Wyniki wyszukiwania - BazTech

1

Tool to identify parameters of insulation system in electrical machines

Kulesz Barbara, Sikora Andrzej, Słota Damian

Przegląd Elektrotechniczny

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2022

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R. 98, nr 11

292--295

EN

The issue of the equivalent scheme parameter identification for the insulation system in an electrical machine is discussed in the paper. The presented method is based upon a recorded voltage waveform, and Artificial Bee Colony algorithm is used in calculations. A numerical example is presented.

PL

W artykule przedstawiono problem identyfikacji parametrów schematu zastępczego układu izolacyjnego maszyny elektrycznej. Zaproponowana metoda identyfikacji wykorzystuje zarejestrowane przebiegi napicia i algorytm roju pszczelego (Artificial Bee Colony Algorithm). Zamieszczono przykład obliczeniowy.

2

Economic dispatch problem using artificial bee colony optimization based on predator and prey concept

Hassani Naima, Bouzeboudja Hamid, Younes Mimoun

Przegląd Elektrotechniczny

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2022

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R. 98, nr 9

32--35

EN

These Artificial bee colony (ABC) algorithm is one of the most recent nature-inspired based algorithms, which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. In this paper, which combines the ABC algorithm and predator-prey (PP) methodology, the PP procedure was incorporated into the ABC algorithm to enhance the process of exploitation. Application of ABC algorithm combined with PP is based on mathematical modelling to solve Economic Dispatch (ED) problems. This combination is tested on 6-Units system. Simulation results are compared with those of other studies reported in the literature, and the comparative results demonstrate our proposed method is more feasible and effective. This method can be deemed to be a promising alternative for solving the (ED) problems in real systems.

PL

Algorytm sztucznej kolonii pszczół (ABC) jest jednym z najnowszych algorytmów inspirowanych naturą, który, jak wykazano, jest konkurencyjny w stosunku do innych algorytmów populacyjnych. Jednak w ABC nadal brakuje równania wyszukiwania rozwiązań, które jest dobre w eksploracji, ale słabe w eksploatacji. W tym artykule, który łączy algorytm ABC i metodologię drapieżnik-ofiara (PP), procedura PP została włączona do algorytmu ABC w celu usprawnienia procesu eksploatacji. Zastosowanie algorytmu ABC w połączeniu z PP opiera się na modelowaniu matematycznym do rozwiązywania problemów Ekonomicznej Dyspozycji (ED). Ta kombinacja jest testowana w systemie 6-jednostkowym. Wyniki symulacji są porównywane z wynikami innych badań opisanych w literaturze, a wyniki porównawcze pokazują, że proponowana przez nas metoda jest bardziej wykonalna i skuteczna. Metoda ta może być uznana za obiecującą alternatywę rozwiązywania problemów (ED) w rzeczywistych systemach.

3

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.

4

Investigation of Applicability of Artificial Bee Colony Algorithm on Rainfall Intensity Duration Frequency Equations

Zeybekoglu Utku, Keskın Asli Ulke

Journal of Ecological Engineering

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2020

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Vol. 21, nr 7

27--33

EN

The main objective of this study was to investigate the applicability and efficiency of an artificial bee colony optimization algorithm to determine two statistical-based rainfall intensity duration frequency equations’ weighting parameters. For this aim, the annual maximum rainfall records were obtained from seven meteorological stations of seven geographic regions in Turkey. It was observed that the Artificial Bee Colony algorithm, which is an alternative technique for solving the rainfall intensity duration frequency equations, gives very good results in selected seven meteorological stations.

5

Artificial bee colony based state feedback position controller for PMSM servo-drive – the efficiency analysis

Tarczewski T., Niewiara L. J., Grzesiak L. M.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2020

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Vol. 68, nr 5

997--1007

EN

This paper presents a state feedback controller (SFC) for position control of PMSM servo-drive. Firstly, a short review of the commonly used swarm-based optimization algorithms for tuning of SFC is presented. Then designing process of current control loop as well as of SFC with feedforward path is depicted. Next, coefficients of controller are tuned by using an artificial bee colony (ABC) optimization algorithm. Three of the most commonly applied tuning methods (i.e. linear-quadratic optimization, pole placement technique and direct selection of coefficients) are used and investigated in terms of positioning performance, disturbance compensation and robustness against plant parameter changes. Simulation analysis is supported by experimental tests conducted on laboratory stand with modern PMSM servo-drive.

6

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.

7

Multi-Objective Optimization of Traffic Signal Timing Using Non-Dominated Sorting Artificial Bee Colony Algorithm for Unsaturated Intersections

Zhao H., He R., Su J.

Archives of Transport

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2018

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Vol. 46, iss. 2

85--96

EN

Vehicle delay and stops at intersections are considered targets for optimizing signal timing for an isolated intersection to overcome the limitations of the linear combination and single objective optimization method. A multi-objective optimization model of a fixed-time signal control parameter of unsaturated intersections is proposed under the constraint of the saturation level of approach and signal time range. The signal cycle and green time length of each phase were considered decision variables, and a non-dominated sorting artificial bee colony (ABC) algorithm was used to solve the multi-objective optimization model. A typical intersection in Lanzhou City was used for the case study. Experimental results showed that a single-objective optimization method degrades other objectives when the optimized objective reaches an optimal value. Moreover, a reasonable balance of vehicle delay and stops must be achieved to flexibly adjust the signal cycle in a reasonable range. The convergence is better in the non-dominated sorting ABC algorithm than in non-dominated sorting genetic algorithm II, Webster timing, and weighted combination methods. The proposed algorithm can solve the Pareto front of a multi-objective problem, thereby improving the vehicle delay and stops simultaneously.

8

Application of artificial bee colony algorithm to auto-tuning of state feedback controller for DC-DC power converter

Tarczewski T., Niewiara Ł. J., Grzesiak L M.

Power Electronics and Drives

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2016

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Vol. 1 (36), No. 2

83--96

EN

The article presents an auto-tuning method of state feedback voltage controller for DC-DC power converter. The penalty matrices employed for calculation of controller’s coefficients were obtained by using nature-inspired artificial bee colony (ABC) optimization algorithm. This overcomes the main drawback of state feedback control related to time-consuming trial-and-error tuning procedure. The optimization algorithm takes into account constraints of selected state and control variables of DC-DC power converter. In order to meet all control objectives (i.e., fast voltage response and chattering-free control signal) an appropriate performance index is proposed. Proper selection of state feedback controller (SFC) coefficients is proven by simulation and experimental tests of DC-DC power converter.

9

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.

10

The prioritisation of production orders under the bee colony algorithm

Jardzioch A., Bulwan K.

Advances in Manufacturing Science and Technology

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2013

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Vol. 37, nr 4

49--59

EN

The paper presents the problem of determining the prioritisation of production orders. The proposed criterion function allows a comprehensive evaluation of various ways of prioritising taking into account both the income derived from the execution of production orders and the penalty for any delays which may occur. The criterion function was implemented in an algorithm based on the operation of a colony of bees. The experiments which have been carried out make it possible to evaluate the solutions obtained through the provided algorithm and compare them with the solutions obtained through the typical heuristic rules. The results show that the prioritisation obtained through the algorithm is characterized by the highest qualities of the criterion function and is definitely superior to that obtained through the simple heuristic rules.

PL

W pracy przedstawiono zagadnienia ustalania kolejności wprowadzania zleceń do produkcji. Zaproponowano zastosowanie kompleksowej funkcji kryterialnej do oceny różnorodnych uszeregowań. Funkcja ta uwzględnia zarówno przychód uzyskany z realizacji zleceń produkcyjnych, jak i ewentualne kary za opóźnienia w ich wykonaniu. Opracowano algorytm oparty na działaniu roju pszczół, w którym zaimplantowano proponowaną funkcję kryterialną. Wykonane eksperymenty pozwoliły na ocenę uszeregowań uzyskiwanych z użyciem algorytmu pszczelego oraz ich porównanie z rozwiązaniami dla typowych reguł heurystycznych. Analiza otrzymanych wyników pozwoliła na stwierdzenie, że uszeregowania uzyskiwane z zastosowaniem opracowanego algorytmu cechowały się największymi wartościami funkcji kryterialnej. Zdecydowanie przewyższały uszeregowania uzyskiwane z wykorzystaniem prostych reguł heurystycznych.