i-manager Publications

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

Current Issue Vol. 12 Issue 1

Thermodynamic and Exergoeconomic Operation Optimization and Simulation of Steam Generation Solar Power Plant
Topology Transformation Approach for Optimal PMU Placement for Monitoring and Control of Power System
Performance Evaluation of Power System with HVDC Integration: Impact of SSSC and STATCOM on Power System Efficiency and Stability
Photovoltaic Systems: A Pollination-Based Optimization Approach for Critical Industrial Applications
Design of a Robust Controller for the Load Frequency Control of Interconnected Power System

Most Cited

Volume 11 Issue 1 April - June 2023

Research Paper

A New Approach for Fault Diagnosis in TCSC Compensated Transmission Lines using FWHT and Machine Learning Techniques

Pankaj Kumar Sahu* , Rajkumar Jhapte**

*-** Department of Electrical & Electronics Engineering, Shri Shankaracharya Technical Campus, Bhilai, Chhattisgarh, India.

Sahu, P. K., and Jhapte, R. (2023). A New Approach for Fault Diagnosis in TCSC Compensated Transmission Lines using FWHT and Machine Learning Techniques. i-manager’s Journal on Power Systems Engineering, 11(1), 1-11. https://doi.org/10.26634/jps.11.1.19451

Abstract

In this research a new approach is introduced for detecting and classifying faults in a Thyristor-Controlled Series Capacitor (TCSC) compensated power system. Our proposed scheme utilizes both the Fast Walsh Hadamard Transform (FWHT) and machine learning algorithms. The FWHT is employed to extract fault features from current data obtained from the TCSC compensated transmission line, while machine learning algorithms such as K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) are used to classify the extracted features for the purpose of fault detection and identification. To evaluate the performance of our proposed scheme, simulation studies were conducted on a test system under various fault conditions. The simulation results demonstrate that our approach is highly effective in accurately and quickly detecting and classifying faults, even when noise and TCSC compensation are present. This scheme has the potential to enhance the reliability and efficiency of power transmission systems.

References

[1]. Abdoos, A. A. (2016). Detection of current transformer saturation based on variational mode decomposition analysis. IET Generation, Transmission & Distribution, 10(11), 2658-2669.

[2]. Biswas, S., & Nayak, P. K. (2018). State of the art on the protection of FACTS compensated high voltage transmission lines: A review. High Voltage, 3(1), 21-30.

[3]. Chen, K., Hu, J., & He, J. (2016b). Detection and classification of transmission line faults based on unsupervised feature learning and convolutional sparse autoencoder. IEEE Transactions on Smart Grid, 9(3), 1748-1758.

[4]. Chen, K., Huang, C., & He, J. (2016a). Fault detection, classification and location for transmission lines and distribution systems: A review on the methods. High Voltage, 1(1), 25-33.

[5]. Dash, P. K., & Samantray, S. R. (2004). Phase selection and fault section identification in thyristor controlled series compensated line using discrete wavelet transform. International Journal of Electrical Power & Energy Systems, 26(9), 725-732.

[6]. Eristi, H. (2013). Fault diagnosis system for series compensated transmission line based on wavelet transform and adaptive neuro-fuzzy inference system. Measurement, 46(1), 393-401.

[7]. Fahim, S. R., Sarker, S. K., Muyeen, S. M., Sheikh, M. R. I., Das, S. K., & Simoes, M. (2021). A robust self-attentive capsule network for fault diagnosis of series- Compensated transmission line. IEEE Transactions on Power Delivery, 36(6), 3846-3857.

[8]. GE Multilin. (2009). D60 Line Distance Relay.

[9]. Guillen, D., Paternina, M. R. A., Zamora, A., Ramirez, J. M., & Idarraga, G. (2015). Detection and classification of faults in transmission lines using the maximum wavelet singular value and Euclidean norm. IET Generation, Transmission & Distribution, 9(15), 2294-2302.

[10]. He, S., Suonan, J., & Bo, Z. Q. (2013). Integrated impedance-based pilot protection scheme for the TCSC-Compensated EHV/UHV transmission lines. IEEE Transactions on Power Delivery, 28(2), 835-844.

[11]. Kasztenny, B., Campbell, B., & Mazereeuw, J. (2000, October). Phase selection for single-pole tripping–Weak infeed conditions and cross country faults. In Proceedings of the 27th Annual Western Protective Relay Conference, Spokane (pp. 1-19).

[12]. Khederzadeh, M., & Sidhu, T. S. (2005). Impact of TCSC on the protection of transmission lines. IEEE Transactions on Power Delivery, 21(1), 80-87.

[13]. Mishra, P. K., Yadav, A., & Pazoki, M. (2018). A novel fault classification scheme for series capacitor compensated transmission line based on bagged tree ensemble classifier. IEEE Access, 6, 27373-27382.

[14]. Mishra, P. K., Yadav, A., & Pazoki, M. (2019). FDOSTbased fault classification scheme for fixed series compensated transmission system. IEEE Systems Journal, 13(3), 3316-3325.

[15]. Mishra, P. K., Yadav, A., & Pazoki, M. (2020). Resilience-oriented protection scheme for TCSC- compensated line. International Journal of Electrical Power & Energy Systems, 121, 106103.

[16]. Moravej, Z., Pazoki, M., & Abdoos, A. A. (2011). A new approach for fault classification and section detection in compensated transmission line with TCSC. European Transactions on Electrical Power, 21(1), 997-1014.

[17]. Muniappan, K., & Kitai, R. (1982). Walsh spectrum measurement in natural, dyadic, and sequency ordering. IEEE Transactions on Electromagnetic Compatibility, EMC-24(1), 46-49.

[18]. Samantaray, S. R. (2009). Decision tree-based fault zone identification and fault classification in flexible AC transmissions-based transmission line. IET Generation, Transmission & Distribution, 3(5), 425-436.

[19]. Shi, S., Zhu, B., Mirsaeidi, S., & Dong, X. (2018). Fault classification for transmission lines based on group sparse representation. IEEE Transactions on Smart Grid, 10(4), 4673-4682.

[20]. Taheri, M. M., Seyedi, H., Nojavan, M., Khoshbouy, M., & Ivatloo, B. M. (2018). High-speed decision tree based series-compensated transmission lines protection using differential phase angle of superimposed current. IEEE Transactions on Power Delivery, 33(6), 3130-3138.

[21]. Unde, S. V., Gawande, P., & Dambhare, S. (2019). New algorithm for protection of double circuit transmission lines using modal currents. IEEE Transactions on Power Delivery, 34(5), 1967-1977.

[22]. Vyas, B. Y., Das, B., & Maheshwari, R. P. (2014b). Improved fault classification in series compensated transmission line: Comparative evaluation of Chebyshev neural network training algorithms. IEEE Transactions on Neural Networks and Learning Systems, 27(8), 1631-1642.

[23]. Vyas, B. Y., Maheshwari, R. P., & Das, B. (2014a). Improved fault analysis technique for protection of Thyristor controlled series compensated transmission line. International Journal of Electrical Power & Energy Systems, 55, 321-330.

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Research Paper

Wireless Charging with Position Alignment for Electric Vehicles

Ajay Sagar M.* , Bharat Hiregoudar, Bipin Chandra S. M.*, Bhushan P.****

*-**** Department of Electronics and Communication Engineering, PESITM, Shimoga, Karnataka, India.

Sagar, M. A., Hiregoudar, B., Chandra, S. M. B., and Bhushan, P. (2023). Wireless Charging with Position Alignment for Electric Vehicles. i-manager’s Journal on Power Systems Engineering, 11(1), 12-19. https://doi.org/10.26634/jps.11.1.19475

Abstract

Electric Vehicles (EVs) have gained popularity in recent years due to its sustainability. However, the charging process for EVs can be inconvenient and time-consuming, particularly when relying on conventional charging stations that necessitate manual vehicle connections. This research aims to develop a wireless charging system for electric vehicles that incorporates position alignment technology. The objective is to enhance the charging experience for EV owners, improving efficiency while reducing the environmental impact associated with traditional charging methods. The system utilizes a combination of wireless charging technology and sensors to detect the position of the EV and align it with the charging pad using Internet of Things (IoT) technology. This ensures an optimized charging process, enabling quick and efficient charging of the EV.

References

[1]. Ahmad, A., Alam, M. S., & Chabaan, R. (2017). A comprehensive review of wireless charging technologies for electric vehicles. IEEE Transactions on Transportation Electrification, 4(1), 38-63.

[2]. Ahmed, A. M., & Khalifa, O. O. (2020, December). Wireless power transfer for electric vehicle charging. In AIP Conference Proceedings, 2306(1), 020026. AIP Publishing LLC.

[3]. Das, S., & Chakraborty, S. (2018, July). Fast charging a 12V battery using wireless power transfer concept. In 2018 International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (ICRIEECE) (pp. 3156-3160). IEEE.

[4]. Das, S., Pal, K., Goswami, P., & Kerawalla, M. A. K. (2018). Wireless power transfer in electric vehicles. International Journal of Applied Environmental Sciences, 13(7), 643-659.

[5]. Fisher, T. M., Farley, K. B., Gao, Y., Bai, H., & Tse, Z. T. H. (2014). Electric vehicle wireless charging technology: A state-of-the-art review of magnetic coupling systems. Wireless Power Transfer, 1(2), 87-96.

[6]. Jang, Y. J., Ko, Y. D., & Jeong, S. (2012, March). Optimal design of the wireless charging electric vehicle. In 2012 IEEE International Electric Vehicle Conference (pp. 1-5). IEEE.

[7]. Lee, J. Y., & Han, B. M. (2014). A bidirectional wireless power transfer EV charger using self-resonant PWM. IEEE Transactions on Power Electronics, 30(4), 1784-1787.

[8]. Machura, P., & Li, Q. (2019). A critical review on wireless charging for electric vehicles. Renewable and Sustainable Energy Reviews, 104, 209-234.

[9]. Nutwong, S., Mujjalinvimut, E., Maiket, S., Khemkhaeng, T., & Wiraphonsawan, N. (2021, March). Position detection with online monitoring system for wireless charging of electric vehicles. In 2021 9th International Electrical Engineering Congress (iEECON) (pp. 173-176). IEEE.

[10]. Qiu, C., Chau, K. T., Liu, C., & Chan, C. C. (2013, November). Overview of wireless power transfer for electric vehicle charging. In 2013 World Electric Vehicle Symposium and Exhibition (EVS27) (pp. 1-9). IEEE.

[11]. Sabki, S. A., & Tan, N. M. L. (2014, March). Wireless power transfer for electric vehicle. In 2014 IEEE 8th International Power Engineering and Optimization Conference (PEOCO2014) (pp. 41-46). IEEE.

[12]. 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.

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Research Paper

A Review of Optimization of Control Strategies for Non-Linear Input Solar PV System

Roshni Rahangdale* , Deeksha Sahare**

*-** Department of Electrical & Electronics Engineering, Shri Shankaracharya Group of Institutions, Durg, India.

Rahangdale, R., and Sahare, D. (2023). A Review of Optimization of Control Strategies for Non-Linear Input Solar PV System. i-manager’s Journal on Power Systems Engineering, 11(1), 20-27. https://doi.org/10.26634/jps.11.1.19418

Abstract

In recent years, solar photovoltaic (PV) units have rapidly grown to become a major contributor to the installed generating capacity worldwide. However, the intermittent and fluctuating nature of renewable generation can negatively impact power quality and generating units. The backbone of these problems is often related to the control system of the entire network. In a solar-based generation, extracting maximum power known as Maximum Power Point Tracking (MPPT), is a major challenge due to its dependence on climatic conditions and location. This research aims to review the steps taken to enhance output by using different control systems for maximum power point extraction and to achieve optimization in the control system technique, resulting in the best output of any location's current solar radiation scenario.

References

[1]. Abdullah, M. Z., Sudiharto, I., & Eviningsih, R. P. (2020, September). Photovoltaic system MPPT using fuzzy logic controller. In 2020 International Seminar on Application for Technology of Information and Communication (iSemantic) (pp. 378-383). IEEE.

[2]. Agorreta, J. L., Reinaldos, L., Gonzalez, R., Borrega, M., Balda, J., & Marroyo, L. (2009). Fuzzy switching technique applied to PWM boost converter operating in mixed conduction mode for PV systems. IEEE Transactions on Industrial Electronics, 56(11), 4363-4373.

[3]. Benkercha, R., Moulahoum, S., & Colak, I. (2017, December). Modelling of fuzzy logic controller of a maximum power point tracker based on artificial neural network. In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 485-492). IEEE.

[4]. Dhanakodi, T., & Sivakumar, M. (2018). A comparative study on efficiency enhanced solar energy harvesting systems. International Journal of Applied Engineering Research, 13(20), 14757-14762.

[5]. Femia, N., Petrone, G., Spagnuolo, G., & Vitelli, M. (2005). Optimization of perturb and observe maximum power point tracking method. IEEE Transactions on Power Electronics, 20(4), 963-973.

[6]. Goel, N., Sharma, P. R., & Bala, S. (2014). Performance analysis of spwm inverter fed 3-phase induction motor drive using matlabsimulink. International Journal of Advanced Technology in Engineering and Science, 2(6), 183-193.

[7]. Gow, J. A., & Manning, C. D. (1999). Development of a photovoltaic array model for use in power-electronics simulation studies. IEE Proceedings-Electric Power Applications, 146(2), 193-200.

[8]. Kermadi, M., & Berkouk, E. M. (2015, May). A maximum power point tracker based on particle swarm optimization for pv-battery energy system under partial shading conditions. In 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT) (pp. 1-6). IEEE.

[9]. Kjaer, S. B., Pedersen, J. K., & Blaabjerg, F. (2005). A review of single-phase grid-connected inverters for photovoltaic modules. IEEE Transactions on Industry Applications, 41(5), 1292-1306.

[10]. Kordestani, M., Mirzaee, A., Safavi, A. A., & Saif, M. (2018, June). Maximum power point tracker (MPPT) for photovoltaic power systems-A systematic literature review. In 2018 European Control Conference (ECC) (pp. 40-45). IEEE.

[11]. Kumar, M. V., & Salma, U. (2020). Modeling and simulation of a three phase grid connected photovoltaic system. European Journal of Molecular & Clinical Medicine, 7(5), 244-261.

[12]. Metry, M., Shadmand, M. B., Balog, R. S., & Abu-Rub, H. (2016). MPPT of photovoltaic systems using sensorless current-based model predictive control. IEEE Transactions on Industry Applications, 53(2), 1157-1167.

[13]. Murdoch, S., & Reynoso, S. (2013). Design and implementation of a MPPT circuit for a solar UAV. IEEE Latin America Transactions, 11(1), 108-111.

[14]. Paz, F., & Ordonez, M. (2016). High-performance solar MPPT using switching ripple identification based on a lock-in amplifier. IEEE Transactions on Industrial Electronics, 63(6), 3595-3604.

[15]. Reisi, A. R., Moradi, M. H., & Jamasb, S. (2013). Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review. Renewable and Sustainable Energy Reviews, 19, 433- 443.

[16]. Schonardie, M. F., & Martins, D. C. (2008, June). Three-phase grid-connected photovoltaic system with active and reactive power control using dq0 transformation. In 2008 IEEE Power Electronics Specialists Conference (pp. 1202-1207). IEEE.

[17]. Selvabharathi, P., Veerakumar, S., & Kannan, V. K. (2021, March). Simulation of DC-DC converter topology for solar PV system under varying climatic conditions with MPPT controller. In IOP Conference Series: Materials Science and Engineering, 1084(1), 012084. IOP Publishing.

[18]. Shadab, S., & Diwan, R. (2022). Analysis and simulation of solar PV connected with grid accomplished with maximum power point tracking system. International Journal of Trend in Scientific Research and Development, 6(5), 1157- 1161.

[19]. Venkatramanan, D., & John, V. (2019). Dynamic modeling and analysis of buck converter based solar PV charge controller for improved MPPT performance. IEEE Transactions on Industry Applications, 55(6), 6234-6246.

[20]. Verma, S., Verma, H. K., & Mohiddin, M. K. (2013). Modeling & Analysis of standalone photovoltaic system. International Journal of Research in Engineering and Technology, 2(11), 259-265.

[21]. Villalva, M. G., Gazoli, J. R., & Ruppert Filho, E. (2009). Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Transactions on Power Electronics, 24(5), 1198-1208.

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Research Paper

A Review of Opposition-Based Whale Optimization Algorithm and Whale Optimization Algorithm Tuned Power System Stabilizer for Multimachine Stability

Sushil Kumhar* , B. Chiranjeev Rao**

*-** Department of Electrical & Electronics Engineering, Shri Shankaracharya Technical Campus Bhilai, Chhattisgarh, India.

Kumhar, S., and Rao, B. C. (2023). A Review of Opposition-Based Whale Optimization Algorithm and Whale Optimization Algorithm Tuned Power System Stabilizer for Multimachine Stability. i-manager’s Journal on Power Systems Engineering, 11(1), 28-34. https://doi.org/10.26634/jps.11.1.19388

Abstract

This research aims to improve the stability of power systems using a power stabilizer. Various methods were used to finetune the conventional Power System Stabilizer (PSS) with a lead-lag compensator by minimizing the integral absolute error of speed deviations of generator rotors. To evaluate the performance of the various methods, different timedomain simulation test cases were conducted and the results were compared with the performance of the Oppositional Whale Optimization Algorithm-based Power System Stabilizer (OWOA-based PSS), Whale Optimization Algorithm-based Power System Stabilizer (WOA-based PSS), and the conventional PSS. The obtained results show that the OWOA-based PSS is more efficient in power oscillation damping than the other methods, including the WOA-based PSS. Overall, the OWOA-based PSS can be considered as a potential solution to enhance the stability of power systems by mitigating power oscillations in generator rotors. However, further studies and experiments may be required to validate all methods and compare them to ensure their effectiveness and efficiency.

References

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[2]. Kaveh, A., & Ghazaan, M. I. (2017). Enhanced whale optimization algorithm for sizing optimization of skeletal structures. Mechanics Based Design of Structures and Machines, 45(3), 345-362.

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[4]. Kumar, C. H., & Rao, R. S. (2016). A novel global MPP tracking of photovoltaic system based on whale optimization algorithm. International Journal of Renewable Energy Development, 5(3), 225-232.

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[10]. Tizhoosh, H. R. (2005, November). Opposition-based learning: a new scheme for machine intelligence. In International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06) (Vol. 1, pp. 695-701). IEEE.

[11]. Touma, H. J. (2016). Study of the economic dispatch problem on IEEE 30-bus system using whale optimization algorithm. International Journal of Engineering Technology and Sciences, 3(1), 11-18.

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Review Paper

Enabling Rural Electrification through a Grid-Interactive Microgrid System with Photovoltaic-Battery Integration

Ruchita Dey* , Simardeep Kaur**

*-** Chhattisgarh Swami Vivekanand Technical University, Bhilai, India.

Dey, R., and Kaur, S. (2023). Enabling Rural Electrification through a Grid-Interactive Microgrid System with Photovoltaic-Battery Integration. i-manager’s Journal on Power Systems Engineering, 11(1), 35-48. https://doi.org/10.26634/jps.11.1.19782

Abstract

The increasing electrification of daily life and the growing number of sensitive or critical loads have led to a rising demand for high-quality electricity and overall energy consumption. Customer demands for increased generating capacity and efficient energy production, distribution, and utilization are driven by the global increase in energy consumption and the depletion of fossil resources. The concept of the microgrid holds promise for addressing significant issues arising from the widespread use of distributed generation in distribution networks. To ensure successful microgrid operation, an appropriate control strategy is essential. MATLAB and other software can be used for simulating and evaluating various load models. This article provides an overview of the previous research on microgrids.

References

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[14]. Divshali, P. H., Alimardani, A., Hosseinian, S. H., & Abedi, M. (2012). Decentralized cooperative control strategy of microsources for stabilizing autonomous VSCbased microgrids. IEEE Transactions on Power Systems, 27(4), 1949-1959.

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

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Volume 11 Issue 1 April - June 2023

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A Review of Optimization of Control Strategies for Non-Linear Input Solar PV System

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A Review of Opposition-Based Whale Optimization Algorithm and Whale Optimization Algorithm Tuned Power System Stabilizer for Multimachine Stability

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Enabling Rural Electrification through a Grid-Interactive Microgrid System with Photovoltaic-Battery Integration

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*-** Chhattisgarh Swami Vivekanand Technical University, Bhilai, India.

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Ajay Sagar M.* , Bharat Hiregoudar, Bipin Chandra S. M.*, Bhushan P.****