Performance Analysis of Solar Energy System with Bidirectional … · Figure 1 shows the arrangement of two buck/boost MPPTs of a single phase bidirectional inverter which is used

International Journal on Electrical Engineering and Informatics - Volume 12, Number 1, March 2020

Performance Analysis of Solar Energy System with Bidirectional Converters and Using Fuzzy Inference Based Modified Inertia PSO Technique

K. Harinadha Reddy

Department of Electrical and Electronics Engineering Lakireddy Bali Reddy College of Engineering (Autonomous)

Mylavaram, Krishna Dt., 521230, Andhra Pradesh, India [email protected]

Abstract: Solar PV energy is the one among the fastest growing renewable energy resources exist in the world. In this paper, analysis is made on the integration of solar PV cells with bi-directional converter by maximum power point tracker (MPPT) to improve efficiency for the both DC and AC distribution system. A bi-directional inverter is connected to dc distribution system in order to control the power flow between ac grid and dc bus, and also to regulate the dc bus voltage to the particular limit. The two buck and boost MPPTs are formed and a thin-film PV panels are used to run the dc bus voltage around 380V. This will vary the voltage of photovoltaic (PV) array from 0 to 600V by reducing the voltage stress of connected inverter. This paper presents an optimal algorithm using fuzzy-modified inertia particle swarm optimization (F-MIPSO). Inertia parameter of PSO is obtained from the fuzzy systems and its control vector is used for calculation of a new inertia parameter. Inertia parameter is calculated from membership functions of fuzzy logic technique and it is designed by using MATLAB/SIMULINK software.

Keywords: Solar PV System, Distributed Generation, Inertia Particle Swarm Optimization, Fuzzy-Modified Inertia Particle Swarm Optimization

1. IntroductionThe energy demand in the world is growing high as fast as the growth of population and

economy in the developed countries. Secondary energy sources are also known as renewable energy sources which are naturally produced energy resources from solar, wind, rain, tides and geothermal heat. Solar energy and wind power are the most developed among the renewable energy resources due to effective cost and also mostly used in many applications. These energy resources are mostly used for being eco-friendly and also world’s electricity produced from the solar PV cells. Solar photovoltaic energy is preferably used in dc distribution system due to the merits of clean, pollution free, low maintenance, minimal wear and tear of components for not having moving parts, lack of noise and no fuel cost [1]. In this paper, a grid-connected PV system is used for extracting energy from the sun by solar cells and interfacing the grid by power converters. In this context, implementation of two PV arrays with two MPPTs because of non-linear behaviour of PV array. Here, MPPT is used draw the maximum power from each PV array. Moreover, a bi-directional inverter is used to control power flow between ac grid and dc bus and it also regulates the dc bus voltage around 380±10V.Mostly, a two-stage configuration is used in PV inverter systems and it operates in by-pass mode. PV array characteristics differ from each other because of this MPPT cannot track maximum power from PV array. To eliminate this problem, a buck and boost converter is combined with MPPT topology. In this paper, proposed MPPT method is perturb and observe (P&O) method for drawing the maximum power from PV array [2]. Inertia term is a major factor for providing the balance in the process of investigation and utilization. Mainly, the inertia term explains the measuring benefit of comparing the present voltage with the previous voltage at very instant. The basic PSO does not have any inertia term. So, the constant inertia term is introduced by Shi and Eberhart in 1998. It is proved that a high value inertia term is used for a social search whereas a low value inertia term is used for a personal search [3-4]. Therefore, it is used for getting the best performance of the system.

Received: March 28th, 2018. Accepted: March 2rd, 2020 DOI: 10.15676/ijeei.2020.12.1.13

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Fuzzy system is used to make precise and limit the operating voltage to a certain value. Reference voltage and derivative voltage of PV array are the inputs given to the fuzzy system then control vector is generated as output. In this paper, two PV array reference voltages and derivative voltages are given as the inputs to the fuzzy system. Then two control vectors are generated and it is added to the calculated inertia term. Therefore, a new inertia term is evolved from added control vectors to inertia term. It will be given as a gate pulse to the bidirectional inverter for improving the overall efficiency of the system. In this paper, power transmission lines are very important for transferring power from converter to grid or vice versa. When the unexpected troubles known as faults which occur in the proposed test system. These faults destroy the power transmission lines of the proposed test system. These faults occur in the situations such as lightning, fog, snow, etc. For protecting these lines, faults should be reduced in order to send the power very efficiently. So, F-MIPSO is introduced to reduce the fault in power transmission lines.

2. Block Diagram Model of Bidirectional Converter and Test System

Mainly in the grid-connected PV systems with ac loads, power electronic devices like inverters are very useful. Here, the inverter choice is the most important unit in order to set the operating voltage of dc bus of the provided system. So, the bidirectional inverter is used to operate the dc bus voltage around 380 ± 10V.In this paper, a full-bridge arrangement of bi-directional inverter is introduced to satisfy both rectification with power factor correction (PFC) and grid-connection. The inverter will sense the dc bus voltage Vdc, line voltage Vs and inductor current ILs with the use of variable inductance for obtaining the inverter control operation stably. When the PV array output power is more than the load then the dc bus voltage will rise. Therefore, the inverter will inject the excess power into ac grid by operating in grid-connection mode. For the other mode, the inverter will operate in rectification mode with PFC which is used to convert ac source to recharge the dc bus. Figure 1 shows the arrangement of two buck/boost MPPTs of a single phase bidirectional inverter which is used to operate in either grid-connection or rectification mode with PFC. The linear control law is used to derive the equations for grid-connection and rectification with PFC are as follows:

Dgc =12

+|VS|2Vdc

+∆ILS. LS(ILS)

2Vdc. TS (1)

Dre =12−

|VS|2Vdc

+∆ILS. LS(ILS)

2Vdc. TS (2)

From the above derived equations, the equation (1) represents the grid-connection mode and the equation (2) represents the rectification mode with PFC. In the above equations, TS represents

PV ARRAY MPPT BASED BUCK-BOOST COVERTER

PV ARRAYMPPT BASED BUCK-BOOST CONVERTER

DC BUS

BIDIRECTIONAL INVERTER

AC GRID

Figure 1. Arrangement of buck-boost MPPTs with solar PV system.

Kadapa Harinadha Reddy

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the switching period and Dgc and Dre are the duty ratios which is used for control operation and LS (ILS) is the inductance which varies with ILs. When the load changes suddenly then it will cause the dc bus voltage to vary below the operating voltage limit. For this reason, the bidirectional inverter is used to make power balance and control the dc-bus voltage by varying the inductor current. In a bidirectional inverter, a gate pulse is given by using fuzzy-modified inertia particle swarm optimization (F-MIPSO).

3. Inertia Based PSO for Test SystemIn 1995, James Kennedy and Russell Eberhart are the first who proposed the particle swarm

optimization algorithm (PSO) as a population-based optimization method.To find the optimal solutions to numerical and qualitative problems by the PSOis known to be a new relative evolutionary algorithm. From the basic rule that the birds will set their directions and velocities birds moving away from the flock to land at the roost will result in the nearby birds to move ~towards the roost. After the discovery of the roost the entire birds will also land at the roost. This method is called as “socio-cognitive view of mind”. The basic PSO has many advantages comparing to other algorithms such as convergence speed for obtaining global best, code implementation is easy, free complex computation platform and parameters will be minimized. The basic PSO is given as follows:

V𝑘𝑘(t + 1) = V𝑘𝑘(t) + a1r1�hbest(t) − xk(t)� + a2r2�mbest(t) − xk(t)� (3) From the above equation the parameters are defined as follows:

k= particle index a1,a2 = acceleration coefficient (0 ≤ a1, a2 ≤ 2) r1,r2= random values (0 ≤ r1, r2 ≤ 1)regenerated for every velocity update Vk(t) = particle’s velocity at instant t xi(t)= particle’s position at instant t hbest = particle’s best position at instant t for personal search mbest = particle’s best position at instant t for social search Inertia term is introduced to overcome the uncountable of particles of PSO by Shi and

Eberhart in 1998. It will improve the performance of PSO and parameters also improved to form the standard PSO equation. Firstly, they introduced the constant inertia term by stating that bigger values of inertia term γ> 1.2 tend to be low investigation and smaller values of inertia term γ> 0.8 tend to be personal search. By this statement, inertia term γ ranges in [1.2, 0.8] which can be used in different applications [5]. The inertia PSO equation is given as follows:

V𝑘𝑘(t + 1) = γV𝑘𝑘(t) + a1r1�hbest(t) − xk(t)� + a2r2�mbest(t) − xk(t)� (4) Where

γ = inertia term The inertia term formula is given as follows:

γ = γstart −γstart − γend

rmax× r

Where γstart = starting value of inertia term

γend = ending value of inertia term r = present iteration rmax= maximum iteration

Updating the voltage along the inertia component Vk(t + 1) = γVk(t) + a1r1(hbest(t) − xk(t) + a2r2�mbest(t) − xk(t)� (5)

Maintains the particle to move in the same direction it was originally moving. The value of the inertia termγ usually varies between 0.8 and 1.2. Velocity update- Cognitive component

Vk(t + 1) = γVk(t) + a1r1(hbest(t) − xk(t) + a2r2�mbest(t) − xk(t)� (6)

Performance Analysis of Solar Energy System with Bidirectional Convertersand

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Act’s as a particle’s memory, causing it to return to its individual best regions of search space. The value of the cognitive coefficient 𝑎𝑎1is close to2. The coefficients limits the size of the step particle takes towards its individual besthbest . Velocity update-Social component

Vk(t + 1) = γVk(t) + a1r1(hbest(t) − xk(t) + a2r2�mbest(t) − xk(t)� (7)This makes the swarm to move to the best position which the swarm has discovered. The value of the social coefficient a2 is close to 2. The coefficients will limit the step size so that the particle takes towards the global best m position. Updating the position of the each particle:

xk(t + 1) = xk(t) + vk(t + 1) (8) Therefore, inertia termγ is calculated from the above formula and equations at every instant. Now, then added to the control vectors which are obtained from the fuzzy system to get the modified inertia term.

4. Fuzzy-Modified Inertia PSOA. Fuzzy System

The system which uses fuzzy mathematics is known as fuzzy system. Fuzzy system is a partof a fuzzy logic and fuzzy set theory. The fuzzy logic is established by depending on the simulation due to the people’s insight and views for control operation of any system. It is the best method toremove uncertainty, lack of precision and precision up to some range of simpler complex systems. FLC (Fuzzy Logic Control) is used to provide a desired voltage up to a certain range of limit with a precise value for DC link. FLC is making useful in applications like control of PV array which is having a non-linear behaviour. FLC analyses the mathematical analog input in the form of logical terms which obtain the constant values in between 0 and 1. FLC has some advantages are as follows: less cost and modelling the system without any knowledge of the correct model to process. FLC improves the performance in another way from previous gained experience and knowledge. The four processes involves in the formation of fuzzy system. Figure 2 shows the four processes which include in the fuzzy system as shown.

Figure 2. Block diagram model of fuzzy system

In the Figure 2, the input variables are described as follows: PV1= reference voltage of PV1 array PV2 = reference voltage of PV2 array dPV1= derivative voltage of PV1

FUZZIFICATION FUZZY INFERENCING

DEFFUZIFICATION

MEMBERSHIP FUNCTION

FUZZY BASE RULES

PV1 + dPV1

PV2 + dPV2

INPUT VARIABLE

U1 U2

OUTPUT VARIABLES


158

dPV2 = derivative voltage of PV2

B. Fuzzy with modified PSOThe exploring activity is irregular and active procedure occurs in PSO. Due to this reason, an

optimization technique should be used according to the changes occur in that activity. The change of position of the particle is proportional to the inertia termγ. Taking the proper value of inertia termγ shows that the social and personal best points will be in balanced condition. For manipulating the inertia term, several methods are applied in the process of optimization. Among all methods, fuzzy if and then rules are applied to control the inertia term of PSO. Four processes happen in fuzzy system as shown in figure 2. These processes are explained in detail as following. Fuzzification:Giving input variables to the first block after that the controller inside the block is Fuzzification. It converts all real input data into the form of membership of fuzzy sets by a table in so many membership functions. For every input and output variables, more than one membership function is described. But mostly three membership functions are described for every input and output variables. In this paper, among more number of membership functions, left-triangle, triangle and right-triangle is used for every input and output variables. The input variable is presented in three semantic levels; low (L), middle (M) and high (H). The output variable will be presented in three sets of semantic values with respective membership functions are shown in figure 3, 4 and 5

Figure 3. Membership function of error

Figure 4. Membership function of change in error


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Figure 5. Membership function of output

B 2: Fuzzy based rules: The fuzzy based rules are a combination of IF-THEN statements. The fuzzy based rules means is the set of rules. In the rules, IF is considered to be as condition where THEN is as result. In this way, computer is capable to perform the given conditions and then result is given based on the input error (E), a derivative error (DE) and control vector voltage (U).The Fuzzy based rule table-1 is given as follows:

Table-1. Fuzzy based rules of data table DE

E VVL VL L M H VH VVH

VVL VVL VVL VVL VVL VL L M VL VVL VVL VVL VL L M H L VVL VVL VL L M H VH M VVL VL L M H VH VVH H VL L M H VH VVH VVH

VH L M H VH VVH VVH VVH VVH M H VH VVH VVH VVH VVH

C. Fuzzy Inference System for PSOThe fuzzy inference Method is used for mapping the elements of input as the outputs. The ANDoperator is used for the set of membership functions of input for the output membershipfunctions. For getting a best inertia term under the fuzzy system, obtained control vectors andpresent inertia term is taken as the input variables. The output variable is the result of the changein inertia term.

γnew = γ + U (9) Where U = U1 + U2 , γ = Inertia term obtained from PSO, U = Control vector of fuzzy system

U1 = control vector obtained from PV1 and its derivative dPV1 U2 = control vector obtained from PV2 and its derivative dPV2 For getting the values in positive and negative terms of inertia term, a small change has to be

done in the inertia term. A small change value range will be in [-0.1 0.1] for obtaining the new inertia term.

γ(𝑡𝑡 + 1) = γ(t) + ∆γ(10) Where ∆γ= Small change value inertia term, γ(t), γ(𝑡𝑡 + 1) = Present value and New value of inertia The equation (10) is calculated and it gives the new inertia term. This new inertia term is applied to the proposed system of figure 1. It will the improve the performance of the proposed system

U


160

The output variable will be defuzzified in order to get a real output value. In this process, the sum of centres of input and output variables membership functions for getting a control signal of the system. This control signal is used in the proposed system.

5. Proposed algorithm for Implementation of F-MIPSO

Figure 6. Flowchart of F-MIPSO

The fuzzy based modified inertia particle swarm optimization (F-MIPSO) technique is mainly introduced for improving the efficiency power of the proposed test system. It serves as the controller for getting the voltage in a particular limit of dc bus. The F-MIPSO is applied for the bidirectional converter as a gate pulse. The inverter can be controlled only through the gate pluses. In inverter mode, it is used to convert the DC to AC. In the rectifier mode it converts the

START

Initialize the position of each particle in search process with stray values and simulate the process

Compute the obtained parameter values and update the inertia term, hbest and mbest

Next iteration t = t+1

Update the position of particles

Get the control vectors from fuzzy system and add to inertia term and simulate the process

Evaluate the simulated results and update the inertia term,hbest and mbest

END

Yes

No

If updated parameters are with the range and best

among them?


161

AC to DC. This conversion can be controlled by gate pulse. So, F-MIPSO is used as a gate pulse for bidirectional converter. F-MIPSO is also used for the reduction of faults which occur in the power transmission lines. In this way, the efficiency will improve in the proposed test system. The following flowchart describes the steps involve in the F-MIPSO technique for the getting the new inertia term.

6. Simulation Results and Discussion

Figure 7. Simulink Module of Test System


162

Figure 8. MPPTTechnique of Test System

Figure 9. Voltage comparison during fault and without fault

The above graph represents the voltage comparison during the fault and without fault. During the fault condition the voltage is decreased from 50V to approximate 0V.

Figure 10. Comparison of the current with and without fault


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Figure 10 indicates the comparative analysis of the current. The analysis is made during the fault and without fault condition. During the fault the current is reduced for a certain period. After the clearance of the fault the current is not raised to the original value. After sometime the current restored to its original value.

Figure 11. Active power comparison during fault and without fault

The above plot indicates the comparitive analysis of the active power. The plot is compared between the fault and without fault condition.Due to the disturbance created in the system the power is varying abruptly. This plot indicates power variation during the fault.The other indicates the power with out fault.

Figure 12. Reactive power comparison during fault and without fault

From the above graph the change in voltage is detrermined easily. When the fault is started the voltage is reduced first.Then amplitude of the voltage is varying enourmously.After the fault is cleared the voltage did not restore to its original value. The above snusoidal graph represents the ac grid voltage. The magnitude of the grid voltage is 100v. This voltage is given to the domestic load. The graph represents the output voltage of the solar pannel. The range of the solar panel varies from the 0 volts to the 600 volts. From the graph the magnitude of the panel voltage is around 320 volts. The mppt topology and the buck boost topology used to boost up the voltage across the panel.


164

Figure 13. Voltage across the ac grid

Figure 14. Output voltage of the solar panel

Figure 15. RMS voltage of the grid

The figure 15 represents the current of the solar panel.The current range varies from the 0amperes to the 20 amperes.


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Figure 16. Current of the solar panel

Figure 17. Voltage comparison with and without use of F-MIPSO

Figure 18. Reactive power comparison with and without using the F-MIPSO


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The graph concludes the reactive power is increased by using the F-MIPSO. The magnitude of the graph is increased while compared to without using the F-MIPSO.

Figure 19. Voltage during the increase in load for F-MIPSO

Figure 20. Voltage during the increase in load without F-MIPSO

Figure 21. Active power with increase in load for by using F-MIPSO


167

The plot represents the difference between the voltage using the F-MIPSO and without using the F-MIPSO. Without using the F-MIPSO the frequency of the voltage waveform is constant, while the frequency of the voltage varies by using the F-MIPSO. From the figures 19 and 20 it is concluded that by using the F-MIPSO technique there is equal distribution of voltage to the load. Due to the non linear load the voltage across the load is also non linear.

Figure 22. Active power with increase in load for without using the F-MIPSO

Figure 23. Reactive power with increase in load by using the F-MIPSO

From the above two figures 21 and 22 the active power using the F-MIPSO technique is linear. The active power is nonlinear due to the nonlinear load. There is unequal distribution of the load across the load. From the figures 23 and 24 it is concluded that the reactive power is linear by using the F-MIPSO. It is nonlinear without using the F-MIPSO. In DC Distribution system, DC type of loads are play very crucial role in the system performance and efficiency. Here with, dc–dc converter control unit, which has includes the rage of voltage and current. The Principle of integration is mainly with voltage loop integrates the primary and the secondary control elements of bidirectional converter of common MPPT controller. The voltage at note node is the amplitude of central voltage, current and power to load to be synchronized based on the given condition. Whereas test system with DC-DC to MPPT control simulation carried out and results with loading condition is depicted in the following figure 25. Here the load increment is taken up to 10% for simulating the proposed F-MIPSO,


168

and is observed the performance. Hence the efficiency of solar integration of solar PV Cell with bidirectional converter is considerably increased as that of without proposed F-MIPSO. The power variations in DC Distribution system are also compared with conventional converter, bidirectional converter and proposed bidirectional converter with F-MIPSO and shown in figure 26. Observations from power variations in DC Distribution system are less with proposed F-MIPSO and hence efficiency is increased.

Figure 24. Reactive power with load without using the F-MIPSO

Figure 25. DC distribution system and Efficiency with proposed F-MIPSO

In AC Distribution system, the frequency of integrated energy system to main grid is important parameter to evaluate the in the system performance and efficiency. Here with, dc–ac converter control unit, which has includes the rage of thresholds for frequency and voltage. The Principle of integration is mainly with frequency droop control during the integration of bidirectional converter of common MPPT controller. Test system with DC-AC to MPPT control simulation carried out and results with loading condition is depicted in the following figure 26.

0.5 0.55 0.6 0.65 0.7 0.75

p.u. Load

0.538

0.5385

0.539

0.5395

0.54

0.5405

0.541

0.5415

0.542

Effi

cien

cy

DC Distribution System-Efficiency

Convetional converter

Bidirectional without F-MIPSO

Bidirectional with F-MIPSO


169

Here the load increment is taken up to 10% for simulating the proposed F-MIPSO, and is observed the test system performance. Hence the efficiency of solar integration of solar PV Cell with bidirectional converter is considerably increased as that of without proposed F-MIPSO for AC distribution system also as shown in figure 27. The power variations in AC Distribution system are also compared with conventional converter, bidirectional converter and proposed bidirectional converter with F-MIPSO and shown in figure 28. Observations from power variations in AC Distribution system are less with proposed F-MIPSO and hence efficiency is increased.

Figure 26. DC distribution system Power Variations with proposed F-MIPSO

Figure 27. AC distribution system and Efficiency with proposed F-MIPSO

1 1.5 2 2.5 3 3.5 4 4.5 5

Time in seconds

0.32

0.33

0.34

0.35

0.36

0.37

0.38

0.39

0.4

Powe

r Var

iatio

ns

DC Distribution System-Power Variations




0.6 0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.76 0.78 0.8

p.u. Load

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Effi

cien

cy

AC Distribution System-Efficiency





170

Figure 28. AC distribution system Power Variations with proposed F-MIPSO

From the principle of DC distribution system and AC distribution system also tested and found efficiency is significantly improved with proposed F-MIPSO. Along the load increased upto 10%, the proposed F-MIPSO holds good in evaluating the performance and efficiency in the principle of both DC distribution system and AC distribution systems.

7. ConclusionThis paper represents the fuzzy modified inertia PSO and power, reactive power, voltage,

current during the fault and the without fault are clearly observed and found. The obtained results are so far better by using the F-MIPSO than the without using the F-MIPSO. Proposed test is also tested for increase in load up to 10%. Fuzzy based inference system proposed with modified inertia terms and also gives the good performance in many operating conditions.

8. References[1]. Subudhi, B. and Pradhan, R., “A Comparative Study on Maximum Power Point Tracking

Techniques for Photovoltaic Power Systems”, IEEE Transactions on Sustainable Energy, Vol.4, No.1, 89-98, 2013.

[2]. S. Ramya and M. Yuvaraj,” Integration of Solar cells with Power Electronic Converters for Power Generation”, IEEE International Conference on Electronics and Communication System (ICECS), 2015.

[3]. J. Kennedy and R. C. Eberhart, “Particle swarm optimization” Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942-1948,1995.

[4]. J. C. Bansal, P. K. Singh, Mukesh Saraswat, Abhishek Verma, Shimpi Singh Jadon, Ajith Abraham, Member, IEE: “Inertia Weight Strategies in Particle Swarm Optimization”, IEEE Conference on Third World Congress on Nature and Biologically Inspired Computing, pp. 640-647, 2011.

[5]. Mohammad Javad Amoshahy, Mousa Shamsi and Mohammad Hossein Sedaaghi, “A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm”, Plos one journal in 2016.http://dx.doi.org/10.1371/journal.pone.0161558.

[6]. K. Harinadha Reddy, G. Srinivasa Rao, “A Review on Design and Development of high Reliable Hybrid Energy Systems with Droop Control Techniques”, International Journal of Power Electronics and Drive System, Vol. 7, No. 3, 69-73, 2016.

[7]. D.F.Li.: TOPSIS-based nonlinear-programming methodology for multiattribute decision making with interval-valued intuitionistic fuzzy sets”, IEEE Transactions on Fuzzy Systems, vol. 18, no. 2, pp. 299—311, 2010.

1 1.5 2 2.5 3 3.5 4 4.5 5

Time in seconds

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[8]. J. Q. Wang and H. Y. Zhang, “Multicriteria decision-making approach based on Atanassov’s intuitionistic fuzzy sets with incomplete certain information on weights”, IEEE Transactions on Fuzzy Systems, vol. 21, no. 3, pp. 510—515, 2013.

[9]. Bing Xue, Mengjie Zhang, and Will N. Browne, “Particle Swarm Optimization for Feature Selection in Classification”, A Multi-Objective Approach: IEEE Transactions on Cybernetics, vol. 43, no. 6, pp. 1656-1671, 2013.

K. Harinadha Reddy was born in India on 2nd July 1974. He received B.E. degree in Electrical and Electronics Engineering from K.U. in 1997, India. He completed M.Tech degree in Electrical Power Systems Emphasis High Voltage Engineering from J. N. T. University, Kakinada Campus, India in the year 2006. He obtained Ph. D degree in Electrical Power Systems from Andhra University Campus in the year 2012. Presently, he is working as Professor in Electrical and Electronics Engineering Department at Lakireddy Bali Reddy

College of Engineering (A), Mylavaram, Krishna District-521230, A.P., India. He published 36 papers in various reputed national and international journals. He is also reviewer for IEEE journals and other reputed SCI journals. His research interest areas are Power and Energy Systems, Islanding Detection in Electrical Power and Energy Systems, Power Electronic Conversion with Power Converters, Integrated Renewable Energy Systems, FACT devices in Electrical Engineering, Electrical Drives and AI Techniques for Electrical Power Engineering applications.


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Performance Analysis of Solar Energy System with Bidirectional … · Figure 1 shows the arrangement of two buck/boost MPPTs of a single phase bidirectional inverter which is used