3,211 research outputs found
DESIGN OF OPTIMAL PID CONTROLLER FOR THREE PHASE INDUCTION MOTOR BASED ON ANT COLONY OPTIMIZATION
Speed control of an induction motor is an important part of the operation of an induction motor. One method of regulating motor speed is the addition of a PID controller. PID parameters must be tuned properly to get the optimal speed. In this study, the PID controller tuning method uses an artificial intelligence method based on Ant Colony Optimization (ACO). ACO algorithm in an intelligent algorithm that is inspired by the behavior of ants looking for food sources in groups with traces of feromone left behind. In this study, food sources are represented as optimal parameters of PID. From the computational results obtained optimal parameters respectively, P (Proportional) 0.5359, I (Integral) 0.1173, D (Derivative) 0.0427. ACO computing found the optimal parameters in the 21st iteration with a minimum fitness function of 11.8914. Case studies are used with two variations of the speed of the induction motor input. With optimal tuning, the performance of the induction motor is increasing, marked by a minimum overshoot of 1.08 pu and a speed variation of both overshoots of 1,201 pu, whereas without control 1.49 pu and 1.28 pu, as well as with PID trial control of 1.22 pu and 1.23 pu respectively. The benefits of this research can be used as a reference for the operation of induction motors, by tuning the Ant Colony intelligent method for the PID controller in real-time with the addition of microcontroller components
An improved Ant Colony System for the Sequential Ordering Problem
It is not rare that the performance of one metaheuristic algorithm can be
improved by incorporating ideas taken from another. In this article we present
how Simulated Annealing (SA) can be used to improve the efficiency of the Ant
Colony System (ACS) and Enhanced ACS when solving the Sequential Ordering
Problem (SOP). Moreover, we show how the very same ideas can be applied to
improve the convergence of a dedicated local search, i.e. the SOP-3-exchange
algorithm. A statistical analysis of the proposed algorithms both in terms of
finding suitable parameter values and the quality of the generated solutions is
presented based on a series of computational experiments conducted on SOP
instances from the well-known TSPLIB and SOPLIB2006 repositories. The proposed
ACS-SA and EACS-SA algorithms often generate solutions of better quality than
the ACS and EACS, respectively. Moreover, the EACS-SA algorithm combined with
the proposed SOP-3-exchange-SA local search was able to find 10 new best
solutions for the SOP instances from the SOPLIB2006 repository, thus improving
the state-of-the-art results as known from the literature. Overall, the best
known or improved solutions were found in 41 out of 48 cases.Comment: 30 pages, 8 tables, 11 figure
Improved particle swarm optimization algorithm for multi-reservoir system operation
AbstractIn this paper, a hybrid improved particle swarm optimization (IPSO) algorithm is proposed for the optimization of hydroelectric power scheduling in multi-reservoir systems. The conventional particle swarm optimization (PSO) algorithm is improved in two ways: (1) The linearly decreasing inertia weight coefficient (LDIWC) is replaced by a self-adaptive exponential inertia weight coefficient (SEIWC), which could make the PSO algorithm more balanceable and more effective in both global and local searches. (2) The crossover and mutation idea inspired by the genetic algorithm (GA) is imported into the particle updating method to enhance the diversity of populations. The potential ability of IPSO in nonlinear numerical function optimization was first tested with three classical benchmark functions. Then, a long-term multi-reservoir system operation model based on IPSO was designed and a case study was carried out in the Minjiang Basin in China, where there is a power system consisting of 26 hydroelectric power plants. The scheduling results of the IPSO algorithm were found to outperform PSO and to be comparable with the results of the dynamic programming successive approximation (DPSA) algorithm
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