An Improved Solution for Reactive Power Dispatch Problem Using Diversity-Enhanced Particle Swarm Optimization

Abstract

Well-structured reactive power policies and dispatch are major concerns of operation and control technicians of any power system. Obtaining a suitable reactive power dispatch for any given load condition of the system is a prime duty of the system operator. It reduces loss of active power occurring during transmission by regulating reactive power control variables, thus boosting the voltage profile, enhancing the system security and power transfer capability, thereby attaining an improvement in overall system operation. The reactive power dispatch (RPD) problem being a mixed-integer discrete continuous (MIDC) problem demands the solution to contain all these variable types. This paper proposes a methodology to achieve an optimal and practically feasible solution to the RPD problem through the diversity-enhanced particle swarm optimization (DEPSO) technique. The suggested method is characterized by the calculation of the diversity of each particle from its mean position after every iteration. The movement of the particles is decided based on the calculated diversity, thereby preventing both local optima stagnation and haphazard unguided wandering. DEPSO accounts for the accuracy of the variables used in the RPD problem by providing discrete values and integer values compared to other algorithms, which provide all continuous values. The competency of the proposed method is tested on IEEE 14-, 30-, and 118-bus test systems. Simulation outcomes show that the proposed approach is feasible and efficient in attaining minimum active power losses and minimum voltage deviation from the reference. The results are compared to conventional particle swarm optimization (PSO) and JAYA algorithms.

An Improved Lightning Attachment Procedure Optimizer for Optimal Reactive Power Dispatch With Uncertainty in Renewable Energy Resources

“…where Г denotes the gamma function; and represent the parameters of the beta PDF which is calculated using (26) and (27)…”

“…ORPD problem is a non-convex, complex, and non-linear optimization problem. Thus, many efforts have been introduced for solving the ORPD by applying numerous optimization techniques including the Backtracking Search Optimizer (BSO) [2], Particle Swarm Optimization (PSO) [3], Ant Lion Optimizer (ALO) [4], Improved Ant Lion Optimization algorithm (IALO) [5] , Whale Optimization Algorithm (WOA) [6], Improved Social Spider Optimization Algorithm (ISSO) [7], Differential Evolution (DE) [8], Moth Swarm Algorithm (MSA) [9], Evolutionary Algorithm (EA) [10], Modified Differential Evolution (MDE) [11], Jaya Algorithm (JA) [12], Modified Sine Cosine Algorithm (MSCA) [13], Lightning Attachment Procedure Optimization (LAPO) [14], Gravitational Search Algorithm (GSA) [15], Biogeography-Based Optimization (BBO) [16], Teaching Learning Based Optimization (TLBO) [17], Harmony Search Algorithm (HAS) [17], Grey Wolf Optimizer (GWO) [18], Comprehensive Learning Particle Swarm Optimization (CLPSO) [19], Chemical Reaction Optimization (CRO) [20], Improved Gravitational Search Algorithm (IGSA) [21], Improved Pseudo-Gradient Search Particle Swarm Optimization (IPG-PSO) [22], Firefly Algorithm (FA) [23], Fractional Particle Swarm Optimization Gravitational Search Algorithm [24], hybrid GWO-PSO optimization [25], Oppositional Salp Swarm Algorithm (OSSA) [26], diversity-enhanced particle swarm optimization (DEPSO) [27].…”

Adaptive Algorithm of a Tap-Changer Controller of the Power Transformer Supplying the Radial Network Reducing the Risk of Voltage Collapse

“…Voltage stability is the ability of a power system to maintain steady acceptable voltages at all buses in the system under normal operating conditions and after being subjected to a disturbance. The main factor causing instability is the inability of the power system to meet the demand for reactive power [20][21][22][23][24][25].…”

Real Power Loss Reduction by Blue Noddy and European Night Crawler Optimization Algorithms

“…In conventional methods inequality constraints are unable to be included successfully and in evolutionary based algorithms balancing the exploration and exploitation are major task to reach the most excellent solution [11][12][13][14][15][16][17][18]. There should be proper trade-off between exploration and exploitation because when trade-off failed then it not at all possible to reach a better solution [21][22][23][24][25]. This paper proposes Blue noddy optimization (BNO) algorithm and European Night crawler optimization (ENO) algorithm for power loss reduction.…”