• 한국지능시스템학회논문지
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• 제26권5호
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• pp.351-360
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• 2016

Rapid population growth with high living standards and high electronics use for personal comfort has raised the electricity demand exponentially. To fulfill this elevated demand, conventional energy sources are shifting towards low production cost and long term usable alternative energy sources. Hybrid renewable energy systems (HRES) are becoming popular as stand-alone power systems for providing electricity in remote areas due to advancement in renewable energy technologies and subsequent rise in prices of petroleum products. Wind and solar power are considered feasible replacement to fossil fuels as the prediction of the fuel shortage in the near future, forced all operators involved in energy production to explore this new and clean source of power. Presented paper proposes fuzzy logic based Energy Management System (EMS) for Wind Turbine (WT), Photo Voltaic (PV) and Diesel Generator (DG) hybrid micro-grid configuration. Battery backup system is introduced for worst environmental conditions or high load demands. Dump load along with dump load controller is implemented for over voltage and over speed protection. Fuzzy logic based supervisory control system performs the power flow control between different scenarios such as battery charging, battery backup, dump load activation and DG backup in most intellectual way.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.67-67
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• 2000

In this paper, a hybrid type fuzzy controller is proposed to maintain molten steel level stable and reliable manner in high speed continuous casting regardless of various disturbances such as casting speed change, tundish weight variation, 치ogging/undoning of SEN(Submerged Entry Nozzle), periodic bulgings, etc. To accomplish this purpose, hardware filter and software filer are carefully designed to eliminate high frequency noise and to smooth input signals from harsh environments. In order to minimize the molten steel level variations from various disturbances the controller uses hybrid type control term: fuzzy logic term, proportional term, differential term and nonlinear feedback compensation tenn. The proposed controller is applied tn commercial mini-mill plant and shows considerable improvement in minimizing the molten steel variation.

• 한국수소및신에너지학회논문집
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• 제15권1호
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• pp.1-11
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• 2004

Hybrid power systems with fuel cells and batteries have the potential to improve the operation efficiency and dynamic response. A proper load management strategy is important to better system efficiency and endurance in hybrid systems. In this paper, a fuzzy logic algorithm has been used to determine the fuel cell output power depending on the external required power and the battery state of charge(SoC). If the required power of the hybrid system is small and the SoC is small, then the greater part of the fuel cell power is used to charge the battery pack. If the required power is relatively big and the SoC is big, then fuel cell and battery are concurrently used to supply the required power. These IF-THEN operation rules are implemented by fuzzy logic for the energy management system of hybrid system. The strategy is evaluated by simulation. The results show that fuzzy logic can be effectively used to optimize the operational efficiency of hybrid system and to maintain the battery SoC properly.

• Journal of Power Electronics
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• 제12권5호
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• pp.787-799
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• 2012

Real and reactive power flows on a transmission line interact inherently. This situation degrades power flow controller performance when independent real and reactive power flow regulation is required. In this study, a quasi multi-pulse interline power flow controller (IPFC), consisting of eight six-pulse voltage source converters (VSC) switched at the fundamental frequency is proposed to control real and reactive power flows dynamically on a transmission line in response to a sequence of set-point changes formed by unit-step reference values. It is shown that the proposed hybrid fuzzy-PI commanded IPFC shows better decoupling performance than the parameter optimized PI controllers with analytically calculated feed-forward gains for decoupling. Comparative simulation studies are carried out on a 4-machine 4-bus test power system through a number of case studies. While only the fuzzy inference of the proposed control scheme has been modeled in MATLAB, the power system, converter power circuit, control and calculation blocks have been simulated in PSCAD/EMTDC by interfacing these two packages on-line.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권10호
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• pp.690-696
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• 2004

This paper is proposed a hybrid intelligent controller based on the vector controlled surface permanent magnet synchronous motor(SPMSM) drive system. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of SPMSM using neural network-fuzzy(NNF) control and speed estimation using artificial neural network(ANN) Controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

• 정보통신설비학회논문지
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• 제3권2호
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• pp.5-10
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• 2004

In this paper, the hybrid-type current controller for inverter TIG systems was implemented and it was shown that the low-current pulse wave forms with high dynamic performance could be obtained. It is not sri easy to obtain the optimum gain tuning of PID controllers in digital PWM control methods. Hybrid control methods which uses automatic tuning techniques after adding fuzzy control methods to traditional PID controllers are chosen to improve the dynamic performance of PID controller's. To demonstrate the practical significance and dynamic performance improvement of the results, some simulation and experimental results are presented.

• Smart Structures and Systems
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• 제33권6호
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• pp.431-447
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• 2024

The Hybrid Mass Damper (HMD) has proven effective in mitigating vibrations in high-rise structures subject to seismic and wind-induced excitations. One derivative configuration of the HMD mounts an Active Mass Damper (AMD) atop a Tuned Mass Damper (TMD). However, the control efficacy of such HMDs may be compromised when confronted with loads that exceed their design parameters. Additionally, the confined structural space within high-rise structures often limits the feasibility and economic viability of retrofitting HMD systems. This study introduces an Acceleration-based Fuzzy Power Approach Rate Sliding Mode Control (AFP-SMC) algorithm aimed at enhancing the control efficacy of HMDs while minimizing their stroke and force output requirements. Employing the Canton Tower as a research prototype, an analytical model incorporating HMDs was established, and a comparative analysis between the AFP-SMC and Linear Quadratic Gaussian (LQG) control algorithms was conducted for efficacy. The control performance of the AFP-SMC control algorithm under different control parameter variations was investigated. Furthermore, by experimentally assessing the AMD subsystem within the Canton Tower, friction and ripple force formulas were derived to bolster the analytical model, thereby validating the robustness of the AFP-SMC algorithm. The results show that the proposed AFP-SMC algorithm effectively reduces the vibration response of the structure and the stroke and control force output of HMDs, and exhibits superior overall control performance and robustness compared to the LQG algorithm.

• Journal of information and communication convergence engineering
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• 제19권4호
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• pp.214-220
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• 2021

In this study, we constructed several scenarios that are required for LED lighting, and we designed and implemented an LED lighting control system to operate these scenarios to confirm their behavior. An LED lighting control system is a hybrid control board that is designed by combining LED controllers and SMPS, consisting of an AC/DC power supply part that converts AC 220 V into DC 12 V, and a drive and control part that controls the scenario and color of the LED module. Conventional LED light controllers have an input power of DC 12 V, so when using the input AC 220 V, the SMPS must be connected to the LED light controller. To eliminate this inconvenience, a hybrid LED lighting control system was configured to combine LED lighting controllers and SMPS into one control system. Furthermore, we designed a control system to represent the most appropriate color according to the input of the distance and illumination using a fuzzy control system to conduct computer simulations.

• International Journal of Automotive Technology
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• 제7권2호
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• pp.167-172
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• 2006

A design of controller for vehicle dynamic control(VDC) and its implementation on the real vehicle were introduced. The controller has been designed using a three-degrees-of-freedom(3DOF) yaw plane vehicle, and the control algorithm was implemented on the vehicle by control prototyping system dSPACE. A hybrid control algorithm, which makes full use of the advantages of robust and fuzzy control, was adopted in the control system. Field test results show that the performance of the vehicle handling dynamics with hybrid controller is improved obviously compared to that without VDC and with simple robust controller on skiddy roads(friction coefficients lower than 0.3).

• International Journal of Control, Automation, and Systems
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• 제3권2호
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• pp.183-194
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• 2005

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the premise part of the rule-based structure of the gHFNN. The consequence part of the gHFNN is designed using PNNs. We distinguish between two types of the linear fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the consequence part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, the models are experimented with a representative numerical example. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when comparing with other neurofuzzy models.