Optimal placement and sizing of capacitor using Limaçon inspired spider monkey optimization algorithm

Optimal allocation and control of fixed and switched capacitor banks on distribution systems using grasshopper optimisation algorithm with power loss sensitivity and rough set theory

“…At different points in distribution systems, an enhancement device must be placed optimally to improve the voltage profile, reduce power losses, improve power factor and reinforce transformers' capabilities. The most common enhancement method is to use the capacitor banks [2,3]. The main challenge of enhancing distribution systems with capacitor banks is the determination of the optimal number, location, size and operation scheduling of capacitor banks in order to increase economic saving as well as to generalise the overall benefit.…”

“…Over the last two decades, capacitors' allocations and control have been studied using a range of heuristic methods such as antcolony optimisation algorithm [4]; particle swarm optimisation (PSO) algorithm [5]; immune algorithm [6]; tabu search algorithm [7]; cuckoo search optimisation algorithm [8]; genetic algorithm (GA) [9,10]; evolutionary algorithm [11]; plant growth algorithm [12]; gravitational search algorithm [13]; flower pollination algorithm [14]; teaching learning optimisation algorithm [15]; bacterial foraging algorithm [16]; honey bee optimisation algorithm [17]; Limaçon inspired spider monkey optimisation algorithm (SMO) [3]; and combined fuzzy-heuristic techniques [2,[18][19][20]. These techniques try to imitate the behaviour of fish schools, bird flocks, ants, wasps, bees etc.…”

“…The connection of the capacitor banks on distribution systems can achieve many advantages once the capacitors are optimally sized and sited. Previous literatures tried to achieve these advantages by optimally allocating capacitor banks for losses minimisation [3,6,7,10,11,[14][15][16][17][21][22][23]; minimise total cost [4-8, 11-14, 24]; maximise annual net saving [2,18]; and improve distribution system voltage stability (VS) [13,19]. These objectives were considered separately as a single objective function [5,7,8,10,13,[20][21][22][23][24] or combined as a multi-objective [2,4,6,7,9,11,[14][15][16][17][18][19].…”

“…After that the deferential evaluation is used to identify the optimal size [3] Limaçon inspired SMO minimise the power loss paper gets the optimal location and size of fixed capacitor banks in distribution system using Limaçon curve inspired local search strategy (LLS) and incorporated to SMO. The main contribution of incorporating LLS and SMO is to improve the exploitation of SMO [4] ant-colony optimisation algorithm minimise the total energy cost and maximise the net saving paper uses two-stage procedure to identify the best allocation of fixed and switched capacitors individually and combination between them using PLS index, to identify the candidate buses for the best location and ant colony to get the best location and size of fixed and switched capacitors [5] PSO algorithm minimise the annual cost of the system paper uses combination between PSO and Gaussian, Caushy probability distribution and chaotic sequence in PSO to get the best location and size of fixed capacitor banks, the Gaussian, Caushy probability have been used to generate random numbers to update the velocity which is helpful in a local optimum avoidance [6] immune optimisation algorithm minimise the total cost, minimise the power losses and maximise the system security margin paper solves the problem of fixed capacitor allocation as in the proposed combination between fuzzy sets and immune algorithm to solve the multi-object optimisation problem [7] tabu search algorithm minimise the power losses and minimise total cost paper aims to get the optimal location, size and number of fixed capacitor banks at different load levels by making combination between energy loss sensitivity analysis and tabu-search optimisation algorithm.…”

Wild Goats Optimization Approach for Capacitor Placement Problem

“…In [6], interactive bi-objective programming with the precious trade-off method has been used to solve the scheduling and capacitor placement problem. Limaçon inspired spider monkey optimization approach has been utilized to find the near-optimal sie and location of capacitor in [7]. VLCI approach based on hybrid PSOGSA optimization method has been displayed to solve the CP problem in [8].…”