Fuzzy logic based power system stabilizer for damping low frequency oscillations in power system

Sreedivya, K. M.; Jeyanthy, P. Aruna; Devaraj, D.

doi:10.1109/icieeimt.2017.8116835

Cited by 11 publications

(5 citation statements)

References 7 publications

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“…Using simulation and case studies, a fuzzy logic-based management and control system for a hybrid power system is devised and confirmed in this work [7,8].…”

Section: Proceedings Of First International Conference On Smart Syste...mentioning

confidence: 85%

“…Because it has various advantages such as dependability, precision, simplicity, and ease of implementation. It is also utilized as a trustworthy tool for power system management, motor speed control, aeronautical control, power conversion control, and photovoltaic systems (PV) system, Maximum Power Point Tracking (MPPT) algorithm, among other things [6,7]. Particle swarm optimization (PSO) technique, multi-agent-based control, modified hill-climbing, fuzzy logic control, methods, and other current noteworthy research tactics in micro grid control include.…”

Section: Proceedings Of First International Conference On Smart Syste...mentioning

confidence: 99%

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Fuzzy Logic Controlled Hierarchical Power Management Scheme for A Solid-State Transformer (SST) Interfaced D.C Micro Grid

Satheesh,

Reddy

2023

First International Conference on Smart Systems and Green Energy Technologies

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The system integration of an advanced, integrated solid-state transformer (SST) micro-grid system is examined and, for the first time, used in this project. A power management strategy is required for this system in order to allow operating in islanding mode operation, SST-enabled operations, as well as the smooth switching between two modes. The recommended power management technique includes three levels of control: First control is for the local controller, second control for recovering the bus voltage of the dc micro grid, and third control for controlling battery state of charge.

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“…Using simulation and case studies, a fuzzy logic-based management and control system for a hybrid power system is devised and confirmed in this work [7,8].…”

Section: Proceedings Of First International Conference On Smart Syste...mentioning

confidence: 85%

Section: Proceedings Of First International Conference On Smart Syste...mentioning

confidence: 99%

Fuzzy Logic Controlled Hierarchical Power Management Scheme for A Solid-State Transformer (SST) Interfaced D.C Micro Grid

Satheesh,

Reddy

2023

First International Conference on Smart Systems and Green Energy Technologies

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“…This proposed regulator makes the best control signal utilizing the structure of the TSK fluffy framework and neural affiliations dependent on association time yield assessment. In [22] the researchers discussed the arranging technique for FPSS and the introduction of FPSS has been differentiated and the yield response of a Relative Indispensable Subsidiary (PID) controlled framework. A Fuzzy Logic Controller for Force System Stabilizer (FPSS) was made to overhaul the presentation of a Singular Machine Unfathomable Vehicle (SMIB) framework under solid state and dynamic states of the electrical power framework.…”

Section: Literature Reviewmentioning

confidence: 99%

Distinct Architectures of Feed Forward Neural Network for Implementing A Human Swing Leg System

2023

IJCCCE

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The artificial intelligence techniques such as neural networks and fuzzy systems play an important role to disconnect flexion & expansion of the swing leg, the earth response force of the other foot has been redesigned. Underthat paper, we think the fuzzy controller plan issue for yield following flawed genuine investigation of nonlinear systems. For examination, an essential fuzzy control plot has been bristly developed dependent on a current methodology delegate under the ﬁeld.In this paper, the Feedforward Neural Network has been implemented with integer, fixed point and floating point data representations. Additionally, The Fuzzy Logic Controllers in both analog and digital forms has been implemented in hardware. Both designs use less hardware resources and operate with reasonable speed compared to other existing designs. The digital implementation of Fuzzy Logic Controller has been tested for a simulated first order liquid level process and the performance results have been compared with those of the Matlab version of Fuzzy Logic Controller. Here, Fuzzy Logic Controller is used as the controller and is trained adaptively for the changes in process parameters using recursive k-means clustering algorithm for updating the centers of the hidden layer and Recursive Least Square algorithm for updating the weights of the output layer, the result of the settling time about 20ms and takes 20 iterations, and the squared error reaches zero at approximately 20 µs. Index Terms— Fuzzy Controller, leg interaction force, Prosthetic legs, Human walking, GyroAccelerate,Fuzzy Logic System.

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“…Thanks to various interesting properties of the FLSs, they have found wide application in various areas, for example, in control systems [1,2], in electrical engineering [3,4], in planning and prognostic [5], and in aviation [6]. FLSs find application in medical health care systems, used for example in human motion capture [7], in which they are counterparts to systems that are based on conventional positioning algorithms [8].…”

Section: Introductionmentioning

confidence: 99%

Parallel, asynchronous, fuzzy logic systems realized in CMOS technology

Talaśka

2019

Adv Comput Math

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Fuzzy systems play an important role in many industrial applications. Depending on the application, they can be implemented using different techniques and technologies. Software implementations are the most popular, which results from the ease of such implementations. This approach facilitates modifications and testing. On the other hand, such realizations are usually not convenient when high data rate, low cost per unit, and large miniaturization are required. For this reason, we propose efficient, fully digital, parallel, and asynchronous (clock-less) fuzzy logic (FL) systems suitable for the implementation as ultra low-power-specific integrated circuits (ASICs). On the basis of our former work, in which single FL operators were proposed, here we demonstrate how to build larger structures, composed of many operators of this type. As an example, we consider Lukasiewicz neural networks (LNN) that are fully composed of selected FL operators. In this work, we propose FL OR, and AND Lukasiewicz neurons, which are based on bounded sum and bounded product FL operators. In the comparison with former analog implementations of such LNNs, digital realization, presented in this work, offers important advantages. The neurons have been designed in the CMOS 130nm technology and thoroughly verified by means of the corner analysis in the HSpice environment. The only observed influence of particular combinations on the process, voltage, and temperature parameters was on delays and power dissipation, while from the logical point of view, the system always worked properly. This shows that even larger FL systems may be implemented in this way. Keywords Fuzzy logic systems • FL operators • FL neural networks • Asynchronous circuits • Parallel circuits • CMOS implementation

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Fuzzy logic based power system stabilizer for damping low frequency oscillations in power system

Cited by 11 publications

References 7 publications

Fuzzy Logic Controlled Hierarchical Power Management Scheme for A Solid-State Transformer (SST) Interfaced D.C Micro Grid

Fuzzy Logic Controlled Hierarchical Power Management Scheme for A Solid-State Transformer (SST) Interfaced D.C Micro Grid

Distinct Architectures of Feed Forward Neural Network for Implementing A Human Swing Leg System

Parallel, asynchronous, fuzzy logic systems realized in CMOS technology

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