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Short-term optimal energy management in stand-alone microgrid with battery energy storage

Navin Kumar Paliwal Asheesh Kumar Singh Navneet Kumar Singh
Archives of Electrical Engineering | 2018 | vol. 67 | No 3 | 499–513 | DOI: 10.24425/123659
Keywords artificial bee colony battery energy storage clean energy sources optimal energy management stand-alone microgrid
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Abstract

The optimal energy management (OEM) in a stand-alone microgrid (SMG) is a challenging job because of uncertain and intermittent behavior of clean energy sources (CESs) such as a photovoltaic (PV), wind turbine (WT). This paper presents the effective role of battery energy storage (BES) in optimal scheduling of generation sources to fulfill the load demand in an SMG under the intermittency of theWT and PV power. The OEM is performed by minimizing the operational cost of the SMG for the chosen moderate weather profile using an artificial bee colony algorithm (ABC) in four different cases, i.e. without the BES and with the BES having a various level of initial capacity. The results show the efficient role of the BES in keeping the reliability of the SMG with the reduction in carbon-emissions and uncertainty of the CES power. Also, prove that the ABC provides better cost values compared to particle swarm optimization (PSO) and a genetic algorithm (GA). Further, the robustness of system reliability using the BES is tested for the mean data of the considered weather profile.

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Authors and Affiliations

Navin Kumar Paliwal
Asheesh Kumar Singh
Navneet Kumar Singh

Condition assessment of transformer insulation using dielectric frequency response analysis by artificial bee colony algorithm

Mehdi Bigdeli Jafar Aghajanloo
Archives of Electrical Engineering | 2016 | vol. 65 | No 1 March | 45-57 | DOI: 10.1515/aee-2016-0004
Keywords artificial bee colony (ABC) algorithm condition assessment dielectric frequency response (DFR) transformer insulation
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Abstract

Transformers are one of the most important components of the power system. It is important to maintain and assess the condition. Transformer lifetime depends on the life of its insulation and insulation life is also strongly influenced by moisture in the insulation. Due to importance of this issue, in this paper a new method is introduced for determining the moisture content of the transformer insulation system using dielectric response analysis in the frequency domain based on artificial bee colony algorithm. First, the master curve of dielectric response is modeled. Then, using proposed method the master curve and the measured dielectric response curves are compared. By analyzing the results of the comparison, the moisture content of paper insulation, electrical conductivity of the insulating oil and dielectric model dimensions are determined. Finally, the proposed method is applied to several practical samples to demonstrate its capabilities compared with the well-known conventional method.
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Authors and Affiliations

Mehdi Bigdeli
Jafar Aghajanloo

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

Chunli Wu Shuai Jiang Chunyuan Bian
Archives of Electrical Engineering | 2021 | vol. 70 | No 4 | 777-790 | DOI: 10.24425/aee.2021.138260
Keywords artificial bee colony algorithm Euclidean distance online identification parameter identification surface-mounted permanent magnet synchronous motor
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Abstract

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.
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Bibliography

[1] Boileau T., Leboeuf N., Nahid-Mobarakeh B., Online identification of PMSM parameters: parameter identifiability and estimator comparative study, IEEE Transactions on Industry Applications, vol. 47, no. 4, pp. 1944–1957 (2011), DOI: 10.1109/TIA.2011.2155010.
[2] Ichikawa S., Tomita M., Doki S., Sensorless control of permanent-magnet synchronous motors using online parameter identification based on system identification theory, IEEE Transactions on Industrial Electronics, vol. 53, no. 2, pp. 363–372 (2006), DOI: 10.1109/TIE.2006.870875.
[3] Jian-fei S., Bao-jun G., Yan-ling L., Research of parameter identification of permanent magnet synchronous motor online, Electric Machines and Control, vol. 22, no. 3, pp. 17–24 (2018), DOI: 10.15938/j.emc.2018.03.003.
[4] Fan S., LuoW., Zou J., A hybrid speed sensorless control strategy for PMSM based on MRAS and fuzzy control, Proceedings of 7th International Power Electronics and Motion Control Conference, Harbin, China, pp. 2976–2980 (2012), DOI: 10.1109/IPEMC.2012.6259344.
[5] Shi Y., Sun K., Huang L., Online identification of permanent magnet flux based on extended Kalman filter for IPMSM drive with position sensorless control, IEEE Transactions on Industrial Electronics, vol. 59, no. 11, pp. 4169–4178 (2012), DOI: 10.1109/TIE.2011.2168792.
[6] Liu K., Zhang J., Adaline neural network based online parameter estimation for surface-mounted permanent magnet synchronous machines, Proceedings of the CSEE, vol. 30, no. 30, pp. 68–73 (2010).
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[10] Liu Z., Wei H., Zhong Q., Parameter estimation for VSI-Fed PMSM based on a dynamic PSO with learning strategies, IEEE Transactions on Power Electronics, vol. 32, no. 4, pp. 3154–3165 (2017), DOI: 10.1109/TPEL.2016.2572186.
[11] Liu Z., Wei H., Li X., Global identification of electrical and mechanical parameters in PMSM drive based on dynamic self-learning PSO, IEEE Transactions on Power Electronics, vol. 33, no. 12, pp. 10858–10871 (2018), DOI: 10.1109/TPEL.2018.2801331.
[12] Sandre-Hernandez O., Morales-Caporal R., Rangel-Magdaleno J., Parameter identification of PMSMs using experimental measurements and a PSO algorithm, IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 8, pp. 2146–2154 (2015), DOI: 10.1109/TIM.2015.2390958.
[13] Liu X., Hu W., Ding W., Research on multi-parameter identification method of permanent magnet synchronous motor, Transactions of China Electrotechnical Society, vol. 35, no. 6, pp. 1198–1207 (2020).
[14] Liu C., Zhou S., Liu K., Permanent magnet synchronous motor multiple parameter identification and temperature monitoring based on binary-modal adaptive wavelet particle swarm optimization, Acta Automatica Sinica, vol. 39, no. 12, pp. 2121–2130 (2013), DOI: 10.3724/SP.J.1004.2013.02121.
[15] Fu X., Gu H., Chen G., Permanent magnet synchronous motors parameters identification based on Cauchy mutation particle swarm optimization, Transactions of China Electrotechnical Society, vol. 29, no. 5, pp. 127–131 (2014).
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[19] Zawilak T., Influence of rotor’s cage resistance on demagnetization process in the line start permanent magnet synchronous motor, Archives of Electrical Engineering, vol. 69, no. 2, pp. 249–258 (2020), DOI: 10.24425/aee.2020.133023.

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Authors and Affiliations

Chunli Wu
1
ORCID: ORCID
Shuai Jiang
1
Chunyuan Bian
1
  1. College of Information Science and Engineering, Northeastern University, China

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

T. Tarczewski L.J. Niewiara L.M. Grzesiak
Bulletin of the Polish Academy of Sciences Technical Sciences | 2020 | 68 | No. 5 (i.a. Special Section on Modern control of drives and power converters) | 997-1007 | DOI: 10.24425/bpasts.2020.134622
Keywords tuning PMSM servo-drive artificial bee colony algorithm linear-quadratic optimization pole placement
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Abstract

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.

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Authors and Affiliations

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

Nonlinear PID controller parameter optimization using modified hybrid artificial bee colony algorithm for continuous stirred tank reactor

Nedumal Pugazhenthi P S. Selvaperumal K. Vijayakumar
Bulletin of the Polish Academy of Sciences Technical Sciences | 2021 | 69 | 3 | e137348 | DOI: 10.24425/bpasts.2021.137348
Keywords artificial bee colony stirred tank reactor genetic algorithm nonlinear PID controller performance measures
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Abstract

The artificial bee colony (ABC) algorithm is well known and widely used optimization method based on swarm intelligence, and it is inspired by the behavior of honeybees searching for a high amount of nectar from the flower. However, this algorithm has not been exploited sufficiently. This research paper proposes a novel method to analyze the exploration and exploitation of ABC. In ABC, the scout bee searches for a source of random food for exploitation. Along with random search, the scout bee is guided by a modified genetic algorithm approach to locate a food source with a high nectar value. The proposed algorithm is applied for the design of a nonlinear controller for a continuously stirred tank reactor (CSTR). The statistical analysis of the results confirms that the proposed modified hybrid artificial bee colony (HMABC) achieves consistently better performance than the traditional ABC algorithm. The results are compared with conventional ABC and nonlinear PID (NLPID) to show the superiority of the proposed algorithm. The performance of the HMABC algorithm-based controller is competitive with other state-of-the-art meta-heuristic algorithm-based controllers in the literature.
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Authors and Affiliations

Nedumal Pugazhenthi P
1
S. Selvaperumal
1
ORCID: ORCID
K. Vijayakumar
2
  1. Department of EEE, Syed Ammal Engineering College, Ramanathapuram, Tamilnadu, India
  2. Department of electronics and instrumentation, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamilnadu, India

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