International Journal of Engineering, Management & Sciences (IJEMS) ISSN:2348 –3733, Volume-2, Issue-2, February 2015
Comparision of Mamdani and Sugeno Fuzzy Inference System for Deciding the Set Point for a Hydro Power Plant Dam Reservoir Based on Power Generation Requirement Kavita Jain, Abhisek Soni Abstract— A Hydro power plant is established for the production of electricity as well as flood control in the areas lying along the course of the river on which dam is constructed. So the water level control in the reservoir and generation of required amount of power are equally important. In this paper a fuzzy logic has been developed using power generation requirement from power plant to control level of reservoir and thus maintaining required water level for power generation and to avoid over flowing as well as flood conditions. The Fuzzy controller is developed using Mamdani-type and Sugeno-type models. The paper outlines the basic difference between Mamdani-type FIS and Sugeno type FIS. The results demonstrated the performance comparison of the two systems and the advantages of using Sugeno- type over Mamdani-type for this case. Index Terms— Spillway Gates, PID, PLC, fuzzification, defuzzification.
I. INTRODUCTION The controller developed in Dam Automation system maintains water’s level at set point and also manages the flow through the spillway gates. The non-linearities that keep coming in the plant are not easy to cope up with to get the desired output. The hydro plant for which the controller is designed is PARBATI-III (Himachal Pradesh). Since the power plant is situated in the higher lying areas of the river Beas (Perennial River) and the flow is at higher side. Hence the design of controller to achieve the synchronized automation is a challenging task. Normally a PLC system is developed to Automation in the plant. Here in this paper a fuzzy logic will be developed for the existing PLC system the architecture of the PLC system is described. The fuzzy logic controller will be added before the existing PLC system which will actually calculate the level of water to be maintained and thus will give the set point to the already existing PLC system having PID controller. The inputs for the fuzzy logic controller will power generation requirement and thus the level need to be maintained. The rules will be made on the basis of a data acquired from the Dam authority which will be having the power generation and corresponding levels to be maintained. This paper is organized as follows. In Section II we describe the concept of FIS with the difference between Mamdani-type and Sugeno-type FIS. Section III and Section IV describe the
development of Mamdani-type FIS and Sugeno-type FIS, respectively. Experimental results and discussions are presented in Section V along with a comparative performance analysis involving the two types of fuzzy logic systems. Finally, Section VI provides some concluding remarks. II. FUZZY INFERENCE SYSTEM Mamdani method is widely accepted for capturing expert knowledge. It allows us to describe the expertise in more intuitive, more human-like manner. However, Mamdani-type FIS entails a substantial computational burden. On the other hand, Sugeno method is computationally efficient and works well with optimization and adaptive techniques, which makes it very attractive in control problems, particularly for dynamic non linear systems. These adaptive techniques can be used to customize the membership functions so that fuzzy system best models the data. The most fundamental difference between Mamdani-type FIS and Sugeno-type FIS is the way the crisp output is generated from the fuzzy inputs. While Mamdani-type FIS uses the technique of defuzzification of a fuzzy output, Sugeno-type FIS uses weighted average to compute the crisp output. The expressive power and interpretability of Mamdani output is lost in the Sugeno FIS since the consequents of the rules are not fuzzy [10]. But Sugeno has better processing time since the weighted average replace the time consuming defuzzification process. Due to the interpretable and intuitive nature of the rule base, Mamdani-type FIS is widely used in particular for decision support application. Other differences are that Mamdani FIS has output membership functions whereas Sugeno FIS has no output membership functions. Mamdani FIS is less flexible in system design in comparison to Sugeno FIS as latter can be integrated with ANFIS tool to optimize the outputs. III. DEVELOPMENT USING SUGENO-TYPE FIS Three inputs are considered here for deciding the rules. The inputs are number of power units on, water level and inflow rate. Corresponding membership functions of the inputs are in
Manuscript received February 03, 2015 Kavita Jain, M. Tech, Scholar, Manipal University ,Jaipur Abhishek Soni, Assistant Professor , RIET , Jaipur, Rajasthan India
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Comparision of Mamdani and Sugeno Fuzzy Inference System for Deciding the Set Point for a Hydro Power Plant Dam Reservoir Based on Power Generation Requirement
table 1 where, MDDL is Minimum draw down level, FRL is Full reservoir level and MWL is Maximum water level. Output membership functions are described in table 2 Table1. membership functions for input Units. On Water. level Inflow. Rate Zero MDDL low One MWL normal Two Three
FRL
high Very High
Table2: membership functions for output (gate opening) Shut v.Small_open Small-Open Open half_open Full_open
Fig2. Membership functions for power generation requirement The power house has three units, when all three of them are running the total power generated is 520MW. Generally, all three units need to be run but there are times when anyone of three or any two is required. If all three units are working properly then the number of units to turn on is decided by the inflow rate of the water. The power generation requirement decides the turn on and off of the units in this manner. a. One – 0 to 174 b. Two – 175 to 345 c. Three – 346 to 520 (Units in MW)
Fig1. Membership functions for water level Fig1. Shows the membership function of water level in the reservoir. As it is clear from the picture that if the water level low then membership function value assigned at this point is 1 and appropriate action will be taken based on the rules defined. In this case the power should not be generated and plant should be in off state.The various water levels defined for the reservoir. a. FRL(Full reservoir level) -1330m
Fig3. Membership functions for flow rate.
b. MWL(maximum water level) - 1330m c. MDDL(minimum draw down level) – 1314m d. Crest level - 1292m Since the plant here is fully submerged type so the maximum water level and full reservoir level are same. Minimum draw down level is the one below which no more water can be drawn for the power production.
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The inflow of water is the major factor in deciding the power generation from the plant and in deciding the status of the spillway gates. The inflow rate with which water comes in the reservoir decides the power production in this way a. low – 44.25cum (plant closed) b. Normal – above 44.25cum c. High – above 89cum d. very high – higher than 132
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International Journal of Engineering, Management & Sciences (IJEMS) ISSN:2348 –3733, Volume-2, Issue-2, February 2015 V. RESULTS The following results were obtained during the simulation of both types fuzzy inference systems. For Sugeno -type FIS, Fig. 5 and for Mamdani-type, Fig. 6 illustrates the surface view of the three-dimensional view of the relationship between the inputs (water level, flow rate and power generation requirement) and the output (gate opening percentage). .
Fig5. Surface view of Sugeno FIS
Fig4. Rules Fig4. Describes the rules for getting the ouptput.
IV. DEVELOPMENT USING MAMDANI-TYPE FIS The initial steps and the setting of Mamdani-type FIS are same as of Sugeno-type FIS. It also consists of three inputs water level, power generation requirement and flow rate. Each of the selected input variables is described by a set of three linguistic fuzzy values, defined by a Trapizium membership function, as in the case of Sugeno-type fuzzy inference system(as already shown in Figs.1-2-3). Unlike the output value range of the Sugenoi-type fuzzy inference system, the range of Sugeno-type output is not between 0 and 1.The output of this system cannot only be either constant or linear in this FIS. Fig6. Surface view of Mamdani FIS
Table3. FIS specifications Parameters
Values (%)
And
Min
OR
Max
Defuzzification
centroid
VI. CONCLUSION This paper described the two most commonly used Fuzzy inference systems to evaluate the gate opening percentage for water hydro-electric power plant dam reservoir. It can be concluded that Mamdani-type FIS and Sugeno-type FIS perform quite similar, but Sugeno-type FIS allows the evaluation of gate opening work at its full capacity with smooth operational performance. Although the designing of both systems is same but the output membership functions of Sugeno-type can only be either constant or linear and also the crisp output is generated in different ways for both FISs.
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Comparision of Mamdani and Sugeno Fuzzy Inference System for Deciding the Set Point for a Hydro Power Plant Dam Reservoir Based on Power Generation Requirement
Sugeno-type FIS has also an advantage that it can integrated with neural networks and genetic algorithm or other optimization techniques so that the system can adapt to system characteristic efficiently. REFERENCES [1] Jyothish Kumar S.Y, “Design and Implementation of Fuzzy Controller on Embedded Computer for Water Level Control”, International Journal of Scientific & Engineering Research, Volume 4, Issue 5, 2013. [2] Avvaru Ravi Kiran, B.Venkat Sundeep, Ch. Sree Vardhan and Neel Mathews,” “The Principle of Programmable Logic Controller and its role in Automation, “International Journal of Engineering Trends and Technology", Volume4, Issue3, 2013. [3] Namrata Dey, Ria Mandal, M Monica Subashini, “Design and Implementation of a Water Level Controller using Fuzzy Logic” International Journal of Engineering and Technology, Vol 5 No 3 Jun-Jul 2013. [4]Alshalaa A. Shleeg, Issmail M. Ellabib, “Comparison of Mamdani and Sugeno Fuzzy Interference Systems for the Breast Cancer Risk”,International Journal of Computer, Information, Systems and Control Engineering Vol:7 No:10, 2013. [5] Arshdeep Kaur, Amrit Kaur, “Comparison of Mamdani-Type and Sugeno-Type Fuzzy Inference Systems for Air Conditioning System”, International Journal of Soft Computing and Engineering, Volume-2, Issue-2, May 2012. [6] Anabik Shome, Dr. S.Denis Ashok, “Fuzzy Logic Approach for Boiler Temperature & Water Level Control” International Journal of Scientific & Engineering Research, Volume 3, Issue 6, June-2012. [7] Om Prakash Verma, Himanshu Gupta, “International Journal of Advanced Research in Computer Science and Software Engineering” Volume 2, Issue 4, April 2012. [8] B.Wayne Bequette, “Process Control Modelling, Design and Simulation”, PHI Learning Private Limited, ISBN-978-81-203-2265-3. [9]Committee on automated monitoring of dams and their foundations, ”Automated dam monitoring systems”, International commission on large dams, bulletein118 , 2000. [10]A. Haman, N. D. Geogranas, “Comparison of Mamdani and Sugeno Fuzzy Inference Systems for Evaluating the Quality of Experienceof Hapto-Audio-Visual Applications”, HAVE 2008 – IEEE International Workshop on Haptic Audio Visual Environments and their Applications, 2008
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