Comparative Analysis of Fuzzy Power System Stabilizer Using Different Membership Functions

Gupta, Neeraj; Jain, Sushma

doi:10.7763/ijcee.2010.v2.147

Cited by 32 publications

(12 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most popular application is fuzzy logic control. There are many studies on comparing the control performance of Gaussian and trapezoidal MFs in T1 fuzzy logic controllers [9], [20]; however, it seems that the conclusion is highly problem dependent, and it is difficult to conclude which MF shape is always better for control performance. We expect the conclusion will be the same for other applications of T1 FLSs, including classification, regression, etc.…”

Section: A Input Mf Shapes: Gaussian or Trapezoidalmentioning

confidence: 99%

Designing practical interval type-2 fuzzy logic systems made simple

Mendel

2014

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

View full text Add to dashboard Cite

Interval type-2 fuzzy logic systems (IT2 FLSs) have become increasingly popular in the last decade, and have demonstrated superior performance in a number of applications. However, the computations in an IT2 FLS are more complex than those in a type-1 FLS, and there are many choices to be made in designing an IT2 FLS, including the shape of membership functions (Gaussian or trapezoidal), number of membership functions, type of fuzzifier (singleton or non-singleton), kind of rules (Mamdani or Takagi-Sugeno-Kang), type of t-norm (minimum or product), method to compute the output (type-reduction or not), and methods for tuning the parameters (gradient-based methods or evolutionary computation algorithms; one-step or two-step). While these choices give an experienced IT2 FLS researcher extensive freedom to design the optimal IT2 FLS, they may look overwhelming and confusing to IT2 beginners. Such a beginner may make an inappropriate choice, obtain unexpected results, and lose interest, which will hinder the wider applications of IT2 FLSs. In this paper we try to help IT2 beginners navigate through the maze by recommending some representative choices for an IT2 FLS design. We also clarify two myths about IT2 FLSs. This paper will make IT2 FLSs more accessible to IT2 beginners.

show abstract

Section: A Input Mf Shapes: Gaussian or Trapezoidalmentioning

confidence: 99%

Designing practical interval type-2 fuzzy logic systems made simple

Mendel

2014

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

View full text Add to dashboard Cite

show abstract

“…Only a few works have discussed about the opportunity of using a certain shape for MFs [5,15,16]. However, performance seems to be application-dependent, and interpretability of fuzzy sets with different shapes has rarely been compared.…”

Section: Fuzzy Sets Shapementioning

confidence: 99%

Insights into Interpretability of Neuro-Fuzzy Systems

Pota¹,

Esposito²

2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

View full text Add to dashboard Cite

Neuro-fuzzy networks revealed their proficiency in learning from data, while offering a transparent and somehow interpretable rule-based model. Recent research focused either on the interpretability of the chosen model or on the system performance. Regarding the interpretability, here an index to control the trade-off between complexity and performance, some insights into fuzzy partitions properties, an ideal fuzzy sets shape, and an evaluation of rules are proposed. All the evaluations are made taking into account the required output and performance. A discussion on results of a system built using the Wisconsin Breast Cancer Dataset is performed as a proof of concept.

show abstract

“…To convert crisp quantity (i.e. rotor speed and its derivative) fuzzy, 07 triangular shape [13][14][15][16] membership functions (Fig. 3) are used.…”

Section: Ieee Indicon 2015 1570203743mentioning

confidence: 99%

PSO based robust Fuzzy power system stabilizer design for single machine infinite bus system

Tayal

Lather

2015

2015 Annual IEEE India Conference (INDICON)

View full text Add to dashboard Cite

A robust PSO optimized fuzzy logic power system stabilizer (PSOFPSS) is proposed in this paper. To damp out the low frequency oscillations and small signal stability enhancement the Fuzzy PSS (FPSS) is added with the excitation system. The single machine infinite bus (SMIB) system is analyzed with Fuzzy PSS (FPSS) and with PSOFPSS. The rotor angular speed deviation and acceleration are considered as inputs to the fuzzy logic controller. The effectiveness of the FPSS is compared with the PSOFPSS. The simulation results obtained over a number of operating conditions indicate the promising performance of PSOFPSS over the FPSS.

show abstract

Comparative Analysis of Fuzzy Power System Stabilizer Using Different Membership Functions

Cited by 32 publications

References 10 publications

Designing practical interval type-2 fuzzy logic systems made simple

Designing practical interval type-2 fuzzy logic systems made simple

Insights into Interpretability of Neuro-Fuzzy Systems

PSO based robust Fuzzy power system stabilizer design for single machine infinite bus system

Contact Info

Product

Resources

About