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Symbiotic organism search algorithm applied to load frequency control of multi-area power system

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Abstract

In this article, a novel powerful metaheuristic optimization technique called symbiotic organism search (SOS) is proposed for the first time to solve the load frequency control problem (LFC). SOS pretends the symbiotic interaction strategies accepted by an organism to sustain in the ecosystem. Initially, an interconnected two-area reheat thermal power plant equipped with proportional–integral–derivative (PID) controller is considered for design and analysis. PID controller gains are optimally selected using SOS algorithm employing integral time absolute error based fitness function. To confirm the superiority of SOS algorithm, an extensive comparative analysis is performed with some newly published optimization methods reported in the literature. Time domain simulation results show that the dynamic stability of the concerned power system is effectively enhanced with SOS. Furthermore, the performance of the proposed method is appraised by changing system loading settings and system constraints in the range of \(\pm 50\% \). To authenticate the tuning ability of proposed SOS algorithm, the study is extended to two more test systems, namely (1) an unequal nonlinear three-area power system and (2) two-area multi-unit thermal-hydro-wind-diesel power plant including generation rate constraint, governor dead band, boiler dynamics, and time delay nonlinearities. Comparison with existing LFC schemes validates the efficacy of SOS algorithm.

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Abbreviations

SOS:

Symbiotic organism search

LFC:

Load frequency control

ACE:

Area control error

PID:

Proportional integral derivative

EA:

Evolutionary algorithm

ITAE:

Integral time absolute error

ITSE:

Integral time square error

ISE:

Integral square error

IAE:

Integral absolute error

TD:

Time delay

GRC:

Generation rate constraint

GDB:

Governor dead band

TD:

Time delay

BD:

Boiler dynamics

SLP:

Step load perturbation

BF:

Beneficial factor

MV:

Mutual vector

\(X_i\) :

ith organism of ecosystem

\(X_{best} \) :

Best organism

MDR:

Minimum damping ratio

\(n_p \) :

Population size

ub :

Upper bound

lb :

Lower bound

\(\dim \) :

Dimension of control variable

\(T_{final} \) :

Final simulation time

J :

Fitness function

\(T_{sg} \) :

Hydraulic time constant in sec

\(T_t \) :

Time constant of steam turbine in sec

\(K_r \) :

Gain of reheat unit

\(T_r \) :

Reheat time constant in sec

\(T_{ps} \) :

Time constant of power system in sec

\(K_{ps}\) :

Gain of power system

\(R_i \) :

Governor speed regulation constant of i th control area in Hz/pu MW

\(B_i \) :

Frequency biasing constant of i th control area in pu MW/Hz

\(\Delta f_i \) :

Frequency deviation of i th control area

\(\Delta P_{tie,i,j} \) :

Tie-line power deviation between ith and jth control areas

\(N_1 ,N_2 \) :

Fourier coefficients

\(\Delta P_D \) :

Load perturbation in pu

\(k_p ,k_i ,k_d \) :

Proportional, integral and derivative gains of PID-controller

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Authors and Affiliations

  1. Department of Electrical Engineering, Dr. B C Roy Engineering College, Durgapur, West Bengal, India

    Dipayan Guha

  2. Department of Electrical Engineering, Kalyani Government Engineering College, Kalyani, West Bengal, India

    Provas Kumar Roy

  3. Department of Electrical Engineering, National Institute of Technology-Durgapur, Durgapur, West Bengal, India

    Subrata Banerjee

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  1. Dipayan Guha
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  2. Provas Kumar Roy
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  3. Subrata Banerjee
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Correspondence to Dipayan Guha.

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Guha, D., Roy, P.K. & Banerjee, S. Symbiotic organism search algorithm applied to load frequency control of multi-area power system. Energy Syst 9, 439–468 (2018). https://doi.org/10.1007/s12667-017-0232-1

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