POWER FACTOR IMPROVEMENT IN THE RUBBER INDUSTRY USING FUZZY LOGIC

GRD Journals | Global Research and Development Journal for Engineering | International Conference on Innovations in Engineering and Technology (ICIET) - 2016 | July 2016

e-ISSN: 2455-5703

Power Factor Improvement in The Rubber Industry using Fuzzy Logic 1P.Loganthurai 2L.Jayamala 1

Professor 2Student Department of Electrical and Electronics Engineering 1,2 K.L.N College of Engineering Pottapalayam , Tamilnadu, India 1,2

Abstract Indian industry is paying one of the highest power tariffs in the world due to the poor quality and lack of power generation, transmission and distribution management. Recent advancements for higher power generation, lesser transmission and distribution cost and saving of energy can benefit significantly by improving the power system through energy efficiency at all levels. The electric billing is a two part tariff, the first part is unit (kWh) cost and the second part is maximum demand (kVA) cost. The maximum demand cost can be reduced by improving power factor. The overall power factor of modern industrial plants is low because of induction motors absorbing more reactive power. When power factor is improved, the reactive power necessary to supply the useful (real) power is automatically reduced. This results in the reduction of maximum demand (kVA) of the entire industrial plant. All the H.T (High Tension) consumers should maintain the power factor above 0.95 in order to avoid the penalization in their electricity bill. In many industries, they are maintaining good power factor (PF) at the incoming terminal of power lines using automatic power factor corrector but measuring power factor at the load side, it is low. Thus, in this proposed work the distributed power factor correction method is used to maintain good power factor at the load side. To select the suitable size of capacitor for compensating the reactive power requirement of induction motors, the Fuzzy Logic (FL) has been used.

Keyword- DSM, Power factor improvement, Fuzzy logic __________________________________________________________________________________________________

I. INTRODUCTION In recent years availability of power in India has both increased and improved but demand has consistently outstripped supply and substantial energy and peak shortages prevailed in 2009-10. There are also various estimates of 25000 to 35000 MW of power being produced by diesel generation to meet the deficits [1]. Electricity shortage is not the only problem. Its spread is an equally serious issue. Although such an approach is essential, there is growing concern about other aspects of power generation such as social, environmental and technological benefits and consequences of the energy source selection. Accordingly, energy audit is an effective means assisting facility managers to develop plans and to achieve goals in energy saving. Moreover, the audit can eliminate the barriers of energy efficiency even in case of inadequate information [2].

Electrical energy management (EEM) is a topic that has reached specific concern in the twenty- first century due to its contribution to economic development and environmental advancement [3].

II. DEMAND SIDE MANAGEMENT Demand Side Management (DSM) refers to a process by which electric utilities, in collaboration with consumers, achieve predictable and sustainable changes in electricity demand. These changes are effected through a permanent reduction in demand levels through energy efficiency as well as time related reductions in demand levels through load management. The practice of Demand Side Management has evolved over the past three decades in response to lessons learned from implementation in different global settings, and also in response to the changing needs of restructured power markets [4].

There are a number of ways to incentives demand reduction and energy conservation. These include,  Tariffs  Technology  Rebates  Quotas  Customer education etc.

DSM programs that emphasize tariffs are aimed at introducing a negative slope in the demand curve in order to let demand and supply balance out at a reasonable price of electricity during tight market conditions[5]. Programs involving demand response to tariffs fall into one of two categories:  Load curtailment programs that pay the customer for reducing peak load during critical times

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Power Factor Improvement in The Rubber Industry using Fuzzy Logic (GRDJE / CONFERENCE / ICIET - 2016 / 026)

 

Dynamic pricing programs that give customers an incentive to lower peak loads in order to reduce their electricity bills.

Both types of programs are largely designed to relieve peak capacity constraints [6].

A. Objectives of DSM Demand-side management (DSM) has been traditionally seen as a means of reducing peak electricity demand so that utilities can delay building further capacity. The main objectives of DSM by Utilities,  Financial benefits  Political benefits  Socio-Economic benefits  Improved quality of electrical services  Avoiding the need for power cuts and rolling blackouts  Improving voltage stability in distribution  DSM strives to improve the efficiency of energy use without any reduction in the services that the energy provides  Conservation includes energy efficiency but also adds reducing energy use through the reduction of nonessential services.

B. Types of DSM Practices 1) Energy Conservation Promote the application of energy efficiency measures, equipment such as lighting, air- conditioning, motors in different customer segments.

2) Load Management Control, curtail or shift the load demand periodically on a daily or seasonal basis according to peak demand requirement or constraints.

3) Increased Electricity Demand It may be used periodically on daily or seasonal basis in order to fill the valleys when the network is fed by run-of-river power stations.

C. Factors Involved In DSM Strategy DSM has numerous advantages but still implementation of DSM strategy is facing following obstacles:  Since electricity price level and its structure are mainly established by government especially electrical price structure.  Government as driving force is responsible for implementation of DSM programmes, but their supporting policies and regulation lag behind the practical situation.  With the deregulation of energy sector, new problem arises which is come who is responsible to invest and sharing the benefit of DSM.  Low awareness of energy efficiency and DSM programmes.  Most of customers are less literate, therefore, not able to understand the future problems.  Since energy efficient appliances and control drives are costly than standard appliances, hence consumers are not showing interest to buy them.

III. ANALYSIS FOR MAXIMUM DEMAND AT RUBBER INDUSTRY India is the 4th largest producer and 2nd largest consumer of natural rubber in the world. The rubber industry comprise of tire and non-tire industries with a turnover of Rs.63,000 crore in 2011-12 with a CAGR of 10% for last 3 years. The Indian rubber industry consists of around 5,500 units1 and is dotted with the presence of several small and tiny units. To ensure availability of skilled human resources to rubber industry, the Rubber Skill Development Council (RSDC) has been formed as a focused national level skill promoting entity under the aegis of National Skill Development Corporation (NSDC)[7].

Rubber is traditionally grown in India in the hinterlands of the South West Coast comprising of the state of Kerala and adjoining Kanyakumari District of Tamil Nadu (TN). Kerala is the single largest rubber producing state in India accounting for 91 per cent of total NR production. Table 1 shows the rubber production in India in different years [8].

In sharp contrast to the fall in production, the consumption rose by 4.0 per cent to 10, 20,910 tonnes during 2014-15 largely driven by the reasonably sound growth attained by the Indian economy and the domestic automobile industry. The consumption of NR grew by 4.4 per cent in the auto-tyre manufacturing sector and 3.3 per cent in the general-rubber-goods sector during the year under review [9]. Production of rubber goods consists of two basic steps: A. Production of rubber itself  Natural rubber is an agricultural crop  synthetic rubbers made from petroleum

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Power Factor Improvement in The Rubber Industry using Fuzzy Logic (GRDJE / CONFERENCE / ICIET - 2016 / 026)

B. Processing into finished goods consists of:  Mixing and compounding  Shaping  Cooling In these rubber industry there are four units are operating continuously. Each unit having three stage of process. They are named as mixing and compounding, shaping and cooling. Latex as found in nature is a milky fluid found in 10% of all flowering plant. It is a complex emulsion consisting of proteins, alkaloids, starches, sugars, oils, tannins, resins, and gums that coagulate on exposure to air. It is usually exuded after tissue injury. In most plants, latex is white, but some have yellow, orange, or scarlet latex.

These Latex are collected and mix with carbon black, sulfur and other chemicals is called stage one process. The mixture is milled and shaping is done in second stage of process. The shaped rubbers are cooled in the third stage.

Every stage has different loads and operating time. The constant load is to be maintained at 42.3 KW. The first stage requires 231 KW load and 240 seconds operating time. The second stage requires 112 KW load and operating time is 120 seconds.

The last stage such as cooling stage requires 6 KW load and 480 seconds of operating time. Every stage has 120 seconds gap for next process.

The industry is operating 20 hours for a day. To complete one cycle, it takes 18 minutes. Each and every stage takes 120 second gap and start the next cycle of process. Totally 66 cycles are completed in 19 hours and 48 minutes. To make an efficient operation, the maintenance gap is given to every 21 cycles. Maintenance gap required for every 21 cycle is 22 minutes.

IV. METHODOLOGY Now-a-days capacitors have been very commonly used to provide reactive power compensation in industrial plants to minimize kVA demand and to improve power factor. The capacitor draws a leading current and partly or completely neutralizes the lagging reactive component of the load current.

This raises the power factor of the load. Capacitors work as reactive current generators "providing" needed reactive power (kVAR) into the power supply. By supplying their own source of reactive power, the industrial users free the utility from having to supply it, and therefore the total amount of apparent power supplied by the utility is minimized. Therefore, capacitors can be utilised to reduce kVA and electrical costs. For three-phase loads, the capacitors can be connected in delta or star. Static capacitors are invariably used for power factor improvement in factories.

V. FRAMEWORK The optimum sizing of capacitors for compensation of lagging reactive power of induction motors results in maximizing the savings by minimization of kVA loading.

Fuzzy logic estimates the capacitor size by minimizing the following objective function. n

min S   P  jQ i

Where,

i 1

S P Q i

- Maximum demand function (KVA) - Real power (KW) - Reactive power (KVAR) - Capacitor rating (KVAR)

A. Constraints 1) Capacitor limit constraints Q Cmin  Q C  Q Cmax Where, Q Cmin 

Minimum rating of

Q Cmax 

Maximum rating of

capacitor capacitor

Q C  0.95 Q no load Where, Q no load  Reactive power drawn by the motor at no - load

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Power Factor Improvement in The Rubber Industry using Fuzzy Logic (GRDJE / CONFERENCE / ICIET - 2016 / 026)

2) Power factor limit constraints PFmin  PF  PFmax

Where, PF  Improved power factor PFmin - Minimum value of power factor PFmax - Maximum value of power factor The above equation is used to reduce maximum demand (KVA).

VI. RESULT ANALYSIS The proposed method is used to minimize the maximum demand by using capacitor placement whenever the power factor leading/lagging. After the minimization of maximum demand, the efficient operation is to be maintained. This operation does not affect the production. Figure 1 shows the load variation at different time period.

Fig. 1: Load curve for one cycle

In order to increase the power factor, the capacitor placement is essential. The capacitor ratings are taken into KVAR. After the placement of capacitor, the reactive power consumption is low and apparent power is also reduced. So the maximum demand is reduced and the power factor is obtained near unity. Figure 2 shows the power factor variation before and after capacitor placement.

Fig. 2: Power factor variation

Table 2 shows the apparent power variation depending upon the capacitor placement. Before the capacitor placement the reactive power consumption of the rubber industry is more. The effect of increasing reactive power reduces the power factor and increases the apparent power [10]. So the penalty is also increases.

VII.

FUZZY APPROACH

The optimum size of the capacitor is determined by using Fuzzy logic approach. The proposed Fuzzy logic based capacitor placement (FLC) scheme for the identification of suitable capacitor sizing is shown in Figure 7 and is simulated in MATLAB/Simulink environment. The existing power factor and kVA loading of the induction machines are the inputs to the Fuzzy Inference System (FIS), which decides the suitable size of the capacitors to be installed in each location of the selected rubber industry. Table 3 shows the fuzzy rules and these are implemented in fuzzy expert system. The input and output variables are assigned to triangular membership function is shown in figure 3 and figure 4.

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Power Factor Improvement in The Rubber Industry using Fuzzy Logic (GRDJE / CONFERENCE / ICIET - 2016 / 026)

Fig. 3: Membership function of power factor

Fig. 4: Membership function of Reactive power

Very low Low

KVAR LOADING Very low Low Medium Very low Low Medium Very low Low Medium

High High High

Medium

Very low

Low

Medium

High

High

Very low

Low

Medium

High

Very high

Very low

Low

Medium

High

POWER FACTOR

AND

Table 3: Fuzzy rules

Fig. 5: Rule viewer

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Power Factor Improvement in The Rubber Industry using Fuzzy Logic (GRDJE / CONFERENCE / ICIET - 2016 / 026)

Fig 6: Surface structure

Figure 5 shows the rules for the given input and output variables. Figure 6 shows the surface structure for the given variables. Figure 7 shows the Matlab simulink model. The fuzzy logic controller choose the suitable capacitor size of the corresponding input variables.

C

Q

(KVAR)

(After C)

298.397

150

5.708

7.99

0.75

0.6578

0.746 111.9 0.746

0.7 0.75 0.75

M2

0.746

M3

Motor

P(KW)

P.F

Q(KVAR)

S(KVA)

S(KVA)

P.F

300 HP

223.8

0.75

197.369

47.369

228.764

0.9783

Hyd.pump

5.595

0.7

3

2.708

6.216

0.9

Oil pump

0.746

0.9946

0.5

0.1578

0.7625

0.9783

Dust coll 150 HP M1

0.761 98.684 0.1578

1.1357 149.1987 0.9946

0.5 75 0.5

0.261 23.68 0.1578

0.7904 114.382 0.7625

0.9438 0.9783 0.9783

0.7

0.761

1.1357

0.5

0.261

0.7904

0.9438

0.746

0.75

0.1578

0.9946

0.5

0.1578

0.7625

0.9783

M4

0.746

0.7

0.761

1.1357

0.5

0.261

0.7904

0.9438

M5

0.746

0.75

0.1578

0.9946

0.5

0.1578

0.7625

0.9783

M6

0.746

0.7

0.761

1.1357

0.5

0.261

0.7904

0.9438

M7

0.746

0.75

0.1578

0.9946

0.5

0.1578

0.7625

0.9783

M8

0.746

0.7

0.761

1.1357 Table 4

0.5

0.261

0.7904

0.9438

Fig. 7: Simulink model

VIII. CONCLUSION The work has been carried out to find the optimal sizing of capacitors to be placed in parallel to the motor terminals to minimize the reactive power requirement of the induction motors. This has obviously resulted in reduced kVA loading and improved power factor of the induction motors. The maximum annual savings has been obtained considering the capacitor cost. The cost of installed capacitors can be paid back within few months, and after that, the savings will reduce the total operating costs. The above problem

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Power Factor Improvement in The Rubber Industry using Fuzzy Logic (GRDJE / CONFERENCE / ICIET - 2016 / 026)

has been solved by using Fuzzy logic techniques. The developed algorithms are effective in deciding the size of capacitors at various locations of the rubber industry.

IX. ACKNOWLEDGEMENT Authors thanks The Principal, K.L.N College of Engineering for providing the facilities in carrying out this project work.

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[3] K. Hoinka and A. Ziebik , ' Mathematical model for the choice of an energy management structure of complex buildings ' , Energy,2010, vol. 35, pp. 1146-1156 [4] J. Terry Cousins , ' Using Time Of Use (Tou) Tariffs In Industrial, Commercial And Residential Applications Effectively.

[5] 'Assignment on Implementation & Impact Analysis of Time of Day (TOD) tariff in India ' , Price water house Coopers India Private Limited, Regulatory Economics Advisory.

[6] P. Dabur, G. Singh and N. K. Yadav, ' Electricity Demand Side Management: Various Concept and Prospects ' , International Journal of Recent Technology and Engineering (IJRTE),2012, ISSN: 2277-3878,Volume-1, pp. 1-6.

[7] Introduction to Rubber Industry ,(2013), Rubber Skill Development Council 2013.

[8] S. Senthilkumar , ' Evaluation and growing prospectus of indian rubber industry ' , Asian journal of multidimensional research, 2012, vol.1, pp. 47-59.

[9] M. G. Sathees Chandran Nair,' Rubber Statistical News ' , vol . 73, No . 12 [10] K . Sapna , G . Vijay Kumar , 'Power Factor Improvement of Induction Motor by Using Capacitors', International Journal of Engineering Trends and Technology (IJETT), 2013,vol. 4 , pp. 2960-2970.

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