• Proceedings of the KIPE Conference
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• 2018.07a
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• pp.382-383
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• 2018

To increase the efficiency of a wind energy conversion system (WECS), the maximum power point tracking (MPPT) algorithm is usually employed. This paper proposes an optimal MPPT algorithm which tracks a sudden wind speed change condition fast. The proposed method can be implemented without the prior information on the wind turbine parameters, generator parameters, air density or wind speed. By investigating the directions of changes of the mechanical output power in wind turbine and rotor speed of the generator, the proposed MPPT algorithm is able to determine an optimal speed to achieve the maximum power point. Then, this optimal speed is set to the reference of the speed control loop. As a result, the proposed MPPT algorithm forces the system to operate at the maximum power point by using a three-phase converter. The simulation results based on the PSIM are given to prove the effectiveness of the proposed method.

• Journal of the Korea Society of Computer and Information
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• v.21 no.1
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• pp.131-138
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• 2016

In this paper, we propose a simple linear bottleneck assignment problems (LBAP) algorithm to find the optimal solution. Generally, the LBAP has been solved by threshold or augmenting path algorithm. The primary characteristic of proposed algorithm is derived the optimal solution of LBAP from linear sum assignment problem (LSAP). Firstly, we obtains the solution for LSAP from the selected minimum cost of rows and moves the duplicated costs in row to unselected row with minimum increasing cost in direct and indirect paths. Then, we obtain the optimal solution of LBAP according to the maximum cost of LSAP can be move to less cost. For the 29 balanced and 7 unbalanced problem, this algorithm finds optimal solution as simple.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.4726-4731
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• 2011

This paper proposed the algorithm to select optimal grinding condition for plunge grinding in the batch production unit. In order to apply to the proposed algorithm, the state variable for plunge grinding process was defined and the optimal grinding condition for each cycle in batch production was decided by genetic algorithm. Based on the optimized grinding condition in each cycle, the optimal grinding condition for whole batch production was selected by dynamic programming. The proposed algorithm was evaluated by computer simulation.

• Journal of Power Electronics
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• v.19 no.5
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• pp.1289-1302
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• 2019

In this paper, an optimal allocation method of a hybrid active power filter in an active distribution network is designed based on the differential evolution algorithm to resolve the harmonic generation problem when a distributed generation system is connected to the grid. A distributed generation system model in the calculation of power flow is established. An improved back/forward sweep algorithm and a decoupling algorithm are proposed for fundamental power flow and harmonic power flow. On this basis, a multi-objective optimization allocation model of the location and capacity of a hybrid filter in an active distribution network is built, and an optimal allocation scheme of the hybrid active power filter based on the differential evolution algorithm is proposed. To verify the effect of the harmonic suppression of the designed scheme, simulation analysis in an IEEE-33 nodes model and an experimental analysis on a test platform of a microgrid are adopted.

• The Journal of Information Technology and Database
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• v.7 no.1
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• pp.42-53
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• 2000

The major goal of this research is to develop an optimal route search algorithm for an intelligent route guidance system, one sub-area of ITS. ITS stands for intelligent Transportation System. ITS offers a fundamental solution to various issues concerning transportation and it will eventually help comfortable and swift moves of drivers by receiving and transmitting information on humans, roads and automobiles. Genetic algorithm, and fuzzy logic are utilized in order to implement the proposed algorithm. Using genetic algorithm, the proposed algorithm searches shortest routes in terms of travel time in consideration of stochastic traffic volume, diverse turn constraints, etc. Then using fuzzy logic, it selects driver-preference optimal route among the candidate routes searched by GA, taking into account various driver's preferences such as difficulty degree of driving and surrounding scenery of road, etc. In order to evaluate this algorithm, a virtual road-traffic network DB with various road attributes is simulated, where the suggested algorithm promptly produces the best route for a driver with reference to his or her preferences.

• Korean Management Science Review
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• v.31 no.3
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• pp.61-76
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• 2014

There are three methods for calculating the optimal level for spare part inventories in a MIME (Multi Indenture and Multi Echelon) system : marginal analysis, Lagrangian relaxation method, and genetic algorithm. However, their solutions are sub-optimal solutions because the MIME system is neither convex nor separable by items. To be more specific, SRUs (Shop Replaceable Units) are required to fix a defected LRU (Line Replaceable Unit) because one LRU consists of several SRUs. Therefore, the level of both SRU and LRU cannot be calculated independently. Based on the limitations of three existing methods, we proposes a improved algorithm applying marginal analysis on determining LRU stock level and genetic algorithm on determining SRU stock level. It can draw optimal combinations on LRUs through separating SRUs. More, genetic algorithm enables to extend the solution search space of a SRU which is restricted in marginal analysis applying greedy algorithm. In the numerical analysis, we compare the performance of three existing methods and the proposed algorithm. The research model guarantees better results than the existing analytical methods. More, the performance variation of the proposed method is relatively low, which means one execution is enough to get the better result.

• Journal of Electrical Engineering and Technology
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• v.3 no.2
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• pp.177-183
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• 2008

The particle swarm optimization(PSO) has been shown to converge rapidly during the initial stages of a global search, but around global optimum, the search process becomes very slow. On the other hand, the genetic algorithm is very sensitive to the initial population. In fact, the random nature of the GA operators makes the algorithm sensitive to initial population. This dependence to the initial population is in such a manner that the algorithm may not converge if the initial population is not well selected. In this paper, we have proposed a new algorithm which combines PSO and GA in such a way that the new algorithm is more effective and efficient and can find the optimal solution more accurately and with less computational time. Optimal location of SVC using this hybrid PSO-GA algorithm is found. We have also found the optimal place of SVC using GA and PSO separately and have compared the results. It has been shown that the new algorithm is more effective and efficient. An IEEE 68 bus test system is used for simulation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.270-275
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• 2016

We present an approach for acceleration actor-critic algorithm for reinforcement learning with continuous action space. Actor-critic algorithm has already proved its robustness to the infinitely large action spaces in various high dimensional environments. Despite that success, the main problem of the actor-critic algorithm remains the same-speed of convergence to the optimal policy. In high dimensional state and action space, a searching for the correct action in each state takes enormously long time. Therefore, in this paper we suggest a search accelerating function that allows to leverage speed of algorithm convergence and reach optimal policy faster. In our method, we assume that actions may have their own distribution of preference, that independent on the state. Since in the beginning of learning agent act randomly in the environment, it would be more efficient if actions were taken according to the some heuristic function. We demonstrate that heuristically-accelerated actor-critic algorithm learns optimal policy faster, using Educational Process Mining dataset with records of students' course learning process and their grades.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.7
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• pp.1802-1814
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• 2012

In order to guide users to select the most optimal access network in heterogeneous wireless networks, a network selection algorithm is proposed which is designed based on multi-objective discrete particle swarm optimization (Multi-Objective Discrete Particle Swarm Optimization, MODPSO). The proposed algorithm keeps fast convergence speed and strong adaptability features of the particle swarm optimization. In addition, it updates an elite set to achieve multi-objective decision-making. Meanwhile, a mutation operator is adopted to make the algorithm converge to the global optimal. Simulation results show that compared to the single-objective algorithm, the proposed algorithm can obtain the optimal combination performance and take into account both the network state and the user preferences.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.10
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• pp.1681-1687
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• 2008

This paper shows application of optimal reconfiguration algorithm for distributing power system to KEPCO system for loss minimization and load balancing. That is, it suggests additional algorithm to check potential problems caused in case of theoretical algorithm being applied to real system and recover from them. Also, comparing the results of reconfiguration algorithm Tabu-Search Algorithm applied to current KEPCO distribution power system and those of Branch Exchange Algorithm using initial operation point suggested in this paper, it shows how much the results are improved in aspects of load balancing, loss reduction and calculating time.