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
1
www.alliedjournals.com