POWER LOSS REDUCTION BY PHEV CHARGING SYSTEM · Block diagram for intelligent integrated system with plug -in hybrid electric vehicle charging station is sho wn in figure 1.step 1

POWER LOSS REDUCTION BY PHEV CHARGING

SYSTEM USING INTELLIGENT INTEGRATED

SYSTEM

K.Hithesh1, R.Hariharan

2, T.Yuvaraj

3

U.G. Scholar, Department of Electrical and Electronics Engineering, Saveetha School of Engineering, Saveetha Institute of Medical And Technical Science, Chennai

1

Assistant Professor, Department of Electrical and Electronics Engineering, Saveetha School of Engineering, Saveetha Institute of Medical And

Technical Science, Chennai 2,3

[email protected], [email protected]

2, [email protected]

ABSTRACT

This paper discuss about the power loss reduction by phev charging system using intelligent

integrated system. Now-a-days Energy loss reduction process is vital role in the distribution side.

To propose the Energy loss compensation by PHEV charging station using Intelligent Integrated

system (IIS). This method is implemented in IEEE 14 radial bus system. PHEV charging station

can able to inject real power in weakness bus with dynamic load performance. Intelligent

integrated system is to identify the energy loss in IEEE 14 radial bus system, then it process,

intelligent system integrated with the weakness bus, to inject real power to compensate the

energy loss in the grid.

Key words: phevs charging system, intelligent integrated system, IEEE14 bus system, power loss

reduction

International Journal of Pure and Applied MathematicsVolume 119 No. 12 2018, 15919-15929ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

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1. Introduction

Power system grids are usually increased due to the building of industries, utilization of power

by consumers and area expansion. Naturally in power girds having drawbacks like power loss,

voltage unbalance, low power factor result and voltage regulation issues[1,2]. Nowadays loss

reduction method plays a vital role in the distribution system. These losses should be minimized

with in short period time. For minimizing these losses we are having different type’s algorithm

and optimization process. Normally we are having distributed generation, capacitor replacement,

network reconfiguration, dstatcom placement methods to minimize these losses [3-6]. Plug-in

hybrid electrical vehicles (phevs) are a new for transportation and future technologies and power

sector and have many profits for economic and environment [7-12]. This paper suggests about

intelligent integrated system with plug-in hybrid electric vehicles charging system. Presently,

due to global warming, spoiling electrical vehicles are much appropriate. Many of the motor

industries are supporting the plug-in hybrid electric vehicle in the public. Plug-in hybrid electric

vehicle charging system is used to provide power to the grid for minimization of losses. This

phev charging system and grid system are observed by intelligent integrated system.

2. Proposed system

Intelligent integrated system observers the grid system state estimation data. Depending upon the

system state, intelligent integrated system reacts. PHEV charging station charges power from the

main grid. Whenever losses occur the intelligent integrated system observes the error in the grid

and the system sends information to the nearest plug-in hybrid electric vehicle charging station to

inject the power for loss minimization.

Block diagram for intelligent integrated system with plug-in hybrid electric vehicle charging

station is shown in figure 1.step 1 initializes the Intelligent Integrated system(IIS), these system

monitor the system grid as Power system parameters, then it process the parameters, it is identify

the energy loss and weak bus. Intelligent integrated system send the actuate signal corresponding

weak bus plug-in hybrid electric vehicle charging station to inject real power to compensate the

energy loss of power system grid.

International Journal of Pure and Applied Mathematics Special Issue

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Figure 1: Block Diagram Of Intelligent Integrated System

2.1 IEEE 14 bus radial distribution system

Figure 2: IEEE 14 bus radial distribution system

In this radial bus system we are going to place plug-in hybrid electric vehicle charging station at

bus 2, bus 3, bus 4 and bus 5. If any bus had effected with power loss these intelligent integrated

system will collects the information and sends the information to the charging station, here in

this charging station the energy has been stored in the battery whenever the loss occur these

charging station will injects the power to reduce the power loss in the distribution system [14].

2.2 Power Balance Equation

Pi(x)- PGi+ PDi=0

Qi(x)- QGi+ QDi=0

Note that Pi(x) and Qi(x) imply the functions that expresses flow from bus i into the gadget in

terms of voltage magnitudes and angles.

While PGi, PDi, QGi, QDi suggest the generations and demand on the bus.


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For a device with a slack bus and the relaxation PQ buses, power go with the flow hassle is to

use the energy stability equations to remedy for the unknown voltage magnitudes and angles in

phrases of the given bus generations and demands, after which use solution to calculate the real

injection on the slack bus.

2.3 Load Factor

Normally we know how load factor will be measured. Load factor is described as the overall

load divided with the aid of the peak load in a precise term. It is a degree of the usage rate, or

performance of electrical power usage; a low load thing suggests that load is not setting a strain

on the electrical device, whereas customers or turbines with that put greater of a pressure on the

electric distribution will have a excessive load thing [13].

Load factor=

2.4 Demand Factor

The ratio of real most call for at the machine to the overall rated load linked with the gadget is

known as demand thing. It is usually less than solidarity. Following system is used to calculate

call for component.

Demand factor=

2.5 Input and Output Variable Bus

Most often, the design of a fuzzy controller is characterized by the fuzzy methodology as

described above. For designing a fuzzy manipulates method from scratch kind of heuristic

methods are to be had. If no informed or operator is to be had, one can't sort out the design

trouble with out some knowledge, natural a mathematical model, of the plant [15-16]. The

controller of the crane has been designed and demonstrated making use of simulation stories with

the aid of the next time-honored method:

Identification of the valuable input and output variables of the controller, i.e. Alternative

of the linguistic variables,

setting of the feasible stages of the input and output values, i.e. scaling of the linguistic

variables,

definition of significant linguistic phrases and their membership capabilities for every

linguistic variable,

developing the rule base, and


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Simulation of the closed loop if feasible or testing at the plant website online.

Figure 3: Input and Output Variable Bus

Fuzzy good judgment systems compact with the fuzziness of the enter and output variables with

the aid of essential fuzzy numbers and fuzzy units that may be conveyed in linguistic variables

(e.g. Small, medium and large).

Fuzzy rule-primarily based method to moulding is mainly established on out loud formulated

policies overlapped for the duration of the limitation space. They use mathematical interruption

to address difficult non-linear associations.

A number of current structures want the policies to be put into words by an expert. Though

guidelines may be additionally generated robotically on the idea of numerical records describing

a positive phenomenon [17].

2.6 Fuzzy Rules for PHEV Optimization

The PHEV optimization of loss by using Fuzzy logic rules. This fuzzy logic vitality organisation

scheme of plug-in hybrid electric vehicles (PHEVs) to make a power splitting between the

charging stations to electric grid. These are good at distributing with model doubtfully and

difficult decisions. This logic controls have been proposed by so many scholars for vehicle

control and energy management.

Fuzzy guidelines are linguistic IF-THEN- structures that have the general form "IF A THEN B"

wherein A and B are (gatherings of) propositions containing linguistic variables. A is known as

the idea and B is the effect of the guideline. In impact, the use of linguistic variables and fuzzy

IF-THEN- guidelines activities the tolerance for fuzziness and improbability. In this recognize,

fuzzy common sense mimics the vital potential of the human thoughts to summarize statistics

and cognizance on choice-applicable statistics.

The below table (1) shows the Rules that are used in the Intelligent Integrated System (IIS), the

table below shows the system of various situations tested with the maximum extreme values to


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determine the working nature and efficiency of the whole charging system. Hence it was

consider being most efficient transmission with minimal losses.

Table 1: Fuzzy Rules Base Matrix

Figure 4: Fuzzy Rules for PHEV Optimization


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Figure 5: Loss Minimization State Simulation Result

3. Simulation result

3.1 Losses state:

When losses occur in the power system, PHEV charging system injects power to the network for

reducing the loss in grid by using intelligent integrated system (IIS).

The amounts of input applied to the PHEVs charging system and required output from the input

with the principle used are clearly seen from this figure 5.

Table2: Input variables Table 3: Output variables

The table 3 shows the extreme quantity of power in the form of per unit value has been stored in

the PHEVs charging station. Whenever the losses occur in the IEEE 14 radial bus system these

PHEVs charging stations are sends energy for the each and every bus in the radial bus system

that can observe in the table 2.

4. Hardware Design


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Figure 6: Hardware design of the PHEVs charging station

From the above figure 6 we can observe the LCD display, diodes, microcontroller, nobs and

LEDs. By using these nobs we are going to vary the source and load in the form of percentage

whenever we have changed we can observe the how much power loss has been occurred in the

grid. If the power loss will be less that 20% the PHEV charging station 1 will be activated to

minimal the power loss in the grid. Whenever loss will be more than 20% and less than 40% the

PHEV charging stations 1, 2 are activated to minimal the power loss in the grid. Whenever loss

will be more that 40% by changing nobs the PHEV charging station 1, 2, 3 are activated to

minimal the power loss in the grid. From figure 7 we can observe result of the hardware design

and which charging stations have been activated we can observe.

Figure 7: Resultant of the hardware design

5. Conclusion

No primary problems are probable to be encountered for several decades in supplying the energy

to rate PHEVs charging station, so long as most motors are charged at night. Generation and

transmission of energy during off-peak hours ought to be ok for lots millions of PHEVs charging

stations, although some distribution circuits may additionally want upgrading if they are to serve


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clusters of PHEVs charging stations. Encouraging PHEV charging station proprietors to charge

their gird whenever the power loss will occur each fee schedules that reward time-suitable

charging and device which can monitor or maybe manipulate -time of use

Reference:

[1] Yuvaraj, T., Devabalaji, K. R., & Ravi, K. (2015). Optimal placement and sizing of

DSTATCOM using harmony search algorithm. Energy Procedia, 79, 759-765.

[2] Devabalaji, K. R., Imran, A. M., Yuvaraj, T., & Ravi, K. (2015). Power loss minimization in

radial distribution system. Energy Procedia, 79, 917-923.

[3] Devabalaji, K. R., Yuvaraj, T., & Ravi, K. (2016). An efficient method for solving the

optimal sitting and sizing problem of capacitor banks based on cuckoo search algorithm. Ain

Shams Engineering Journal.

[4] Yuvaraj, T., Ravi, K., & Devabalaji, K. R. (2017). Optimal allocation of DG and

DSTATCOM in radial distribution system using cuckoo search optimization

algorithm. Modelling and Simulation in Engineering, 2017.

[5] Yuvaraj, T., Devabalaji, K. R., & Ravi, K. (2018). Optimal Allocation of DG in the Radial

Distribution Network Using Bat Optimization Algorithm. In Advances in Power Systems and

Energy Management (pp. 563-569). Springer, Singapore.

[6] Yuvaraj, T., Ravi, K., & Devabalaji, K. R. (2017). DSTATCOM Allocation in the Radial

Distribution Networks with Different Stability Indices using Bat Algorithm. GAZI UNIVERSITY

JOURNAL OF SCIENCE, 30(4), 314-328.

[7] Hariharan, R., & Rani, P. U. (2017, March). Blackout restoration process by PHEV charging station integrated system using virtual instrumentation. In Signal Processing, Communication and Networking (ICSCN), 2017 Fourth International Conference on (pp. 1-5). IEEE.

[8] Hariharan, R. (2012). Design of controlling the charging station of PHEV system based on

virtual instrumentation.

[9] Baharin, N., & Abdullah, T. A. R. T. (2013). Challenges of PHEV penetration to the

residential network in Malaysia. Procedia Technology, 11, 359-365.

[10] Taylor, J., Maitra, A., Alexander, M., Brooks, D., & Duvall, M. (2009, July). Evaluation of

the impact of plug-in electric vehicle loading on distribution system operations. In Power & Energy Society General Meeting, 2009. PES'09. IEEE (pp. 1-6). IEEE.

[11] Farmer, C., Hines, P., Dowds, J., & Blumsack, S. (2010, January). Modeling the impact of increasing PHEV loads on the distribution infrastructure. In System Sciences (HICSS), 2010 43rd

Hawaii International Conference on (pp. 1-10). IEEE.

[12] Grahn, P., Munkhammar, J., Widén, J., Alvehag, K., & Söder, L. (2013). PHEV home-

charging model based on residential activity patterns. IEEE Transactions on power Systems, 28(3), 2507-2515.


15927

[13] Yuvaraj, T., Ravi, K., & Devabalaji, K. R. (2015). DSTATCOM allocation in distribution networks considering load variations using bat algorithm. Ain Shams Engineering Journal.

[14] Committee, D., I. Power and E. Society, 2009. IEEE recommended practice for monitoring electric power quality. IEEE Std 1159TM-2009.

[15] Hariharan, R., & Rani, P. U. (2006). GRAPH THEORY BASED POWER SYSTEM RESTORATION USING LABVIEW.

[16] R.Hariharan, P.Usha Rani, s.usman “A Complete restoration methodology using Virtual

instrumentation” IJCTA, 9(2) 2016, pp.313-318, International Science Press.

[17] Hariharan, R. (2013, February). Design of controlling the smart meter to equalize the power

and demand based on virtual instrumentation. In Power, Energy and Control (ICPEC), 2013 International Conference on (pp. 505-508). IEEE.

[18] Hariharan, R., and P. Usha Rani. "Optimal Generation Start-Up strategy for Blackout

Restoration Using Virtual Instrumentation." Indian Journal of Public Health Research &

Development 8.4 (2017).

[19] K.Nanthini, 2D.Prabhakaran, 3C.Ramkumar AN INTELLIGENT SYSTEM FOR

FORESTFIREDETECTION USING ANISOTROPICDIFFUSION AND FUZZY C-MEANS

ALGORITHM International Journal of Innovations in Scientific and Engineering Research (IJISER)


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POWER LOSS REDUCTION BY PHEV CHARGING SYSTEM · Block diagram for intelligent integrated system with plug -in hybrid electric vehicle charging station is sho wn in figure 1.step 1