Browsing by Author "Das, M. K."

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Analysis of transient performance for DFIG wind turbines under the open switch faults /
(IEEE, 2010-12-01) Das, M. K.
The fast development of grid-integrated wind power introduces new requirements for the operation and control of power networks. In order to maintain the reliability of a host power grid, it is preferred that the grid-connected wind turbine should restore its normal operation with minimized power losses in events of grid fault. This paper presents the results for the transient performance of a 2MW doubly-fed induction generator (DFIG), a type of variable-speed wind turbine. The paper concentrates on transient performance of the said generator technology under open-switch grid faults. The simulation was performed using MATLAB - Simulink software. The results obtained have shown that the control schemes employed for the DFIG wind turbines played an effective role in the restoration of the normal operation for the wind turbine in response to grid faults. The results for both during and after the grid fault will be discussed in this paper.
Discrete mode AGC of restructured power system using P-I controller with governor dead-band nonlinearity /
(IEEE, 2016-10-01) Das, M. K.
The paper presents the application of proportional-integral (P-I) Controller for the discrete mode Automatic Generation Control (AGC) problems in the deregulated environment. Governor dead-band non-linearity has been considered in a two area deregulated power system while designing the P-I controller. Imperialist Competitive Algorithm (ICA) has been used for optimizing the gains of P-I controller and the dynamics responses are compared with and without considering optimized values of the controller gains. Simulation results show that P-I controller optimized using ICA gives better dynamic performances compared to P-I controller without optimized using ICA.
Expert-based FMEA of wind turbine system /
(IEEE, 2011-12-01) Das, M. K.
This paper presents the results of a Failure mode and effects analysis (FMEA) conducted for wind turbine (WT) systems. The FMEA was performed on the functional modes of WT sub-systems in order to understand its performance of each sub-system, and to identify weaknesses in the respective sub-systems of WT considered. Sensitivity analysis had been done to investigate the cross-spreading of unreliability between sub-systems in the WT and a ranking of critical subassemblies was prepared on the basis of data supplied by experts that took part in the study. Overall, it was observed that crowbar protection and gearbox were found to be the two most critical components of WT given a reference to the Risk Priority Number (RPN) value of 200. Also, some design and improvement of these components could be envisaged to help improve the performance of the entire WT system by means of reducing the number of failures.
Oppositional Krill Herd Algorithm-Based RLNN Controller for Discrete-Mode AGC in Deregulated Hydrothermal Power System Using SMES /
(Springer Nature, 2018-06-27) Das, M. K.
This paper presents the application of oppositional krill herd algorithm (OKHA)-based reinforced learning neural network (RLNN) controller to study the discrete-mode automatic generation control (AGC) problems in the deregulated environment considering superconducting magnetic energy storage (SMES) system for three-area hydrothermal power system. The dynamic responses using OKHA-based RLNN controller for various loading conditions are compared with the proportional–integral–derivative (P–I–D) controllers whose gains are also optimized using OKHA. Area control error (ACE) is used as input to both P–I–D and RLNN controllers, and the weights of neural networks have been adjusted online for RLNN controllers. Sensitivity analyses have been performed to investigate the robustness of the controllers that are subject to change in SMES parameters and loading conditions. Investigation reveals that OKHA-based RLNN controllers give better dynamic performances compared to gains of P–I–D controllers obtained using OKHA considering SMES units for different loading conditions.