i-manager Publications

i-manager's Journal on Power Systems Engineering (JPS)

Current Issue Vol. 12 Issue 2

Optimal Placement of Distributed Products to Improve Network Reliability under Network Operation Conditions
Optimized Multi-Antenna Wireless Communication: Enhancing Beam Forming, Security and Energy Efficiency
Maximum Power Point Tracking for a Photovoltaic Power System using the DIRECT Algorithm
Optimizing Multigenerational Renewable Energy Systems through Integrated Geothermal and Wind Energy Solutions
Wireless Charging Stations Utilizing Solar Energy for Electric Vehicles

Most Cited

Volume 12 Issue 2 July - September 2024

Research Paper

Optimal Placement of Distributed Products to Improve Network Reliability under Network Operation Conditions

Mehdi Salman*

Diyala, Iraq.

Salman, M. (2024). Optimal Placement of Distributed Products to Improve Network Reliability under Network Operation Conditions. i-manager’s Journal on Power Systems Engineering, 12(2), 1-7. https://doi.org/10.26634/jps.12.2.21639

Abstract

Considering the study of optimal location of distributed units to improve network reliability under network operation conditions and the focus on using the genetic algorithm method for locating distributed generation units in the distribution system, there is no comprehensive and relevant research in this field. In this research, to improve network reliability, location with variable numbers and sizes of distributed generation resources is performed. In the proposed method, multi-objective optimization using weight coefficients to combine two objective functions as the overall objective function is used.

References

[1]. Abdelaziz, M. M. A., Farag, H. E., & El-Saadany, E. F. (2015). Optimum reconfiguration of droop-controlled islanded microgrids. IEEE Transactions on Power Systems, 31(3), 2144-2153.

[2]. Alonso, F. R., Oliveira, D. Q., & De Souza, A. Z. (2014). Artificial immune systems optimization approach for multiobjective distribution system reconfiguration. IEEE Transactions on Power Systems, 30(2), 840-847.

[3]. Asrari, A., Wu, T., & Lotfifard, S. (2016). The impacts of distributed energy sources on distribution network reconfiguration. IEEE Transactions on Energy Conversion, 31(2), 606-613.

[4]. Dall'Anese, E., & Giannakis, G. B. (2014). Risk-constrained microgrid reconfiguration using group sparsity. IEEE Transactions on Sustainable Energy, 5(4), 1415-1425.

[5]. Guimaraes, M. A. N., Castro, C. A., & Romero, R. (2010). Distribution systems operation optimisation through reconfiguration and capacitor allocation by a dedicated genetic algorithm. IET Generation, Transmission & Distribution, 4(11), 1213-1222.

[6]. Gupta, N., Swarnkar, A., Niazi, K. R., & Bansal, R. C. (2010). Multi-objective reconfiguration of distribution systems using adaptive genetic algorithm in fuzzy framework. IET Generation, Transmission & Distribution, 4(12), 1288-1298.

[7]. Mendoza, J. E., Lopez, M. E., Coello Coello, C. A., & Lopez, E. A. (2009). Microgenetic multiobjective reconfiguration algorithm considering power losses and reliability indices for medium voltage distribution network. IET Generation, Transmission & Distribution, 3(9), 825-840.

[8]. Rupolo, D., & Mantovani, J. R. S. (2015). Reconfiguration of radial electric power distribution system via a scatter search algorithm. IEEE Latin America Transactions, 13(4), 1022-1028.

[9]. Tolabi, H. B., Ali, M. H., & Rizwan, M. (2014). Simultaneous reconfiguration, optimal placement of DSTATCOM, and photovoltaic array in a distribution system based on fuzzy-ACO approach. IEEE Transactions on Sustainable Energy, 6(1), 210-218.

[10]. Tsai, M. S., & Hsu, F. Y. (2009). Application of grey correlation analysis in evolutionary programming for distribution system feeder reconfiguration. IEEE Transactions on Power Systems, 25(2), 1126-1133.

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Research Paper

Optimized Multi-Antenna Wireless Communication: Enhancing Beam Forming, Security and Energy Efficiency

Fulgencio Estefan*

Department of Electrical Engineering, University of Matanzas, Matanzas, Cuba.

Estefan, F. (2024). Optimized Multi-Antenna Wireless Communication: Enhancing Beam Forming, Security and Energy Efficiency. i-manager’s Journal on Power Systems Engineering, 12(2), 8-14. https://doi.org/10.26634/jps.12.2.21664

Abstract

Wireless communication systems have seen exponential growth due to the increasing demand for high-speed data transmission, improved spectral efficiency, and enhanced energy optimization. This paper explores optimization techniques for reliable data communication in multi-antenna wireless systems, particularly focusing on Multiple-Input Multiple-Output (MIMO) and Multiple-Input Single-Output (MISO) architectures. The study integrates beamforming, energy harvesting, and secure wireless information transmission through novel mathematical optimization frameworks. Key contributions include coordinated multi-cell beamforming, SINR balancing for energy-harvesting systems, and secrecy wireless information and power transfer (SWIPT) in MIMO channels. Experimental simulations demonstrate significant improvements in signal-to-noise ratio (SNR), power efficiency, and security enhancement, making these methods viable for next-generation 5G and 6G networks.

References

[1]. AbdelRahman, A. A., Meftin, N. K., Alak, E. K., Jawad, H. M., Hatim, Q. Y., Khlaponin, Y., & Al-Jawher, W. A. M. (2024, October). AI-Driven cloud networking optimizations for seamless LTE connectivity. In 2024 36th Conference of Open Innovations Association (FRUCT) (pp. 13-23). IEEE.

[2]. Chai, L., Bai, L., Bai, T., Shi, J., & Nallanathan, A. (2023). Secure RIS-aided MISO-NOMA system design in the presence of active eavesdropping. IEEE Internet of Things Journal, 10(22), 19479-19494.

[3]. Chen, S., He, M., Ran, L., Li, H., Xi, F., Tian, S., & Liu, Z. (2024). Transmit beampattern synthesis for active RIS-aided MIMO radar via waveform and beamforming optimization. IEEE Transactions on Aerospace and Electronic Systems (pp. 1-13).

[4]. Kumar, N. A., & Daniel, R. (2024). Artificial intelligence-based energy efficiency models in green communications towards 6G. In Towards Wireless Heterogeneity in 6G Networks (pp. 158-179). CRC Press.

[5]. Li, Q., Zhang, L., Zhang, C., Tian, Y., Fan, Y., Li, B., & Wang, Z. L. (2024a). Compact, robust, and regulated-output hybrid generators for magnetic energy harvesting and self-powered sensing applications in power transmission lines. Energy & Environmental Science, 17(8), 2787-2799.

[6]. Li, R., Wang, L., Chen, K., Xu, L., & Fei, A. (2024b). Full-Duplex NOMA enabled integrated sensing and communication: Joint transmit and receive beamforming optimization. IEEE Internet of Things Journal, 11(16), 27015 – 27029.

[7]. Li, S., Mu, B., Wang, X., & Wang, A. (2021). Recent researches on natural pigments stabilized by clay minerals: A review. Dyes and Pigments, 190, 109322.

[8]. Liu, L., Li, S., Wei, M., Xu, J., & Yu, B. (2024a). Joint beam-forming optimization for active-RIS-assisted internet-of-things networks with SWIPT. Future Internet, 16(1), 20.

[9]. Liu, Y., Zhang, S., Li, X., Huang, Y., Fang, Y., & Cao, H. (2024b, April). Deep reinforcement learning-based beamforming design in ISAC-assisted vehicular networks. In 2024 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1-6). IEEE.

[10]. Niu, Y., Wei, Z., Wang, L., Wu, H., & Feng, Z. (2024). Interference management for integrated sensing and communication systems: A survey. IEEE Internet of Things Journal (pp. 1-1).

[11]. Rehman, S. U., Ahmad, J., Manzar, A., & Moinuddin, M. (2024). Beamforming techniques for MIMO-NOMA for 5G and beyond 5G: Research gaps and future directions. Circuits, Systems, and Signal Processing, 43(3), 1518-1548.

[12]. Sun, Y., Zhang, A., Yang, H., Tian, D., & Xia, H. (2024). Near-optimal low-complexity hybrid precoding for THz massive MIMO systems. KSII Transactions on Internet & Information Systems, 18(4), 1042-1058.

[13]. Tang, Z., Song, T., & Hu, J. (2024). V2I physical layer security beamforming with antenna hardware impairments under RIS assistance. Computers, Materials & Continua, 81(1), 1835-1854.

[14]. Xiao, Q., Zhao, J., Feng, S., Li, G., & Hu, A. (2024). Securing NextG networks with physical-layer key generation: A survey. Security and Safety, 3, 2023021.

[15]. Yun, J., Kim, I., & Kim, D. (2023). Triboelectric energy harvesting using inductor towards self-powered real-time wireless communication system. Nano Energy, 115, 108760.

[16]. Zhang, J., Qi, X., Cai, Y., & Chen, C. (2023). Secure transmission performance analysis of multi-hop cognitive relay radio networks with energy harvesting and artificial noise aided. Wireless Networks, 29(8), 3829-3837.

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Research Paper

Maximum Power Point Tracking for a Photovoltaic Power System using the DIRECT Algorithm

Muchen Yuze*

* Department of Material Science, Qingdao University, Shandong, China.

** Linköping University of Creative Technology, Selangor, Malaysia.

Yuze, M., and Nadia, F. (2024). Maximum Power Point Tracking for a Photovoltaic Power System using the DIRECT Algorithm. i-manager’s Journal on Power Systems Engineering, 12(2), 15-28. https://doi.org/10.26634/jps.12.2.21682

Abstract

Photovoltaic (PV) power generation systems require effective maximum power point tracking (MPPT) algorithms to ensure optimal energy conversion efficiency. This research presents a novel MPPT algorithm based on the Dividing Rectangles (DIRECT) algorithm, which offers improved tracking performance under rapidly changing insolation and partial shading conditions. Compared to conventional techniques such as Perturb & Observe (P&O) and Incremental Conductance (INC), the proposed method effectively identifies and tracks the global maximum power point (GMPP) while minimizing steady-state oscillations. The effectiveness of the approach is validated through simulation and experimental results, demonstrating enhanced tracking speed and energy efficiency.

References

[1]. Alajmi, B. N., Ahmed, K. H., Finney, S. J., & Williams, B. W. (2010). Fuzzy-logic-control approach of a modified hill-climbing method for maximum power point in microgrid standalone photovoltaic system. IEEE Transactions on Power Electronics, 26(4), 1022-1030.

[2]. Ali, A. N. A., Saied, M. H., Mostafa, M. Z., & Abdel-Moneim, T. M. (2012, May). A survey of maximum PPT techniques of PV systems. In 2012 IEEE Energytech (pp. 1-17). IEEE.

[3]. Atkin, P., & Farid, M. M. (2015). Improving the efficiency of photovoltaic cells using PCM infused graphite and aluminium fins. Solar Energy, 114, 217-228.

[4]. Chen, J., Zhang, X., & Wen, C. (2016). Harmonics attenuation and power factor correction of a more electric aircraft power grid using active power filter. IEEE Transactions on Industrial Electronics, 63(12), 7310-7319.

[5]. Esram, T., & Chapman, P. L. (2007). Comparison of photovoltaic array maximum power point tracking techniques. IEEE Transactions on Energy Conversion, 22(2), 439-449.

[6]. Gosselin, L., & Dussault, J. M. (2017). Correlations for glazing properties and representation of glazing types with continuous variables for daylight and energy simulations. Solar Energy, 141, 159-165.

[7]. Hohm, D. P., & Ropp, M. E. (2003). Comparative study of maximum power point tracking algorithms. Progress in Photovoltaics: Research and Applications, 11(1), 47-62.

[8]. International Energy Agency. (2022). World Energy Outlook 2022.

[9]. Jones, D. R., Perttunen, C. D., & Stuckman, B. E. (1993). Lipschitzian optimization without the Lipschitz constant. Journal of Optimization Theory and Applications, 79, 157-181.

[10]. Koad, R. B., Zobaa, A. F., & El-Shahat, A. (2016). A novel MPPT algorithm based on particle swarm optimization for photovoltaic systems. IEEE Transactions on Sustainable Energy, 8(2), 468-476.

[11]. Kobayashi, K., Takano, I., & Sawada, Y. (2003, July). A study on a two-stage maximum power point tracking control of a photovoltaic system under partially shaded insolation conditions. In 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No. 03CH37491), 4, 2612-2617. IEEE.

[12]. Leelavathi, M., & Devi, B. (2023, December). Maximizing solar energy output: A comparative analysis of MPPT strategies for partially shaded PV systems. In 2023 International Conference on Energy, Materials and Communication Engineering (ICEMCE) (pp. 1-7). IEEE.

[13]. Miyatake, M., Veerachary, M., Toriumi, F., Fujii, N., & Ko, H. (2011). Maximum power point tracking of multiple photovoltaic arrays: A PSO approach. IEEE Transactions on Aerospace and Electronic Systems, 47(1), 367-380.

[14]. Mohan, N., Undeland, T. M., & Robbins, W. P. (2003). Power Electronics: Converters, Applications, and Design. John Wiley & Sons.

[15]. Mohapatra, A., Nayak, B., Das, P., & Mohanty, K. B. (2017). A review on MPPT techniques of PV system under partial shading condition. Renewable and Sustainable Energy Reviews, 80, 854-867.

[16]. Nguyen, T. L. (2010). Maximum Power Point Tracking for a Photovoltaic Power System (Doctoral dissertation, Nanyang Technological University).

[17]. Patel, H., & Agarwal, V. (2008). MATLAB-based modeling to study the effects of partial shading on PV array characteristics. IEEE Transactions on Energy Conversion, 23(1), 302-310.

[18]. Pilawa-Podgurski, R. C., Li, W., Celanovic, I., & Perreault, D. J. (2015). Integrated CMOS energy harvesting converter with digital maximum power point tracking for a portable thermophotovoltaic power generator. IEEE Journal of Emerging and Selected Topics in Power Electronics, 3(4), 1021-1035.

[19]. Salas, V., Olías, E., Barrado, A., & Lazaro, A. (2006). Review of the maximum power point tracking algorithms for stand-alone photovoltaic systems. Solar Energy Materials and Solar Cells, 90(11), 1555-1578.

[20]. Sera, D., Kerekes, T., Teodorescu, R., & Blaabjerg, F. (2006, July). Improved MPPT method for rapidly changing environmental conditions. In 2006 IEEE International Symposium on Industrial Electronics, 2, 1420-1425. IEEE.

[21]. Sera, D., Mathe, L., Kerekes, T., Spataru, S. V., & Teodorescu, R. (2013). On the perturb-and-observe and incremental conductance MPPT methods for PV systems. IEEE Journal of Photovoltaics, 3(3), 1070-1078.

[22]. Solar Energy Industries Association. (2021). U.S. Solar Market Insight Report.

[23]. Subudhi, B., & Pradhan, R. (2012). A comparative study on maximum power point tracking techniques for photovoltaic power systems. IEEE Transactions on Sustainable Energy, 4(1), 89-98.

[24]. Villegas-Mier, C. G., Rodriguez-Resendiz, J., Álvarez-Alvarado, J. M., Rodriguez-Resendiz, H., Herrera-Navarro, A. M., & Rodríguez-Abreo, O. (2021). Artificial neural networks in MPPT algorithms for optimization of photovoltaic power systems: A review. Micromachines, 12(10), 1260.

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Research Paper

Optimizing Multigenerational Renewable Energy Systems through Integrated Geothermal and Wind Energy Solutions

Kanaga Bharathi Nageswaran* , Devi Vighneshwari, M. Abirami*, Hariprasath Manoharan****

* Department of Electrical and Electronics Engineering, Bangalore College of Engineering and Technology, Bengaluru, India.

Department of Electrical Engineering, Annamalai University, Chidambaram, Tamil Nadu, India.

* Department of Electrical Engineering, Annamalai University, Chidambaram, Tamil Nadu, India.

**** Department of Electronics and Communication Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India.

Bharathi, K. N., Vighneshwari, D., Abirami, M., and Manoharan, H. (2024). Optimizing Multigenerational Renewable Energy Systems Through Integrated Geothermal and Wind Energy Solutions. i-manager’s Journal on Power Systems Engineering, 12(2), 29-36. https://doi.org/10.26634/jps.12.2.21505

Abstract

In order to effectively tackle the increasing energy demands resulting from urbanization, modernization, and economic activities, it is imperative to develop novel solutions that simultaneously mitigate greenhouse gas emissions and meet energy needs. This study investigates the feasibility of integrating wind and geothermal energy sources to enhance the efficacy and effectiveness of multigenerational renewable energy systems. Case Study 1 investigates the application of a geothermal-wind hybrid system to maximize the efficacy of the Kalina cycle, demonstrating significant improvements in both energy and exergy utilization. Case Study 2 conducts a sensitivity analysis to evaluate the system's performance, providing critical information pertaining to wind velocities and thermal efficiencies. This study emphasizes the importance of thorough modeling and optimization, in addition to the potential of incorporating renewable energy sources to drive progress in sustainable energy.

References

[1]. Bamisile, O., Dongsheng, C., Li, J., Mukhtar, M., Wang, X., Duo, J., & Huang, Q. (2022). An innovative approach for geothermal-wind hybrid comprehensive energy system and hydrogen production modeling/process analysis. International Journal of Hydrogen Energy, 47(27), 13261-13288.

[2]. Das, D. C., Roy, A. K., & Sinha, N. (2012). GA-based frequency controller for solar thermal–diesel–wind hybrid energy generation/energy storage system. International Journal of Electrical Power & Energy Systems, 43(1), 262-279.

[3]. Fargali, H. M., Fahmy, F. H., & Hassan, M. A. (2008). A simulation model for predicting the performance of PV/wind-powered geothermal space heating system in Egypt. The Online Journal on Electronics and Electrical Engineering (OJEEE), 2(4), 173-177.

[4]. Olabi, A. G., Mahmoud, M., Soudan, B., Wilberforce, T., & Ramadan, M. (2020). Geothermal-based hybrid energy systems, toward eco-friendly energy approaches. Renewable Energy, 147, 2003-2012.

[5]. Sawle, Y., Gupta, S. C., & Kumar Bohre, A. (2016). PV-wind hybrid system: A review with case study. Cogent Engineering, 3(1), 1189305.

[6]. Sharmin, T., Khan, N. R., Akram, M. S., & Ehsan, M. M. (2023). A state-of-the-art review on geothermal energy extraction, utilization, and improvement strategies: Conventional, hybridized, and enhanced geothermal systems. International Journal of Thermofluids, 18, 100323.

[7]. Wendt, D. S., Neupane, G., Davidson, C. L., Zheng, R., & Bearden, M. A. (2018). GeoVision Analysis Supporting Task Force Report: Geothermal Hybrid Systems.

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Research Paper

Wireless Charging Stations Utilizing Solar Energy for Electric Vehicles

N. Swathi* , Ajay Kumar Dharmireddy**

*-** Department of Electronics and Communication Engineering, Sir C. R. Reddy College of Engineering, Eluru, Andhra Pradesh, India.

Swathi, N., & Dharmireddy, A. K. (2024). Wireless Charging Stations Utilizing Solar Energy for Electric Vehicles. i-manager’s Journal on Power Systems Engineering, 12(2), 37-43. https://doi.org/10.26634/jps.12.2.21613

Abstract

The emergence of electric vehicles (EVs) signifies a transformative potential for the future of transportation on a global scale. The transition from fuel-dependent transportation to electric-powered systems marks a significant breakthrough in energy efficiency, conservation, and the facilitation of smooth energy transitions. This initiative also aims to reduce harmful emissions that have significant environmental impacts, including influencing weather patterns and contributing to global warming. Advancements in technology have led numerous companies to manufacture electric vehicles (EVs). However, charging remains a significant challenge for EVs. Various methods have been proposed to address this concern, with one conventional approach being the charging of vehicles at designated stations. However, this method is limited by the time required for charging and the restricted travel distances. As a result, wireless charging has been proposed as an alternative solution. Several factors influence the efficacy of wireless charging, with various techniques developed that rely on electromagnetic induction or inductive power transfer. Although passive wireless charging has been implemented, challenges related to the placement of charging pads and stations continue to arise. The parameters influencing these charging methods and the positioning of charging infrastructure are critical to the system’s success. This system operates through affordable inductive coupling between two coils, known as the transmitter and receiver coils. In the context of EV charging, transmitter coils are typically embedded in the roadway, while receiver coils are installed within the vehicle.

References

[1]. Abdolmaleki, M., Masoud, N., & Yin, Y. (2019). Vehicle-to-vehicle wireless power transfer: Paving the way toward an electrified transportation system. Transportation Research Part C: Emerging Technologies, 103, 261-280.

[2]. Barman, S. D., Reza, A. W., Kumar, N., Karim, M. E., & Munir, A. B. (2015). Wireless powering by magnetic resonant coupling: Recent trends in wireless power transfer system and its applications. Renewable and Sustainable Energy Reviews, 51, 1525-1552.

[3]. Chakradhar, A., Dharmireddy, A., Sudhkar, H., Lavanyai, K., Krishna, M. V., Madhava Rao, C. H., & Hareesh, P. (2024, June). Arduino based GPS tracking alert system for women’s protection. In All Conference Proceedings, 2971(1).

[4]. Chhawchharia, S., Sahoo, S. K., Balamurugan, M., Sukchai, S., & Yanine, F. (2018). Investigation of wireless power transfer applications with a focus on renewable energy. Renewable and Sustainable Energy Reviews, 91, 888-902.

[5]. Clemente, C., & Soraghan, J. J. (2012). Range doppler and chirp scaling processing of synthetic aperture radar data using the fractional fourier transform. IET Signal Processing, 6(5), 503-510.

[6]. Dharmireddy, A. K., & Suneetha, N. (2023). A blockchain based cyber thread detection system for IIoT networks. I-manager’s Journal on IoT and Smart Automation, 1(2), 16.

[7]. Dharmireddy, A. K., Larraine, K. J. S., Maddumala, R. K., & Lavanya, K. (2024a). Pesticide prediction and disease identification with AIoT. In The Future of Agriculture: IoT, AI and Blockchain Technology for Sustainable Farming (pp. 62-85).

[8]. Dharmireddy, A. K., Ravikumar, M., & Kumar, B. V. (2024b). Identifying chronic kidney failure through machine learning. I-manager’s Journal on IoT and Smart Automation, 2(1), 1.

[9]. Dharmireddy, A. K., Srinivasulu, P., Greeshma, M., & Shashidhar, K. (2024c). Soft sensor-based remote monitoring system for industrial environments. In Blockchain Technology for IoT and Wireless Communications (pp. 103-112).

[10]. Dharmireddy, A., & Gotlipalli, M. D. (2023). Social networking sites fake profiles detection using machine learning techniques. Asian Journal for Convergence in Technology (AJCT), 9(3), 09-15.

[11]. Dharmireddy, A., Chakradhar, A., Akram, S. V., Deepak, R. S., Akash, S., Rajasekhar, T., & Anatha, T. (2024d, June). Dermatological disease detection and preventative measures using deep convolution neural networks. In AIP Conference Proceedings, 2971(1).

[12]. Elfourni, F. M., Becherif, M., Djerdit, A., & Ramadan, H. S. (2021). The key issues of electric vehicle charging via hybrid power sources: Techno-economic viability, analysis, and recommendations. Renewable and Sustainable Energy Reviews, 138, 110534.

[13]. Kumar, M., Narayana, K., & Dharmireddy, D. (2024). High performance approximate multiplier using reversible logic gates. Asian Journal for Convergence in Technology (AJCT), 10(3), 1-5.

[14]. Kurs, A., Karalis, A., Moffatt, R., Joannopoulos, J. D., Fisher, P., & Soljacic, M. (2007). Wireless power transfer via strongly coupled magnetic resonances. Science, 317(5834), 83-86.

[15]. Prithvi, J. M., & Dharmireddy, A. K. (2013). Multitrack simulator implementation in FPGA for ESM system. International Journal of Electronics Signals and Systems, (pp. 81-84).

[16]. Shashidhar, K., Dharmireddy, A. K., & Rao, C. M. (2024). Anti-theft fingerprint security system for motor vehicles. In Blockchain Technology for IoT and Wireless Communications (pp. 89-101).

[17]. Swathi, V., Dharmireddy, A. K., Kumar, M. R., Kumar, B. V., & Prasad, P. S. (2024). Suggestion of tablets with machine learning and IoT for women. I-manager’s Journal on IoT and Smart Automation, 2(2), 13.

[18]. Tuchnitz, F., Ebell, N., Schlund, J., & Pruckner, M. (2021). Development and evaluation of a smart charging strategy for an electric vehicle fleet based on reinforcement learning. Applied Energy, 285, 116382.

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[21]. Zhang, X., Yuan, Z., Yang, Q., Li, Y., Zhu, J., & Li, Y. (2016). Coil design and efficiency analysis for dynamic wireless charging system for electric vehicles. IEEE Transactions on Magnetics, 52(7), 1-4.

i-manager's Journal on Power Systems Engineering (JPS)

Current Issue Vol. 12 Issue 2

Most Read

Most Cited

Volume 12 Issue 2 July - September 2024

Optimal Placement of Distributed Products to Improve Network Reliability under Network Operation Conditions

Mehdi Salman*

Diyala, Iraq.

Salman, M. (2024). Optimal Placement of Distributed Products to Improve Network Reliability under Network Operation Conditions. i-manager’s Journal on Power Systems Engineering, 12(2), 1-7. https://doi.org/10.26634/jps.12.2.21639

Abstract

References

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Optimized Multi-Antenna Wireless Communication: Enhancing Beam Forming, Security and Energy Efficiency

Fulgencio Estefan*

Department of Electrical Engineering, University of Matanzas, Matanzas, Cuba.

Estefan, F. (2024). Optimized Multi-Antenna Wireless Communication: Enhancing Beam Forming, Security and Energy Efficiency. i-manager’s Journal on Power Systems Engineering, 12(2), 8-14. https://doi.org/10.26634/jps.12.2.21664

Abstract

References

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Maximum Power Point Tracking for a Photovoltaic Power System using the DIRECT Algorithm

Muchen Yuze*

* Department of Material Science, Qingdao University, Shandong, China. ** Linköping University of Creative Technology, Selangor, Malaysia.

Yuze, M., and Nadia, F. (2024). Maximum Power Point Tracking for a Photovoltaic Power System using the DIRECT Algorithm. i-manager’s Journal on Power Systems Engineering, 12(2), 15-28. https://doi.org/10.26634/jps.12.2.21682

Abstract

References

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Optimizing Multigenerational Renewable Energy Systems through Integrated Geothermal and Wind Energy Solutions

Kanaga Bharathi Nageswaran* , Devi Vighneshwari**, M. Abirami***, Hariprasath Manoharan****

Bharathi, K. N., Vighneshwari, D., Abirami, M., and Manoharan, H. (2024). Optimizing Multigenerational Renewable Energy Systems Through Integrated Geothermal and Wind Energy Solutions. i-manager’s Journal on Power Systems Engineering, 12(2), 29-36. https://doi.org/10.26634/jps.12.2.21505

Abstract

References

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Wireless Charging Stations Utilizing Solar Energy for Electric Vehicles

N. Swathi* , Ajay Kumar Dharmireddy**

*-** Department of Electronics and Communication Engineering, Sir C. R. Reddy College of Engineering, Eluru, Andhra Pradesh, India.

Swathi, N., & Dharmireddy, A. K. (2024). Wireless Charging Stations Utilizing Solar Energy for Electric Vehicles. i-manager’s Journal on Power Systems Engineering, 12(2), 37-43. https://doi.org/10.26634/jps.12.2.21613

Abstract

References

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* Department of Material Science, Qingdao University, Shandong, China.

** Linköping University of Creative Technology, Selangor, Malaysia.

Kanaga Bharathi Nageswaran* , Devi Vighneshwari, M. Abirami*, Hariprasath Manoharan****