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This paper proposes the design of a feedback controller for a voltage controlled Induction Motor (IM) drive by using the Ant Colony Optimization (ACO) technique. ACO is an evolutionary stochastic combinatorial computational discipline inspired by the behavior of ant colonies. A model of the induction motor drive is developed and its performance is evaluated while using a conventional PI controller. However it can well be seen that the system works satisfactorily for a particular operating point while its performance deteriorates for other operating points. This facilitates the need for online tuning. To accomplish this task a feedback controller is designed that uses ACO for online tuning. Comparison between two variants of ACO is also presented in this paper.

This paper demonstrates an effective application of artificial neural networks for online identification of a multimachine power system. The paper presents a recurrent neural network as the identifier of the benchmark two area, four machine system. This neural identifier is trained using the static Backpropagation algorithm. The trained neural identifier is then tested using datasets generated by simulating the system under consideration at different operating points and a different loading condition. The test results clearly establish a satisfactory performance of the trained neural identifier in identification of the power system considered.

This paper presents the model of car triple inverted pendulum system using Lagrange equation in detail. This model is then linearized to design an LQR controller to maintain the triple inverted pendulum on a cart around its unstable equilibrium position using single control input. The controllability of the triple inverted pendulum system is investigated and the choice of weights in LQR is also discussed. The system is simulated in MATLAB environment and the simulation results establish the satisfactory performance of the LQR controller in stabilizing the system.

This paper investigates the effectiveness of Model Predictive Controller and its ability to damp out the low frequency oscillations and improve the small signal stability of single machine infinite bus system. The damping functions of the UPFC with various alternative control signals are investigated on the basis of linearized Heffron Phillips model of SMIB equipped with UPFC. The performance of model predictive controller has been compared to that of a controller based on phase compensation technique and is found to be superior as it uses the optimized control inputs to bring stability to the plant. The performance effectiveness of various control signals is evaluated and the best way of controlling the UPFC is proposed.

The power system is a dynamic system that is constantly being subjected to disturbances. It is important that these disturbances do not drive the system to unstable conditions. The Unified Power Flow Controller (UPFC), which is a FACTS device, simultaneously controls the real and reactive power flows on a transmission line as well as regulates voltage at the bus where it is connected. This device creates a tremendous quality impact on power system stability. In this paper, a systematic approach based on phase compensation technique, Robust control technique and Fuzzy logic control technique for designing Unified Power Flow Controller (UPFC) based damping controllers (based on m B and m E ) have been considered. Investigations on comparing the response of controllers based on all the techniques reveal that the performance of Robust Controller and Fuzzy Logic Controller is superior to the controller based on Lead-Lag compensation technique.