New Formulation for Representing Higher Order TSK Fuzzy Systems

Heydari, Gholamali; Gharaveisi, Ali Akbar; Vali, Mohammad Ali

doi:10.1109/tfuzz.2015.2486813

Cited by 9 publications

(2 citation statements)

References 25 publications

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“…where x i represents the system variables and µ j and σ j are the mean and variance of the Gaussian function, respectively. Further, the set of rules is defined considering the first order Sugeno formulation, and the corresponding output is thus given by Equation ( 16) [28].…”

Section: Fuzzy Inference Systemmentioning

confidence: 99%

Leveraging a Genetic Algorithm for the Optimal Placement of Distributed Generation and the Need for Energy Management Strategies Using a Fuzzy Inference System

Katyara

Shaikh

et al. 2021

Electronics

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With the rising load demand and power losses, the equipment in the utility network often operates close to its marginal limits, creating a dire need for the installation of new Distributed Generators (DGs). Their proper placement is one of the prerequisites for fully achieving the benefits; otherwise, this may result in the worsening of their performance. This could even lead to further deterioration if an effective Energy Management System (EMS) is not installed. Firstly, addressing these issues, this research exploits a Genetic Algorithm (GA) for the proper placement of new DGs in a distribution system. This approach is based on the system losses, voltage profiles, and phase angle jump variations. Secondly, the energy management models are designed using a fuzzy inference system. The models are then analyzed under heavy loading and fault conditions. This research is conducted on a six bus radial test system in a simulated environment together with a real-time Power Hardware-In-the-Loop (PHIL) setup. It is concluded that the optimal placement of a 3.33 MVA synchronous DG is near the load center, and the robustness of the proposed EMS is proven by mitigating the distinct contingencies within the approximately 2.5 cycles of the operating period.

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Section: Fuzzy Inference Systemmentioning

confidence: 99%

Leveraging a Genetic Algorithm for the Optimal Placement of Distributed Generation and the Need for Energy Management Strategies Using a Fuzzy Inference System

Katyara

Shaikh

et al. 2021

Electronics

View full text Add to dashboard Cite

show abstract

“…In this case, the ANFIS is trained by means of PSO, and it demonstrates a very effective performance at dealing with the uncertainties of electrochemical cells. In [32], an effective method to synthesize higher order Takagi-Sugeno-Kang (TSK) fuzzy systems using first-order TSK models is described. The proposed model allows to train high order TSK fuzzy systems in popular softwares (e.g., Matlab R ⃝ ) that notably allow the implementation of low order TSK fuzzy systems only.…”

Section: Introductionmentioning

confidence: 99%