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

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.

Fractional Dynamics of Stuxnet Virus Propagation in Industrial Control Systems

“…In the future, one may exploit the strength of stochastic numerical solvers [56][57][58][59][60][61] based on fractional evolutionary and swarming techniques [62][63][64][65][66][67] for a detailed analysis of the designed fractional-order Stuxnet virus model. Additionally, new definitions of the fractional operator, such as Yang-Machado [35] and Yang-Abdel-Aty-Cattani [36] fractional derivatives looks promising for the development of new computing solvers for the numerical solution of the fractional-order Stuxnet virus model and other fractionalorder systems with better theoretical justifications, a better applicability domain, proof of the accuracy, convergence, stability, and robustness.…”

Diffusion Based Channel Gains Estimation in WSN Using Fractional Order Strategies

“…(3). To introduce fractional part [47][48][49][50][51], we introduce the Caputo fractional derivative of order ν such that n − 1 < ν < n of a function g(t) = t p , for p ≥ 0 as given in [14,[46][47][48][49][50][51] as:…”

“…We consider modelling of the channel gains [8,19], which are generated using the path loss and log-normal effects [38][39][40][41][42][43][44][45][46]; and then the estimation of these gains through the FO DLMS. Recently, similar techniques for non-distributed environments published in reputed databases includes RC circuit modelled with nonlinear differential order system [47], design of multi-innovation fractional LMS algorithm [48], fractional evolutionary processing [49], power signal parameter estimation [50], and design of momentum fractional LMS for Hammerstein nonlinear system identification [51]. Evaluation through numerical experimentation is performed for different number of sleep cycles, compare the results with conventional DLMS and for different fractional orders.…”

Solving Reactive Power Scheduling Problem using Multi-objective Crow Search Algorithm

“…Differential evolution (DE) based productive stochastic search approach for solving the ORPD problem is presented in [12]. A fractional evolutionary based method that achieves the objectives of ORPD problem by including the FACTS controllers is presented in [13]. The author in [14] proposes a stochastic fractal search methodology for the solution of ORPD problem considering the voltage stability index (VSI), voltage deviation and power losses objectives.…”