Symbiotic organisms search algorithm for economic dispatch problems

Das, Diptanu; Bhattacharya, Aniruddha; Ray, Rup Narayan

doi:10.1109/icecct.2017.8118029

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…Then, an organism with the best fitness is chosen to be initialized as the best organism. Lastly, the chaotic search space radius is computed by using (13), which consists of (14) and (15).…”

Section: Step 1: Initializationmentioning

confidence: 99%

“…The performance of SOS has been acknowledged by several studies conducted. In previous studies, SOS has been implemented to solve optimal SVC allocation for voltage profile improvement [5], economic dispatch [14,15], optimal power flow [16] as well as civil engineering design [17]. Further modifications are proposed on SOS to improve its performance.…”

Section: Introductionmentioning

confidence: 99%

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Chaotic immune symbiotic organisms search for SVC installation in voltage security control

Zamani

Musirin

Suliman

et al. 2019

IJEECS

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<span>Parallel with the urbanization of the world, energy demand in the world also increased. The increase in energy demand will require a power system to be operated near its stability limit. To mitigate the problem, Flexible Alternating Current Transmission System (FACTS) devices can be installed as a compensation scheme to improve voltage security in a power system. For an effective compensation, FACTS devices should be optimally allocated in a power system. Although optimization techniques can be implemented to optimally allocate these devices, problems have been reported which would affect the performance of the optimization techniques in terms of producing high quality solutions. This paper presents the implementation of Chaotic Immune Symbiotic Organisms Search for solving optimal Static VAr Compensator (SVC) allocation problem for voltage security control. The optimization is validated in IEEE 26-Bus Reliability Test System (RTS) realizes the capability of CISOS in solving the optimization problem. Comparative studies with respect to Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) resulting in good agreement on the results and demonstrated superior performance of CISOS. Results of the study can be beneficial to power system community in terms of compensation planning prior to real world implementation.</span>

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Section: Step 1: Initializationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Chaotic immune symbiotic organisms search for SVC installation in voltage security control

Zamani

Musirin

Suliman

et al. 2019

IJEECS

View full text Add to dashboard Cite

show abstract

“…The superior performance of SOS over well-known optimization techniques are affirmed in the original paper using some unconstrained mathematical problems and structural engineering design problems [20]. Besides, the SOS algorithm and its modified versions have been successfully employed in the optimization of various benchmarks and real life engineering problems, such as load frequency control of multi-area power system [21,22], load dispatch problem with valve-point effect [23], off-line optimization of PI parameters for DSP-based DC motor drives [24], optimization of pinjointed structures [25], economic dispatch problem [26], design of planar concentric circular antenna arrays [27], efficient PID based automatic voltage regulator design [28,29], static optimal power flow problem [30], and optimal placement and sizing of distributed generators [31]. Nonetheless, in the light of our literature review, SOS has not been yet applied to optimal design of multiple linear regression model in attempting to estimate synchronous motor excitation current for power factor correction task.…”

Section: Mutualist Relation Parasitist Relationmentioning

confidence: 99%

Estimation of Synchronous Motor Excitation Current Using Multiple Linear Regression Model Optimized by Symbiotic Organisms Search Algorithm

Çelik

2018

Mugla Journal of Science and Technology

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In this paper, an effective and simple means of estimating the excitation current of a synchronous motor (SM) is presented for power factor correction task. First, a multiple linear regression model with four predictor variables such as motor load current, actual power factor, power factor error and excitation current change is formed to estimate the SM excitation current. Then, recently introduced symbiotic organisms search (SOS) algorithm is benefitted in the hope of searching better values of regression coefficients in that model using the data collected from the prepared experimental setup. The supremacy of SOS over some recently published algorithms such as genetic algorithm, artificial bee colony and gravitational search algorithm is widely attested through comparative computer simulations for the similar compensation system. The results exhibited in this article show that the presented technique outperforms the other reported popular algorithms from the aspects of simplicity, robustness and accuracy. In view of this, the suggested tuning of regression coefficients of the multiple linear regression model yields a better estimating performance of SM excitation current than the earlier studies.

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Optimal Planning of Electric Vehicle Charging Station in Radial Distribution Network considering System Average Interruption Frequency Index

Chakraborty

Das

2022

2022 IEEE 6th International Conference on Condition Assessment Techniques in Electrical Systems (CATCON)

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Symbiotic organisms search algorithm for economic dispatch problems

Cited by 4 publications

References 23 publications

Chaotic immune symbiotic organisms search for SVC installation in voltage security control

Chaotic immune symbiotic organisms search for SVC installation in voltage security control

Estimation of Synchronous Motor Excitation Current Using Multiple Linear Regression Model Optimized by Symbiotic Organisms Search Algorithm

Optimal Planning of Electric Vehicle Charging Station in Radial Distribution Network considering System Average Interruption Frequency Index

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