Static var compensator planning using artificial immune system for loss minimisation and voltage improvement

Ishak, Siti Zaharah; Abidin, Ahmad Farid; Rahman, Titik Khawa Abdul

doi:10.1109/pecon.2004.1461613

Cited by 12 publications

(9 citation statements)

References 4 publications

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“…The voltage deviation index is defined as the ratio of difference between the bus voltage with the reference voltage of the bus to the reference voltage of the bus. In [16], the voltage deviation index considers the voltage deviation at all buses. In this paper, the voltage deviation index only considers the voltage deviation of load buses only, while slack bus, P-V buses and connecting bus are not considered in the index.…”

Section: Problem Formulationmentioning

confidence: 99%

“…In the study, robustness of DE has been proven via its low value of standard deviation, while CoDE has revealed its superiority over DE in terms of computational time. The same approach was implemented by Nguyen et al where the authors have conducted a study to solve optimal SVC sizing and placement using Self-Organizing Hierarchical PSO with Time-Varying Acceleration Coefficients, IHS, and Cuckoo Search Algorithm (CSA) [16]. In the study conducted by Nguyen et al, CSA is found out to be more powerful as compared to other techniques discussed in [16] in terms of results quality.…”

mentioning

confidence: 99%

“…The same approach was implemented by Nguyen et al where the authors have conducted a study to solve optimal SVC sizing and placement using Self-Organizing Hierarchical PSO with Time-Varying Acceleration Coefficients, IHS, and Cuckoo Search Algorithm (CSA) [16]. In the study conducted by Nguyen et al, CSA is found out to be more powerful as compared to other techniques discussed in [16] in terms of results quality. Ishak et al [17] has proven the capability of Artificial Immune System (AIS) in improving voltage profile of transmission system while minimizing the total power loss via optimal sizing of SVC.…”

mentioning

confidence: 99%

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Symbiotic Organisms Search Technique for SVC Installation in Voltage Control

Zamani

Musirin

Suliman

2017

IJEECS

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Section: Problem Formulationmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Symbiotic Organisms Search Technique for SVC Installation in Voltage Control

Zamani

Musirin

Suliman

2017

IJEECS

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“…GA is applied for placement of only one SVC to minimize power loss, voltage deviation and cost within a power system. In [22] AIS based approach is applied to improve voltage profile and minimize losses by optimally placement of SVC. Multi-objective GA is used to find optimal locations of FACTS devices in a power system [23].…”

Section: Introductionmentioning

confidence: 99%

Optimal Placement of SVC using NSGA-II

Dixit¹,

Srivastava²,

Agnihotri³

2016

IJGDC

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Improving voltage stability, reducing real power loss (PL) and voltage deviation (VD) are the most important tasks in the operation of electrical power systems. Voltage instability and voltage collapse are the severe problems which may take place because of deficit reactive power at load buses due to increased loading or contingencies. In this paper, the problem of obtaining optimal location and size of SVC is formulated as true multi-objective optimization problem for simultaneous minimization of the two objectives namely real power losses and load bus voltage deviation. The two algorithms real coded genetic algorithm (RCGA) and non-dominated sorting genetic algorithm-II (NSGA-II) with a feature of adoptive crowding distance have been used for solving nonlinear constrained multi-objective optimization problem. Both the algorithms have been used for obtaining optimal location and sizing of SVC. Voltage security of the power system has also been analyzed separately for all placement of SVC to ensure secure operation of the system. The proposed approaches have been implemented on IEEE 30-bus test system. The simulation results of the two algorithms have been compared for solution quality, computational complexity and computational time. It has been found that NSGA-II presents better performance in solving multi-objective optimization problem and also in obtaining a diverse set of solutions which converge near the true Pareto-optimal front. The simulation results of NSGA-II have also been presented to exhibit the capabilities of the algorithm to generate well-distributed Pareto-optimal front.

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“…In [10], GA is used to determine the best location of only one SVC within a power system, in which the objective function is to reduce the power loss, voltage deviation, and cost. An AIS technique is used to minimize the total loss and improve the voltage in a power system [11] by determining the correct SVC placement. The well-known PSO is explored in [12] to obtain the optimal locations of SVCs in the IEEE 30 bus system.…”

Section: Introductionmentioning

confidence: 99%