Optimal location and setting of SVC and TCSC devices using non-dominated sorting particle swarm optimization

Benabid, Rabah; Boudour, Mohamed; Abido, M. A.

doi:10.1016/j.epsr.2009.07.004

Cited by 154 publications

(110 citation statements)

References 16 publications

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“…Due to the imprecise nature of the decision maker's (DM) judgment, it is natural to assume that DM may have fuzzy or imprecise nature goals of each objective function [14] Hence, the membership functions are introduced to represents the goals of each objective function; each membership function is defined by the experiences and intuitive knowledge of the decision maker. In this study, a simple linear membership function was considered for each of the objective functions.…”

Section: Nsga-ii Optimization Principlementioning

confidence: 99%

Optimal Integration of the Renewable Energy to the Grid by Considering Small Signal Stability Constraint

Wartana¹,

Agustini²,

Singh³

2017

IJECE

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In recent decades, one of the main management's concerns of professional engineers is the optimal integration of various types of renewable energy to the grid. This paper discusses the optimal allocation of one type of renewable energy i.e. wind turbine to the grid for enhancing network's performance. A multi-objective function is used as indexes of the system's performance, such as increasing system loadability and minimizing the loss of real power transmission line by considering security and stability of systems' constraints viz.: voltage and line margins, and eigenvalues as well which is representing as small signal stability. To solve the optimization problems, a new method has been developed using a novel variant of the Genetic Algorithm (GA), specifically known as Non-dominated Sorting Genetic Algorithm II (NSGA-II). Whereas the Fuzzy-based mechanism is used to support the decision makers prefer the best compromise solution from the Pareto front. The effectiveness of the developed method has been established on a modified IEEE 14-bus system with wind turbine system, and their simulation results showed that the dynamic performance of the power system can be effectively improved by considering the stability and security of the system.

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Section: Nsga-ii Optimization Principlementioning

confidence: 99%

Optimal Integration of the Renewable Energy to the Grid by Considering Small Signal Stability Constraint

Wartana¹,

Agustini²,

Singh³

2017

IJECE

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“…2a. The basic idea behind power flow control with the TCSC is to vary the overall line's effective series impedance, by adding capacitive or inductive impedance [16], [22].…”

Section: Model Of Tcscmentioning

confidence: 99%

“…ii) Optimization-based method: use either conventional or heuristic optimization methods such as simulated annealing, Tabu search, or particle swarm optimization (PSO) [11]- [16]. The objective function can be single or multi-objective optimizing certain technical/economic operational goals [17], [18].…”

Section: Introductionmentioning

confidence: 99%

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Long‐term economic model for allocation of FACTS devices in restructured power systems integrating wind generation

Elmitwally

Eladl

Morrow

2016

IET Generation, Transmission & Distribution

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Abstract-This paper proposes an approach to optimally allocate multiple types of flexible AC transmission system (FACTS) devices in market-based power systems with wind generation. The main objective is to maximize profit by minimizing device investment cost, and the system's operating cost considering both normal conditions and possible contingencies. The proposed method accurately evaluates the long-term costs and benefits gained by FACTS devices installation to solve a large-scale optimization problem. The objective implies maximizing social welfare as well as minimizing compensations paid for generation rescheduling and load shedding. Many technical operation constraints and uncertainties are included in problem formulation. The overall problem is solved using both Particle Swarm Optimizations (PSO) for attaining optimal FACTS devices allocation as main problem and optimal power flow as sub optimization problem. The effectiveness of the proposed approach is demonstrated on modified IEEE 14-bus test system and IEEE 118-bus test system.

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“…To satisfy these two crucial goals, flexible alternative current transmission system (FACTS) devices are found to be the most suitable and appropriate solution that works near the steady stability limit [2,3]. Placing FACTS devices appropriately in the power system and paying attention to their size is what guarantees the complete goal.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of power flow using FACTS systems

Sekhane¹,

Djamel²

2017

GJCS

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In this paper, both standard IEEE test systems 57-bus and Algerian 59-bus are considered. To enhance the power flow of these two considerable networks in terms of voltage profile and reduce the real and reactive total transmission losses, the inclusion of flexible alternating current transmission systems (FACTS) devices is one of the best solutions. For this, a static synchronous compensator (STATCOM) is proposed. Our code is written in the MATLAB computing environment, based on finding the weakest buses in the network, and placing one or two STATCOMs in an appropriate place; in the next step, there would be recalculation of the power flow again. The results of power flow compared with the popular MATPOWER software environment show the exactitude of our code calculation, and the enhancement of voltage profile, especially in buses where STATCOM is placed. Furthermore, the reduction of real and reactive losses shows the effectiveness of the FACTS device proposed.

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Optimal location and setting of SVC and TCSC devices using non-dominated sorting particle swarm optimization

Cited by 154 publications

References 16 publications

Optimal Integration of the Renewable Energy to the Grid by Considering Small Signal Stability Constraint

Optimal Integration of the Renewable Energy to the Grid by Considering Small Signal Stability Constraint

Long‐term economic model for allocation of FACTS devices in restructured power systems integrating wind generation

Enhancement of power flow using FACTS systems

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