Soft Computing Techniques and Applications in Electrical Drives Fuzzy logic, and Genetic Algorithm

Author Name(s): S. Sakunthala, R.Kiranmayi M.Tech, P.Nagaraju Mandadi
Author Email: shakuntala.btech@gmail.com

Abstract

Present days’ technology requires new innovations which are implemented by using soft computing methods. The particular schemes have brought industrial capabilities to new changes in applications. Controlling mode is a significant use of several industries to control the complicated structure specifications. Present day’s soft computing schemes are growing with good performance. These soft techniques are using the combination of conventional techniques which is used for develop machinery for resolving current controller difficulties. Soft computing schemes yield a power to make determinations and research from the certain knowledge or expert’s experience. This paper mainly focuses on soft computing and hard computing techniques and the comparison of these techniques when applying for electrical motor drives. 

Introduction

Hard computing schemes are conventional computing schemes set on fundamentals of definiteness, serenity, and rigidness. Present days complications which process with uncertain of knowledge and estimated performance cannot be managed by such systems. It is observed that these schemes can have capability to solve tangible complications. despite, affecting considerable condition of hard computing schemes is that it takes a lot of estimation time to deal with complications as real-life problems are developed to hold estimated and undetermined data. There are different analytical models presented for dealing pre-determined requirements of real-life complications. However, real scenario presents that the physical complications occur in a non-ideal environment. several concurrent complications do not contain hard computing schemes for precise solutions. 

Intelligent System (IS) can be represented as the structure that integrates intelligence into utilizations being directed by electrical systems. To integrate intelligence into machine Utilizations, qualities such as interpretation, research, and adjustments are desired. Distinct from these aspects, search and optimization is another greater ability of IS. In order to understand with complicated real-world complications and to expose intelligence; an intelligent system desires a compound of knowledge, and schemes from many origins.. which deal with a defect, indefinite, low-cost solution, partial truth, and robustness can be developed by implementing soft computing techniques.

This technique is a developing selection of schemes, which aim to deed tolerance for the defect, indefinite, and partial truth to obtain power, conformity, and entire low cost. soft computing methods mimic recognition and acknowledge in various main considerations: they can learn from experience; they can generalize into fields where explicit observation is removed; and complete lateral computer designs to affect living mechanism, they can grow the mapping from inputs to the outputs faster than naturally serial systematic representations [3,4].

This technique is a crowd of computing methodologies that provide a support for the representation, design, and implementation of intelligent systems to provide economical and feasible solutions with reduced complexity. Figure 2 presents Different combinations of techniques from such ring have provided good results these techniques have their own strengths and limitations. Integration of three or more methods can provide important advantages for intelligent system design.  for designing intelligent systems; e.g. soft computing methods.

Conclusion

This paper consists various techniques of soft computing like the fuzzy logic controller and genetic algorithm. This soft computing technique is compared with hard computing techniques. The fuzzy logic controller (FLC) is implemented in PMSM instead of PI controller. By implementing this fuzzy controller, we can estimate the errors of flux and torque in PMSM. Errors can be minimized and compared by using the fuzzy logic(FLC) and the torque ripples in PMSM is reduced. In soft computing techniques, a genetic algorithm is also studied. How to implement this algorithm along with fuzzy controller in PMSM drives.  The genetic algorithm can optimize the problems more easily than a Fuzzy controller and the advantages and applications of soft computing techniques are studied.by using this controller we can reduce the flux ripples and torque in the machine than conventional controllers.

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