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1

Online parameter identification of SPMSM based on improved artificial bee colony algorithm

Wu Chunli, Jiang Shuai, Bian Chunyuan

Archives of Electrical Engineering

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2021

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Vol. 70, nr 4

777--790

EN

The artificial bee colony (ABC) intelligence algorithm is widely applied to solve multi-variable function optimization problems. In order to accurately identify the parameters of the surface-mounted permanent magnet synchronous motor (SPMSM), this paper proposes an improved ABC optimization method based on vector control to solve the multi-parameter identification problem of the PMSM. Because of the shortcomings of the existing parameter identification algorithms, such as high computational complexity and data saturation, the ABC algorithm is applied for the multi-parameter identification of the PMSM for the first time. In order to further improve the search speed of the ABC algorithm and avoid falling into the local optimum, Euclidean distance is introduced into the ABC algorithm to search more efficiently in the feasible region. Applying the improved algorithm to multi-parameter identification of the PMSM, this method only needs to sample the stator current and voltage signals of the motor. Combined with the fitness function, the online identification of the PMSM can be achieved. The simulation and experimental results show that the ABC algorithm can quickly identify the motor stator resistance, inductance and flux linkage. In addition, the ABC algorithm improved by Euclidean distance has faster convergence speed and smaller steady-state error for the identification results of stator resistance, inductance and flux linkage.

2

Comparison of different approaches in traffic forecasting models for the D-200 highway in Turkey

Dogan E., Korkmaz E., Akgungor A. P.

Zeszyty Naukowe. Transport / Politechnika Śląska

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2018

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z. 99

25--42

EN

Short-term traffic estimations have a significant influence in terms of effectively controlling vehicle traffic. In this study, short-term traffic forecasting models have been developed based on different approaches. Seasonal autoregressive integrated moving average (SARIMA), artificial bee colony (ABC) and differential evolution (DE) algorithms are the techniques used in the optimization of models, which have been developed by using observation data for the D-200 highway in Turkey. 80% of the data were used for training, with the remaining data used for testing. The performances of the models were illustrated with mean absolute errors (MAEs), mean absolute percentage errors (MAPEs), the coefficient of determination (R2) and the root-mean-square errors (RMSEs). It is understood that all the models provided consistent and useful results when the developed models were compared with the statistical results. In the models created separately for two lanes, the R2 values of the models were calculated to be approximately 92% for the right lane, which is generally used by heavy vehicles, and 88% for the left lane, which is used by less traffic. Based on the MAE and RMSE values, the model developed by the ABC algorithm gave the lowest error and showed more effective performance than the other approaches. Thus, the ABC model showed that it is appropriate for use on other highways in Turkey.

3

Application of artificial bee colony algorithm to auto-tuning of linear-quadratic regulator for PMSM position control

Tarczewski T., Grzesiak L M.

Przegląd Elektrotechniczny

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2016

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R. 92, nr 6

57--62

EN

In this paper auto-tuning process of linear-quadratic regulator (LQR) for position control of permanent magnet synchronous motor (PMSM) is presented. The novelty of the proposed solution lies in use of artificial bee colony (ABC) optimization algorithm to calculate gain coefficients of controller. In order to maintain selected state and control variables of drive in a permissible level, the ABC algorithm has been extend for solving constrained optimization problem. Proper operation of auto-tuning procedure was investigated by numerical simulations.

PL

W artykule przedstawiono proces samostrojenia regulatora liniowo-kwadratowego przeznaczonego do regulacji położenia silnika synchronicznego o magnesach trwałych. Oryginalność proponowanego rozwiązania polega na zastosowaniu algorytmu optymalizacyjnego sztucznej kolonii pszczół do wyznaczenia współczynników wzmocnień regulatora. W celu utrzymywania wartości wybranych zmiennych stanu i sygnałów sterujących w dopuszczalnym zakresie, algorytm optymalizacyjny rozszerzono o możliwość rozwiązywania problemów optymalizacyjnych z ograniczeniami. Poprawne działanie algorytmu automatycznego strojenia zostało zbadane w testach symulacyjnych.

4

Macromodelling of fermentation process of municipal solid organic waste at biogas plants on the acidogenesis stage

Hural I., Dyvak M., Stakhiv P.

Journal of Applied Computer Science

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2016

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Vol. 24, nr 1

17--35

EN

In this work the task of modeling processes of anaerobic microbiological fermentation with keeping the balance on the stages of hydrolysis and acidogenesis was considered. It was proposed and justified to build the model at the stage of acidogenesis in the form of interval difference operator (IDO). The structure identification method of IDO on the principles of honey bee colony functioning in the food search process, which provides the possibility of obtaining simple IDO structures with specified prognostic properties relative to existing methods, was substantiated.

5

Interval model of dynamics of dispersion of harmful pollution from vehicular traffic

Dyvak M., Stakhiv P., Pukas A., Voytyuk I., Porplytsya N., Maslyiak Y.

Journal of Applied Computer Science

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2016

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Vol. 24, nr 3

49--60

EN

The method of structure identification of interval discrete dynamic models, based on principles of the bee colonies functioning is represented. An example of the implementation of method for modeling of air pollution by harmful vehicle emissions is considered.

6

Zastosowanie algorytmów rojowych do optymalizacji parametrów w modelach układów regulacji

Tomera M.

Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej

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2015

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Nr 46

97--102

PL

W pracy przedstawione zostały algorytmy rojowe, takie jak: algorytm mrówkowy, zmodyfikowany algorytm mrówkowy, algorytm sztucznej kolonii pszczół oraz algorytm optymalizacji rojem cząstek. Dla tych algorytmów przygotowane zostało oprogramowanie w Matlabie, pozwalające na optymalizację parametrów poszukiwanych modeli matematycznych, wyznaczanych na podstawie przeprowadzonych testów identyfikacyjnych lub na optymalizację parametrów regulatorów zastosowanych w modelach matematycznych układów sterowania.

EN

The paper presents the swarm intelligence algorithms, such as: ant colony algorithm (ACO), the modified ant colony algorithm (MACO), the artificial bee colony algorithm (ABC) and the particle swarm optimization algorithm (PSO). Ant colony optimization (ACO) based upon the observation of the behavior of ant colonies looking for food in the surrounding anthill. Feeding ants it is based on finding the shortest path transitions between a food source and the anthill. In the process of foraging ants on their paths crossing from the nest to a food source and back, they leave a pheromone trail. The work presents also the modified ant colony algorithm (MACO). This algorithm is based on searching the solution space surrounded by the best solution obtained in the previous iteration. If you find a local minimum, the proposed algorithm uses pheromone to find a new solution space, while retaining the position information current local minimum. The artificial bee colony algorithm is one of the well-known swarm intelligence algorithms. In the past decade there has been created several different algorithms based on the observation of the behavior of cooperative bees. Among them, the most frequently analyzed and used is bee algorithm proposed in 2005 by Dervis Karaboga and was be used in the proposed paper. The particle swarm optimization algorithm (PSO) is based on adjusting the change speed of the moving particles to a speed of particles movement in the neighborhood. Particle optimization algorithm is one of the computational techniques derived on the basis of swarm behavior such as flocks of birds and schools of fish, which is the basis for the functioning of the exchange of information to enable them to cooperate. It was noticed that the animals in the herd tend to maintain the optimum distance from their neighbors, by appropriate adjustment of their speed. This method allows the synchronous and collision-free motion, often accompanied by sudden changes of direction and due to the rearrangement of the optimal formation. For these algorithms has been prepared the software in Matlab, allowing to optimization of the mathematical models designated on the basis of the carried out identification tests and control parameters used in the mathematical model of the control system.