LFC based adaptive PID controller using ANN and ANFIS techniques

Mosaad, Mohamed I.; Salem, Fawzan

doi:10.1016/j.jesit.2014.12.004

Cited by 61 publications

(26 citation statements)

References 8 publications

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“…The adaptive network-based fuzzy inference system (ANFIS) serves as a fundamental for constructing a set of fuzzy rules with appropriate membership functions to generate the prearranged input-output pairs (Jang, 1993;Lee, 1990). Various types of fuzzy rules have been projected in the earlier period (Jang, 1993;Kumar & Vani, 2014;Mosaad & Salem, 2014;Rao, 2012). Adaptive networks are evolving, dynamic networks, in which the topology changes in dependence of the dynamic state of the nodes, while the dynamics of the state depends on the topology.…”

Section: Adaptive Network-based Fuzzy Inference Systemmentioning

confidence: 99%

ACS algorithm tuned ANFIS-based controller for LFC in deregulated environment

Selvaraju

Ganapathy

2017

Journal of Applied Research and Technology

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In this paper, an artificial cooperative search (ACS) algorithm tuned adaptive network-based fuzzy inference system (ANFIS) controller for optimal gain tuning of load frequency control (LFC) operation in deregulated scenario has been offered. The conventional controllers for load frequency control operation are having fixed gain values intended for nominal operating conditions of the power system and they do not afford effective and efficient performance over a large range of operating scenarios in the deregulated environment. To progress the system performance to its near optimum for all probable operating circumstances of the power system, the controller gains have to be computed for the equivalent operating conditions by using the restructured parameters. For this intention, a controller based on an adaptive network-based fuzzy inference system seems to be the most excellent and valuable preference. The ANFIS is trained by off-line data obtained using a new optimization technique, artificial cooperative search optimization algorithm and the corresponding gains are updated in real-time as per the changing operating conditions. ACS is a swarm intelligence algorithm developed for solving numerical optimization problems. The swarm intelligence philosophy behind ACS algorithm is based on the migration of two artificial superorganisms as they biologically interact to achieve the global minimum value pertaining to the problem. To exhibit the competence and robustness of the projected ACS algorithm tuned ANFIS controller, the controller has been implemented on a two-area two-unit interconnected deregulated power system having one reheat unit and one non-reheat unit in each area. The simulation results exhibit the ability of the designed ACS algorithm tuned ANFIS controller for online LFC operation in deregulated environment.

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Section: Adaptive Network-based Fuzzy Inference Systemmentioning

confidence: 99%

ACS algorithm tuned ANFIS-based controller for LFC in deregulated environment

Selvaraju

Ganapathy

2017

Journal of Applied Research and Technology

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“…MUGA (Multi objective uniform diversity genetic algorithm) algorithm is implemented to optimize the PI/PID controller parameters [6]. In [8][9][10][11][12][13][14] PI/PID controller parameters are optimized using fuzzy logic in combination with intelligent techniques.…”

Section: Introductionmentioning

confidence: 99%

“…In this method, possible uncertainty in large-scale complex systems is considered and type-2 fuzzy sets are used. In order to optimize the PID controller parameters, GA, ANN and ANFIS algorithms are used [10]. Results show that the ANFIS algorithm has a more appropriate response.…”

Section: Introductionmentioning

confidence: 99%

Load frequency control of multi area interconnected power system using differential evolution algorithm

Sina

Kaur

2019

Teh. glas. (Online)

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In this paper, Proportional Integral Derivative (PID) controller is designed using Differential Evolution (DE) algorithm to Load Frequency Control (LFC) in three areas of an interconnected power system. The proposed controller has appropriate dynamic response, so it increases damping in transient state in unhealthy conditions. Different generators have been used in three areas. Area 1 includes thermal non-reheat generator and two thermal reheat generators; area 2 includes hydro and thermal non-reheat generators, and area 3 includes hydro and thermal reheat generators. In order to evaluate the performance of the controller, Sim/Matlab software is used. Simulation results show that the controller designed using DE algorithm is not affected by load changes, disturbance, or system parameters changes. Comparing the results of proposed algorithm with other load frequency control algorithms, such as PSO and GA, it has been found that this method has a more appropriate response and satisfactory performance

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“…10,11 The details of existing unbalance detection methods are reviewed in several publications. [12][13][14][15] Also, fault diagnosis of a rotor-bearing system for misalignment and unbalance was investigated in a steady state. 36 In spite of some techniques available to diagnosis unbalance, localization of the unbalance and the identification of how much is the eccentric masses generating unbalances are very useful.…”

Section: Introductionmentioning

confidence: 99%

Unbalance Rotor Parameters Detection Based on Artificial Neural Network

2019

The International Journal of Acoustics and Vibration

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Unbalance is an important fault that can damage or shut down vital rotary systems such as the gas turbine, compressors, and others, so to avoid this trouble, the balancing process is very crucial, even though it is time-consuming and costly. Thus, having a technique which can predict the unbalance location and its parameters will be valuable and practical. The current study represents a model that can identify the unbalance’s mass, radius, and location of the eccentric mass based on the artificial neural network (ANN) model. The inputs of the proposed ANN, which is based on a feed forward with back propagation model, is the bearing acceleration signal in the frequency domain. It has 10 hidden layers with 10 neurons through each layer. The accuracy in prediction was acquired at 96%, 96%, and 94% for the disc number (plane), the eccentric radius, and eccentric mass values, respectively.

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LFC based adaptive PID controller using ANN and ANFIS techniques

Cited by 61 publications

References 8 publications

ACS algorithm tuned ANFIS-based controller for LFC in deregulated environment

ACS algorithm tuned ANFIS-based controller for LFC in deregulated environment

Load frequency control of multi area interconnected power system using differential evolution algorithm

Unbalance Rotor Parameters Detection Based on Artificial Neural Network

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