Adaptive Dragonfly Optimization Based Placement of Capacitor Banks for Voltage Stability Enhancement in Distribution Networks

The present day ever escalating power demand moves the operating point of the distribution networks in the vicinity of the voltage stability boundaries. Any small reactive power unbalance between the generation and demand may trigger voltage instability, which makes the voltage magnitude to fall slowly until a rapid change occurs. This paper presents an adaptive dragonfly optimization based method for placement of shunt capacitor banks with a view of improving the voltage stability besides enhancing the voltage profile and lowering the network losses. Simulation results on 33 and 69-node distribution networks exhibit the greater performances of the suggested algorithm.

Particle swarm optimisation for reactive power compensation on Oman 6 bus electrical grid

<span lang="EN-US">The consistent problem with operators and planners for power systems has been related to minimizing transmission losses. An important role is played by reactive power by keeping voltage stability and reliability in the system in order to support the transfer of real power. The optimal reactive power dispatch is associated with the problem of non-linear optimization along with many constraints. In this paper, a study is highlighted for an algorithm that optimizes reactive power with the help of particle swarm algorithm and compare the result with Newton-Raphson method. Reduction of system active power loss is the goal of the function in the projected algorithm. Here, the control variables identified are transformer tap positions, generator bus voltages, and shunt capacitor banks with switch. This projected algorithm is performed on Oman 6 bus electrical grid as oman electricity transmission company has an instability voltage issue in chosen 6 bus.</span>

Energy Saving Maximization of Balanced and Unbalanced Distribution Power Systems via Network Reconfiguration and Optimum Capacitor Allocation Using a Hybrid Metaheuristic Algorithm

The main aim of this work was the maximization of the energy saving of balanced and unbalanced distribution power systems via system reconfiguration and the optimum capacitor's bank choice, which were estimated by using a new algorithm: modified Tabu search and Harper sphere search (MTS-HSSA). The results demonstrated that the proposed method is appropriate for energy saving and improving performance compared with other methods reported in the literature for IEEE 33-bus adopted systems, including large scale systems such as IEEE 119 and the IEEE 123 unbalanced distribution system. Moreover, it can be used for unbalanced distribution systems distributed generators (DGs). The results demonstrated that the proposed method (the optimal choice of shunt capacitor(SC) banks and the optimal reconfiguration via the proposed algorithm) is appropriate for energy saving compared with different strategies for energy saving, which included distributed generation (DG) at different cost levels.