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In Perspective of Combining Chaotic Particle Swarm Optimizer and Gravitational Search Algorithm Based on Optimal Power Flow in Wind Renewable Energy

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Soft Computing Techniques and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1248))

Abstract

Optimizing the power flow is one of the recent and emerging problems need to solve immediately to enhance the power flow in any power system applications. To do this, various earlier researchers have used many artificial intelligence methods or optimization methods. The obtained results are not cost and time effective. Still the real-time power system applications need a solution with cost and time effective. This paper aimed to integrate the chaotic particle swarm optimizer (CPSO) and gravitational search algorithm to solve the OPF problems. The proposed methods are simulated in MATLAB software, tested on IEEE buses of 30 and 57, and the results are verified. From the obtained results, it is identified that the GSA method is concluded as better method for solving OPF problems in power system applications.

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Abbreviations

NPV :

Number of voltage controlled buses

NPQ :

Number of PQ buses

NTL :

Number of transmission lines

P G :

Active power output of generators at PV bus

V minLi and V maxLi :

Minimum and maximum load voltage of ith unit

S li :

Apparent power flow of ith branch

\(cr_{1}\) and \(cr_{2}\):

Chaotic variables

V G :

Terminal voltages at generation bus bars

Q C :

Output of shunt VAR compensators

T :

Tap setting of the tap regulating transformers

Q minCi and Q maxCi :

Minimum and maximum \(V_{\text{ar}}\) injection limits of \(i{\text{th}}\) shunt capacitor

T mini and T maxi :

Minimum and maximum tap settings limits of ith transformer

S maxli :

Maximum apparent power flow limit of ith branch

x lim :

Limit value of the dependent variable x

G:

Initial value (G0) and time (t)

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar, India

    C. Shilaja

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  1. C. Shilaja
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Correspondence to C. Shilaja .

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Editors and Affiliations

  1. Sikkim Manipal Institute of Technology, Majhitar, Sikkim, India

    Samarjeet Borah

  2. Sikkim Manipal Institute of Technology, Majhitar, Sikkim, India

    Ratika Pradhan

  3. Department of Computer Science and Engineering, JIS University, Kolkata, West Bengal, India

    Nilanjan Dey

  4. GLA University, Mathura, Uttar Pradesh, India

    Phalguni Gupta

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Shilaja, C. (2021). In Perspective of Combining Chaotic Particle Swarm Optimizer and Gravitational Search Algorithm Based on Optimal Power Flow in Wind Renewable Energy. In: Borah, S., Pradhan, R., Dey, N., Gupta, P. (eds) Soft Computing Techniques and Applications. Advances in Intelligent Systems and Computing, vol 1248. Springer, Singapore. https://doi.org/10.1007/978-981-15-7394-1_44

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