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Number of results: 5
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Abstract

Static Var Compensator (SVC) is a popular FACTS device for providing reactive power support in power systems and its placement representing the location and size has significant influence on network loss, while keeping the voltage magnitudes within the acceptable range. This paper presents a Firefly algorithm based optimization strategy for placement of SVC in power systems with a view of minimizing the transmission loss besides keeping the voltage magnitude within the acceptable range. The method uses a self-adaptive scheme for tuning the parameters in the Firefly algorithm. The strategy is tested on three IEEE test systems and their results are presented to demonstrate its effectiveness.

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Authors and Affiliations

R. Selvarasu
M. Surya Kalavathi
C. Christober Asir Rajan
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Abstract

This paper presents the application of Flexible Alternating Current Transmission System (FACTS) devices based on heuristic algorithms in power systems. The work proposes the Autonomous Groups Particle Swarm Optimization (AGPSO) approach for the optimal placement and sizing of the Static Var Compensator (SVC) to minimize the total active power losses in transmission lines. A comparative study is conducted with other heuristic optimization algorithms such as Particle Swarm Optimization (PSO), Timevarying Acceleration Coefficients PSO (TACPSO), Improved PSO (IPSO), Modified PSO (MPSO), and Moth-Flam Optimization (MFO) algorithms to confirm the efficacy of the proposed algorithm. Computer simulations have been carried out on MATLAB with the MATPOWER additional package to evaluate the performance of the AGPSO algorithm on the IEEE 14 and 30 bus systems. The simulation results show that the proposed algorithm offers the best performance among all algorithms with the lowest active power losses and the highest convergence rate.
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Bibliography

[1] Vera S.M., Nuez I., Hernandez-Tejera M., A FACTS devices allocation procedure attending to load share, Energies, vol. 13, no. 8 (2020), DOI: 10.3390/en13081976.
[2] Singh B., Kumar R., A comprehensive survey on enhancement of system performances by using different types of FACTS controllers in power systems with static and realistic load models, Energy Reports, vol. 6, pp. 55–79 (2020).
[3] Shehata A.A., Ahmed M.K., State estimation accuracy enhancement for optimal power system steady state modes, IOP Conference Series: Materials Science and Engineering, vol. 643 (2019), DOI: 10.1088/1757-899X/643/1/012049.
[4] Sreedharan S., Joseph T., Joseph S., Chandran C.V., Vishnu J., Das V., Power system loading margin enhancement by optimal STATCOM integration – A case study, Computers and Electrical Engineering, vol. 81, no. 106521 (2019).
[5] Al Ahmad A., Sirjani R., Optimal placement and sizing of multi-type FACTS devices in power systems using metaheuristic optimisation techniques: An updated review, Ain Shams Engineering Journal (2019), DOI: 10.1016/j.asej.2019.10.013.
[6] Belazzoug M., Boudour M., Sebaa K., FACTS location and size for reactive power system compensation through the multi-objective optimization, Archives of Control Sciences, vol. 20, no. 4, pp. 473–489 (2010).
[7] Kotsampopoulos P., Georgilakis P., Lagos D.T., Kleftakis V., Hatziargyriou N., FACTS providing grid services: applications and testing, Energies, vol. 12, no. 13 (2019), DOI: 10.3390/en12132554
[8] Kavitha K.,Neela R., Optimal allocation of multi-type FACTS devices and its effect in enhancing system security using BBO, WIPSO & PSO, Journal of Electrical Systems and Information Technology, vol. 5, no. 3, pp. 777–793 (2018).
[9] Shehata A.A., Korovkin N.V., An accuracy enhancement of optimization techniques containing fractional-polynomial relationships, 2020 International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE), pp. 1–5 (2020).
[10] Dash S.P., Subhashini K.R., Satapathy J.K., Optimal location and parametric settings of FACTS devices based on JAYA blended moth flame optimization for transmission loss minimization in power systems, Microsystem Technologies, vol. 26, no. 5, pp. 1543–1552 (2020).
[11] Saurav S., Gupta V.K., Mishra S.K., Moth-flame optimization based algorithm for FACTS devices allocation in a power system, 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), pp. 1–7 (2017).
[12] Jyotshna D.K., Madhuri N., Optimal allocation of SVC for enhancement of voltage stability using harmony search algorithm, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 4, no. 7, pp. 6693–6701 (2015).
[13] Ravi K., Rajaram M., Optimal location of FACTS devices using Improved Particle Swarm Optimization, International Journal of Electrical Power and Energy Systems, vol. 49, pp. 333–338 (2013).
[14] Mathad V.G., Ronad B.G., Jangamshetti S.H., Review on comparison of FACTS controllers for power system stability enhancement, International Journal of Scientific and Research Publications, vol. 3, no. 3, pp. 2250–3153 (2013).
[15] Murali D., Rajaram M., Reka N., Comparison of FACTS devices for power system stability enhancement, International Journal of Computer Applications, vol. 8, no. 4, pp. 30–35 (2010).
[16] Rezaee J.A., Particle swarm optimisation (PSO) for allocation of FACTS devices in electric transmission systems: A review, Renewable and Sustainable Energy Reviews, vol. 52, pp. 1260-1267 (2015).
[17] Shaheen A.M., Spea S.R., Farrag S.M., Abido M.A., A review of meta-heuristic algorithms for reactive power planning problem, Ain Shams Engineering Journal, vol. 9, no. 2, pp. 215–231 (2018).
[18] Suresh V., Janik P., Jasinski M., Metaheuristic approach to optimal power flow using mixed integer distributed ant colony optimization, Archives of Electrical Engineering, vol. 69, no. 2, pp. 335–348 (2020).
[19] Benchabira A., Khiat M., A hybrid method for the optimal reactive power dispatch and the control of voltages in an electrical energy network, Archives of Electrical Engineering, vol. 68, no. 3, pp. 535–551 (2019).
[20] Ziyu T., Dingxue Z., A modified particle swarm optimization with an adaptive acceleration coefficient, 2009 Asia-Pacific Conference on Information Processing, vol. 2, pp. 330–332 (2009).
[21] Mirjalili S., Lewis A., Sadiq A.S., Autonomous particles groups for particle swarm optimization, Arabian Journal for Science and Engineering, vol. 39, no. 6, pp. 4683–4697 (2014). [22] The IEEE 14 and 30 Bus Test Systems, available online at: http://labs.ece.uw.edu/pstca.
[23] Cui Z., Zeng J., Yin Y., An improved PSO with time-varying accelerator coefficients, 2008 8th International Conference on Intelligent Systems Design and Applications, vol. 2, pp. 638–643 (2008).
[24] Bao G.Q., Mao K.F., Particle swarmoptimization algorithm with asymmetric time varying acceleration coefficients, 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO), no. 3, pp. 2134–2139 (2009).
[25] Mirjalili S., Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm, Knowledge-Based Systems, vol. 89, pp. 228–249 (2015).
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Authors and Affiliations

Ahmed A. Shehata
1
ORCID: ORCID
Ahmed Refaat
2
ORCID: ORCID
Mamdouh K. Ahmed
1
ORCID: ORCID
Nikolay V. Korovkin
1
ORCID: ORCID

  1. Institute of Energy, Peter the Great Saint-Petersburg Polytechnic University, Russia
  2. Electrical Engineering Department, Port-Said University, Egypt
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Abstract

Conventional field-orientated Induction motor drives operate at rated flux even at low load. To improve the efficiency of the existing motor it is important to regulate the flux of the motor in the desired operating range. In this paper a loss model controller (LMC) based on the real coded genetic algorithm is proposed, it has the straightforward goal of maximizing the efficiency for each given load torque. In order to give more accuracy to the motor model and the LMC a series model of the motor which consider the iron losses as a resistance connected in series with the mutual inductance is considered. Digital computer simulation demonstrates the effectiveness of the proposed algorithm and also simulation results have confirmed that this algorithm yields the optimal efficiency.

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Authors and Affiliations

Z. Rouabah
B. Abdelhadi
F. Anayi
F. Zidani
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Abstract

The radial distribution system is a rugged system, it is also the most commonly used system, which suffers by loss and low voltage at the end bus. This loss can be reduced by the use of a capacitor in the system, which injects reactive current and also improves the voltage magnitude in the buses. The real power loss in the distribution line is the I2R loss which depends on the current and resistance. The connection of the capacitor in the bus reduces the reactive current and losses. The loss reduction is equal to the increase in generation, necessary for the electric power provided by firms. For consumers, the quality of power supply depends on the voltage magnitude level, which is also considered and hence the objective of the problem becomes the multi objective of loss minimization and the minimization of voltage deviation. In this paper, the optimal location and size of the capacitor is found using a new computational intelligent algorithm called Flower Pollination Algorithm (FPA). To calculate the power flow and losses in the system, novel data structure load flow is introduced. In this, each bus is considered as a node with bus associated data. Links between the nodes are distribution lines and their own resistance and reactance. To validate the developed FPA solutions standard test cases, IEEE 33 and IEEE 69 radial distribution systems are considered.

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Authors and Affiliations

V. Tamilselvan
T. Jayabarathi
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Abstract

Induction motors (IMs) experience power losses when a portion of the input power is converted to heat instead of driving the load. The combined effect of copper losses, core losses, and mechanical losses results in IM power losses. Unfortunately, the core losses in the motor, which have a considerable impact on its energy efficiency, are not taken into account by the generally employed dynamic model in the majority of the studies. Due to this, the motor rating often corresponds to the worst-case load in applications, but the motor frequently operates below rated conditions. A hybridized model reference adaptive system (MRAS) with sliding mode control (SMC) is used in this study for sensorless speed control of an induction motor with core loss, allowing the motor to operate under a variety of load conditions. As a result, the machine can run at maximum efficiency while carrying its rated load. By adjusting the ��-axis current in the �� - �� reference frame in vector-controlled drives, the system’s performance is enhanced by running the motor at its optimum flux. Regarding the torque and speed of both induction motors with and without core loss, the Adaptive Observer Sliding Mode Control (AOSMC) has been constructed and simulated in this case. The AOSMC with core loss produced good performance when the proposed controller was tested.
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Authors and Affiliations

Tadele Ayana
1
ORCID: ORCID
Lelisa Wogi
1
ORCID: ORCID
Marcin Morawiec
1
ORCID: ORCID

  1. Faculty of Electrical and Control Engineering, Gdansk University of Technology, Gabriela Narutowicza 11/12, 80-233 Gdansk, Poland

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