Robust decentralized neural networks based LFC in a deregulated power system

This paper presents a novel control approach of hybrid neuro-fuzzy (HNF) for load frequency control (LFC) of four-area power system. The advantage of this controller is that it can handle the non-linearities, and at the same time it is faster than other existing controllers. The effectiveness of proposed controller in increasing the damping of local and inter area modes of oscillation is demonstrated in four area interconnected power system. Area-1 and area-2 consist of thermal reheat power plant whereas area-3 and area-4 consist of hydro power plant. Performance evaluation is carried out by using fuzzy, ANN, ANFIS and conventional PI and PID control approaches. The performances of the controllers are simulated using MATLAB/Simulink package. The result shows that intelligent HNF controller is having improved dynamic response and at the same time faster than ANN, fuzzy and conventional PI and PID controllers.

Simulation based neuro-fuzzy hybrid intelligent PI control approach in four-area load frequency control of interconnected power system

Load Frequency Control of an Interconnected Reheat Thermal system using Type-2 fuzzy system including SMES units

This paper present analysis on dynamic performance of Load Frequency Control (LFC) of three area interconnected hydrothermal reheat power system by the use of Artificial Intelligent and PI Controller. In the proposed scheme, control methodology developed using conventional PI controller, Artificial Neural Network (ANN) and Fuzzy Logic controller (FLC) for three area interconnected hydro-thermal reheat power system. In this paper area-1 and area-2 consists of thermal reheat power plant whereas area-3 consists of hydro power plant. In this proposed scheme, the combination of most complicated system like hydro plant and thermal plant with reheat turbine are interconnected which increases the nonlinearity of the system. The performances of the controllers are simulated using MATLAB/SIMULINK package. A comparison of PI controller, Fuzzy controller and ANN controller based approaches shows the superiority of proposed ANN based approach over Fuzzy and PI for same conditions. To enhance the performance of PI, Fuzzy and neural controller sliding surface is included. The simulation results also tabulated as a comparative performance in view of settling time and peak over shoot.

/pdf/load-frequency-control-of-three-area-interconnected-hydro-57gmo8ypbx.pdf

Load frequency control of three area interconnected hydro-thermal reheat power system using artificial intelligence and PI controllers

This paper presents the use of artificial intelligence to study the load frequency control of interconnected power system. In the
proposed scheme, a control methodology is developed using Artificial Neural Network (ANN) and Fuzzy Logic controller
(FLC) for interconnected hydro-thermal power system. The control strategies guarantees that the steady state error of frequencies and inadvertent interchange of tie-lines power are maintained in a given tolerance limitations. The performances of the controllers are simulated using MATLAB/SIMULINK package. A comparison of Fuzzy controller and ANN controller based approaches shows the superiority of proposed ANN based approach over Fuzzy one for different loading conditions (1% and 2% step load variations). The simulation results also tabulated as a comparative performance in view of settling time and peak over shoot.

/pdf/application-of-artificial-intelligence-in-load-frequency-3e3946e1nj.pdf

Application of artificial intelligence in load frequency control of interconnected power system

This paper describes a nonlinear radial basis function neural networks (RBFNN) controller based on /spl mu/ synthesis technique to load frequency control (LFC) of the power systems. Power systems such as other industrial plants have some uncertainties and deviations due to multivariable operating conditions and load variations that for controller design had to take the uncertainties into account For this reason, in design of the proposed loud frequency controller the idea of /spl mu/ synthesis theory is being used. The motivation of using the /spl mu/-based robust controller for training of the RBFNN controller is to take the large parametric uncertainties and modeling error into account The proposed controller is effective and can guarantee the stability of overall system in the presence of plant parameter changes and system nonlinearities. The simulation results on a two-area power system show that the proposed RBFNN controller gives good dynamic responses and is superior to the conventional PI and /spl mu/-based robust controllers.

Power system load frequency control using RBF neural networks based on /spl mu/-synthesis theory

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Power system load frequency control using RBF neural networks based on /spl mu/-synthesis theory