et al. 2023

Preprint

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Using flexible AC transmission system (FACTS) devices in power systems while adhering to some equality and inequality constraints, researchers around the world sought to address this issue with the objectives of improving the voltage profile, reducing power losses in transmission lines, and increasing system reliability and safety. The recent development of FACTS controllers opens up new perspectives for safer and more efficient operation of electrical power networks by continuous and rapid action on power systems parameters, such as phase angle shifting, voltage injection and line impedance compensation. Thus, an improvement on voltage profile and enhancement of power transfer capability can be obtained. It is for that, the idea behind the FACTS concept is to enable the transmission system to be an active element in increasing the flexibility of power transfer requirements and in securing stability of integrated power system. It may also be effective in transient stability improvement, power oscillations damping and balancing power flow in parallel lines. The primary issue that has significantly piqued the interest of a number of researchers working in this field is the FACTS optimization problem, which involves determining the optimal type, location, and size of FACTS devices in electrical power systems. For solving this mixed integer, nonlinear and non-convex optimization problem, this paper provides an in-depth and comprehensive review of the various optimization techniques covered in published works in the field. In this review, a classification of optimization techniques in five main groups that are widely used, such as classical optimization techniques or conventional optimization approaches, Meta heuristic methods, analytic methods or sensitive index methods and mixed or hybrid methods, is summarized. In addition, a performance descriptions and comparison of these different optimization techniques are discussed in this study. Finally, some advice is offered for future research in this field.

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Section: Summary Of Meta Heuristic Methods Related To Facts Devices O...mentioning

confidence: 99%

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

Ismail¹,

Guesmi²,

Alshammari³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Particularly noteworthy are [129][130][131], in which the authors focused on ensuring system security through reinforcement learning. The assessment of the transient stability of energy systems using ML techniques was also discussed in articles [132][133][134][135][136]. For this purpose, CNN was used in [132], RNN was used in [133] and LSTM was used in [134,135].…”

Section: Power System Securitymentioning

confidence: 99%

“…Low-frequency oscillations that are commonly observed in systems can cause their instability; this is why it is so important to detect them quickly and suppress them. For this purpose, an original approach to tuning the parameters of the power system stabiliser was proposed in [136]. To perform it, the ensemble learning method was used, which combines many machine learning techniques, namely extreme machine learning, neurogenetic (NG) system and multi-gene genetic programming (MGGP).…”

Section: Power System Securitymentioning

confidence: 99%

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Pijarski,

Belowski

2024

Energies

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The challenges currently faced by network operators are difficult and complex. Presently, various types of energy sources with random generation, energy storage units operating in charging or discharging mode and consumers with different operating characteristics are connected to the power grid. The network is being expanded and modernised. This contributes to the occurrence of various types of network operating states in practice. The appearance of a significant number of objects with random generation in the power system complicates the process of planning and controlling the operation of the power system. It is therefore necessary to constantly search for new methods and algorithms that allow operators to adapt to the changing operating conditions of the power grid. There are many different types of method in the literature, with varying effectiveness, that have been or are used in practice. So far, however, no one ideal, universal method or methodology has been invented that would enable (with equal effectiveness) all problems faced by the power system to be solved. This article presents an overview and a short description of research works available in the literature in which the authors have used modern methods to solve various problems in the field of power engineering. The article is an introduction to the special issue entitled Advances in the Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering. It is an overview of various current problems and the various methods used to solve them, which are used to cope with difficult situations. The authors also pointed out potential research gaps that can be treated as areas for further research.

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“…Moreover, an ensemble method was proposed in ref. [108] incorporating MGGP with two other approaches, a neurogenetic (NG) system and an extreme learning machine (ELM), for estimating the PSS parameters in real time to damp out the unwanted oscillations in the electrical power system's stability. Two single-machine power system models, one with PSS only and one with PSS-UPFC, were used to verify the robustness of this selected ensemble method.…”

Section: Evolutionary Algorithmsmentioning

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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Stability improvement of the PSS-connected power system network with ensemble machine learning tool

Cited by 19 publications

References 43 publications

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

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

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

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

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