Identification of Dynamic Load Model Parameters Using Particle Swarm Optimization

Kim, Young-Gon; Song, Hwachang; Lee, Byongjun

doi:10.5391/ijfis.2010.10.2.128

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…7(d) shows the limited current value considering the rated current of the switching elements. According to the increase in the dclink current in the mode-2 section to increase the active power, it can be confirmed that the peak of the ac-side current reference is reduced by the current limit condition of (23). In the case of the mode-3 section to the grid-side undervoltage conditions, the active power is reduced by the current limitation condition considering the arm capacitor voltage ripple.…”

Section: Current Limit Methods Considering Sm Capacitor Voltage Ripplementioning

confidence: 62%

Design and Control Method for Sub-module DC Voltage Ripple of HVDC-MMC

Gwon¹,

Park²,

Kang³

et al. 2016

Journal of Electrical Engineering and Technology

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-This paper proposes a design and control method for a high-voltage direction current modular multilevel converter (HVDC-MMC) considering the capacitor voltage ripple of the submodule (SM). The capacitor voltage ripple consists of the line frequency and double-line-frequency components. The double line-frequency component does not fluctuate according to the active power, whereas the line-frequency component is highly influenced by the grid-side voltage and current. If the grid voltage drops, a conventional converter increases the current to maintain the active power. A grid voltage drops, current increment, or both occur with a capacitor voltage ripple higher than the limit value. In order to reliably control an MMC within a limit value, the SM capacitor should be designed on the basis of the capacitor voltage ripple. In this paper, the capacitor voltage ripple according to the grid voltage and current are analyzed, and the proposed control method includes a current limitation method considering the capacitor voltage ripple. The proposed design and control method are verified through simulation using PSCAD/EMTDC.

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Section: Current Limit Methods Considering Sm Capacitor Voltage Ripplementioning

confidence: 62%

Design and Control Method for Sub-module DC Voltage Ripple of HVDC-MMC

Gwon¹,

Park²,

Kang³

et al. 2016

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

show abstract

“…The BP algorithm is suitable for solving the complicated nonlinear problem, but the algorithm usually falls into local minimum, resulting in training failure. In [26], the authors proposed a method for estimating the parameters of dynamic models for induction motor dominating loads. Based on PSO, the method finds the adequate set of parameters that best fit the sampling data from the measurement for a period of time, minimizing the error of the outputs and active and reactive power demands.…”

Section: Optimization Of Initial Value Of Adjustable Parametersmentioning

confidence: 99%

Fuzzy Neural Network Control of Thermostatically Controlled Loads for Demand-Side Frequency Regulation

et al. 2019

Energies

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In this paper, a fuzzy neural network controller for regulating demand-side thermostatically controlled loads (TCLs) is designed with the aim of stabilizing the frequency of the smart grid. Specifically, the balance between power supply and demand is achieved by tracking the automatic generation control (AGC) signal in an electric power system. The particle swarm optimization (PSO) and error back propagation (BP) algorithms are used to optimize the control parameters and consequently reduce the tracking errors. The fuzzy neural network can be applied to solve load control problems in power systems, since its self-learning and associative storage functions can deal with the highly nonlinear relationship between input and output. Simulation results show the advantage of the fuzzy neural network control scheme in terms of frequency regulation error and consumer comfort.

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Identification of Dynamic Load Model Parameters Using Particle Swarm Optimization

Cited by 2 publications

References 17 publications

Design and Control Method for Sub-module DC Voltage Ripple of HVDC-MMC

Design and Control Method for Sub-module DC Voltage Ripple of HVDC-MMC

Fuzzy Neural Network Control of Thermostatically Controlled Loads for Demand-Side Frequency Regulation

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