A dsPIC based optimal sizing of solar PV plant using ultra capacitors for transient power delivery

Microprocessors and Microsystems 71 (2019) 102893

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A dsPIC based optimal sizing of solar PV plant using ultra capacitors for transient power delivery K. Karthikeyan a,∗, S.K. Patnaik b, M. Baskar c, E. Jeyashree b a

Department of Electrical and Electronics Engineering, KCG College of Technology, Karapakkam, Chennai 600097, India Department of Electrical Engineering, College of Engineering, Guindy, Chennai 600025, India c Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India b

a r t i c l e

i n f o

Article history: Received 27 June 2019 Revised 5 September 2019 Accepted 7 September 2019 Available online 9 September 2019 Keywords: Solar PV Ultra capacitor PID BLDC Motor Transient power dsPIC controller

a b s t r a c t Solar PV sources are being increasingly implemented in many applications due to the growing concern of environmental pollution. The capacity of solar PV depends both on the transient power and nominal power required by any type of motor loads. The present investigation reports on a method to optimize the required capacity of Solar Panel by augmenting an Ultra capacitor bank for powering the transient needs of BLDC Motor. A PID based high quality dynamic response characteristics controller was designed to improve the speed transient response of the BLDC motor thereby reducing the transient energy requirement of the BLDC Motor. Tuning of PID parameters is critical to optimise the transient behaviour of the motor. The under damped step response tuning method has achieved an optimal PID parameters without any loss in speed transients when compared with other major tuning methods. PID controller is programmed in dsPIC controller that has high-resolution control and minimal control loop delays, thereby reduces torque ripples, harmonics and improves the dynamic behaviour in all speed ranges. The proposed optimal design of PV system augmented with Ultra capacitor for transient power delivery has been modelled and simulated in MATLAB/Simulink, which resulted in a reduction of plant size by 50 percent. The results obtained in simulation are verified experimentally. © 2019 Elsevier B.V. All rights reserved.

1. Introduction Solar energy is a ubiquitous form of energy available everywhere and could be used for power generation. But the efficiency of PV modules to convert solar irradiance to electricity is found to be as low as 12–17% as mentioned in [1]. Due to such a low operating efficiency the area occupied by solar panels is very large even for low power applications. This excessive requirement of area has been one of the major drawbacks of solar PV plant. In real time the PV panel output is much lesser and depends on irradiation, PV cell temperature, shadowing, dust etc., [2,3]. Hence the area required for same amount of power differs for different environmental conditions. Power converters play a vital role in PV tracking [4] and maintaining constant output as required for the load as the solar irradiation is seasonal and fluctuating in nature. The power converters designed for a PV system should possess good voltage gain with high efficiency and should support for large range of input voltage and temperature variations. Hence the design of highly efficient DC–DC converters has been a challenging task [5,6]. Various ∗

Corresponding author. E-mail address: [email protected] (K. Karthikeyan).

https://doi.org/10.1016/j.micpro.2019.102893 0141-9331/© 2019 Elsevier B.V. All rights reserved.

Maximum Power Point (MPPT) algorithms are available to extract the maximum power from the PV system. Considering a motor load, that may demand power spikes of up to six to seven times their rated power at starting [7], the PV systems designed should have the capacity to meet the transient needs. For mobile applications – where the solar PV system is mounted on an electric vehicle for transportation [8] or used to power a water pump for irrigation, space available plays an imperative role in determining the plant capacity. For such machine loads, BLDC motor is mostly preferred due to its better speedtorque characteristics, less maintenance, high efficiency, and extensive operating speed range [9,10]. The most versatile PID controllers are used as controllers in such motor control systems. The peak power demanded by the machine is observed to be much higher than the nominal power, though it exists only for few seconds, it mandates the design of solar PV system for peak load considerations which demands more power and area. PID controller can damp the transient behaviour of the motor to be controlled [11,12], thereby reducing the transient power demand. If that demand is supplied by other means, then the PV system can be designed for the nominal load power. Ultra capacitors have been prevalent contender for meeting the transient power demand [13], they have an efficiency of around 95% with high power

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Fig. 1. Optimized PV system with Ultra capacitor.

density. For last few decades Ultra capacitor has become a vital part of low voltage power converters that frequently demand highpower from DC storage device during transients [8,14]. Hence Ultra capacitor would be the best choice to assist the Solar PV system in satisfying the transient power mandate. Víctor et al. [15], showed that Ultra capacitors in comparison to any type of batteries, have a better charge/discharge performance for HESS (Hybrid Energy Storage Systems) smaller than 5 kW, thereby reducing the system losses, size of the batteries and increasing the lifetime. In another study, Roncero-Sánchez et al. [16] suggested that Ultra capacitors can perform well with much higher current than batteries and for a great number of charge and discharge cycles compared to lead-acid batteries with only a few thousand cycles. The sharing of power between Ultra capacitors, batteries and fuel cells is an optimistic solution for enhancing the performance of the system due to the dynamic behaviour of the Ultra capacitors with longer life span [17]. Fuel cells and Ultra-capacitors have high power density suitable for supplementing the fluctuating power sources such as solar and wind. The polarization curve of fuel cells illustrates the three regions of operation such as region of activation polarization, region of ohmic polarization and region of concentration polarization, which indicates that the fuel cells are slower in dynamic response compared to Ultra-capacitors. The Ultra capacitors stores the energy electro statically and hence can charge/discharge faster to meet the transient power demands at start-ups, acceleration and sudden changes in the load which can’t be met by fuel cells as the principle of operation of fuel cells is similar to conventional batteries. Hence Ultra-capacitors outweigh the fuel cells in case of BLDC motor drives. Majority of investigations in literature propose Battery energy storage supported by Ultra capacitor for peak power demand, but optimal sizing of solar PV plant is missing which is a growing concern. In view of the above, the present study summarizes the methodology employed to size the photovoltaic panels optimally and Ultra capacitors that assists in gratifying the transient power demands of motor loads by controlling Ultra capacitors’ energy, thereby enabling the optimal sizing of solar PV.

Fig. 2. PV cell model.

implement this system effectively, the solar irradiation is converted to electricity by an optimized solar PV system consisting of a PV module, a highly efficient DC-DC converter which converts PV output to the desired regulated DC voltage [18], PID controller with BLDC Power Drive to optimise the transient response of the motor, and an Ultra capacitor module that supplements the PV system during the transient power demands. The proposed system to optimize the solar PV system with Ultra capacitor is as illustrated in Fig. 1. Transient power required by the BLDC motor exist only for a small interval of time after which nominal power is sufficient to run the BLDC motor. Hence in the proposed model this transient power is supplied by the Ultra capacitor as a supplementary system with PV system. 3. System modelling 3.1. PV array modelling A solar cell is modelled with a current source and a diode connected in anti-parallel [2] as shown in Fig. 2. The series resistance shown depicts limitations offered to the flow of electrons from n to p-junction and parallel resistance depicts existence of leakage current. For simplicity, single diode model with a series resistance (Rs) is considered [2,19]. The parallel resistance (Rp) is neglected as it has a negligible effect [20].

2. System configuration 3.2. (a) MPPT tracking The proposed standalone system comprises of Solar PV system, DC-DC converter with Incremental Conduction Algorithm based MPPT and Ultra capacitor for supplying power to BLDC motor. To

Normally without tracking the conversion in solar is in the range of 30–40%, this can be improved either by mechanical track-

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respectively. The converter system parameters are designed for a power rating of 10 0 0 W and the specifications are mentioned in the Table 1. 3.3. Ultra capacitor modelling An Ultra capacitor whose model is as shown in Fig. 4, is an electrochemical capacitor with double layer, capable of holding thousand times more electrical charge than a typical capacitor [8,14]. But only 20% energy density of a battery, and shares the characteristics of both batteries and conventional capacitors. They have longer life span than a battery and negligible losses. However, the breakdown voltage of Ultra capacitor cannot be easily "pre-set", because it essentially depends on the electrolyte type and remarkably small charge separation distance. Due to these hitches they are restricted to operating voltages of about 3 V as proved in [22]. So they must be connected in series to reach a required working voltage. The Ultra capacitor is used as an auxiliary storage device to store and discharge energy when required. The required value of the Ultra capacitor is given by,

Fig. 3. Basic boost converter.

Capacitance = Fig. 4. Ultra capacitor equivalent circuit.

ESR = ing to maintain the panel at normal to sun’s irradiance or by electronically varying the load resistance thereby maintaining maximum conversion efficiency at all time, now a days the second option is more predominant, among various Maximum power point racking algorithm, incremental conductance algorithm proves to provide maximum efficiency with less ripples in the output [3]. 3.2. (b) DC–DC converter The DC/DC converter is liable for the power conversion. The converter is of boost topology as in Fig. 3 to have the output voltage higher than that of the input. It controls the output voltage with variable duty cycle [21]. This provides a constant voltage, even if there is any variation in the output of solar PV module. The basic design equations of Boost converter operating in Continuous Conduction Mode (CCM) is shown in Eq. (1) [18].

Lmin =

( 1 − D )2 D R L 2Fs

and Cmin =

D R FsVr

(1)

=

Id Td Vd

V f − Vmin Id

(2) (3)

where, Vw is initial working voltage, Vmin is minimum voltage under load, Id is discharge current, Vf is final Voltage, 5 s after removal of load and td is time to discharge from initial to minimum voltage. 3.4. Brushless direct current motor BLDC motors are available in various configurations among them the 3φ motor is the most commonly used type as it has low torque ripple with high efficiency [10]. It also has an accurate control with limited power electronic devices for the control of its stator currents [23]. BLDC motors shares the best characteristics of both the DC and AC motors like high torque, no mechanical commutated hence smaller volume, and provide high speed support. The mathematical model of the BLDC motor is done as in Eq. (4) for the parameters from Table 1.

G (s ) =

where Lmin is the minimum inductance, Cmin is the minimum capacitance, D is the duty cycle, RL is load resistance, and Fs is the switching frequency, and Vr is the Ripple Voltage. The Duty cycle of the converter is varied in accordance with incremental conductance MPPT algorithm by the controller, thereby providing maximum output from solar PV panel at all times. The values of inductor (L) and capacitor (C) of Boost converter are designed for a current ripple of 5% and a voltage ripple of 10%

Id Td

(Vw − V f )

1 Ke

τm τe s 2 + τm s + 1

(4)

The values of Ke , τ e and τ m need to be calculated to obtain the motor model.

τe =

Lm , 3Rm

(5)

but τ m is a function of R, J and Ke

τm =

3Rφ J Ke. Kt

(6)

Table 1 Design specifications: Solar PV, Boost converter and BLDC motor. S. no.

Solar panel

Boost converter

Motor parameter

1 2 3 4 5 6 7 8 9 10

STC power rating: 340 W STC power per unit of area: 16.2 W/ft2 (174.9 W/m2 ) Peak efficiency: 17.49% Power tolerances: 0% to ±3% Number of cells: 72 Imp: 8.91 A Vmp: 38.2 V Isc: 9.22 A Voc: 47.5 V

Power rating: 1000 W Input voltage range: 20–30 V Switching frequency: 50 kHz Inductance (L): 470 μH Capacitance(C): 470 μF

No of poles: 4 Stator phase resistance Rm : 0.65/ph Stator Phase inductance Lm : 965 μH/ph Torque constant: 0.082 Nm/A Rated speed: 420 RPM Rated voltage: 48 V Rated current: 6 A Rated torque: 0.42 Nm Rotor Inertia: 29.15 × 10−6 kg m2 Frictional coefficient: 0.01 Nm/rad/s

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Fig. 5. Simulink block diagram of BLDC motor with PID controller.

The parameters such as resistance (Rm ) and inductance (Lm ) values were obtained by blocked rotor test on a 250 W, 48 V BLDC motor and listed as in Table 1. These obtained specifications of the BLDC motor are used in the MATLAB simulation.

Table 2 Tuning parameters and peak over shoot of both the tuning methods. Method

Controller

Kp

Ti

Td

PO%

Proposed Ziegler–Nichols

PID (δ = 0.05) PID

0.2051 0.3

0.002s 0.0031 s

0.003s 0.0042 s

5.0 49.5

3.5. PID controller for BLDC motor PID regulators are extensively used for processes and motion control system in industries. Moreover, these controllers are to be modified for an operating range with different loads [24]. The major challenge in the design of controllers is in obtaining the model of the system [16,25] and their PID parameters. The governing equation of PID controller is given by

u(t ) = K p e(t ) +

1 Ti



t 0

e(τ )dτ + Td

d e(t ) dt

(7)

where u(t) and e(t) denote the control and the error signals respectively and Kp (Proportional term), Ti (Integral time) and Td (Differential time), are the tuning parameters. The corresponding transfer function is given as

K (s ) = K p





1 1+ + Td s sTi

(8)

and setting

Td =

1 2ζ 4Ti , Ti = , and K p = 2ζ ωn ωn K Ts

(9)

From the mathematical model of BLDC motor, the tuning parameters were determined. With the real time controller the tuning is performed by increasing Kp up to a value for which either the settling time increases or the percent overshoot reaches a value higher than the prescribed for the second time. This makes the BLDC motor to perform better. 4. Modelling and simulation 4.1. Tuning of PID The tuning of PID parameters aims to minimise the rise and settling times, steady state error and voltage ripples of converter for step changes in load torque and speed. Fig. 5 shows the Simulink Block diagram of BLDC Motor with PID Controller. A robust tuning approach was employed in designing the controller in order to effectively improve the transient performance of the BLDC motor. Initially the tuning of PID controller parameters was performed according to refined Ziegler–Nichols method

[26] as shown in Table 2, implemented a approach based on automated and machine learning technique [27,28] which resulted with the transient behaviour of the system as depicted in Fig. 6(a). The PID tuning was also performed with under damped step response method as suggested in [29–31] using the relations to calculate the PID parameters, damping coefficient ζ , and natural frequency ωn as given in Eqs. (9)–(11)

ζ = 

ωn =

1 1+

 2 π 2  ln d

2π

Tp



(10)

1 − ζ2

(11)

The results of under damped step response method is as shown in Fig. 6(b) with the calculated PID parameters as tabulated in Table 2. The results with PID controller tuned by both the methods are summarised in Table 3 which shows that the transient currents under different operating speed were found to be superior when compared to the existing method. 4.2. Transient power extraction of BLDC motor To optimize the value of Ultra capacitor, the transient power drawn by the motor is to be extracted. The transient powers drawn are measured with PID controllers tuned with Ziegler–Nichols and under-damped step response methods with half and full load conditions using MATLAB/Simulink and listed in Table 4. The time and the power drawn during transient give the total energy required by the motor. The excess power required at transient time is to be supported by the Ultra capacitor, whereas the nominal power is delivered by the Solar PV system. The results obtained from simulation are used in the optimal sizing of Ultra capacitor. 4.3. Optimal design of Ultra capacitor From the simulation results, maximum transient current and time (td ) are found to be 14.32 A and 4.55 ms respectively giving

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Fig. 6. Performance of BLDC with PID (a) Tuned with Ziegler–Nichols method and (b) Tuned with an under-damped step response method under full load and 370 RPM.

Table 3 Comparison of stator current at different speed and load. Transient peak of stator current Speed (rpm)

370 390 410

Half load (0.21 Nm)

Full load (0.42 Nm)

PID 1

PID 2

% Diff

PID 1

PID 2

% Diff

12.36A 13.02A 13.83A

11.25A 11.9A 12.55A

9.9% 9.4% 10.2%

14.07A 14.87A 15.65A

12.92A 13.62A 14.32A

8.9% 9.2% 9.3%

Table 4 Simulation results on performance parameters of the BLDC motor with PID controller. S. no

Speed (rpm)

Transient current (A)

Transient time (ms)

Transient peak power (W)

1 2 3

410 390 370

12.92 13.62 14.32

4.8 4.6 4.55

620.24 653.76 687.36

the transient energy requirement of 3.12 J. From the design Eqs. (2) and (3), the value of Capacitance is found to be C = 5.92 mF. Since each Ultra capacitor cell is typically rated at 5.5 V, the total Capacitance for a 50 V system module would be 59.2 mF as they are connected in series that can provide a max of 720 W. The losses are neglected as ESR is not considered and hence the optimum value of capacitance is chosen slightly higher than the calculated value.

4.4. Simulation of proposed system Modelling of the proposed system in Simulink is as shown in Fig. 7, which was tested for various loading conditions. The BLDC motor was tested under full load for verifying its transient performance and settling time, after controlling it with the PID controller tuned by under-damped step response method. The system is powered by PV system along with the designed Ultra capacitor. The

current driven by Ultra capacitor along with the speed, stator current and torque were measured in this test as shown in Fig. 9. 5. Experimental verification The experimental setup was implemented using a Polycrystalline solar panel of 340 Wp, and a Boost converter of 10 0 0 W, coupled with the standard Ultra capacitor module of 0.1 F,50 V instead of calculated 59.2 mF and a PID controller programmed in dsPIC30F4011 controller as shown in Fig. 8. The dsPIC30F4011 is a 16-bit MCU 120 MHz digital signal controller mostly opted for embedded motor control applications. Induction Motors, BLDC motors are few types for which the dsPIC30F4011 processors have been designed for. It consists of dedicated Motor control PWM outputs, and ADC’s with up to 4 simultaneous sampling capabilities, and multiple serial communications: UART, I2 C and SPI thereby, DSP can be faster in control loop implementation in real time.

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Fig. 7. Complete simulation diagram of solar PV panel with Ultra capacitor.

Table 5 Experimental load test results of BLDC motor. Test type

Transient current (A)

Steady state current (A)

Transient time (ms)

Speed (rpm)

No load Load

8.3 13.2

3.9 4.6

2.7 3.81

410 370

the motor for different loads using the developed hardware and the results were as shown in Fig. 12. 6. Results and discussions

Fig. 8. Experimental setup of the proposed module.

A dsPIC30F4011 16-bit 120 MHz digital signal controller with a maximum of 30 MIPS from Microchip was chosen for control of the BLDC motor. It possesses 5 × 16-bit and 2 × 32-bit timers, 6PWM motor control channels, ADCs and DACs which was suitable for this application. The PID control algorithms were implemented in the dsPIC. From the load test of BLDC motor module, the transient power requirement under different load conditions were obtained by measuring the voltage and transient current supplied to BLDC motor, this is tabulated in Table 5. The test results were used to calculate the transient power for the optimal sizing of PV system and Ultra capacitor. Transient current and speed were measured from

The proposed system has been simulated using MATLAB/Simulink as shown in Fig. 7. Fig. 8 shows that the modeled Ultra capacitor of the calculated value was sufficient to deliver the transient power requirement of the BLDC motor under loaded condition. The current, speed and torque curves of the motor have shown the optimized transient behavior of the motor with no change in speed of response. The solar panel of (255 W) 340 Wp and Ultra capacitor of 720 W have proved sufficient to deliver the load requirement of 250 W and the transient power of 652.8 W, this proves a reduction of around 50% in PV size. This was verified from the delivery of transient current of 13.6 A in simulation, by the Ultra capacitor as shown in the Fig. 11. Experimental result of phase current in Fig. 10 shows the comparison of transient currents drawn in both tuning methods. The transient current was found to be 12.4 A with the under-damped step response method of PID controller, which is 12.6% less than the Ziegler–Nichols method. The results obtained were similar to those of simulation. From the rating of BLDC motor, the required nominal power from panel was found to be 250 W. A Solar PV panel of 340 W was selected to provide the requisite nominal power, after considering an average insolation of 0.75 kW/m2 . From the load test results, the maximum transient power required for starting the BLDC motor was obtained as 652.8 W for a duration of 3.1 ms. To meet this, an Ultra capacitor module was fabricated by connecting 10 ultra-capacitors of 1 F, 5.5 V rating in series for a max voltage of 50 V. Tables 4 and 5, validates the proposed system by simulation and experimental verification respectively, and the transient

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Fig. 9. (a) Current supplied by an Ultra capacitor during transient time (b) Speed (c) Stator current and (d) Torque of BLDC motor. Table 6 Transient power during starting condition from experimental results. Transient power-starting condition (W)

Transient duration (ms)

Load power required (W)

PV power (W)

Ultra capacitor power (W)

% Reduction in PV size

652.8

3.1

250

255∗

720

50

∗

Output from panel varies with insolation and time, tested at 11am.

Fig. 10. Transient current drawn by BLDC with PID controller (a) Tuned with Ziegler–Nichols method and (b) Tuned with under-damped step response method under full load.

current measured in both the cases were found to be similar. The transient power was calculated and tabulated in Table 6. After augmenting Ultra Capacitor with the output of solar panel, the transient current was supplied completely by the Ultra capaci-

Fig. 11. Simulated results - current transients of BLDC motor during (a) No load test (b) Load test.

tor. The current drawn from the PV panel for both the no load and loaded condition were found to be only the nominal current and this was verified with the current measured from Ultra capacitor as shown in Fig. 13.

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(i) Design of PID controller with optimized tuning method to minimize the transients in the motor current, without compromising its performance under loaded condition. (ii) Determination of transient power requirement of BLDC motor, and design of Ultra capacitor module for the transient power need. (iii) Optimizing the PV panel sizing for supplying the nominal power to BLDC motor. The Ultra capacitor bank was utilized as an energy buffer to deliver power at transients due to sudden changes in the load and/or during fluctuations in solar irradiation. Hence, an Ultra capacitor bank can be employed to minimize the size of an independent photovoltaic system for a high peak to average power load conditions. The results from this proposed system thus pave way for off grid applications without battery support. Declaration of Competing Interest All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version.

Fig. 12. Measured no load and load currents of BLDC motor.

Acknowledgement The Authors would like to thank the management of KCG College of Technology for providing the Research lab setup for conducting the experimental verification of this research work. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.micpro.2019.102893. References

Fig. 13. Measured transient current supplied by the Ultra capacitor.

Fig. 11a and b shows the current waveforms of the BLDC motor under no load and loaded condition.

7. Conclusion The present investigation depicts the optimization process of PV plant sizing for a peak power load. A reduction of 10% in transient current was realized by optimal tuning of PID controller with the underdamped step response method. A highperformance dsPIC30F4011 microcontroller and digital signal controller has shown a good performance in the controller implementation. When the PV power was augmented with Ultra capacitor module, the optimized PV panel requirement was found to be 340 Wp for a 250 W BLDC motor, and if the BLDC motor was to be driven with PV power alone, then the PV power requirement would have been 652.8 Wp. As, area required is the utmost factor for solar PV plant design, for a small load of 250 W, with the proposed system, there is a reduction in PV panel power requirement by 50%. When this is considered at larger scale, there would be a huge reduction in the number of PV panels, thereby drop in area requirement of PV panel installation can be achieved. The key contributions of this investigation are in:

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K. Karthikeyan received the M E. degree from Madras University in Applied electronics. Currently he is pursuing his PhD in Anna University. He is working in the area of Renewable energy optimization. His research interests include power electronics, DC-DC converters, Special Electrical Machines. He is the life member of ISTE.

S.K. Patnaik received the Ph.D degree in Electrical Engineering from Anna University, India in 2008. Since 2000, he has been with the Department of Electrical Engineering, College of Engineering Guindy, Anna University, where he is currently a Professor. His current research interest includes application of robust, optimal, sliding mode control in switching systems and power electronics applications.

M. Baskar received B.E. Computer Science and Engineering from Anna University, Chennai, M.Tech. Information Technology from Sathyabama University, Chennai and Ph.D., (Information and Communication Engineering) from Anna University, Chennai. His Area of research interest includes Computer Networks and Security, Parallel and Distributed Systems, Image Processing, Big Data, Machine Learning and IoT. He is published 19 Research Article in reputed International Journals and 10 Article in International Conferences. He is acting as a reviewer in Cluster Computing, Journal of Web Engineering, Multimedia Tools and Applications, Neural Processing Letters and Concurrency and Computation: Practice and Experience. He is a Life time Professional body member of CSI, ISTE, IET, ISRD, IRED, IACSET, IAENG, SDIWC and UACEE. J. Jeyashree completed the Masters in Electrical Engineering in Anna University, India in 2018. Her current research interests includesinclude PID Controllers, and Special Electrical Machines.