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i-manager's Journal on Electrical Engineering (JEE)

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PV-grid Performance improvement through Integrated Intelligent Water Drop Optimization with Neural Network for Maximum Power Point Tracking
A Digital Healthcare Monitoring System with Real-Time Analysis
Advancements in Smart Meter Design and Integration for Enhanced Energy Management and Efficiency
Electric Vehicles in Modern Transportation: Environmental Impacts, Configurations, and Future Trends – A Review
GTO Technique Based Hybrid Power System Controller Design

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Volume 16 Issue 1 July - September 2022

Research Paper

Chaotic Particle Swarm Optimization with Attractive Search Space Border Points for Optimal Reactive Power Dispatch

P. Lokender Reddy* , G. Yesuratnam**

*-** Department of Electrical Engineering, University College of Engineering, Osmania University, Hyderabad, Telangana, India.

Reddy, P. L., and Yesuratnam, G. (2022). Chaotic Particle Swarm Optimization with Attractive Search Space Border Points for Optimal Reactive Power Dispatch. i-manager’s Journal on Electrical Engineering, 16(1), 1-14. https://doi.org/10.26634/jee.16.1.19099

Abstract

This paper presents a reliable approach for optimal reactive power dispatch based on chaotic particle swarm optimization with attractive search space border points, where the particles are randomly attracted to the boundary points of the search space in each direction avoiding stagnation of the population. The introduction of chaotic dynamics improves the stability and rate of convergence. The algorithm is further improved by using Latin Hypercube Sampling (LHS) to create diversity in the population. The proposed algorithm is used for optimal reactive power dispatch with three objective functions, namely: minimization of real power loss, voltage stability index, and sum squared voltage deviations. The algorithm is tested on a standard 30-bus system of the Institute of Electrical and Electronics Engineers (IEEE) and on a practical 75-bus Indian Power System. The results obtained with the proposed algorithm are compared with the conventional interior point method and the basic particle swarm optimization algorithm, and the effectiveness of the proposed algorithm is demonstrated.

References

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[19]. Rajan, A., & Malakar, T. (2015). Optimal reactive power dispatch using hybrid Nelder–Mead simplex based firefly algorithm. International Journal of Electrical Power & Energy Systems, 66, 9-24. https://doi.org/10.1016/j.ijepes.2014.10.041

[20]. Roy, P. K., & Paul, C. (2015). Optimal power flow using krill herd algorithm. International Transactions on Electrical Energy Systems, 25(8), 1397-1419. https://doi.org/10.1002/etep.1888

[21]. Saddique, M. S., Bhatti, A. R., Haroon, S. S., Sattar, M. K., Amin, S., Sajjad, I. A., ... & Rasheed, N. (2020). Solution to optimal reactive power dispatch in transmission system using meta-heuristic techniques-Status and technological review. Electric Power Systems Research, 178, 106031. https://doi.org/10.1016/j.epsr.2019.106031

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Research Paper

Performance Comparison of Slim Drive with ANFIS Controller

M. Nagaraju* , G. Durga Sukumar, M. Ravindrababu*

* Vignan's Lara Institute of Technology & Science, Andhra Pradesh, India.

Vignan Institute of Technology and Science, Deshmuki, Yadadri, Bhuvanagiri, Telengana, India.

* University College of Engineering Narsasraopet, JNTUK, Guntur, Andhra Pradesh India.

Nagaraju, M., Sukumar, G. D., and Ravindrababu, M. (2022). Performance Comparison of Slim Drive with ANFIS Controller. i-manager’s Journal on Electrical Engineering, 16(1), 15-26. https://doi.org/10.26634/jee.16.1.19165

Abstract

Normally speed control of a Single-Sided Linear Induction Motor (SLIM) by an indirect vector control scheme is difficult because the motor's parameters are time-dependent and the performance depends on various factors such as end effect, saturation, location of primary losses, and iron losses. Traditional PI current regulators are commonly used in vector regulators, but there is a tuning problem due to the oscillation of an operating point. This problem can be overcome by substituting an adaptive neuro-fuzzy-based current controller, and this controller improves the operation of a SLIM, such as its motor speed and thrust force. In this adaptive neuro-fuzzy controller, the ID and IQ errors and the error delay are inputs, and its outputs are Vds and Vqs, respectively. It is trained based on available values. A SLIM's dynamic modelling is implemented by dividing current (I) and flux-linkages into two terms. In these two terms, one is dependent on the end effect, and the other is independent of the end effect. The function of a Voltage Source Inverter (VSI)-fed indirect vector-controlled SLIM drive is simulated in MATLAB/Simulink, and its operation under various operating conditions is studied using an adaptive neuro-fuzzy current controller. These results are compared to a traditional P-I controller. The Pulse Width Modulation (PWM) technology that is used for controlling the VSI is called Space Vector Modulation (SVM).

References

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[8]. Gang, L., Zhiming, L., & Shouguang, S. (2016). Analysis of torques in single-side linear induction motor with transverse asymmetry for linear metro [J]. IEEE Transactions on Energy Conversion, 31(1), 165-173. https://doi.org/10.1109/TEC.2015.2470561

[9]. Hamedani, P., & Shoulaie, A. (2013, February). Indirect field oriented control of linear induction motors considering the end effects supplied from a cascaded Hbridge inverter with multiband hystersis modulation. In 4th Annual International Power Electronics, Drive Systems and Technologies Conference (pp. 13-19). IEEE.

[10]. Li, D., Li, W., Fang, J., Zhang, X., & Cao, J. (2013). Performance evaluation of a low-speed single-side HTS linear induction motor used for subway system. IEEE Transactions on Magnetics, 50(5), 1-9. https://doi.org/10.1109/TMAG.2013.2291543

[11]. Li, J. Q., Li, W. L., Deng, G. Q., & Ming, Z. (2016). Continuous-behavior and discrete-time combined control for linear induction motor-based urban rail transit. IEEE Transactions on Magnetics, 52(7), 1-4. https://doi.org/10.1109/TMAG.2016.2533439

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Research Paper

Controlling Speed in Induction Motors with Fuzzy Logic and a PI Controller

Deepika Dewangan* , Alka Mishra, Surekha Bhusnur*

*-*** Department of Electrical & Electronics Engineering, Bhilai Institute of Technology, Durg, Chhattisgarh, India.

Dewangan, D., Mishra, A., and Bhusnur, S. (2022). Controlling Speed in Induction Motors with Fuzzy Logic and a PI Controller. i-manager’s Journal on Electrical Engineering, 16(1), 27-37. https://doi.org/10.26634/jee.16.1.19132

Abstract

The purpose of this paper is to illustrate the dynamical response of speed using the design of a Fuzzy Logic Controller (FLC) to regulate the motor speed, while the load changes. Induction motor power control has become popular in recent years in high-performance drive systems. It is due to its amazing qualities, such as high-performance, a high energy factor, and significant toughness. The overall performance of the controller is estimated using MATLAB or Simulink software and a common Proportional Integral (PI) management technique. This work discusses the design and construction of a voltage supply inverter-based Space Vector Pulse Width Modulation (SVPWM) system for regulating the speed of an induction motor. This observation also incorporates a fuzzy controller into the SVPWM to maintain the motor speed constant even when the load varies. FLC is used to alter the pulse width of the Pulse Width Modulation (PWM) converter, which controls the motor pace. This paper describes the application of a rule-based Mamdani type FLC to a closed loop induction motor model.

References

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Review Paper

A Review on Concentrated Solar Power (CSP) and Emerging Technology

D. Rene Dev* , D. Sam Harison**

* Department of Electronics and Communication Engineering, C. S. I Institute of Technology, Thovalai, Kanyakumari District, Tamil Nadu, India.

** Department of Electrical and Electronics Engineering, Rohini College of Engineering and Technology, Kanyakumari District, Tamil Nadu, India.

Dev, D. R., and Harison, D. S. (2022). A Review on Concentrated Solar Power (CSP) and Emerging Technology. i-manager’s Journal on Electrical Engineering, 16(1), 38-44. https://doi.org/10.26634/jee.16.1.19196

Abstract

Solar power generation is one of the fastest-growing sources of renewable energy in the world. The economic benefits of technologies used to capture sunlight are increasing every year, expanding the opportunities for cleaner power generation. The global energy production model is changing from fossil fuels to renewable and nuclear energy. This paper provides a brief overview of the solar power generation system called Concentrated Solar Power (CSP), which is an emerging technology that is leading the way. The energy extracted from CSP technology is very clean, reliable, and environmentally friendly. This growth implies the complexity and size of systems and therefore requires an increase in maintenance tasks to ensure reliability, availability, maintainability, and security. This paper describes the various configurations of CSP, and the main causes and consequences of the CSP components are also analyzed.

References

[1]. Abbas, R., Montes, M. J., Piera, M., & Martínez-Val, J. M. (2012). Solar radiation concentration features in linear Fresnel reflector arrays. Energy Conversion and Management, 54(1), 133-144. https://doi.org/10.1016/j.enconman.2011.10.010

[2]. Akbarimoosavi, S. M., & Yaghoubi, M. (2014). 3D Thermal-structural analysis of an absorber tube of a parabolic trough collector and the effect of tube deflection on optical efficiency. Energy Procedia, 49, 2433-2443. https://doi.org/10.1016/j.egypro.2014.03.258

[3]. Dong, R. F., Sun, L. G., Zhe, L. I. U., Wang, X. L., & Liu, Q. Y. (2008). Microstructures and properties of X60 grade pipeline strip steel in CSP plant. Journal of Iron and Steel Research, International, 15(2), 71-75. https://doi.org/10.1016/S1006-706X(08)60035-7

[4]. He, Y., Tian, G., Zhang, H., Alamin, M., Simm, A., & Jackson, P. (2012). Steel corrosion characterization using pulsed eddy current systems. IEEE Sensors Journal, 12(6), 2113-2120. https://doi.org/10.1109/JSEN.2012.2184280

[5]. Jiang, P., Dong, J., & Huang, H. (2020). Optimal integrated demand response scheduling in regional integrated energy system with concentrating solar power. Applied Thermal Engineering, 166, 114754. https://doi.org/10.1016/j.applthermaleng.2019.114754

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Concept paper

Prototype of Energy Consumption Monitoring and Energy Theft Detection Based on IoT

M. Shiny*

Department of Computer Science, DMI College of Engineering, Aralvaimozhi, Tamil Nadu, India.

Shiny, M. (2022). Prototype of Energy Consumption Monitoring and Energy Theft Detection Based on IoT. i-manager’s Journal on Electrical Engineering, 16(1), 45-51. https://doi.org/10.26634/jee.16.1.19195

Abstract

Energy management in home is gaining more attention as people try to use sustainable energy sources. This attempt to improve the use of infrastructure for energy production and distribution is expected to include smart grid systems. The incorporation of Internet of Things (IoT)-enabled smart devices into an ecosystem designed for maximum energy efficiency has been proposed for Smart Home Energy Management Systems (SHEMS). Smart homes are expensive, but smart plugs can change normal home devices relatively smart. This paper proposes a prototype design for a SHEMS it monitors and controls the energy consumption of smart devices to reduce the electricity bill and also detect energy theft.

References

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[2]. Corno, F., & Razzak, F. (2012). Intelligent energy optimization for user intelligible goals in smart home environments. IEEE Transactions on Smart Grid, 3(4), 2128-2135. https://doi.org/10.1109/TSG.2012.2214407

[3]. Gottwalt, S., Ketter, W., Block, C., Collins, J., & Weinhardt, C. (2011). Demand side management—A simulation of household behavior under variable prices. Energy Policy, 39(12), 8163-8174. https://doi.org/10.1016/j.enpol.2011.10.016

[4]. Hamed, B. (2012). Design & implementation of smart house con-trol using labview. International Journal of Soft Computing and Engineering (IJSCE), 1(6), 98-108.

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[6]. Oracle. (2022). What is IoT? Retrieved from https://www.oracle.com/in/internet-of-things/what-is-iot/

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[8]. Veleva, S., Davcev, D., & Kacarska, M. (2011, December). Wireless smart platform for home energy management system. In 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies (pp. 1-8). IEEE. https://doi.org/10.1109/ISGTEurope.2011.6162798

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i-manager's Journal on Electrical Engineering (JEE)

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Volume 16 Issue 1 July - September 2022

Chaotic Particle Swarm Optimization with Attractive Search Space Border Points for Optimal Reactive Power Dispatch

P. Lokender Reddy* , G. Yesuratnam**

*-** Department of Electrical Engineering, University College of Engineering, Osmania University, Hyderabad, Telangana, India.

Reddy, P. L., and Yesuratnam, G. (2022). Chaotic Particle Swarm Optimization with Attractive Search Space Border Points for Optimal Reactive Power Dispatch. i-manager’s Journal on Electrical Engineering, 16(1), 1-14. https://doi.org/10.26634/jee.16.1.19099

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Performance Comparison of Slim Drive with ANFIS Controller

M. Nagaraju* , G. Durga Sukumar**, M. Ravindrababu***

* Vignan's Lara Institute of Technology & Science, Andhra Pradesh, India. ** Vignan Institute of Technology and Science, Deshmuki, Yadadri, Bhuvanagiri, Telengana, India. *** University College of Engineering Narsasraopet, JNTUK, Guntur, Andhra Pradesh India.

Nagaraju, M., Sukumar, G. D., and Ravindrababu, M. (2022). Performance Comparison of Slim Drive with ANFIS Controller. i-manager’s Journal on Electrical Engineering, 16(1), 15-26. https://doi.org/10.26634/jee.16.1.19165

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Controlling Speed in Induction Motors with Fuzzy Logic and a PI Controller

Deepika Dewangan* , Alka Mishra**, Surekha Bhusnur***

*-*** Department of Electrical & Electronics Engineering, Bhilai Institute of Technology, Durg, Chhattisgarh, India.

Dewangan, D., Mishra, A., and Bhusnur, S. (2022). Controlling Speed in Induction Motors with Fuzzy Logic and a PI Controller. i-manager’s Journal on Electrical Engineering, 16(1), 27-37. https://doi.org/10.26634/jee.16.1.19132

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A Review on Concentrated Solar Power (CSP) and Emerging Technology

D. Rene Dev* , D. Sam Harison**

* Department of Electronics and Communication Engineering, C. S. I Institute of Technology, Thovalai, Kanyakumari District, Tamil Nadu, India. ** Department of Electrical and Electronics Engineering, Rohini College of Engineering and Technology, Kanyakumari District, Tamil Nadu, India.

Dev, D. R., and Harison, D. S. (2022). A Review on Concentrated Solar Power (CSP) and Emerging Technology. i-manager’s Journal on Electrical Engineering, 16(1), 38-44. https://doi.org/10.26634/jee.16.1.19196

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Prototype of Energy Consumption Monitoring and Energy Theft Detection Based on IoT

M. Shiny*

Department of Computer Science, DMI College of Engineering, Aralvaimozhi, Tamil Nadu, India.

Shiny, M. (2022). Prototype of Energy Consumption Monitoring and Energy Theft Detection Based on IoT. i-manager’s Journal on Electrical Engineering, 16(1), 45-51. https://doi.org/10.26634/jee.16.1.19195

Abstract

References

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M. Nagaraju* , G. Durga Sukumar, M. Ravindrababu*

* Vignan's Lara Institute of Technology & Science, Andhra Pradesh, India.

Vignan Institute of Technology and Science, Deshmuki, Yadadri, Bhuvanagiri, Telengana, India.

* University College of Engineering Narsasraopet, JNTUK, Guntur, Andhra Pradesh India.

Deepika Dewangan* , Alka Mishra, Surekha Bhusnur*

* Department of Electronics and Communication Engineering, C. S. I Institute of Technology, Thovalai, Kanyakumari District, Tamil Nadu, India.

** Department of Electrical and Electronics Engineering, Rohini College of Engineering and Technology, Kanyakumari District, Tamil Nadu, India.