INTERNATIONAL JOURNAL and Technology (IJCIET), ISSN 0976 – 6308  International Journal of Civil Engineering OF CIVIL ENGIN...
International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308(Print), ISSN 0976 – 6316(Online) Volu...
International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308(Print), ISSN 0976 – 6316(Online) Volu...
International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308(Print), ISSN 0976 – 6316(Online) Volu...
International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308(Print), ISSN 0976 – 6316(Online) Volu...
International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308(Print), ISSN 0976 – 6316(Online) Volu...
International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308(Print), ISSN 0976 – 6316(Online) Volu...
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Optimization of reservoir operation using neuro fuzzy techniques

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Optimization of reservoir operation using neuro fuzzy techniques

  1. 1. INTERNATIONAL JOURNAL and Technology (IJCIET), ISSN 0976 – 6308 International Journal of Civil Engineering OF CIVIL ENGINEERING AND (Print), ISSN 0976 – 6316(Online) Volume 4, Issue 2, March - April (2013), © IAEME TECHNOLOGY (IJCIET)ISSN 0976 – 6308 (Print)ISSN 0976 – 6316(Online)Volume 4, Issue 2, March - April (2013), pp. 149-155 IJCIET© IAEME: www.iaeme.com/ijciet.aspJournal Impact Factor (2013): 5.3277 (Calculated by GISI) © IAEMEwww.jifactor.com OPTIMIZATION OF RESERVOIR OPERATION USING NEURO- FUZZY TECHNIQUES S. K. Hajare Principal, Someshwar Polytechnic College, Someshwarnagar, Tal. Baramati, Dist. Pune ABSTRACT Water resource engineering is mainly concerned with planning, designing and operation of water resources system. This paper explores the use of soft computing tools for water availability and operation studies, deciding suitable advanced optimization techniques for understanding a real life study related to water resources engineering and extension of these techniques to reservoir operation studies Keywords: Water resource engineering; soft computing; optimization; ANN. I. INTRODUCTION Water resource Engineering is mainly concerned with planning, designing and operation of water resources system. Uncertainty in availability of water in space and time poses challenges for efficient planning and design of water resources systems. The basic techniques used in water resources systems analysis are optimization and simulation, where optimization techniques are meant to give global optimum solutions and simulation is a trial and error approach leading to the identification of the best possible solution. Optimization models are characterized by a mathematical statement of the objective function and a formal search procedure to determine the values of decision variables for optimizing the objective function (Gupta and Gupta Amit, 1994). The principal optimization techniques are, A) Linear programming B) Nonlinear programming C) Dynamic programming. 149
  2. 2. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308(Print), ISSN 0976 – 6316(Online) Volume 4, Issue 2, March - April (2013), © IAEME Water resources development is a production process and the purpose ofproduction is to convert a set of inputs to set of outputs, e.g. output for water resourcesare irrigation hydropower generation and flood damage alleviation and the examples ofinflow are natural stream flow. A reservoir operating policy is a sequence of decisions inoperational periods (such as months) specified as a functions of the state of thesystem.The state of the system in a period is generally defined by the reservoir storage atthe beginning of the period and the inflow to the reservoir during the period (Vedula andMujumdar, 2005)Once the operating policy is known the reservoir operation can bestimulated in time with a given inflow sequence. Traditionally reservoir operation isbased on heuristic procedures, embracing rule curves and to certain extent subjectivejudgment by the operator. As such it does not take into account the randomness orstochasticity of reservoir inflow and rainfall in the irrigated area, inter -seasonalcompetition for water among multiple croops.The conventional methods are based onmass curve analysis and time series analysis and have limitations such as. • Paucity of data and quality of data can be major impediment for reliability of results. • Inability to handle or manage a complex tasks under significant uncertainty. • Assumes a definite sequence of events as in rainfall data but it is subjected to considerable time variations • In many approaches the structures and parameters of the model usually do not have any physical significance. • Dose not considers the inception by the recognition that the human brain taking into account such as nonlinear modeling, classification association and control. In order to find remedies for such limitations it needs to explore use of softcomputing as well as advanced optimization tools and techrliques such as artificial neuralnetworks ,fuzzy logic, Neuro-fuzzy etc.II. POLICES FOR RESERVOIR OPERATION2.1 Standard Operating Policy The standard operating policy (SOP) aims to best meet the demands in each periodbased on the water availability in that period. It thus used no foresight on what is likely tobe the scenario during future period in a year. Let D and R represent respectively thedemand and release in a period. Let the capacity of the reservoir be K.Then the standardoperating policy for the periods is represented in fig.1. The available water in any periodis the sum of storages S at the beginning of the period and inflow Q during the period.The release is made as per the line OABC on the Fig.1 150
  3. 3. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308(Print), ISSN 0976 – 6316(Online) Volume 4, Issue 2, March - April (2013), © IAEME Fig.1: Standard operating policyAlong OA : Release = water available; Reservoir will be empty after release.Along AB : Release = demand; Excess water is stored in the reservoir (fill up phase)At.A : Reservoir is empty after release.At.B : Reservoir is full after release.Along BC : Release = demand + excess of availability over the capacity (spill) The release in any time period is equal to the availability ,S + Q or demand, Dwhichever is less as along as the availability, does not exceed the sum of the demand and thecapacity. The standard operating policy no optimization criterion is used in the releasedecisions, for highly stressed systems standard operating performs poorly.III. OPTIMAL OPERATING POLICY One of the classical problems in water resources systems modeling is the derivation ofan optimal operating policy for reservoir to meet a long term objective. Modeling techniquesto be used depend on whether the reservoir inflows are treated deterministic or stochastic. Asshown in Fig.2 given single simplified reservoir system of known capacity K and sequence ofinflows. Fig2: Single Reservoir Operation The reservoir operation problem involves determining the sequence of release Rt thatoptimizes an objective function. In general the objective function may be a function of thestorage volume and /or the release. 151
  4. 4. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308(Print), ISSN 0976 – 6316(Online) Volume 4, Issue 2, March - April (2013), © IAEME3.1 Rule curves for reservoir operation A rule cure indicates the desired releases or storage volume at given period of the yearin a steady state condition, some rules identify storage volume targets that the operation is tomaintain, as far as possible and others identifies storage zones ,each associated with aparticular release policy. The rule curves are best derived through simulation for a specifiedobjective, although for simple cases it may possible to derive them using optimization.IV. LITERATURE REVIEW4.1 Linear programming In reservoir operation linear programming (LP) is well known most favoredoptimization techniques as it is easy to understand and does not require any initial solution.For optimal reservoir operation, objective function is to maximize net benefits of the cropsubjected to various constraints such as storage capacity, canal capacity, Area under crop,Evaporation losses Seepage losses, Inflow and its dependability. Demand of water for varioususes such as industrial, water supply, irrigation etc. An intraseasonal allocation model can beused to maximize sum of relative yields of all crops, for given state of system by using LPnumber of models developed on LP such as integrated model for optimal reservoir operationdeveloped deterministic LP. Model for short term annual operation (Long Le Ngo,2006).Limitation of LP is that it cannot consider stochastic as well as random nature ofinflow and demand.(Vedula and Nageshkumar,1996).4.2 Dynamic Programming /Real Time Reservoir Operation Dynamic Programming (DP) is a sequential or multistage decision making processworks on a divide and conquer manner. it gives the steady state operational policy of singlereservoir by using backward recursive equations. Steady state models are useful in derivingpolices for maximizing long-term benefits from irrigation system. The steady state operationmodel developed by (Vedula and Muzumdar, 1992 ) focuses on main bases for real timeoperation model and it is better in case of critical low flow years. It is useful in findingsteady state policy selecting stage as a time period in a particular year (after which it isassumed that reservoir is no longer useful).The real time operation is formulated for solutionones at the beginning of each inter seasonal period. It uses forecasted inflow for the currentperiod and all subsequent period in the year. (Muzumdar and Ramesh, 1997)4.3 Limitations i) Assumption that the decision made at one stage is dependent only on the state variables and is independent of the decision taken in other stages. ii) The dynamic programming will not be an appropriate technique where decision is4.4 Evolutionary Algorithm (EA) Real world reservoir problem mostly involves complexities like discrete, continuousor mixed variables, multiple conflicting objectives, non-linearity, discontinuity etc. for such a 152
  5. 5. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308(Print), ISSN 0976 – 6316(Online) Volume 4, Issue 2, March - April (2013), © IAEMEsituation stochastic search as EA is used. EA provides not only a single best solution but 2ndbest 3rd best and so on as required ,also gives quick approximate solution and may beincorporated with other local search alogorithms.To handle limitations of EA and theconvergence It is required to carry out problem oriented sensitivity analysis to find out therange in which model is effective. (Reddy Janga &Nagesh kumar,2006).V. ARTIFICIAL NEUTRAL NETWORKS Artificial Neutral Network (ANN) is structured to resemble the biological neutralnetworkTwo aspects:i) Knowledge acquisition through a learning process andii) Storage of knowledge through connections known as synapade weights. The artificial neutron has two modes of operation ,the training mode and using modein The training mode, the neutron can be trained to fire (or not) for particular input patterns.When a taught input pattern is deleted at the input, its associated output to become theCurrent output (Zurada,2006). Artificial neural networks are also used successfully for Singlereservoir as well as multireservoir operation(Raman and chandramouli1996). ANNs areparticularly useful as pattern recognition tools for generalization of input Outputrelationship. In the water recourses engineering most common application of ANNsIncludes thus for rainfall runoff relationships, stream flow forecasting and reservoirOperation (Vedula and Mujumdar,2005). For reservoir operation the ANN is trained by theavailable in flow with related to out flow At various demand of water. Then it can beapplicable for the stochastic nature of inflow as Well as demand.VI. FUZZY LOGIC CONTROL Fuzzy logic is extension of classical set theory and element is the member of severalsets. At the different degree. Fuzzy sets are defined by labels and membership functions. TheFuzzy rues will be relies on human experts to express knowledge of appropriate controlStrategies (Ross, 1995) .Many successful applications of fuzzy systems were reported in theliterature especially in control and modeling (Fontana etai, 1997). The main advantage of thefuzzy control method is to control the processes that are too Complex to be mathematicallymodeled. Reservoir operation mode fuzzy logic haveFollowing distinct steps (Vedula andmujumdar, 2005). i) Fuzzification of inputs where the crisp in puts such as the inflow, reservoir Storage and release are transferrin to fuzzy variables. ii) Formulation of fuzzy set, based on a expert knowledge base. iii) Application of a fuzzy operation to obtain one number representing the premises of each rule. iv) Shaping of the consequence of the rule by implication and defuzzification. The fuzzy neural network (FNN) model has been developed to study the behavior ofoptimal release operating policies formulated through DP (Deka and Chandramouli,2009) 153
  6. 6. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308(Print), ISSN 0976 – 6316(Online) Volume 4, Issue 2, March - April (2013), © IAEME6.1 Integration of Models The challenge of stochastic DP locates that it is usually associated with difficulties indata availability and solution efforts, especially when there are more thAn two state variables(Ravikumar and Venugopal,1998)The Fuzzy provides a convinient approach for optimizingcomplex reservoir system with low quality information where the uncertainty cannot berepresented adequately by probability theory (Liu,2011). so that a methodology byintegration chance constrained programming and factorial design is used to account foruncertainties in reservoir operation and management. The impact of system reliability andinitial reservoir storage as well as their interaction are examined through a set of factorialdesign experiments and complex interrelationships among water release, initial reservoirstorage and reliabilitylevel are examined through implementing the developed chanceconstrained model based on variety of system condition (Li,2003)6.2 Suggested Methodology From the literature review some of typical problem associated with operation forvarious methods are as indicated below.GAPS • LP cannot consider stochastic as well as random nature of inflow and demand. • DP/ Real time reservoir operation assumes that the decision made at one stage is dependent only on the state variables and is independent of the decision taken in other stages but in reservoir operation decision taken at one period effects on the other. • For EA techniques problem oriented sensitivity analysis should be carried out to find out the range in which model is effective. • SDP is associated with difficulties in data availability and solution efforts when there are more than two state variables. Hence it is suggested to explore the following options:-Consider water users in different seasons under different reliability levels and initial storageconditions may emphasis on design of cropping pattern according to seasonal as well asinitial availability of water. Explore use of soft computing techniques for reservoir operationand management under uncertainties by effectively relating the information quality of systemvariable to the reservoir operation decisions. The physical system may be single reservoir ormulti-reservoir with stochastic nature of inflow to meet demand.VII. EXPECTED OUTCOMES Explore the use of soft computing tools for water availability and operation studies.Deciding suitable advanced optimization techniques for understanding a real life studyrelated to water resources engg. and extension of these techniques to reservoir operationstudies. Use of human brain intelligent to solve large complex problem in water resourcesengineering by use of rational decision in a environment of uncertainty. Attempt forapplication to real life problem involving a water resources system in the state of Maharashtrastate. 154
  7. 7. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308(Print), ISSN 0976 – 6316(Online) Volume 4, Issue 2, March - April (2013), © IAEMEREFERENCE [1] Deha Paresh Chandrs and Chandramouli V.(2009) Fuzzy Neural Network Modeling of oir Operation ASCEJ.Water Resource .Plng.Mgt,135(1),5-12. [2] DmtanceD.G.,Gates T.K. and Moncada,E.(1997) Planning reservoir operation with imprecise objectives.J.Water Resour Plng.Mgt,123(3)432-510. [3] Gupta B.L. and Gupta Amit (1994) water resource system management standard pub. Distributor;new Delhi. [4] L.J.B.(2003)Integration of stochastic programming and factorial design for optionalreservoir, Operation,ISEIS-J. Environmentalinformation,1(2),12-17. [5] Ltu,B(2001)Fuzzy random chanced-constrained programming,IEEE Trans fuzzy system 9 (5),713-720. [6] Long Le Neg(2006).Optimizing reservoir operation (Ph.D.Thesis),A case study of Binnreservoir,Vietnam (Institute of Environment and Resources) Technical University Denmark. [7] Mujurmdar P.P.and Ramesh T.S.V.(1997) Real Time reservoir operation for irrigation ASCE, J. Water Resour Plann.Manag122(5),342-347. [8] Ravikumar V.and Venigopal k.(1998)Optimal reservoir operation using mulit objective evolutionary algorithm Springer J,Water Resour,Manag 20(1)86-8.8. [9] Ross.T.J.(1995)Fuzzy logic with engineering application Tata McGraw Hit New Delhi. [10] Vedula S and Mujumadar P.P.(1992).Optional reservoir operation for irrigation of multiple crop,J.Water REsour.Res.28(1)1-9. [11] Vedula S and Nageshkumar D.(1996)An integrated model for optimal reservoir operation for irrigation of multiple crops,water Resource.Res,32(4),1101-1108. [12] VeddiaS.and Mujumadar P.P.(2005)Water resources system, modeling technique and analysis, Tata McGraw-Hill, New Delhi. 155

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