Department of Electrical and Electronics Engineering

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Author: search.filters.author.Mishra, PuneetSubject: search.filters.subject.Fuzzy logic controller

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    An improved nonlinear deloading approach based on the fuzzy controller for wind turbine generators in an islanded microgrid
    (Elsevier, 2023-11) Mishra, Puneet; Mathur, Hitesh Datt
    The wind turbine generators (WTG’s) incapability of primary frequency support during system contingencies due to its decoupled nature from the system frequency causes profound integration and stability issues. The present study focuses on resolving such issues by enabling the WTGs to participate in long-term frequency support under the derated operation of WTGs. The deloading operation of WTGs can provide a specific reserve power margin by reducing its rotor speed, which can be utilized during system contingencies. In literature, linear and quadratic deloading techniques have been proposed but these fail to replicate the nonlinear characteristics of the WTG accurately, thereby making deloading ineffective. To effectively implement the deloading, this work uses a more-accurate higher-order Newton’s interpolation polynomial (HNIP), to cope with the highly nonlinear characteristics of WTG. The proposed deloading approach is also augmented with a fuzzy-based intelligent supplementary control structure to handle the inherent and incorporated nonlinearities in WTG. The microgrid system, consisting of a conventional energy source with WTG, has been considered as system under investigation. The integral time absolute error for step wind profile and variable speed wind profile was found to be improved by 97.65% and 97.29%, respectively, with the proposed novel deloading technique with fuzzy-PID compared to PID. Further, to ensure the implementation viability of the proposed control scheme, real-time validation of the same is carried out on OPAL-RT 4510, having a Xilinx Kintex-7 FPGA board. It was found that for all the scenarios considered for real-time digital simulation purposes, the results unerringly matched with MATLAB/Simulink.
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    Comparative study of some optimization techniques applied to Jacketed CSTR control
    (IEEE, 2015-09) Mishra, Puneet
    In this paper, the performance of four optimization techniques i.e. Grey Wolf Optimizer (GWO), Backtracking Search Algorithm (BSA), Differential Evolution (DE), and Bat Algorithm (BA) have been investigated for optimizing the scaling factors of fuzzy proportional-integral controller (FPIC). Jacketed continuous stirred tank reactor (CSTR) has been considered for step set-point and trajectory tracking of reactor temperature. The present work has been simulated in LabVIEW™. The performance of aforementioned algorithms has been evaluated by comparing the cost function Integral of Absolute Error for step set-point and trajectory tracking. On the basis of simulation results, it can be inferred that, GWO outperformed other optimization algorithms for all considered cases.
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    A fractional order fuzzy PID controller for binary distillation column control
    (Elsevier, 2015-12) Mishra, Puneet
    Expert and intelligent control schemes have recently emerged out as a promising solution with robustness which can efficiently deal with the nonlinearities, along with various types of modelling uncertainties, present in different real world systems e.g. binary distillation column. This paper is an attempt to propose an intelligent control system which takes the form of a fractional order fuzzy proportional–integral–derivative (FOFPID) controller which is investigated as a solution to deal with the complex dynamic nature of the distillation column. The FOFPID controller is an extension of an existing formula based self tuning fuzzy proportional integral controller structure, which varies its gains at run time in accordance with the instantaneous error and rate of change of error. The FOFPID controller is a Takagi–Sugeno (TS) model based fuzzy adaptive controller comprising of non-integer order of integration and differentiation operators used in the controller. It has been observed that inclusion of non-integer order of the integration and differentiation operators made the controller scheme more robust. For the performance evaluation of the proposed scheme, the performance of FOFPID controller is compared with that of its integer order counterpart, a fuzzy proportional–integral–derivative (FPID) controller. The parameters of both the controllers were optimized for minimum integral of absolute error (IAE) using a bio-inspired global optimization algorithm, genetic algorithm (GA). Intensive LabVIEWۛ simulation studies were performed which included setpoint tracking with and without uncertainties, disturbance rejection, and noise suppression investigations. For testing the parameter uncertainty handling capability of the proposed controller, uncertain and time varying relative volatility and uncertain tray hydraulic constant were applied. Also, for the disturbance rejection studies, intensive simulations were conducted, which included two most common causes of disturbance i.e. variation in feed composition and variation in feed flow rate. All the simulation investigations clearly suggested that FOFPID controller provided superior performance over FPID controller for each case study i.e. setpoint tracking, disturbance rejection, noise suppression and parameter uncertainties.
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    A Novel Augmented Fractional-Order Fuzzy Controller for Enhanced Robustness in Nonlinear and Uncertain Systems with Optimal Actuator Exertion
    (Springer, 2021-03) Mishra, Puneet
    An appropriate balance between the controller’s performance and its robustness is a complex design challenge. In order to enhance the control system performance, vigorous and rapid variations in controller output are often employed; however, this poses a critical challenge for practical implementations of the control schemes, as it may affect the actuator badly and may reduce its lifetime. To handle this issue, the current work presents an innovative control structure that aims to strike an apt balance between the control output variability while maintaining the desired control performance. The proposed control scheme exploits the abilities of fuzzy logic to cope with uncertainties in the system and along with the use of fractional calculus to enhance the control performance. Further, to evaluate the performance of the proposed improved fractional-order fuzzy controller (IFOFC), extensive simulation studies have been carried out on a multi-input–multi-output nonlinear system for a wide variety of test scenarios. An exhaustive comparative study with an inline state-of-art controller, i.e., fractional-order fuzzy PID (FOFPID) controller has also been carried out on two interesting performance measures. These performance measures include integral of time-weighted absolute error (ITAE) and another measure which corresponds to undesirable variations in the controller output, i.e., integral of the absolute change of torque (IACT). Based on the detailed simulation studies, it was found that the proposed control structure provides a fair balance between controller output aggression and control performance and also completely outperformed the FOFPID controller, even under large parametric variations.