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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

At present, the back-propagation (BP) network algorithm widely used in the short-term output prediction of photovoltaic power stations has the disadvantage of ignoring meteorological factors and weather conditions in the input. The existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. The back-propagation (BP) neural network is easy to fall into local optimization thus reducing the prediction accuracy in photovoltaic power prediction. In order to solve this problem, an improved grey wolf optimization (GWO) algorithm is proposed to optimize the photovoltaic power prediction model of the BP neural network. So, an improved grey wolf optimization algorithm optimized BP neural network for a photovoltaic (PV) power prediction model is proposed. Dynamic weight strategy, tent mapping and particle swarm optimization (PSO) are introduced in the standard grey wolf optimization (GWO) to construct the PSO–GWO model. The relative error of the PSO–GWO–BP model predicted data is less than that of the BP model predicted data. The average relative error of PSO–GWO–BP and GWO–BP models is smaller, the average relative error of PSO–GWO–BP model is the smallest, and the prediction stability of the PSO–GWO–BP model is the best. The model stability and prediction accuracy of PSO–GWO–BP are better than those of GWO–BP and BP.
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Authors and Affiliations

Ping He
1
ORCID: ORCID
Jie Dong
1
ORCID: ORCID
Xiaopeng Wu
1
ORCID: ORCID
Lei Yun
1
ORCID: ORCID
Hua Yang
1
ORCID: ORCID

  1. Zhengzhou University of Light Industry, College of Electrical and Information Engineering, China
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Abstract

In order to improve the utilization rate of coal resources, it is necessary to classify coal and gangue, but the classification of coal is particularly important. Nevertheless, the current coal and gangue sorting technology mainly focus on the identification of coal and gangue, and no in-depth research has been carried out on the identification of coal species. Accordingly, in order to preliminary screen coal types, this paper proposed a method to predict the coal metamorphic degree while identifying coal and gangue based on Energy Dispersive X-Ray Diffraction (EDXRD) principle with 1/3 coking coal, gas coal, and gangue from Huainan mine, China as the research object. Differences in the phase composition of 1/3 coking coal, gas coal, and gangue were analyzed by combining the EDXRD patterns with the Angle Dispersive X-Ray Diffraction (ADXRD) patterns. The calculation method for characterizing the metamorphism degree of coal by EDXRD patterns was investigated, and then a PSO-SVM model for the classification of coal and gangue and the prediction of coal metamorphism degree was developed. Based on the results, it is shown that by embedding the calculation method of coal metamorphism degree into the coal and gangue identification model, the PSO-SVM model can identify coal and gangue and also output the metamorphism degree of coal, which in turn achieves the purpose of preliminary screening of coal types. As such, the method provides a new way of thinking and theoretical reference for coal and gangue identification.
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Authors and Affiliations

Yanqiu Zhao
1
ORCID: ORCID
Shuang Wang
1
Yongcun Guo
1
Gang Cheng
1
Lei He
1
Wenshan Wang
1

  1. School of Mechanical Engineering, Anhui University of Science and Technology, China
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Abstract

The study aims to estimate metal foam microstructure parameters for the maximum sound absorption coefficient (SAC) in the specified frequency band to obtain optimum metal foam fabrication. Lu’s theory model is utilised to calculate the SAC of metallic foams that refers to three morphological parameters: porosity, pore size, and pore opening. After Lu model validation, particle swarm optimisation (PSO) is used to optimise the parameters. The optimum values are obtained at frequencies 250 to 8000 Hz, porosity of 50 to 95%, a pore size of 0.1 to 4.5 mm, and pore opening of 0.07 to 0.98 mm. The results revealed that at frequencies above 1000 Hz, the absorption efficiency increases due to changes in the porosity, pore size, and pore opening values rather than the thickness. However, for frequencies below 2000 Hz, increasing the absorption efficiency is strongly correlated with an increase in foam thickness. The PSO is successfully used to find optimum absorption conditions, the reference for absorbent fabrication, on a frequency band 250 to 8000 Hz. The outcomes will provide an efficient tool and guideline for optimum estimation of acoustic absorbents.
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Authors and Affiliations

Rohollah Fallah Madvari
1
Mohsen Niknam Sharak
2
Mahsa Jahandideh Tehrani
3
Milad Abbasi
4

  1. Occupational Health Research Center, Department of Occupational Health Engineering, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
  2. Department of Mechanical Engineering, University of Birjand, Birjand, Iran
  3. Australian Rivers Institute, Griffith University, Queensland, Australia
  4. Social Determinants of Health Research Center, Saveh University of Medical Sciences, Saveh, Iran
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Abstract

This paper presents the resolution of the optimal reactive power dispatch (ORPD) problem and the control of voltages in an electrical energy system by using a hybrid algorithm based on the particle swarmoptimization (PSO) method and interior point method (IPM). The IPM is based on the logarithmic barrier (LB-IPM) technique while respecting the non-linear equality and inequality constraints. The particle swarmoptimization-logarithmic barrier-interior point method (PSO-LB-IPM) is used to adjust the control variables, namely the reactive powers, the generator voltages and the load controllers of the transformers, in order to ensure convergence towards a better solution with the probability of reaching the global optimum. The proposed method was first tested and validated on a two-variable mathematical function using MATLAB as a calculation and execution tool, and then it is applied to the ORPD problem to minimize the total active losses in an electrical energy network. To validate the method a testwas carried out on the IEEE electrical energy network of 57 buses.

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Authors and Affiliations

Aissa Benchabira
Mounir Khiat
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Abstract

Economic dispatch (ED) is an essential part of any power system network. ED is howto schedule the real power outputs from the available generators to get the minimum cost while satisfying all constraints of the network. Moreover, it may be explained as allocating generation among the committed units with the most effective minimum way in accordance with all constraints of the system. There are many traditional methods for solving ED, e.g., Newton-Raphson method Lambda-Iterative technique, Gaussian-Seidel method, etc. All these traditional methods need the generators’ incremental fuel cost curves to be increasing linearly. But practically the input-output characteristics of a generator are highly non-linear. This causes a challenging non-convex optimization problem. Recent techniques like genetic algorithms, artificial intelligence, dynamic programming and particle swarm optimization solve nonconvex optimization problems in a powerful way and obtain a rapid and near global optimum solution. In addition, renewable energy resources as wind and solar are a promising option due to the environmental concerns as the fossil fuels reserves are being consumed and fuel price increases rapidly and emissions are getting higher. Therefore, the world tends to replace the old power stations into renewable ones or hybrid stations. In this paper, it is attempted to enhance the operation of electrical power system networks via economic dispatch. An ED problem is solved using various techniques, e.g., Particle Swarm Optimization (PSO) technique and Sine-Cosine Algorithm (SCA). Afterwards, the results are compared. Moreover, case studies are executed using a photovoltaic-based distributed generator with constant penetration level on the IEEE 14 bus system and results are observed. All the analyses are performed on MATLAB software.
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Bibliography

[1] Zee-Lee Gaing, Particle swarm optimization to solving the economic dispatch considering the generator limits, IEEE Trans. Power Syst., vol. 18, pp. 1187–1195 (2003).
[2] Nidul Sinha, Chakrabarti R., Chattopadhyay P.K., Evolutionary programming techniques for economic load dispatch, IEEE Transactions on Evolutionary Computation, vol. 7, iss. 1, pp. 83–94 (2003).
[3] Jeyakumar D., Jayabarathi T., Raghunathan T., Particle swarm optimization for various types of economic dispatch problems, International Journal of Electrical Power Energy System, vol. 36, pp. 42–28 (2006).
[4] Leandro dos Santos Coelho, Chu-Sheng Lee, Solving economic load dispatch problems in power system using chaotic and Gaussian particle swarm optimization approaches, Elsevier, International Journal of Electrical Power and Energy Systems (IJEPES), vol. 30, iss. 5, pp. 297–307 (2008).
[5] Vishnu Prasad, Amita Mahor, Saroj Rangnekar, Economic dispatch using particle swarm optimization: A review, Renewable and Sustainable Energy Reviews, vol. 13, pp. 2134–2141 (2009).
[6] Kumar C., Alwarsamy T., Dynamic Economic Dispatch – A Review of Solution Methodologies, European Journal of Scientific Research, ISSN 1450-216X, vol. 64, no. 4, pp. 517–537 (2011).
[7] Deep K., Bansal J.C., Solving Economic Dispatch Problems with Valve-point Effects using Particle Swarm Optimization, J. UCS, vol. 18, no. 13, pp. 1842–1852 (2012).
[8] Timothy Ganesan, Pandian Vasant, Irraivan Elamvazuthy, A hybrid PSO approach for solving nonconvex optimization problems, Archives of Control Sciences, vol. 22 (LVIII) (2012).
[9] Jie Meng, Geng-yin Li, Shi-jun Cheng, Economic Dispatch for Power Generation System Incorporating Wind and Photovoltaic Power, Applied Mechanics and Materials, vol. 441, pp. 263–267 (2014).
[10] Kumar C., Anbarasan A., Karpagam M., Alwarsamy T., Artificial Intelligent Techniques in Economic Power Dispatch Problems, International Journal of Applied Engineering Research, ISSN 0973-4562, vol. 10, no. 9, pp. 23243–23254 (2015).
[11] Zeinab G. Hassan, Ezzat M., Almoataz Y. Abdelaziz, Solving Unit Commitment and Economic Load Dispatch Problems Using Modern Optimization Algorithms, International Journal of Engineering, Science and Technology, vol. 9, no. 4, pp. 10–19 (2017).
[12] Quande Q., Cheng S., Xianghua C., Solving non-convex/non-smooth economic load dispatch problems via an enhanced particle swarm optimization, Applied Soft Computing, vol. 59, no. 1, pp. 229–242 (2017).
[13] Sanjoy R., The maximum likelihood optima for an economic load dispatch in presence of demand and generation variability, Energy, vol. 147, pp. 915–923 (2018).
[14] Jagat Kishore Pattanaik, Mousumi Basu, Deba Prasad Dash, Dynamic economic dispatch: a comparative study for differential evolution, particle swarm optimization, evolutionary programming, genetic algorithm, and simulated annealing, Pattanaik et al., Journal of Electrical Systems and Information Technology (2019).
[15] Bishwajit Dey, Shyamal Krishna Roy, Biplab Bhattacharyya, Solving multi-objective economic emission dispatch of a renewable integrated microgrid using latest bio-inspired algorithms, Engineering Science and Technology, International Journal 22, pp. 55–66 (2019).
[16] Aissa Benchabira, Mounir Khiat, 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).
[17] Patel N., Bhattacharjee K., A comparative study of economic load dispatch using sine cosine algorithm, Scientia Iranica International Journal of Science and Technology, vol. 27, no. 3, pp. 1467–1480 (2020).
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[19] Anurag Gupta, Himanshu Anand, Analysis of scheduling of solar sharing for economic/environmental dispatch using PSO, INDICON IEEE (2015).
[20] Hafez A.I., Zawbaa H.M., Emary E., Hassanien A.E., Sine cosine optimization algorithm for feature selection, International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) (2016).
[21] Ajay Wadhawan, Preeti Verma, Sonia Grover, Himanshu Anand, Economic Environmental Dispatch with PV Generation Including Transmission Losses using PSO, IEEE Power India International Conference (PIICON) (2016).
[22] Suid M.H., Ahmad M.A., Ismail M.R.T.R., Ghazali M.R., Irawan A., Tumari M.Z., An Improved Sine Cosine Algorithm for Solving Optimization Problems, IEEE Conference on Systems, Process and Control (ICSPC) (2018).
[23] Jiajun Liu, Bo Song,Ye Li, An Optimum Dispatching for Photovoltaic-thermal Mutual-Complementing Power Plant Based on the Improved Particle Swarm Knowledge Algorithm, IEEE Conference on Industrial Electronics and Applications (ICIEA) (2018).
[24] Kennedy J., Particle swarm optimization, Encyclopedia in Machine Learning, pp. 760–766 (2010).
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Authors and Affiliations

Abrar Mohamed Hafiz
1
ORCID: ORCID
M. Ezzat Abdelrahman
1
Hesham Temraz
1

  1. Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Egypt
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Abstract

In recent years, due to the increasing number of renewable energy sources, which are characterised by the stochastic nature of the generated power, interest in energy storage has increased. Commercial installations use simple deterministic methods with low economic efficiency. Hence, there is a need for intelligent algorithms that combine technical and economic aspects. Methods based on computational intelligence (CI) could be a solution. The paper presents an algorithm for optimising power flow in microgrids by using computational intelligence methods. This approach ensures technical and economic efficiency by combining multiple aspects in a single objective function with minimal numerical complexity. It is scalable to any industrial or residential microgrid system. The method uses load and generation forecasts at any time horizon and resolution and the actual specifications of the energy storage systems, ensuring that technological constraints are maintained. The paper presents selected calculation results for a typical residential microgrid supplied with a photovoltaic system. The results of the proposed algorithm are compared with the outcomes provided by a deterministic management system. The computational intelligence method allows the objective function to be adjusted to find the optimal balance of economic and technical effects. Initially, the authors tested the invented algorithm for technical effects, minimising the power exchanged with the distribution system. The application of the algorithm resulted in financial losses, €12.78 for the deterministic algorithm and €8.68 for the algorithm using computational intelligence. Thus, in the next step, a control favouring economic goals was checked using the CI algorithm. The case where charging the storage system from the grid was disabled resulted in a financial benefit of €10.02, whereas when the storage system was allowed to charge from the grid, €437.69. Despite the financial benefits, the application of the algorithm resulted in up to 1560 discharge cycles. Thus, a new unconventional case was considered in which technical and economic objectives were combined, leading to an optimum benefit of €255.17 with 560 discharge cycles per year. Further research of the algorithm will focus on the development of a fitness function coupled to the power system model.
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Authors and Affiliations

Dominika Kaczorowska
1
ORCID: ORCID
Jacek Rezmer
1
ORCID: ORCID
Przemysław Janik
1
ORCID: ORCID
Tomasz Sikorski
1
ORCID: ORCID

  1. Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland
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Abstract

This paper presents unsupervised change detection method to produce more accurate change map from imbalanced SAR images for the same land cover. This method is based on PSO algorithm for image segmentation to layers which classify by Gabor Wavelet filter and then K-means clustering to generate new change map. Tests are confirming the effectiveness and efficiency by comparison obtained results with the results of the other methods. Integration of PSO with Gabor filter and k-means will providing more and more accuracy to detect a least changing in objects and terrain of SAR image, as well as reduce the processing time.
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Bibliography


[1] Feng Gao, Junyu Dong, Bo Li, Qizhi Xu, Cui Xie, “Change detection from synthetic aperture radar images based on neighborhood-based ratio and extreme learning machine,” J. Appl. Remote Sens. 10(4), 046019 (2016), https://doi.org/10.1117/1.JRS.10.046019.
[2] Turgay Celik, " Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and k-Means Clustering", IEEE geoscience and remote sensing letters, vol. 6, no. 4, October 2009.
[3] Xinzheng Zhang, Guo Liu, Ce Zhang, Peter M. Atkinson, Xiaoheng Tan, Xin Jian, Xichuan Zhou and Yongming Li, " Two-Phase Object-Based Deep Learning for Multi-Temporal SAR Image Change Detection", Remote Sensing. 2020.
[4] Karpenko A.P., Seliverstov E.Yu. Review of the particle swarm optimization method (PSO) for a global optimization problem. Nauka i obrazovanie. MGTU im. N.E. Baumana [Science and Education of the Bauman MSTU], 2009, no. 3 (in Russ.). https://doi.org/10.7463/00309.0116072.
[5] Xinzheng Zhang, Hang Su, Ce Zhang, Peter M. Atkinson, Xiaoheng Tan, Xiaoping Zeng and Xin Jian." A Robust Imbalanced SAR Image Change Detection Approach Based on Deep Difference Image and PCANet", arXiv.org > cs > arXiv:2003.01768, 2020
[6] Feng Gao, Xiao Wang, Junyu Dong, Shengke Wang, " SAR Image Change Detection Based on Frequency Domain Analysis and Random Multi-Graphs", Journal of Applied Remote Sensing, 2017
[7] Feng Gao, Junyu Dong, Bo Li, and Qizhi Xu, " Automatic Change Detection in Synthetic Aperture Radar Images Based on PCANet", IEEE geoscience and remote sensing letters, vol. 13, no. 12,2016.
[8] Li Yufeng & He Wei, " Research on SAR image change detection algorithm based on hybrid genetic FCM and image registration", Springer Science+Business Media New York 2017.
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[14] Nameirakpam Dhanachandra, Yambem Jina Chanu, "An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm", Springer Science+Business Media, LLC, part of Springer Nature 2020.
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Authors and Affiliations

Jinan N. Shehab
1
Hussein A. Abdulkadhim
1

  1. University of Diyala, College of Engineering, Dept. of Communication Engineering, Iraq
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Abstract

The proportional-integral-derivative (PID) controller is widely used in various industrial applications such as process control, motor drives, magnetic and optical memory, automotive, flight control and instrumentation. PID tuning refers to the generation of PID parameters (Kp, Ki, Kd) to obtain the optimum fitness value for any system. The determination of the PID parameters is essential for any system that relies on it to function in a stable mode. This paper proposes a method in designing a predictive PID controller system using particle swarm optimization (PSO) algorithm for direct current (DC) motor application. Extensive numerical simulations have been done using the Mathwork’s Matlab simulation environment. In order to gain full benefits from the PSO algorithm, the PSO parameters such as inertia weight, iteration number, acceleration constant and particle number need to be carefully adjusted and determined. Therefore, the first investigation of this study is to present a comparative analysis between two important PSO parameters; inertia weight and number of iteration, to assist the predictive PID controller design. Simulation results show that inertia weight of 0.9 and iteration number 100 provide a good fitness achievement with low overshoot and fast rise and settling time. Next, a comparison between the performance of the DC motor with PID-PSO, with PID of gain 1, and without PID were also discussed. From the analysis, it can be concluded that by tuning the PID parameters using PSO method, the best gain in performance may be found. Finally, when comparing between the PID-PSO and its counterpart, the PI-PSO, the PID-PSO controller gives better performance in terms of robustness, low overshoot (0.005%), low minimum rise time (0.2806 seconds) and low settling time (0.4326 seconds).

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Authors and Affiliations

Norhaida Mustafa
Fazida Hanim Hashim
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Abstract

Recently, interest in incorporating distributed generators (DGs) into electrical distribution networks has significantly increased throughout the globe due to the technological advancements that have led to lowering the cost of electricity, reducing power losses, enhancing power system reliability, and improving the voltage profile. These benefits can be maximized if the optimal allocation and sizing of DGs into a radial distribution system (RDS) are properly designed and developed. Getting the optimal location and size of DG units to be installed into an existing RDS depends on the various constraints, which are sometimes overlapping or contradicting. In the last decade, meta-heuristic search and optimization algorithms have been frequently developed to handle the constraints and obtain the optimal DG location and size. This paper proposes an efficient optimization technique to optimally allocate multiple DG units into a RDS. The proposed optimization method considers the integration of solar photovoltaic (PV) based DG units in power distribution networks. It is based on multi-objective function (MOF) that aims to maximize the net saving level (NSL), voltage deviation level (VDL), active power loss level (APLL), environmental pollution reduction level (EPRL), and short circuit level (SCL). The proposed algorithms using various strategies of inertia weight particle swarm optimization (PSO) are applied on the standard IEEE 69-bus system and a real 205-bus Algerian distribution system. The proposed approach and design of such a complicated multi-objective functions are ultimately to make considerable improvements in the technical, economic, and environmental aspects of power distribution networks. It was found that EIW-PSO is the best applied algorithm as it achieves the maximum targets on various quantities; it gives 75.8359%, 28.9642%, and 64.2829% for the APLL, EPRL, and VDL, respectively, with DG units’ installation in the IEEE 69-bus test system. For the same number of DG units, EIW-PSO gives remarkable improved performance with the Adrar City 205-bus test system; numerically, it shows 72.3080%, 22.2027%, and 63.6963% for the APLL, EPRL, and VDL, respectively. The simulation results of this study prove that the proposed algorithms exhibit higher capability and efficiency in fixing the optimum DG settings.
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Authors and Affiliations

Mohamed Zellagui
1
ORCID: ORCID
Adel Lasmari
2
ORCID: ORCID
Ali H. Kasem Alaboudy
3
ORCID: ORCID
Samir Settoul
2
ORCID: ORCID
Heba Ahmed Hassan
4
ORCID: ORCID

  1. Department of Electrical Engineering, Faculty of Technology, University of Batna 2, Algeria
  2. Department of Electrotechnic, Faculty of Technology, Mentouri University of Constantine, Algeria
  3. Electrical Department, Faculty of Technology and Education, Suez University, Egypt
  4. Electrical Power Engineering Department, Faculty of Engineering, Cairo University, Egypt
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Abstract

In this paper, the performance of Low-Density Parity-Check (LDPC) codes is improved, which leads to reduce the complexity of hard-decision Bit-Flipping (BF) decoding by utilizing the Artificial Spider Algorithm (ASA). The ASA is used to solve the optimization problem of decoding thresholds. Two decoding thresholds are used to flip multiple bits in each round of iteration to reduce the probability of errors and accelerate decoding convergence speed while improving decoding performance. These errors occur every time the bits are flipped. Then, the BF algorithm with a low-complexity optimizer only requires real number operations before iteration and logical operations in each iteration. The ASA is better than the optimized decoding scheme that uses the Particle Swarm Optimization (PSO) algorithm. The proposed scheme can improve the performance of wireless network applications with good proficiency and results. Simulation results show that the ASAbased algorithm for solving highly nonlinear unconstrained problems exhibits fast decoding convergence speed and excellent decoding performance. Thus, it is suitable for applications in broadband wireless networks.
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Authors and Affiliations

Ali Jasim Ghaffoori
1
Wameedh Riyadh Abdul-Adheem
1

  1. Department of Electrical Power Techniques Engineering, AL_Ma’moon University College, Baghdad, Iraq

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