Available Transfer Capability Enhancement by FACTS Devices Using Metaheuristic Evolutionary Particle Swarm Optimization (MEEPSO) Technique

Gupta, Deeksha; Jain, Sushma

doi:10.3390/en14040869

Cited by 20 publications

(13 citation statements)

References 55 publications

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“…Simulation results have been obtained from 14-bus test system, where, it can have noted an improvement on voltage profile, reduction on transmission line losses, increase of ATC and maximization loading and voltage stability margin. Divya Gupta et al [235] presented an approach of calculation for Enhanced ATC, using TCSC FACTS device by controlling the transmission line reactance. In this research work, the Power loss-based Congestion Reduction (SPCR) index and the Metaheuristic Evolutionary Particle Swarm Optimization (MEEPSO) technique have been combined successfully to finding the appropriate placement of TCSC and the augmentation of ATC respectively.…”

Section: Summary Of Mixed Methods Related To Facts Devices Optimizati...mentioning

confidence: 99%

“…In general, there are some improvements in computational speed and accuracy as a result of hybridization [254]. However, a number of authors favor the hybrid approach, employing numerous open-source hybrid methods Sai Ram Inkollu et al [219], Ahmad Abubakar SADIQ et al [229], Parizad et al [234], Divya Gupta et al [235], etc. For the time being, it outperforms other heuristic, sensitive, and classical methods when it comes to finding the optimal solution for FACTS device optimization problems in a hyper search space with stable convergence characteristics and high computational efficiency.…”

Section: Benefitsmentioning

confidence: 99%

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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 Mixed Methods Related To Facts Devices Optimizati...mentioning

confidence: 99%

Section: Benefitsmentioning

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

“…In order to extract the operation rules of the transmission section limit transmission power total transmission capacity, in the knowledge base construction stage, the calculation of the limit transmission power of the specified transmission section needs to be carried out for random working conditions. The total transmission capacity calculation methods include continuous power flow method [15,16] and optimal power flow method [17,18]. In actual operation, important transmission sections are often constrained by transient stability.…”

Section: Repeated Power Flow Calculationmentioning

confidence: 99%

An Optimized Algorithm for Renewable Energy Forecasting Based on Machine Learning

Ali¹,

Galal²,

Alkhalaf³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The large-scale application of renewable energy power generation technology brings new challenges to the operation of traditional power grids and energy management on the load side. Microgrid can effectively solve this problem by using its regulation and flexibility, and is considered to be an ideal platform. The traditional method of computing total transfer capability is difficult due to the central integration of wind farms. As a result, the differential evolution extreme learning machine is offered as a data mining approach for extracting operating rules for the total transfer capability of tie-lines in wind-integrated power systems. K-medoids clustering under the two-dimensional "wind powerload consumption" feature space is used to define representative operational scenarios initially. Then, using stochastic sampling and repetitive power flow, a knowledge base for total transfer capability operating rule mining is created. Then, a novel method is used to filter redundant characteristics and find features that are closely associated to the total transfer capability in order to decrease the ultra-high dimensionality of operational features. Finally, by feeding the training data into the proposed algorithm, the total transfer capability operation rules are derived from the knowledge base. It can be seen that, the proposed algorithm can optimize the system performance with good accuracy and generality, according to numerical data.

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“…Recent years witnessed changes in optimal power flow (OPF) calculation as typified by artificial intelligence (AI) techniques 11 . A variety of AI methods for ATC calculation are genetic algorithm, 12 artificial neural network, 13,14 bee algorithm (BE), particle swarm optimization, 11,15 grey wolf optimizer, cuckoo search algorithm, 16,17 and evolutionary planning. Although AI methods are fast indeed, they suffer the exaggerated issue of finding the right training method and the need for copious training information.…”

Section: Introductionmentioning

confidence: 99%

A reliable and efficient holomorphic approach to evaluate dynamic available transfer capability

Eidiani

2021

Int Trans Electr Energ Syst

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Summary In the present paper, the static approximate Newton‐Raphson‐Sidel (ANRS) and the developed down‐hill (DDH) algorithm methods were used. To take advantage of both methods, ANRS‐DDH and the revised holomorphic embedding load flow (RHELF) were combined. An approximation of the potential energy boundary surface (APEBS) was also employed in combination with determine transient stability and correct the previous methods. A combination of static method and transient stability led to a combined approach for dynamic available transfer capability (C‐DATC) computation. It is shown that a small correction for these four methods makes the proposed method faster or more accurate. The average root mean square error of the proposed method is half the other methods and the average relative speed of calculations in the proposed methods is 10% higher than the other method. These were confirmed in the networks of Iowa State (145 bus), modified IEEE 118‐bus, and the west of Iran (848 bus) with a large number of wind farms. Finally, the present paper demonstrated that the suggested approach is ideal not only for the operation mode but also for the planning mode and the existence of wind farms.

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Available Transfer Capability Enhancement by FACTS Devices Using Metaheuristic Evolutionary Particle Swarm Optimization (MEEPSO) Technique

Cited by 20 publications

References 55 publications

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

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

An Optimized Algorithm for Renewable Energy Forecasting Based on Machine Learning

A reliable and efficient holomorphic approach to evaluate dynamic available transfer capability

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