Design of fractional evolutionary processing for reactive power planning with FACTS devices

Yasir Muhammad,J A Tenreiro Machado,Rahimdad Khan,Muhammad Asif Zahoor Raja,Farman Ullah,Rizwan Akhtar

doi:10.1038/s41598-020-79838-2

Abstract

Reactive power dispatch is a vital problem in the operation, planning and control of power system for obtaining a fixed economic load expedition. An optimal dispatch reduces the grid congestion through the minimization of the active power loss. This strategy involves adjusting the transformer tap settings, generator voltages and reactive power sources, such as flexible alternating current transmission systems (FACTS). The optimal dispatch improves the system security, voltage profile, power transfer capability and overall network efficiency. In the present work, a fractional evolutionary approach achieves the desired objectives of reactive power planning by incorporating FACTS devices. Two compensation arrangements are possible: the shunt type compensation, through Static Var compensator (SVC) and the series compensation through the Thyristor controlled series compensator (TCSC). The fractional order Darwinian Particle Swarm Optimization (FO-DPSO) is implemented on the standard IEEE 30, IEEE 57 and IEEE 118 bus test systems. The power flow analysis is used for determining the location of TCSC, while the voltage collapse proximity indication (VCPI) method identifies the location of the SVC. The superiority of the FO-DPSO is demonstrated by comparing the results with those obtained by other techniques in terms of measure of central tendency, variation indices and time complexity.

Highlights

The validity and applicability of the Fractional Order Darwinian PSO (FO-Darwinian PSO (DPSO)) is analyzed for the reactive power scheduling in the IEEE 30, IEEE 57 and IEEE 118 buses while incorporating the Static Var compensator (SVC) and Thyristor controlled series compensator (TCSC) devices at weak buses
The theoretical and simulation analyses are presented for 10 values of the fractional order including the integer order case, i.e., α = 1, where the fractional DPSO transformed to standard DPSO
The FO-DPSO was explored for the minimization of the active power losses and the operating costs, together with the installation cost of FACT, while the voltage profile is maintained within the allowable limits through minimizing voltage deviation index in the standard IEEE-30, 57 and 118 bus systems

Summary

Introduction

The Darwinian PSO (DPSO) is an evolutionary mechanism that improves the standard PSO by increasing its capability to escape from local optimum either by natural assortment, or by persisting those with high fitness values. The performance of DPSO is superior to the one exhibited by the PSO, Parameters Particle dimensions or variables Swarm, set of particles Fractional order Inertia weight Global acceleration factor Local acceleration factor Iterations or cycles for statistics Vmax but has the disadvantage of a higher computational complexity. Pires et al combined the DPSO with the concept of fractional calculus (FC) to improve learning ability of the DPSO mechanism by designing the Fractional Order Darwinian PSO (FO-DPSO)[54]. The integro-differential operator defined by the Grünwald-Letnikov, Caputo and Riemann–Liouville formulations are classical expressions that are adopted in science and engineering. The Grüwald-Letnikov interpretation of the fractional derivative can be expressed as[35]

Methods

Results

Conclusion