Improvement of Dynamic Modeling of Supercapacitor by Residual Charge Effect Estimation

Torregrossa, Dimitri; Bahramipanah, Maryam; Namor, Emil; Cherkaoui, Rachid; Paolone, Mario

doi:10.1109/tie.2013.2259780

Cited by 79 publications

(54 citation statements)

References 16 publications

(39 reference statements)

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“…The dynamic characteristic of supercapacitor and its nonlinear leakage current behaviour limit the advantages of this model. There are studies that have tried to improve this model with more complex and elaborate RC nets [18]. …”

Section: Equivalent Electrical Modelmentioning

confidence: 99%

Supercapacitor Electro-Mathematical and Machine Learning Modelling for Low Power Applications

et al. 2018

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Low power electronic systems, whenever feasible, use supercapacitors to store energy instead of batteries due to their fast charging capability, low maintenance and low environmental footprint. To decide if supercapacitors are feasible requires characterising their behaviour and performance for the load profiles and conditions of the target. Traditional supercapacitor models are electromechanical, require complex equations and knowledge of the physics and chemical processes involved. Models based on equivalent circuits and mathematical equations are less complex and could provide enough accuracy. The present work uses the latter techniques to characterize supercapacitors. The data required to parametrize the mathematical model is obtained through tests that provide the capacitors charge and discharge profiles under different conditions. The parameters identified are life cycle, voltage, time, temperature, moisture, Equivalent Series Resistance (ESR) and leakage resistance. The accuracy of this electro-mathematical model is improved with a remodelling based on artificial neuronal networks. The experimental data and the results obtained with both models are compared to verify and weigh their accuracy. Results show that the models presented determine the behaviour of supercapacitors with similar accuracy and less complexity than electromechanical ones, thus, helping scaling low power systems for given conditions.

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Section: Equivalent Electrical Modelmentioning

confidence: 99%

Supercapacitor Electro-Mathematical and Machine Learning Modelling for Low Power Applications

et al. 2018

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“…Thus, the capacitance CH will discharge faster than the capacitance CD. Therefore, there will be a charge flow between CH and CD until the equilibrium state is reached [14].…”

Section: Equivalent Circuit Of Supercapacitormentioning

confidence: 99%

Measurement of Noise in Supercapacitors

Szewczyk

2017

Metrology and Measurement Systems

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A developed method and measurement setup for measurement of noise generated in a supercapacitor is presented. The requirements for noise data recording are considered and correlated with working modes of supercapacitors. An example of results of low-frequency noise measurements in commercially available supercapacitors are presented. The ability of flicker noise measurements suggests that they can be used to assess quality of tested supercapacitors.

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“…In order to better show the performance of the proposed method, we replaced one uncontrollable load with a high power/low energy storage device (ESS2) that represents a supercapacitor array (SC). We model the behaviour of the SC based on [11].…”

Section: Performance Evaluationmentioning

confidence: 99%

Real-time control of microgrids with explicit power setpoints: Unintentional islanding

Bernstein

Boudec

Reyes-Chamorro

et al. 2015

2015 IEEE Eindhoven PowerTech

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Abstract-We propose a method to perform a safe unintentional islanding maneuver of microgrids. The method is derived in the context of a framework for the real-time control of microgrids, called Commelec, recently proposed by the Authors. The framework uses a hierarchy of software agents that communicate with each other using a common, device independent protocol in order to define explicit power setpoints without the need of droop controllers. We show that the features of the framework allow to design a generic control method for treating unintentional islanding with the following properties. First, the method is able to choose the best candidate slack resource, based on the information obtained from the agents. Second, as the agent responsible for the grid has a global view of the network's status and its resources, it is possible to optimize the performance of the network during and after the islanding transition. Third, after the islanding maneuver it allows for the online switching of the slack resource to that with the best capabilities to face the network's needs. Finally, the method is suitable for inertia-less systems as the control is performed using explicit power setpoints and it does not rely on the frequency signal. We illustrate the benefits of the proposed method via simulation on the LV microgrid benchmark defined by the CIGRÉ Task Force C6.04.02, by comparing its performance to that of the standard droop-based method called load drop anticipator.

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Improvement of Dynamic Modeling of Supercapacitor by Residual Charge Effect Estimation

Cited by 79 publications

References 16 publications

Supercapacitor Electro-Mathematical and Machine Learning Modelling for Low Power Applications

Supercapacitor Electro-Mathematical and Machine Learning Modelling for Low Power Applications

Measurement of Noise in Supercapacitors

Real-time control of microgrids with explicit power setpoints: Unintentional islanding

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