small hydropower fuzzy logic mcda

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Volume 4 No. 1 January 2015

E.S.I.A. AND ENVIRONMENTAL AUDIT

FOR SMALL HYDRO POWER PROJECTS –

M.C.D.M. APPROACH

Priyabrata Adhikary Pankaj Kr Roy Asis Mazumdar

Mechanical, G.M.I.T. (WBUT), Kolkata S.W.R.E., Jadavpur University, Kolkata

ABSTRACT

The purpose of determining environmental or social impact signicance for a project or doing environmentalaudits is to place value on its environmental and socio-economic effects. E.S.I.A. and E.A. are the processes

that judges whether impact signicance is acceptable or not in accordance with the scientic facts regardingenvironment, ecology and socio-economic effects described in “Environmental Impact Statements” (E.I.S.).

The purpose of this paper is to describe the development of the Multi Criteria Decision Making (MCDM) orFuzzy Logic approach for E.S.I.A. or E.A. decision making and its usefulness for experts, environmental

engineers or managers by illustrating its application both manually and by software to environmentalperformance appraisals of renewable energy generation. Article illustrates an example of MCDM or FuzzyLogic based E.S.I.A. or E.A. of small hydro power generation in Himalayan Region in order to prove the

usefulness of the model. To the best of the authors knowledge this novel MCDM or Fuzzy Logic approachof E.S.I.A. or E.A for small hydro power generation is absent in environmental uid mechanics literatures

due to its assessment complexity.

Keywords : Fuzzy logic; small hydro power plant; S.H.P.; E.S.I.A.; Environmental Audit; M.C.D.M.

INTRODUCTION

The total installed power generating capacity in India

during June 2014 was reported as 2,49,488 MW out

of which only 40,730 MW is through hydro power. Thecost of clean-green-friendly hydroelectricity is relativelylow, i.e., Rs.1.5/kW to Rs. 2.5/kW, compared to others

and thus making it a competitive source of renewableenergy. We know from a small hydro electric powergeneration project, consumers require power at rated

frequency and voltage. To maintain these parameterswithin the prescribed limits, various controls are

required. Voltage is maintained by control of excitationof the generator and frequency is maintained by

eliminating mismatch between generation and loaddemand as a result of the river ow and head throughturbine. Power can be controlled by controlling ow

through turbine and dams are maintained safely thrucontrolling spillway gates.

In water or hydro power generation “Water-the whitecoal” is used non-destructively by the force of gravity,which is a totally carbon-free and inexhaustible resource

to generate power. Naturally owing rivers and streams,ow towards lesser elevation and thus provide suitable

site for hydropower generation. The falling water ofwaterfalls can be used directly to drive turbines due to its

sharp elevation. If the natural fall is not steep, a head is

created articially by damming the river or stream, makinga reservoir, and diverting its water to a nearby location

with a penstock where the water is made to fall undergravity, driving a turbine for power generation.

Hydro power became increasingly popular as an

advantageous clean – green – friendly renewableenergy resource. Unlike thermal power plants there is

no pollution of gaseous or y-ash emissions in-caseof hydropower. Again in nuclear power plants, thereare radioactive wastes. The water used in hydro power

generation remains fully intact and utilizable or reusableafterwards. Setting up of reservoirs by damming rivers had

also appeared to be a safe and wise strategy because itpromised to enable utilizing the river-ow to a maximum

extent by ood control, ensure year round availability ofwater for irrigation-cultivation, navigation, entertainment,

sh culture etc.

Latter, it was observed that interference with the hydrologyof a river by putting a dam for power generation and

converting the upstream portion into a big reservoir didnot occur without serious impacts in all the three major

dimensions of ecosystem or environment – physical,chemical and biological. Rivers and their ecosystemsused for hydropower generation were fundamentally

transformed due to the fragmenting of channels andalteration in river ow. Degradation in water quality,

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evaporation water losses, bank or coastal erosion, harm

to sheries, siltation and loss of storage capacity, invasionby weeds, GHG emission, and facilitation of the growth of

mosquitoes are most common effects. Socio-economicadverse impacts are caused by hydropower projects in

all the four habitats associated with the projects – the

catchments area, the articial reservoir, the upstreamas well as the downstream reaches. Several of them

are traumatic, as they resulted from the displacement ofa large number of people, and also due to the sudden

change in the patterns of natural resource availability andtheir culture or heritage.

Environmental and social impact assessment

(E.S.I.A.) was made legislation in USA in the NationalEnvironmental Policy Act (NEPA) 1969 though it started

in 1960s. There is a need for stronger foundation ofEIA practice through training for managers, expert

practitioners and environmental engineers till date. TheMinistry of Environment and Forests (MoEF) of India

has been in a great effort in EIA in India. The CentralPollution Control Board is responsible body for this.E.S.I.A. needs a signicant amount of primary and

secondary environmental and socio-economic data.The primary data are those which need to be collected

in the eld to dene the status of the environment (likeair quality data, water quality data etc). The secondarydata are those data which have been collected from

past records gathered over the years and can be usedto understand the existing environmental scenario.

The E.S.I.A. studies are conducted over a shortperiod of time and therefore the understanding of

the environmental trends, based on a few months ofprimary data, has major limitations. Ideally, the primarydata has to be considered along with the secondary

data for complete understanding of the existingenvironmental and socio-economic status of the area.

Environmental Information Centre (EIC) has been setup to serve as a professionally managed clearing house

of environmental information in India.

E.S.I.A. experience in India indicates that thelack of timely availability of reliable and authentic

environmental data has been a major problem inachieving the full benets of it. The environment being

a multi-dimensional topic, various organisations are

involved in collection of those environmental data.Further, those data is not available in ready made orvalue added forms that can enhance the quality ofthe E.S.I.A. This in turn adversely affects as the time

delay and wastage of efforts and ultimately the delayin timely environmental clearances by the regulators.

E.S.I.A. of a hydro power generation project can bedened as the systematic identication and evaluation

of the potential impacts or effects (both positive andnegative) of proposed hydro power project. It includes

plans, programs, or legislative actions relative to the

physical, chemical, biological, ecological, cultural andsocio-economic components of the total system. The

E.S.I.A. process essentially involves:

• Screening

• Scoping

• Planning

• Evaluation and execution

Screening is the process of identifying the signicantenvironmental or social impacts. Scoping determineswhich components are to be included in the E.S.I.A. and

alternatives to be considered. This is followed by planand execution. A baseline condition, namely the existing

environment, is recognized as a standard or benchmarkby which the future conditions of project alternatives are

compared for taking decision.

On the other hand, Environmental audit (E.A.) is a

general term that can reect various types or evaluationsintended to identify environmental compliance and

management system implementation gaps, along with

related corrective actions. In this way they perform ananalogous (similar) function to nancial audits. Thereare generally two different types of E.A.: compliance

audits and management systems audits. Complianceaudits tend to be the primary type in the US or within

US-based multinationals. As the name implies, theseaudits are intended to review the site’s/company’s legal

compliance status in an operational context. Complianceaudits generally begin with determining the applicable

compliance requirements against which the operations

will be assessed. This tends to include federal regulations,state regulations, permits and local ordinances/codes.

In some cases, it may also include requirements withinlegal settlements. Compliance audits may be multimedia

or programmatic.

Audits are also focused on operational aspects of acompany/site, rather than the contamination status

of the real property. Assessments, studies, etc.that involve property contamination/remediation are

typically not considered an E.A. The term “protocol”means the checklist used by environmental auditors

as the guide for conducting the audit activities. There

is no standard protocol, either in form or content.Typically, companies develop their own protocols

to meet their specic compliance requirements andmanagement systems. Audit rms frequently develop

general protocols that can be applied to a broad rangeof companies/operations.

Conducting an E.A. is no longer an option but a sound

precaution and a proactive measure in today’s heavilyregulated environment. Indeed, evidence suggests that

E.A. has a valuable role to play, encouraging systematic

E.S.I.A. and Environmental Audit for Small Hydro Power Projects – M.C.D.M. Approach



26 IASH Journal


incorporation of environmental perspectives into many

aspects of an organisation’s overall operation, helpingto trigger new awareness and new priorities in policies

and practices.

An E.A. is typically undertaken in three phases:

• Pre-audit

• On-site audit

• Post-audit

Each of these phases comprises a number of clearlydened Objectives. Each objective is to be achieved

through specic actions, and these actions yielding resultsin the form of outputs after each phase.

Among the numerous M.C.D.A. or M.C.D.M. methodsavailable for E.I.A. or E.A. of energy management analysis,the most prevalent are Macbeth, AHP, Promethee,

Electre, and MCDM or Fuzzy Logic as observed. It uses asubjective assessments of relative importance converted

to a set of overall scores (weights), arranging in this waythe structure of the problem in a hierarchy way. MCDM

or Fuzzy Logic can be one of the most powerful decisionanalysis methods in industrial engineering. In this paper,an applied environmental engineering problem is solved

using Fuzzy Logic both manually and thru MATLAB FISeditor for validation.

MATERIALS AND METHODS

E.S.I.A. as well as E.A. methods need specificparameters and variables to be measured to estimate

intensity of impact indicators. However many of the

environment impact properties cannot be measuredon a scale e.g. ecology quality, lifestyle quality,

social-economic acceptance etc. and moreover theseindicators are very imprecise and subjective. Thus

to assess the impacts we may need to take the helpof information from similar E.S.I.A. as well as E.A.,

available database, expert criteria, sensitivity of affectedpopulation or area. etc. To treat this information, whichis generally subjective, inaccurate and imprecise, we

need approximate reasoning methods such as FuzzyLogic or fuzzy sets, which can be utilised effectively.

Such semi-structured assessment or decision-makingin E.S.I.A. as well as E.A. is a combination of both

standard solution procedures and human judgment.E.S.I.A. as well as E.A. of water power generationis also a complex semi-structured decision-making

process. Hence it should not only consider thescientific aspect (quantitative decision-making) but

also reflect the response of political values and socialacceptability (qualitative decision-making).

For E.S.I.A., the term Environmental Impacts or Effects-

Input relates to Biodiversity, Erosion, and Water Qualityetc. Again the term Social Impacts or Effects-Input relates

to Employment, Visual impact, Noise pollution etc. Here

output will be Project Feasibility.

For E.A., the term Project Strength-Input relates to its

Size, Planning, and Scheduling etc. Again the termProject Impact-Input relates to Environmental, social andeconomical etc. Here output will be Project Rank for the

existing one.

THEORY AND CALCULATIONS

Fuzzy logic : The main advantage of the MCDM or

Fuzzy Logic method is to control the processes thatare too complex to be mathematically modelled. The

membership functions must be optimally determinedto design an efcient MCDM or Fuzzy Logic for aproblem. Many factors related to Run-off River or

hydro power are subjective and difcult to quantify inthis type of process such as Water Level or Depth is

at “Below Danger Level-Danger Level-Above DangerLevel”. Similarly the water ow rate is “Slow-Normal-

Fast”, “Standard – High – Maximum” etc. Still MCDMor Fuzzy Logic enables the evaluator or the decisionmaker to incorporate this information in the environment

performance evaluation system which is imprecise,vague and subjective. Therefore, the MCDM or Fuzzy

Logic method is a very suitable method for small hydroelectric power generation problem. The rule base and

membership functions have a great inuence on theperformance of fuzzy logic. The fuzzy linguistic variableperformance can be easily characterized by common

terms as: “Good – Moderate – Bad; Strong – Average– Weak; High-Medium-Low” etc. Each term is called a

linguistic modier. Hence a fuzzy set is formed when alinguistic variable is combined with a linguistic modier.

Fuzzy arithmetic can be solved either manually or inMATLAB Software.

Application of MCDM or Fuzzy Logic in hydro power

comprised in three stages:

1. Fuzzication

(Assigning input and output variables; Converts theCrisp Values to Fuzzy Set)

2. MCDM or Fuzzy Logic Rules and Fuzzy Inference

Methods

(Mamdani Inference Method)3. Defuzzication

(Converts the Fuzzy Set to Classical or Crisp

Values)

Fuzzication : Usually, a fuzzication of mathematicalconcepts is based on the generalization of these concepts

from characteristic functions to membership functions.Let us assume M and N be two fuzzy subsets of X.Intersection (MWN) and union (MUN) are dened as

follows:




(MWN)(x) = min(M(x),N(x)), (MUN)(x)=max(M(x),N(x)).

A simple fuzzication is usually based on min and

max operations. The triangular MF is a function thatdepends on three scalar parameters a, b and c. Fuzzy

sets and fuzzy operators are the subjects and verbsof fuzzy logic. The If-Then rule statements are used

to formulate the conditional statements that comprisefuzzy logic.

Ri: IF Xi is Ai AND Yi is Bi

THEN Zi is Ci

Where A and B are linguistic values dened by fuzzy setson the ranges X and Y, respectively.

The If-Then part of the rule “x is A” is called the antecedentor premise, while the then-part of the rule “y is B” iscalled the consequent or conclusion. µA and µB are

the membership function of the fuzzy sets of A and B,respectively.

The AND operator is used here because the two featuresmust be captured simultaneously and applied in decisionmaking by fuzzy logic.

Fuzzy Rules and Fuzzy Inference Methods : Mamdani

inference method, as dened for solving either manuallyor in MATLAB Software, expects the output membership

functions to be fuzzy sets. After the aggregationprocess, there is a fuzzy set for each output variable that

needs defuzzication. It enhances the efciency of the

defuzzication process because it greatly simplies thecomputation required. There are several ways to dene

the result of a rule, but one of the most common andsimplest is the “max-min” inference method, in which the

output membership function is given as the truth valuegenerated by the premise.

Defuzzication : It is the process of producing a crisp or

quantiable result or output in fuzzy logic, from obtainedfuzzy output sets and corresponding membership degrees.

Most common and useful defuzzication technique is“Centre of Gravity Method”. In “Centre of Gravity Method”,

the rst step of defuzzication typically is to “cut off”parts of the triangular graphs to form trapezoids (or other

shapes). Then, “The Centroid” of this shape, called thefuzzy centroid, is evaluated. The x coordinate of “TheCentroid” is the defuzzied value. The “Centre of Gravity

Method” is very popular and is used widely for calculation.To get the crisp value we use the following equitation for

the defuzzication: C.O.G. = ∑µi*µ(i) / ∑µi

Tabu Search Algorithm : It is a meta-heuristic local searchalgorithm that can be used for solving combinatorial

optimization problems (problems where an optimalordering and selection of options is desired). Tabu search

uses a local or neighbourhood search procedure toiteratively move from one potential solution to an improved

solution in the neighbourhood of, until some stoppingcriterion has been satised (generally, an attempt limit or ascore threshold). Local search procedures often become

stuck in poor-scoring areas. In order to avoid these pitfallsand explore regions of the search space that would be

left unexplored by other local search procedures, “TabuSearch” carefully explores the neighbourhood of eachsolution as the search progresses. The solutions admitted

to the new neighbourhood, are determined through theuse of memory structures. These memory structures form

what is known as the “Tabu List”, a set of rules and bannedsolutions used to lter which solutions will be admitted

to the neighbourhood to be explored by the search. In

its simplest form, a “Tabu List” is a short-term set of thesolutions that have been visited in the recent past. Thememory structures used in “Tabu Search” can be dividedinto three categories:

Short-term : The list of solutions recently considered. If apotential solution appears on this list, it cannot be revisited

until it reaches an expiration point.

Intermediate-term : A list of rules intended to bias thesearch towards promising area’s of the search space.

Again the fuzzy integer linear programming model can

be stated as:

The objective function maximizes the total return on

investment. It includes the scores of project rank,attractiveness, competitive advantage, feasibility and

nancial potential.




28 IASH Journal


Long-term : Rules that promote diversity in the search

process (i.e., regarding resets when the search becomesstuck in a plateau or a suboptimal dead-end).

The Fuzzy-Delphi Method : It is a semi-structuredcommunication method, developed as a systematic,interactive forecasting method which relies on experts,

engineers or managers. In the standard method, theexperts answer the queries in two or more phase.

After each phase, a facilitator provides an anonymoussummary of the experts’ detailed forecasts report.

Thus, experts are encouraged to revise their earlieranswers in light of the replies of other members of theirpanel. During this process the range of the answers

will decrease and the group will converge towardsthe “correct” solution. Finally, the process is stopped

after a pre-dened stop criterion. The mean or medianscores of the nal phase or rounds determine the nal

results. Delphi is based on the principle that forecasts(or decisions) from a structured group of individuals

are more accurate than those from unstructured groupsand has been mentioned as “collective intelligence”.The technique can also be adapted for use in meeting

individuals and is then termed as mini-Delphi.Delphi has been widely used for project or business

forecasting and has certain advantages over anotherstructured forecasting approach, prediction markets.First applications of the “Delphi Method” were in the

eld of science and technology forecasting. The mainobjective of “Delphi Method” was to combine expert

opinions on likelihood and expected development time,of the particular technology, in a single indicator. It

was also applied successfully and with high accuracyin project or business forecasting. Quantitativemethods produced errors of 10–15%, and traditional

unstructured forecast methods had errors of about 20%where “Delphi Method” had errors of 3-5%. Overall

the track record of the “Delphi Method” is mixed. Lateron, several extensions to the “Delphi Method” were

developed to address this drawback that takes intoconsideration the possibility that the occurrence of

one event may change probabilities of other eventscovered in the survey. Still the “Delphi Method” canbe used most successfully in forecasting single scalar

indicators. Despite these shortcomings, today the

“Delphi Method” is a widely accepted forecasting tooland has been used successfully for thousands ofstudies and researches.

CASE STUDY

Considering a project of “MCDM or Fuzzy Logic

approach to E.S.I.A. and E.A. study of a small hydropower generation project in the Himalayan region within

India”.

In the rst step of this method, the system variables,

inputs, and outputs are determined according to

expert’s views. The second step is to determinelinguistic values of system variables (inputs and

output). Then the fuzzy intervals of the input and outputvariables are characterized. According to the expert’s

poll and based on obtained data of the measurement,

past experiences and calculation in the workplace,their membership function and other parameters

are obtained. The linguistic variables, their linguisticvalues and related fuzzy intervals are then tabulated

or dened. The most popular triangular membershipfunctions for all inputs and outputs revealed. As they

are symmetrical, evenly spaced and overlapping. Someexperimentation was done with different numbers andshapes of membership functions, but the increase

in complexity was not adequately rewarded by aperformance improvement. As already discussed for

E.S.I.A., the term Environmental Impacts or Effects-Input relates to Biodiversity, Erosion, and Water Quality

etc. Again the term Social Impacts or Effects-Inputrelates to Employment, Visual impact, Noise pollutionetc. Here output will be Project Feasibili ty. For E.A., the

term Project Strength-Input relates to its Size, Planning,and Scheduling etc. Again the term Project Impact-Input

relates to Environmental, social and economical etc.Here output will be Project Rank for the existing one.

Case-1: E.S.I.A. of SHP Project

Let us consider the following Membership Functions and

their denations for E.S.I.A. as shown:

MCDM or Fuzzy Logic Modelling-E.S.I.A. ofS.H.P.

Here we have considered following fuzzy conditions:

Denitions of Environmental Impacts:

Low trimf (0 15 35); Medium trimf (30 45 65); High trimf(60 75 100)

Denitions of Social Impacts:

Low trimf (0 15 35); Medium trimf (30 45 65); High trimf(60 75 100)

Now let us consider following condition:

Environmental Impact (63 Unit): Medium (0.1) & High (0.2)

Social Impact (32 Unit):

Low (0.15) & Medium (0.133)



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Rules red are 4, 5, 7 and 8 as shown:

Strength of rule 4: [0.1 ^ 0.15] = 0.1

Strength of rule 5: [0.1 ^ 0.133] = 0.1

Strength of rule 7: [0.2 ^ 0.15] = 0.15

Strength of rule 8: [0.2 ^ 0.133] = 0.133

Now, Output C.O.G.= ∑µi*µ(i) / ∑µi

= (65*0.1+0.1*20+0.15*65+0.133*65)/ (0.1+0.1+0.15+0.133)

C.O.G. = 56% (Approx) i.e. “Medium”.

Validating above calculation through MATLAB FIS

Editor

Inputs : E.S.I.A. methods need specific parametersand input variables to be measured to estimate “projectfeasibility” as shown in Fig. 1(a) and Fig. 1(b). Here

two inputs are “Environmental Impacts” and “SocialImpacts”.

Fig. 1 (e) : Fuzzy Rule Viewer-Graphical

Surface Viewer-Graphical : Fig. 1(f) shows 3-D plot of 2

inputs i.e. “Environmental Impacts” and “Social Impacts”for their 1 output i.e. “Project Feasibility”.


Fig. 1 (a) : Environmental Impacts-Input

Fig. 1 (b) : Social Impacts-Input

Rule Editor : In this study, total number of active rulesobtained is equal to 9 rules (= 32 = pq ; where p = maximum

number of overlapped fuzzy sets and q = number ofinputs) as shown in Fig. 1(c). The rules are based on

“Mamdani Inference Method”.

Fig. 1 (c) : Fuzzy Mamdani-Rule Editor

Output : Fig. 1(d) shows the relation of 2 inputs, i.e.

“Environmental Impacts” and “Social Impacts” for their 1output i.e. “Project Feasibility”.

Fig. 1 (d) : Project Feasibility-Output

Rule Viewer-Graphical : Fig. 1(e) shows the relation of 2inputs i.e. “Environmental Impacts” and “Social Impacts”

for their 1 output i.e. “Project Feasibility” through graphical

rule viewer. Result shows 57% i.e. “Medium – Output”.Hence we nd after validation that calculative accuracy ofthe fuzzy model is very reasonable as shown above.

Fig. 1 (f) : Fuzzy Rule Viewer-3D Surface



30 IASH Journal


Fig. 2(b) : Project Impacts-Input

Rule Editor : In this study, total number of active rules

obtained is equal to 9 rules (= 32 = pq ; where p = maximumnumber of overlapped fuzzy sets and q = number of

inputs) as shown in Fig. 2 (c). The rules are based on“Mamdani Inference Method”.

Case 2 : E.A. of SHP Project

Similarly as shown above let us consider the followingMembership Functions and their denations for E.A. asshown:

Calculation through MATLAB FIS Editor

Inputs : E.A. methods need specic parameters and input

variables to be measured to estimate “Project Rank” asshown in Fig. 2(a) and Fig. 2(b). Here two inputs are“Project Strength” and “Project Impacts”.

Fig. 2(a) : Project Strength-Input

Fig. 2(c) : Fuzzy Mamdani-Rule Editor

Output: Fig. 2(d) shows the relation of 2 inputs, i.e.“Project Strength” and “Project Impacts” for their 1 output

i.e. “Project Rank”.

Fig. 2 (d) : Project Rank-Output

Rule Viewer-Graphical : Fig. 2(e) shows the relation of 2

inputs i.e. “Project Strength” and “Project Impacts” for their1 output i.e. “Project Rank” through graphical rule viewer.

Result shows 11% i.e. “Low – Project Rank” for Input-1 asLow i.e. 20 Unit and Input-2 as High, i.e., 80 Unit.

Fig. 2 (e) : Fuzzy Rule Viewer-Graphical

Surface Viewer-Graphical : Fig. 2(f) shows 3-D plot of2 inputs i.e. “Project Strength” and “Project Impacts” fortheir 1 output i.e. “Project Rank”.



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Hydro electric power generation is one of the earliest

known renewable energy sources and hence have asignicant role in the economic-social development of

countries and they have found special importance dueto their relatively clean-green-friendly characteristics.

The model here is fundamental importance to

understand physical system. In this paper, E.S.I.A.or E.A. method based on MCDM or Fuzzy Logic is

proposed for the small hydro power generation project.Because optimization of the membership functions is

an important factor, for the success of optimum hydroelectric generation and reservoir control during various

power demand and overows, a TSA is also used inthis work. The simulation results show that the fuzzylogic-based control optimized by a TSA provides an

appropriate alternative and is not based on humanoperators. Therefore, the drawbacks of the human

based E.S.I.A. or E.A. study system do not exist in thismethod. Moreover, the parameters of the membership

functions are optimized by using a TSA and the degreeof automation of the fuzzy control system may increase.This work can be extended to develop a method for

relating fuzzy logic-linguistic variables with variousother renewable energy generations E.S.I.A. or E.A.

process in future.

ACKNOWLEDGEMENT

The authors declare that there is no conict of interests.

REFERENCES

1. Adhikary, P., Roy, P.K., and Mazumdar, A.: Safe and

efcient control of hydro power plant by fuzzy logic,IJESAT, Vol.2, Issue-5, pp. 1270-1277 (2012)

2. Adhikary, P., Roy, P.K., and Mazumdar, A.: MCDA

of manpower shift scheduling for cost effective hydropower generation, IJETED, Vol.7, Issue-2, pp. 116-

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521-528 (2012)

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generation for decentralized rural electrification,IJETT, Vol.4, Issue-4, pp. 507-511 (2013)

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logic based blade angle control of Kaplan turbine fora hydro power project, ICERTSD 2013 (BESUS),Paper No: ICERTSD-13-109 (2013)

Fig. 2 (f) : Fuzzy Rule Viewer-3D Surface

DISCUSSION

The purpose of determining environmental or social

impact signicance for a project or doing environmentalaudits is to place value on its environmental and socio-

economic effects. E.S.I.A. and E.A. are the processesthat judges whether impact signicance is acceptable

or not in accordance with the scientic facts regardingenvironment, ecology and socio-economic effects. Themain aim of the small hydro power generation and dam

or reservoir control system is to keep the system withinpredetermined ranges by adjusting the ow through

a spillway gate at the dam and inow through turbinevalves and guide vanes in any condition for safety as

well as efcient electricity generation without affectingthe environment and society. Triangular membership

functions are used because of their simplicity for fuzzyvalues. The rule base is intuitively constructed by ringoptimum no. of rules. Initially, membership functions

and rule base are dened randomly. Presently, TSAis used to choose the most appropriate rules and

parameter values characterizing the fuzzy membershipfunctions. The predictive accuracy of the fuzzy model isvery reasonable as shown in either manual calculation

or MATLAB FIS Editor. It was well understood that thedata scarcity in modelling SHP operation inuence

the estimation of proper release policies. But still fromthe very approximate data, the model is capable of

generating reasonably accurate result. These resultsdemonstrate that the MCDM or Fuzzy Logic is a veryuseful method for assessing and not enforced to

evaluate with a crisp number.

6. CONCLUSION

The present study intends to contribute for the

improvement of MCDM or Fuzzy Logic applicationthrough use of MATLAB FIS editor or manual

calculations for E.S.I.A. or E.A. hydro power generation.




32 IASH Journal


7. Adhikary, P., Roy, P.K., and Mazumdar, A.: Hydraulic

transient analysis of SMALL HYDROPOWERPROJECT: A MCDM application for optimum

penstock design, IWMSID 2013 (IIT-Bhubaneswar),Paper No: IWMSID / WRE / 16 (2013)

8. Adhikary, P., Roy, P.K., and Mazumdar, A.: Indian

small hydropower Project Planning And Development: A Review Of DSS Tools, IJERT, Vol.2, Issue-6, pp.1386-1391 (2013)

9. Adhikary, P., Roy, P.K., and Mazumdar, A.: Fuzzy

Logic based optimum penstock design: Elastic Watercolumn theory approach, ARPN-JEAS, Vol.8, Issue-7,

pp. 563-568 (2013)

10. Adhikary, P., Roy,P.K., and Mazumdar, A.: Optimum

selection of Hydraulic Turbine Manufacturer forSHP:MCDA or MCDM Tools, IDOSI-WASJ, Vol.28,

Issue-7, pp. 914-919 (2013)

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