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 8 Issue 2 May - July 2020

Research Paper

An Approach on Hybrid Storage System in Standalone AC/DC Micro-Grid with Super-Capacitor

This paper has been removed due to plagiarized work because of the dual submission by author.

Research Paper

Reactive Power Compensation of Wind Power Plant using Neuro Fuzzy Controller

Raina Jain * , Reshmita Sharma , Abhishek Mishra *

- Department of Electrical and Electronics Engineering, SSGI, Bhilai, India.

Department of Electronics and Communication Engineering, GGU, Bilaspur, India.

Jain, R., Sharma, R., and Mishra, A. (2020). Reactive Power Compensation of Wind Power Plant using Neuro Fuzzy Controller. i-manager's Journal on Power Systems Engineering, 8(2), 1-15. https://doi.org/10.26634/jps.8.2.17605

Abstract

Power system consist of generation, transmission, and distribution of power to clients. To meet the increasing demand the power sector has developed improving the quantity of tools, especially costly parts needed for power systems. Power generation and demand should be constantly adjusted to improve the operating effectiveness. The power system has disadvantages such as poor power quality and the significant expense of generation, which face difficulties in meeting the objective of supply of electrical energy. In this paper, a wind power plant model using MATLAB has been taken. When wind turbine is connected to existing power systems, wind turbines can cause a number of issues related to the system stability and power quality. To maintain the output power of system at desired values, an ANFIS system is designed as a controller for the power output of a variable speed wind power generation system. Membership functions are the building block of ANFIS, hence ANFIS has been trained with different membership function and results are obtained. Along with that, in an isolated wind plant, the variation in wind speed synchronous condenser need to increase the VAR generation due to which larger capacity synchronous condenser has to be installed. So ANFIS controller is designed to change the pitch angle so as to maintain the VAR generation of synchronous generator. The adequate result has been obtained, and it is found that the Gaussian type membership function gives the best result at higher wind speed and the variable generation has been controlled.

References

[1]. AbdElhafez, A. A., Alruways, S. H., Alsaif, Y. A., Althobaiti, M. F., Alotaibi, A. B., & Alotaibi, N. A. (2017). Reactive power problem and solutions: An overview. Journal of Power and Energy Engineering, 5(5), 40-54. https://doi.org/10.4236/ jpee.2017.55004

[2]. Aissaoui, A. G., Tahour, A., Abid, M., Essounbouli, N., & Nollet, F. (2013, October). Using neuro fuzzy PI techniques in wind turbine control. In 2013, International Conference on Renewable Energy Research and Applications (ICRERA) (pp. 605-610). IEEE. https://doi.org/10.1109/ICRERA.2013. 6749827

[3]. Aldair, A. A., Rashid, M. T., Halihal, A. F., & Mokayef, M. (2019). Design of pitch angle controller for wind turbine based on PI neurofuzzy model. Indonesian Journal of Electrical Engineering and Computer Science, 15(3), 1664- 1670. https://doi.org/10.11591/ijeecs

[4]. Al-Saffar, M., & Musilek, P. (2016, May). Fuzzy logic controller for large, grid-integrated wind farm under variable wind speeds. In 2016 17^th International Scientific Conference on Electric Power Engineering (EPE) (pp. 1-6). IEEE. https://doi.org/10.1109/EPE.2016.7521767

[5]. Amin, I. K., Uddin, M. N., & Marsadek, M. (2019, May). ANFIS based neuro-fuzzy control of DFIG for wind power generation in standalone mode. In 2019, IEEE International Electric Machines & Drives Conference (IEMDC) (pp. 2077- 2082). IEEE. https://doi.org/10.1109/IEMDC.2019.8785334

[6]. Arif, E. M. H., Hossen, J., Ramana, G., Bhuvaneswari, T., Velrajkumar, P., & Venkataseshaiah, C. (2018). A survey on neuro-fuzzy controllers for solar panel tracking systems. Far East Journal of Electronics and Communications, 18(7), 981-1003.

[7]. Ghiasvand, O., & Ghiasvand, A. (2011, October). Wind speed short term forecast by neuro fuzzy modeling with aid of mutual information at Manjil Wind Power Plant. In 2011, 1^st International eConference on Computer and Knowledge Engineering (ICCKE) (pp. 1-5). IEEE. https://doi.org/10.11 09/ICCKE.2011.6413314

[8]. Jassbi, J. J., Serra, P. J., Ribeiro, R. A., & Donati, A. (2006, July). A comparison of mandani and sugeno inference systems for a space fault detection application. In 2006, World Automation Congress (pp. 1-8). IEEE. https://doi.org/ 10.1109/WAC.2006.376033

[9]. KangaraniFarahani, M., & Mehralian, S. (2013, August). Comparison between artificial neural network and neuro-fuzzy for gold price prediction. In 2013, 13^th Iranian Conference on Fuzzy Systems (IFSC) (pp. 1-5). IEEE. https:// doi.org/10.1109/IFSC.2013.6675635

[10]. Mughal, M. H., & Guojie, L. (2015, August). Review of pitch control for variable speed wind turbine. In 2015, IEEE 12^th International Conference on Ubiquitous Intelligence and Computing and 2015 IEEE 12^th International Conference on Autonomic and Trusted Computing and th 2015 IEEE 15 Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATCScalCom) (pp. 738-744). IEEE. https:// doi.org/10.1109/UICATC- ScalCom-CBDCom-IoP2015.148

[11]. Muneer, A., & Kadri, M. B. (2013, October). Pitch angle control of DFIG using self tuning neuro fuzzy controller. In 2013, International Conference on Renewable Energy Research and Applications (ICRERA) (pp. 316-320). IEEE. https://doi.org/10.1109/ICRERA.2013.6749772

[12]. Nauck, D. D., & Nürnberger, A. (2013). Neuro-fuzzy systems: A short historical review. In Computational Intelligence in Intelligent Data Analysis (pp. 91-109). Heidelberg, Berlin: Springer. https://doi.org/10.1007/978-3- 642-32378-2_7

[13]. Navarrete, E. C., Perea, M. T., Correa, J. J., Serrano, R. C., & Moreno, G. R. (2019). Expert control systems implemented in a pitch control of wind turbine: A review. IEEE Access, 7, 13241-13259. https://doi.org/10.1109/ACC ESS.2019.2892728

[14]. Parikh, A. (2019). Over 12 GW of Wind Capacity Awarded as of October 2019: MNRE. Mercom India. Retrieved from https://mercomindia.com/wind-capacityawarded- october-mnre/

[15]. Rajendran, S., & Jena, D. (2014). Control of variable speed variable pitch wind turbine at above and below rated wind speed. Journal of Wind Energy, 1-14. https://doi.org/10.1155/2014/709128

[16]. Ranga, K., Sukumar, G., Pakkiraiah, B., & Rao, M.S. (2016). Neuro fuzzy based PS & QS controller for doubly fed induction generator with wind turbine. In 2016, International Conference on Electrical Power and Energy Systems (ICEPES) (pp. 139-144). https://doi.org/10.1109/ ICEPES.2016.7915920

[17]. Schubel, P. J., & Crossley, R. J. (2012). Wind turbine blade design review. Wind Engineering, 36(4), 365-388. https://doi.org/10.1260/0309-524X.36.4.365

[18]. Son, Y. S., Kim, H. J., & Kim, J. T. (2018). ANFIS-based rate adaptation scheme for adaptive streaming over HTTP. EURASIP Journal on Wireless Communications and Networking, 2018(1), 261. https://doi.org/10.1186/s13638- 018-1279-y

[19]. Subhedar, M., & Birajdar, G. (2013). Comparison of mamdani and sugeno inference systems for dynamic spectrum allocation in cognitive radio networks. Wireless Personal Communications, 71(2), 805-819. https://doi.org/ 10.1007/s11277-012-0845-6

[20]. Topaloğlu, F., & Pehlıvan, H. (2018, March). Comparison of Mamdani type and Sugeno type fuzzy inference systems in wind power plant installations. In 2018, 6^th International Symposium on Digital Forensic and Security (ISDFS) (pp. 1-4). IEEE. https://doi.org/10.1109/ISD FS.2018.8355384

[21]. Veeramani, C., & Mohan, G. (2013). A fuzzy based pitch angle control for variable speed wind turbines. International Journal of Engineering and Technology (IJET), 5(2), 1699-1703.

[22]. Vieira, J., Dias, F. M., & Mota, A. (2004, March). Neuro fuzzy systems: A survey. In 5^th WSEAS NNA International Conference on Neural Networks and Applications (pp. 87- 92).

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Research Paper

AI Based Static Security Assessment of Power System

N. Aishwarya * , Shekhappa G. Ankaliki **

*-** Department of Electrical and Electronics Engineering, SDM college of Engineering and Technology, Dharwad, Karnataka, India.

Aishwarya, N., and Ankaliki, S. G. (2020). AI Based Static Security Assessment of Power System. i-manager's Journal on Power Systems Engineering, 8(2), 16-23. https://doi.org/10.26634/jps.8.2.17629

Abstract

Contingency selection and ranking using fast and accurate method has become a main issue to ensure the secured operation of power systems. This work proposes multi-layer feed forward artificial neural network (MLFFN) and radial basis function network (RBFN) to demonstrate the static security assessment. In this, the composite security index which is capable of accurately differentiating the secure and insecure cases of power system based on contingency selection, and ranking are done. For each contingency in power system, the composite security index is computed using conventional Newton-Raphson load flow analysis. The MLFFN and RBFN takes all possible loading conditions and probable contingencies as input and assess the system security by screening the credible contingencies and ranking them in the order of severity based on composite security index. The proposed approach is demonstrated by considering the IEEE 30 Bus system using MATLAB simulation.

References

[1]. Balu, N., Bertram, T., Bose, A., Brandwajn, V., Cauley, G., Curtice, D., ... Wrubel, J. N. (1992). On-line power system security analysis. Proceedings of the IEEE, 80(2), 262-282. https://doi.org/10.1109/5.123296

[2]. Kalyani, S., & Swarup, K. S. (2013). Static security assessment in power systems using multi-class SVM with parameter selection methods. International Journal of Computer Theory and Engineering, 5(3), 465-471.

[3]. Li, Y., Li, Y., & Sun, Y. (2018). Online static security assessment of power systems based on lasso algorithm. Applied Sciences, 8(9), 1442-1466. https://doi.org/10.3390 /app8091442

[4]. Morison, K. (2002, July). Power system security in the new market environment: Future directions. In IEEE Power Engineering Society Summer Meeting (Vol. 3, pp. 1416- 1417). IEEE.

[5]. Morison, K., Wang, L., & Kundur, P. (2004). Power system security assessment. IEEE Power and Energy Magazine, 2(5), 30-39.

[6]. Nara, K., Tanaka, K., Kodama, H., Shoults, R. R., Chen, M. S., Van Olinda, P., & Bertagnolli, D. (1985). On-line contingency selection algorithm for voltage security analysis. IEEE Transactions on Power Apparatus and Systems, 4, 846-856. https://doi.org/10.1109/TPAS.1985. 319085

[7]. Sekhar, P., & Mohanty, S. (2016). An online power system static security assessment module using multi-layer perceptron and radial basis function network. International Journal of Electrical Power & Energy Systems, 76, 165-173. https://doi.org/10.1016/j.ijepes.2015.11.009

[8]. Sidhu, T. S., & Cui, L. (2000). Contingency screening for steady-state security analysis by using FFT and artificial neural networks. IEEE Transactions on Power Systems, 15(1), 421-426. https://doi.org/10.1109/59.852154

[9]. Song, H. (2006, May). Static analysis of vulnerability and security margin of the power system. In 2005/2006 IEEE/PES Transmission and Distribution Conference and Exhibition (pp. 147-152). IEEE. https://doi.org/10.1109/TDC.2006.166 8474

[10]. Srivani, J., & Swarup, K. S. (2008). Power system static security assessment and evaluation using external system equivalents. International Journal of Electrical Power & Energy Systems, 30(2), 83-92.

[11]. Sunitha, R., Kumar, R. S., & Abraham, T. M. (2009, January). Development of a composite security index for static security evaluation. In TENCON 2009-2009 IEEE Region 10 Conference (pp. 1-6). IEEE. https://doi.org/10.1 109/TENCON.2009.5396229

[12]. Sunitha, R., Kumar, R. S., & Mathew, A. T. (2011). A composite security index for on-line steady-state security evaluation. Electric Power Components and Systems, 39(1), 1-14. https://doi.org/10.1080/15325008.2010. 492448

[13]. Sunitha, R., Kumar, R. S., & Mathew, A. T. (2013). Online static security assessment module using artificial neural networks. IEEE Transactions on Power Systems, 28(4), 4328- 4335. https://doi.org/10.1109/TPWRS.2013.2267557

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Research Paper

Design and Performance Analysis of Grid-Connected PV Power Plant with Different Mechanisms for Tracking System

Yadavalli S. V. Venkateswara Rao* , M. Sai Veerraju **

*-** Department of Electrical and Electronics Engineering, Sagi Ramakrishnam Raju Engineering College, Bhimavaram, Andhra Pradesh, India.

Rao, Y. S. V. V., and Veerraju, M. S. (2020). Design and Performance Analysis of Grid-Connected PV Power Plant with Different Mechanisms for Tracking System. i-manager's Journal on Power Systems Engineering, 8(2), 24-33. https://doi.org/10.26634/jps.8.2.17684

Abstract

Solar panels are being increasingly installed on roof tops to generate electricity. This paper analyses the performance of grid connected PV system which consists of 3 case studies for the same plant with different orientations. The importance for the change in the collector plane orientations is briefly analyzed by different case studies. Based on these case studies the optimal tilt angle for the installation of PV plant can be selected focusing on the customer's choice and requirements. The on-grid system will be installed on the top of a 5 staired building. The coordinates of site where the study is conducted are 16.54° N and 81.50° E. The grid connected PV system with a capacity of 39.2 kWp is proposed based on the user's needs. The different field orientations of the PV panel namely fixed tilt, seasonal tilt and N-S axis tracking are defined briefly with losses. The performance of these on-grid photovoltaic systems during an entire year are analyzed and the performance parameters are measured. The data is analyzed by using the simulation software Pvsyst. The different parameters including efficiencies of module, inverter, array's initial yield, final array yield, and the performance ratio of PV system are analyzed for the 3 case studies.

References

[1]. IS/IEC. (1998). Photovoltaic System Performance Monitoring - Guidelines for Measurement, Data exchange and Analysis (IS/IEC 61724). International Electrotechnical Commission. Retrieved from https://law.resource.org/pub/ in/bis/S05/is.iec.61724.1998.pdf

[2]. Kumar, B. S., & Sudhakar, K. (2015). Performance evaluation of 10 MW grid connected solar photovoltaic power plant in India. Energy Reports, 1, 184-192. https:// doi.org/10.1016/j.egyr.2015.10.001

[3]. Rao, Y. S. V. V., & Veerraju, M. S. (2020). Performance analysis of 33 kW grid connected solar roof top power plant. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 9(5), 1765-1770. https://doi. org/10.35940/ijitee.E2923.039520

[4]. Sharma, R., & Goel, S. (2017). Performance analysis of a 11.2 kWp roof top grid-connected PV system in Eastern India. Energy Reports, 3, 76-84. https://doi.org/10.1016 /j.egyr.2017.05.001

[5]. Sharma, V., & Chandel, S. S. (2013). Performance analysis of a 190 kWp grid interactive solar photovoltaic power plant in India. Energy, 55, 476-485. https://doi.or g/10.1016/j.energy.2013.03.075

[6]. Shukla, A. K., Sudhakar, K., & Baredar, P. (2016a). Simulation and performance analysis of 110 kWp gridconnected photovoltaic system for residential building in India: A comparative analysis of various PV technology. Energy Reports, 2, 82-88. https://doi.org/10.1016/j.egyr.20 16.04.001

[7]. Shukla, A. K., Sudhakar, K., & Baredar, P. (2016b). Design, simulation and economic analysis of standalone roof top solar PV system in India. Solar Energy, 136, 437- 449. https://doi.org/10.1016/j.solener.2016.07.009

[8]. Sundaram, S., & Babu, J. S. C. (2015). Performance evaluation and validation of 5 MWp grid connected solar photovoltaic plant in South India. Energy Conversion and Management, 100, 429-439. https://doi.org/10.1016/j. enconman.2015.04.069

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Research Paper

Transient Stability Enhancement by using Statcom and Stabilizer for Multi Machine Power System Network

Ajay Kumar Tiwari* , Shimpy Ralhan , Mahesh Singh *, Dovendra Kumar Verma ****

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

Tiwari, A. K., Ralhan, S., Singh, M., and Verma, D. K. (2020). Transient Stability Enhancement by using Statcom and Stabilizer for Multi Machine Power System Network. i-manager's Journal on Power Systems Engineering, 8(2), 34-40. https://doi.org/10.26634/jps.8.2.17708

Abstract

In this paper, the performance of the four machine six bus system rotor angle with static synchronous compensator and different controllers like genetic power system stabilizer and multiband PSS have been analysed. By using STATCOM and multi band power system stabilizer, damping oscillations of the system rotor angle are improved which helps to improve efficiency of the power system network. It will be more effective for large power system network because cost of the STATCOM is high, so for small system it is costly as compared to SVC. Four generator systems is to be taken for the analysis of the generator rotor angle oscillations which is also called multi machine system. For this analysis we are using the software MATLAB/SIMULINK in which this model run and the outcome of the individual generator rotor angle response has been analysed. This four machine 6 bus systems are the reassembled model of the Western System Coordinated Council (WSCC). This multi machine system is analysed for different fault conditions like LG, LL, and LLG fault.

References

[1]. Ahmadinia, M., & Ghazi, R. (2018, May). Coordinated control of STATCOM and ULTC to reduce capacity of STATCOM. In Iranian Conference on Electrical Engineering (ICEE) (pp. 1062-1066). IEEE. https://doi.org/10.1 109/ICEE.2018.8472712

[2]. Dhal, P. K., & Rajan, C. C. A. (2014, February). Design and analysis of STATCOM for reactive power compensation and transient stability improvement using intelligent controller. In 2014 International Conference on Electronics and Communication Systems (ICECS) (pp. 1-6). IEEE. https://doi.org/10.1109/ECS.2014.6892604

[3]. Hingorani, N. G., & Gyugyi, L. (1999). Understanding FACTS: Concepts and Technology of Flexible AC Transmission Systems. Wiley-IEEE Press.

[4]. Jadhav, G. B., Bangal, C. B., & Kanungo, S. (2017). Transient stability analysis with SVC and STATCOM in multimachine power systems with and without PSS using Matlab/Simulink. International Journal of Engineering Development and Research, 5(4), 1196-1205.

[5]. Jyothsna, T. R., & Vaisakh, K. (2008, December). Improving multi-machine transient stability using a nonlinear power system stabilizer under different operating conditions. In 15^th National Power System Conference (pp. 16-18).

[6]. Kamdar, R., Kumar, M., & Agnihotri, G. (2014). Transient stability analysis and enhancement of IEEE-9 bus system. Electrical & Computer Engineering: An International Journal (ECIJ), 3(2), 41-51. https://doi.org/10.14810/ecij. 2014.3204

[7]. Kaur, S., Kanthwal, A., & Khela, R. S. (2014). System stability using FACTS. International Journal of Engineering Research, 3(3), 1704-1708.

[8]. Kumar, D., & Kumar, A. (2014). Stability analysis of multi-machine system using STATCOM. International Journal of Enhanced Research in Science Technology & Engineering, 3(8), 148-151.

[9]. Kundur, P., Balu, N. J., & Lauby, M. G. (1994). Power system stability and control (Vol. 7). New York: McGraw-Hill.

[10]. Mithulananthan, N., Canizares, C. A., Reeve, J., & Rogers, G. J. (2003). Comparison of PSS, SVC, and STATCOM controllers for damping power system oscillations. IEEE Transactions on Power Systems, 18(2), 786- 792. https://doi.org/10.1109/TPWRS.2003.811181

[11]. Muzaffer, I., & Mufti, M. (2017, August). Modeling of a multi-machine system aided with power system stabilizers and shunt compensator for transient stability enhancement. In 2017, International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) (pp. 1716-1721). IEEE. https://doi.org/10.1109/ICECDS.2017.8389743

[12]. Pahade, A., & Saxena, N. (2013). Transient stability improvement by using shunt FACT device (STATCOM) with Reference Voltage Compensation (RVC) control scheme. International Journal of Electrical, Electronics and Computer Engineering, 2(1), 7-12.

[13]. Rao, P., Crow, M. L., & Yang, Z. (2000). STATCOM control for power system voltage control applications. IEEE Transactions on Power Delivery, 15(4), 1311-1317. https:// doi.org/10.1109/61.891520

[14]. Singh, M., Verma, D. K., Ralhan, S., & Patil, M. (2019, November). Performance analysis of SVC supplementary controller in multi-machine power system network. In 2019, International Conference on Electrical, Electronics and Computer Engineering (UPCON) (pp. 1-6). IEEE. https://doi. org/10.1109/UPCON47278.2019.8980089

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Research Paper

Novel Approach for Control of Renewable Power Generation Systems by Synchronous Power Controller

Mulumudi Rajesh* , A. Lakshmi Devi **

*-** Department of Electrical and Electronics Engineering, Sri Venkateswara University College Of Engineering, Tirupati, Andhra Pradesh, India.

Rajesh, M., and Devi, A. L. (2020). Novel Approach for Control of Renewable Power Generation Systems by Synchronous Power Controller. i-manager's Journal on Power Systems Engineering, 8(2), 41-46. https://doi.org/10.26634/jps.8.2.17709

Abstract

The increasing amount of renewable power generation systems is a challenging issue for the control and operation of the electrical networks. One of the main issues is lack of inertia, which is becoming a greater problem as much as the share of the power plants based on traditional synchronous generators gets reduced. In this regard, the new grid codes ask these plants to provide new functionalities such as the frequency support and inertia emulation. In this paper, a synchronous power controller for grid-connected converters is proposed as a good solution for the renewable generation systems with energy storage. It provides inertia, damping, and flexible droop characteristics. Different from the faithful replication of the swing equation of synchronous machines, an alternative control structure is proposed, by which the damping and inherent droop slope can be configured independently to meet the requirements in both dynamics and frequency regulations. Analysis and experimental results are both shown to validate the proposed controller.

References

[1]. De Brabandere, K., Bolsens, B., Van den Keybus, J., Woyte, A., Driesen, J., & Belmans, R. (2007). A voltage and frequency droop control method for parallel inverters. IEEE Transactions on Power Electronics, 22(4), 1107-1115. https://doi.org/10.1109/TPEL.2007.900456

[2]. Glover, J. D., Overbye, T. J., & Sarma, M. S. (2017). Power system analysis & design. Boston, MA: Cengage Learning.

[3]. Guerrero, J. M., Vasquez, J. C., Matas, J., De Vicuña, L. G., & Castilla, M. (2010). Hierarchical control of droopcontrolled AC and DC microgrids: A general approach toward standardization. IEEE Transactions on Industrial Electronics, 58(1), 158-172.

[4]. Kundur, P., Balu, N. J., & Lauby, M. G. (1994). Power system stability and control (Vol. 7). New York: McGraw-hill.

[5]. Loix, T., De Brabandere, K., Driesen, J., & Belmans, R. (2007, November). A three-phase voltage and frequency droop control scheme for parallel inverters. In IECON 2007- rd 33 Annual Conference of the IEEE Industrial Electronics Society (pp. 1662-1667). IEEE. https://doi.org/10.1109/IECON.2007.4460216

[6]. Mascioli, M., Pahlevani, M., & Jain, P. K. (2014, September). Frequency-adaptive current controller for grid-connected renewable energy systems. In 2014, IEEE th 36 International Telecommunications Energy Conference (INTELEC) (pp. 1-6). IEEE. https://doi.org/10.1109/INTLEC.20 14.6972190

[7]. Rocabert, J., Luna, A., Blaabjerg, F., & Rodriguez, P. (2012). Control of power converters in AC microgrids. IEEE Transactions on Power Electronics, 27(11), 4734-4749. https://doi.org/10.1109/TPEL.2012.2199334

[8]. Rodriguez, P., Candela, I., & Luna, A. (2013, September). Control of PV generation systems using the synchronous power controller. In 2013, IEEE Energy Conversion Congress and Exposition (pp. 993-998). IEEE. https://doi.org/10.1109/ECCE.2013.6646811

[9]. Zhang, W., Cantarellas, A. M., Rocabert, J., Luna, A., & Rodriguez, P. (2016). Synchronous power controller with flexible droop characteristics for renewable power generation systems. IEEE Transactions on Sustainable Energy, 7(4), 1572-1582. https://doi.org/10.1109/TSTE.20 16.2565059

[10]. Zhong, Q. C., & Weiss, G. (2009, March). Static synchronous generators for distributed generation and renewable energy. In 2009, IEEE/PES Power Systems Conference and Exposition (pp. 1-6). IEEE. https://doi.org/ 10.1109/PSCE.2009.4840013

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

Current Issue Vol. 12 Issue 1

Most Read

Most Cited

Volume 8 Issue 2 May - July 2020

An Approach on Hybrid Storage System in Standalone AC/DC Micro-Grid with Super-Capacitor

This paper has been removed due to plagiarized work because of the dual submission by author.

Reactive Power Compensation of Wind Power Plant using Neuro Fuzzy Controller

Raina Jain * , Reshmita Sharma **, Abhishek Mishra ***

*-** Department of Electrical and Electronics Engineering, SSGI, Bhilai, India. *** Department of Electronics and Communication Engineering, GGU, Bilaspur, India.

Jain, R., Sharma, R., and Mishra, A. (2020). Reactive Power Compensation of Wind Power Plant using Neuro Fuzzy Controller. i-manager's Journal on Power Systems Engineering, 8(2), 1-15. https://doi.org/10.26634/jps.8.2.17605

Abstract

References

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AI Based Static Security Assessment of Power System

N. Aishwarya * , Shekhappa G. Ankaliki **

*-** Department of Electrical and Electronics Engineering, SDM college of Engineering and Technology, Dharwad, Karnataka, India.

Aishwarya, N., and Ankaliki, S. G. (2020). AI Based Static Security Assessment of Power System. i-manager's Journal on Power Systems Engineering, 8(2), 16-23. https://doi.org/10.26634/jps.8.2.17629

Abstract

References

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Design and Performance Analysis of Grid-Connected PV Power Plant with Different Mechanisms for Tracking System

Yadavalli S. V. Venkateswara Rao* , M. Sai Veerraju **

*-** Department of Electrical and Electronics Engineering, Sagi Ramakrishnam Raju Engineering College, Bhimavaram, Andhra Pradesh, India.

Rao, Y. S. V. V., and Veerraju, M. S. (2020). Design and Performance Analysis of Grid-Connected PV Power Plant with Different Mechanisms for Tracking System. i-manager's Journal on Power Systems Engineering, 8(2), 24-33. https://doi.org/10.26634/jps.8.2.17684

Abstract

References

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Transient Stability Enhancement by using Statcom and Stabilizer for Multi Machine Power System Network

Ajay Kumar Tiwari* , Shimpy Ralhan **, Mahesh Singh ***, Dovendra Kumar Verma ****

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

Tiwari, A. K., Ralhan, S., Singh, M., and Verma, D. K. (2020). Transient Stability Enhancement by using Statcom and Stabilizer for Multi Machine Power System Network. i-manager's Journal on Power Systems Engineering, 8(2), 34-40. https://doi.org/10.26634/jps.8.2.17708

Abstract

References

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Novel Approach for Control of Renewable Power Generation Systems by Synchronous Power Controller

Mulumudi Rajesh* , A. Lakshmi Devi **

*-** Department of Electrical and Electronics Engineering, Sri Venkateswara University College Of Engineering, Tirupati, Andhra Pradesh, India.

Rajesh, M., and Devi, A. L. (2020). Novel Approach for Control of Renewable Power Generation Systems by Synchronous Power Controller. i-manager's Journal on Power Systems Engineering, 8(2), 41-46. https://doi.org/10.26634/jps.8.2.17709

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Raina Jain * , Reshmita Sharma , Abhishek Mishra *

- Department of Electrical and Electronics Engineering, SSGI, Bhilai, India.

Department of Electronics and Communication Engineering, GGU, Bilaspur, India.

Ajay Kumar Tiwari* , Shimpy Ralhan , Mahesh Singh *, Dovendra Kumar Verma ****