Multi objective self adaptive optimization method to maximize ampacity and minimize cost of underground cables

Abstract

This study presents a novel algorithm for the optimal placement of underground cables in a concrete duct bank to simultaneously maximize ampacity and minimize cable system cost for the first time. The self-adaptive particle swarm optimization (SAPSO) method -which has been used to solve multi-objective optimization problems- is used to solve the multi-objective problem. The main novelty of this paper is finding optimal cable placement by finding maximum ampacity and minimum cable system cost, simultaneously. The proposed method is used in a test case to show how the cable placement is affected by the total ampacity and cable cost in the duct bank. The obtained results show the total fundamental ampacity decrease and the total cable cost increase, on the existence of power system harmonics. The output of the optimization problem is a Pareto optimal solution which gives the cable placement for the concurrent maximized ampacity and minimized total cost. As a case in point, at a specific ampacity of the worst cable configuration, the total cable system cost is more than the total cost of the cable configuration obtained from the optimal Pareto solution, based on simulation results. Highlights An analytical formula is studied to compute the temperature of underground cables. The Thermal Margin Index (TMI) is introduced and the probability of unreliability is calculated. Obtained simulation results have shown that HPEM requires less input data.

Multiobjective optimization of underground power cable systems

“…Vahidi [14] PSO algorithm calculating the optimal configuration set of underground cables in the concrete duct bank, simultaneously maximizing the ampacity and minimizing the cost of the system Table 2 Design variables and their change ranges considered in the computation.…”

“…Two optimization algorithms, PSO and Shuffled Frog Leaping algorithms, were used. In the next paper, Zarchi and Vahidi [14] proposed an algorithm for calculating the optimal configuration set of underground cables in the concrete duct bank, simultaneously maximizing the ampacity and minimizing the cost of system. A Particle Swarm Optimization was employed.…”