Load frequency control of a diverse energy source integrated hybrid power system with a novel hybridized harmony search-random search algorithm designed Fuzzy-3D controller

Bhatta, Sunil Kumar; Mohapatra, Srikanta; Sahu, Prakash Chandra; Swain, Sarat Chandra; Panda, Sidhartha

doi:10.1080/15567036.2021.1970860

Cited by 23 publications

(7 citation statements)

References 30 publications

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“…However, the hybrid EV control scheme did not improve the output voltage. [27][28][29][30] To overcome these several issues, the quantile regressive extreme seeking cat swarm optimized Mamdani fuzzy PI controller (QRESCSO-MFPIC) approach is proposed. Simulation results of FL controller-based proportional-integral sampling waveform show better timing and less peak overshoot.…”

Section: Motivation and Research Gapmentioning

confidence: 99%

“…To manage energy sources effectively with the load demand by generating a nonlinear controller based on fuzzy logic (FL). However, the hybrid EV control scheme did not improve the output voltage 27–30 . To overcome these several issues, the quantile regressive extreme seeking cat swarm optimized Mamdani fuzzy PI controller (QRESCSO‐MFPIC) approach is proposed.…”

Section: Introductionmentioning

confidence: 99%

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A novel cat swarm optimization‐based fuzzy PI controller for improving dynamic response of converter

Priya,

Balakumaran

2023

Asia-Pacific J Chem Eng

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Nowadays, the use of fuel cells in various applications has become very widespread. Some of their most important applications are in electric vehicles (EVs) and local off‐grid power systems. An EV works as an electric motor that burns a mixture of fuel and gases to generate electricity. One of the challenges with fuel cells is the slow dynamic response to load power changes. In the past few decades, the modeling and control of DC‐DC converters have undergone extensive research and development. However, the error, settling time, and peak overshoot performance are not reduced by the robust nonlinear controller and current mode controller. To overcome these problems, quantile regressive extreme seeking cat swarm optimized Mamdani fuzzy PI controller (QRESCSO‐MFPIC) approach is developed. The goal of the QRESCSO‐MFPIC approach is to reduce the integral time absolute error (ITAE) for tuning the fuzzy PI controller. Initializing the fuzzy PI controller parameter is an input in the QRESCSO‐MFPIC approach. For every parameter value, the fitness function is determined and quantile regression analysis is carried out. The efficiency of the QRESCSO‐MFPIC approach is measured by evaluating the settling time and peak overshoot. Tuning of the PI controller is carried out with cat swarm optimization (CSO) and particle swarm optimization (PSO) based on the objective function of ITAE. The result analysis shows that the QRESCSO‐MFPIC approach improves the efficiency of an optimized Fuzzy PI controller compared to the existing methods. Peak overshoot reduction and settling time are improved through the QRESCSO‐MFPIC approach than the proportional integral particle swarm optimization controller (PI‐PSO).

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Section: Motivation and Research Gapmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel cat swarm optimization‐based fuzzy PI controller for improving dynamic response of converter

Priya,

Balakumaran

2023

Asia-Pacific J Chem Eng

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“…Hypothetical vibration method is adopted to obtain an approximate solution to the blade vibration equation. The hypothetical vibration method considered that the solution to the vibration problem of a continuous system is a spatial coordinate function multiplied by a time-independent generalized coordinate, assuming that the solution of the rotating blade is shown in Equation (7).…”

Section: Wind Turbine Blade Modelmentioning

confidence: 99%

“…Due to the difficulties in modeling wind turbine blade systems, 5 modeling methods that do not depend on the system model have been welcomed by experts such as fuzzy control, neuronal network control, auto-disturbance resistant control, and so forth. 6 Fuzzy control 7 and neural network control are generally used in conjunction with other controllers and are often used to observe disturbances or to optimize controller parameters. Song et al proposed an improved immpa algorithm to find the optimal weight coefficients to optimize the fuzzy regulator and improve the accuracy of the fuzzy regulator, thus reducing the torque fluctuation of the engine.…”

Section: Introductionmentioning

confidence: 99%

ADRC‐based optimized control system for wind turbine power generator

2023

Adv Control Appl

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As a renewable and clean energy source, wind energy can be converted into electricity by wind turbines. Currently, it is challenging to suppress the edgewise vibration of the wind turbine blades to improve the efficiency and service life of wind turbines. In this paper, active disturbance rejection control (ADRC)is applied to vibration suppression at the blade edge of wind turbines. First, the proposed system is mathematically modeled and analyzed in the frequency domain. Second, the ADRC is designed for the proposed system. Then, time domain analysis, frequency domain analysis, and uncertainty analysis of the closed‐loop system were carried out. It has been shown that the proposed controller has 12.2% less overshoot and 55.8% less setting time when subjected to perturbations than the PID controller. And the proposed controller has 33.9% less overshoot and 55.9% less setting time when subjected to perturbations than the SMC. Furthermore, the resonance frequency range of the proposed controller has been effectively reduced, and the peak resonance has been reduced by 41.7%. This reflects that the ADRC has a significant vibration suppression effect on blade edgewise vibrations compared with PID controllers and SMC.

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“…Hydro unit ; The individual components of the hydro system are modeled 31 as.

Governor modeling G_{G} ((), s) goodbreak= \frac{∆ P_{V} ((), s)}{∆ P_{g} ((), s)} goodbreak= \frac{1}{T_{gh} ((), s) + 1}

Droop modeling G_{D} ((), s) goodbreak= \frac{∆ P_{V 1} ((), s)}{∆ P_{V} ((), s)} goodbreak= \frac{T_{rs} ((), s) + 1}{T_{rh} ((), s) + 1}

Penstock modeling G_{P} ((), s) goodbreak= \frac{∆ P_{T} ((), s)}{∆ P_{V 1} ((), s)} goodbreak= \frac{- T_{w} ((), s) + 1}{0.5 T_{w} ((), s) + 1}

…”

Section: System Modelingmentioning

confidence: 99%

Analysis of Gaussian fuzzy logic‐sliding model control and flexible AC transmission systems controllers for automatic generation control of hybrid power system under chaotic‐water cycle algorithm approach

Mahapatra¹,

Samal

Mohapatra

et al. 2021

Int Trans Electr Energ Syst

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The article has suggested a hybridized fuzzy logic-sliding model control (FL-SMC) approach for automatic generation control (AGC) of a hybrid power system under various uncertainties. The hybrid structure makes modern power system more complex, and the inherent uncertain of renewable sources further causes power imbalance and frequency oscillations in the system. The AGC incorporates power generation monitoring and consequent frequency control of the hybrid system irrespective of the load demand. To obtain automatic control over power generation, various control actions are employed as secondary loop in the system. This study proposes FL-SMC as secondary controller for monitoring power generation of multigrid power system. Further, a novel chaotic-water cycle algorithm (C-WCA) has been suggested for optimum parameter selection of the proposed controller under different disturbances. The work also has implemented an interline power flow controller to improve AGC of hybrid system. The effectiveness of the proposed controller over sliding mode controller, conventional fuzzy-PID, and PID controller has been synthesized to justify its superiority. The robustness of the proposed controller has been examined through a critical sensitive analysis. Finally, it has been observed that proposed controller advances the settling time of ΔF 1 by 41%, 61.4%, and 86.4% over sliding mode controller, fuzzy-PID, and PID controller, respectively.

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Load frequency control of a diverse energy source integrated hybrid power system with a novel hybridized harmony search-random search algorithm designed Fuzzy-3D controller

Cited by 23 publications

References 30 publications

A novel cat swarm optimization‐based fuzzy PI controller for improving dynamic response of converter

A novel cat swarm optimization‐based fuzzy PI controller for improving dynamic response of converter

ADRC‐based optimized control system for wind turbine power generator

Analysis of Gaussian fuzzy logic‐sliding model control and flexible AC transmission systems controllers for automatic generation control of hybrid power system under chaotic‐water cycle algorithm approach

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