Distribution Network Reconfiguration Using Binary Particle Swarm Optimization to Minimize Losses and Decrease Voltage Stability Index

Abstract

Power losses and voltage drop are existing problems in radial distribution networks. This power losses and voltage drop affect the voltage stability level. Reconfiguring the network is a form of approach to improve the quality of electrical power. The network reconfiguration aims to minimize power losses and voltage drop as well as decreasing the Voltage Stability Index (VSI). In this research, network reconfiguration uses binary particle swarm optimization algorithm and Bus Injection to Branch Current-Branch Current to Bus Voltage (BIBC-BCBV) method to analyze the radial system power flow. This scheme was tested on the 33-bus IEEE radial distribution system 12.66 kV. The simulation results show that before reconfiguration, the active power loss is 202.7126 kW and the VSI is 0.20012. After reconfiguration, the active power loss and VSI decreased to 139.5697 kW and 0.14662, respectively. It has decreased the power loss for 31.3136% significantly while the VSI value is closer to zero.

Distribution network reconfiguration considering DGs using a hybrid CS-GWO algorithm for power loss minimization and voltage profile enhancement

“…Depending on the number of tie switches, five loops have been formed as 𝐿 1 to 𝐿 5 , these switches are operated during fault cases, load balancing conditions and to reduce the system losses. L1= [ 3,4,5,6,7,8,9,36,37,38,39,40,41,42,35]; L2= [ 11,12,13,14,44,43,45]; L3 = [ 15,16,17,18,19,20]; L4= [ 21,22,23,24,25,26,59,60,61,62,63,64]; L5 = [ 47,48,49,53,54,55,56,57,52,46, To evaluate the effectiveness of proposed method, test system 2 is also simulated at different load levels such as light (0.5), nominal (1.0), and heavy (1.6), and obtained results are conferred in Table 3. From Table 3, we can infer that base case PL in the DS (in kW) at light load is 51.60, which is reduced to 23.43, 18.14, 11.54 and 9.545 using scenarios II, III, IV, and V, respectively.…”

“…In the past decade of literature, many metaheuristic techniques have been proposed by the researchers to find the optimal solution for the DNR problem separately without DGs allocation, with an aim to minimize active PL and enhance the voltage profile of system. Some of the most popularly used metaheuristic algorithms to solve DNR problem alone with different objective functions are listed as Refined genetic algorithm [5], Harmony search algorithm [6], Minimum current circular-updating mechanism method [7], Discrete PSO algorithm [8], Improved adaptive imperialist competitive algorithm [9], Catfish PSO algorithm [10], Fireworks algorithm [11], Enhanced genetic algorithm [12], NSGA-II algorithm [13], Cuckoo search algorithm [14], Genetic algorithm with varying population size [15], Runner-root algorithm [16], Biased random key genetic algorithm [17], Binary PSO algorithm [18], Modified culture algorithm [19], Feasibility-preserving evolutionary optimization [20], graphically-based network reconfiguration [21]. Moreover, this algorithm needs many control parameters to be tuned for each problem to obtain an optimal solution.…”

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