scholarly journals Analysis of the effect of ionic conductivity of electrolyte materials on the solid oxide fuel cell performance

2021 ◽  
Vol 3 (6 (111)) ◽  
pp. 41-52
Author(s):  
Mega Nur Sasongko ◽  
Fahrizal Perdana ◽  
Widya Wijayanti
Keyword(s):  
Ionic Conductivity ◽  
Power Output ◽  
Simulation Analysis ◽  
Operating Temperature ◽  
Calculation Error ◽  
Power Performance ◽  
Fuel Cell Performance ◽  
Yttria Content ◽  
Ysz Electrolyte ◽  
A Current

SOFC solid electrolytes are known for their ionic conductivity characteristics, which increase with increasing SOFC operating temperature. Using COMSOL Multiphysics numerical simulation, analysis of SOFC power performance with yttria-stabilized zirconia (YSZ) and lithium sodium carbonate – gadolinium-doped ceria ({LiNa}2CO3-GDC) electrolytes was conducted to determine the potential of these electrolytes in their application in SOFC. The ionic conductivity of YSZ was differentiated based on the mole value of the yttria content, namely 8, 8.95, 10 and 11.54 mol. Meanwhile, GDC varied based on the (LiNa)2CO3 content such as 7.8, 10, 16.8 and 30 %. With the numerical model, the calculation error is an average of 7.32 % and 6.89 % for the experimental power and voltage values. In SOFC with the YSZ electrolyte, it was found that the power output can increase 26.4–35 times with an increase in operating temperature from 500 °C to 750 °C. SOFC with 8YSZ can produce the highest power compared to other YSZ, which is 123 A/m2 at a current of 198 A/m2 with an operating temperature of 500 °C and 3,440 A/m2 at a current of 5,549 A/m2 with an operating temperature of 750 °C. Whereas in SOFC with the GDC electrolyte, it was found that the power output can increase 18.6–22.6 times with an increase in operating temperature from 500 °C to 750 °C. SOFC with 30 % (LiNa)2CO3-GDC produced the highest power compared to other GDC, which is 231 A/m2 at a current of 444 A/m2 with an operating temperature of 500 °C and 5,240 A/m2 at a current of 10,077 A/m2 with an operating temperature of 750 °C. YSZ also showed the potential for an increase in power output as the SOFC temperature increases above 750 °C, while the 30 % variation (LiNa)2CO3-GDC shows a limited increase in ionic conductivity at 750 °C

Geometrical Optimization of Double Layer LSM/LSM-YSZ Cathodes by Electrochemical Impedance Spectroscopy

2009 ◽  
Vol 7 (1) ◽  
Author(s):  
Rui Antunes ◽  
Tomasz Golec ◽  
Mirosław Miller ◽  
Ryszard Kluczowski ◽  
Mariusz Krauz ◽  
...  
Keyword(s):  
Electrochemical Impedance Spectroscopy ◽  
Impedance Spectroscopy ◽  
Layer Thickness ◽  
Electrochemical Impedance ◽  
Operating Temperature ◽  
Contact Layer ◽  
Cell Performance ◽  
Functional Grade ◽  
Functional Layer ◽  
Ysz Electrolyte

The present-day high-temperature solid oxide fuel cells (SOFCs), based on yttria-stabilized zirconia (YSZ) electrolyte, a lanthanum-strontium manganite (LSM) cathode and a nickel-YSZ cermet anode, operate at 800–1000°C. Cathode materials are restricted to doped lanthanum manganites due to their stability in oxidizing atmosphere, sufficient electrical conductivity, and thermal expansion match to the YSZ electrolyte. Reduction in the operating temperature of SOFCs is desirable to lower the costs and to overcome the technological disadvantages associated with elevated temperatures. However, as the operating temperature is reduced, the decrease in the LSM conductivity and increase in interfacial polarization resistances between the LSM cathode and YSZ electrolyte become critical. Therefore, different approaches have been proposed to improve interfacial quality and electrochemical performance of the LSM/YSZ cathode. The length of the triple-phase boundary (TPB) correlates well with the interfacial resistances to electrochemical oxidation of hydrogen at the anode and reduction in oxygen at the cathode. The extension of the TPB or the number of active reaction sites becomes, therefore, a determining factor in improving electrode performance. This can be achieved by developing electrode materials of higher ambipolar conductivity and by optimizing the microstructure of the electrodes. In order to improve SOFC performance, both composition and structure of the LSM/YSZ interface and of the cathode should be optimized. Recently, functional grade materials (FGMs) were introduced for SOFC technology. However, all studies reported in the literature so far, were focused on cathodes with only compositional gradient. On the other hand, intuitionally the best structure for a functional SOFC should be characterized by both compositional and porosity gradients. Fine grains (and high surface area) close to the electrode/electrolyte surface and large grains (and thus large pore size) at the air/oxygen side are expected to be of advantage. In the present study, “symmetrical” cathode-electrolyte-cathode SOFC single cells were fabricated. The cells consisted of the functional grade LSM cathode with YSZ/LSM cathode functional layer and LSM contact layer. The effects of various geometrical and microstructural parameters of cathode/functional layers on the overall cell performance were systematically investigated. The parameters investigated were the (1) cathode functional layer thickness and grain size and (2) the LSM contact layer thickness. Cathode performances were tested by means of electrochemical impedance spectroscopy (EIS) over a temperature range of 650–950°C, using air as oxidant. The dependence of cell performance on various parameters was rationalized by a comprehensive microscale model. A cathode polarization corresponding to 0.14–0.4 Ω cm2 at 750°C was achieved in this manner.

scholarly journals Short Time Solar Power Forecasting Using Persistence Extreme Learning Machine Approach

2021 ◽  
Vol 294 ◽  
pp. 01002
Author(s):  
Xiaoyan Xiang ◽  
Yao Sun ◽  
Xiaofei Deng
Keyword(s):  
Solar Energy ◽  
Extreme Learning Machine ◽  
Power Output ◽  
Smart Grids ◽  
Large Scale ◽  
Solar Power ◽  
Learning Algorithm ◽  
Power Performance ◽  
Energy Prediction ◽  
Learning Machine

Solar energy in nature is irregular, so photovoltaic (PV) power performance is intermittent, and highly dependent on solar radiation, temperature and other meteorological parameters. Accurately predicting solar power to ensure the economic operation of micro-grids (MG) and smart grids is an important challenge to improve the large-scale application of PV to traditional power systems. In this paper, a hybrid machine learning algorithm is proposed to predict solar power accurately, and Persistence Extreme Learning Machine(P-ELM) algorithm is used to train the system. The input parameters are the temperature, sunshine and solar power output at the time of i, and the output parameters are the temperature, sunshine and solar power output at the time i+1. The proposed method can realize the prediction of solar power output 20 minutes in advance. Mean absolute error (MAE) and root-mean-square error (RMSE) are used to characterize the performance of P-ELM algorithm, and compared with ELM algorithm. The results show that the accuracy of P-ELM algorithm is better in short-term prediction, and P-ELM algorithm is very suitable for real-time solar energy prediction accuracy and reliability.

Fabrication of high performance oxygen sensors using multilayer oxides with high interfacial conductivity

2016 ◽  
Vol 4 (29) ◽  
pp. 11422-11429 ◽  
Author(s):  
Lei Yao ◽  
Gang Ou ◽  
Wei Liu ◽  
Xiaohui Zhao ◽  
Hiroki Nishijima ◽  
...  
Keyword(s):  
Ionic Conductivity ◽  
High Performance ◽  
Operating Temperature ◽  
Oxygen Sensors

Fabrication of oxygen sensors using Ce0.8Sm0.1Nd0.1O2−δ/Al2O3multilayered electrolytes with high interfacial ionic conductivity which endows the sensors with low operating temperature and excellent performances.

Design of Modifications for Constructional Elements of Manipulator Arm Aimed at Measuring of Operational Torque

2015 ◽  
Vol 816 ◽  
pp. 261-269
Author(s):  
Miroslav Pástor ◽  
František Trebuňa ◽  
František Šimčák ◽  
Ľubomír Gabáni ◽  
Ladislav Vargovčík
Keyword(s):  
Power Output ◽  
Engineering Practice ◽  
Robot Arm ◽  
Technical Device ◽  
Constructional Element ◽  
Manipulator Arm ◽  
Current Operation ◽  
The Individual ◽  
The Given ◽  
A Current

There are often designed such constructional elements in the engineering practice that are specified for sensing of operational loading and for a precise setting of the individual components in the given technical device, for example components of a robot arm. Positioning of the robot arms is regulated by means of power output setting (or torque setting) of the driving parts (motors) predominately. Driving effect is transferred between the individual components by means of the connecting parts designed for transfer of force flow. There is described in this paper such design of modification for constructional element of the robot arm, which enables to perform measuring of torque in the joining parts of the robot arm during a current operation.

Study of Influence on Power System Voltage Stability of Wind Farm Power Output Fluctuation

2013 ◽  
Vol 694-697 ◽  
pp. 846-849
Author(s):  
Jian Yuan Xu ◽  
Wei Fu Qi ◽  
Yun Teng
Keyword(s):  
Power System ◽  
Wind Power ◽  
Power Output ◽  
Voltage Stability ◽  
Wind Farm ◽  
Reactive Power ◽  
Simulation Analysis ◽  
Power Grid ◽  
Reactive Power Compensation ◽  
Grid Integration

This paper mainly studies wind power fluctuations how to affect voltage stability after the wind power grid integration, and reactive power compensation equipment on improving effect. In certain parts of the wind farm, for example, firstly, analyzing the wind farm reactive power problems. Then introduce the reactive power compensation equipment that used in the wind farm. Finally, with PSCAD software, making a simulation analysis about the influence on the power grid voltage according to adopting the different reactive power compensation devices or not.

scholarly journals Monitoring offshore wind farm power performance with SCADA data and an advanced wake model

2017 ◽  
Vol 2 (1) ◽  
pp. 175-187 ◽  
Author(s):  
Niko Mittelmeier ◽  
Tomas Blodau ◽  
Martin Kühn
Keyword(s):  
Power Output ◽  
Wind Farm ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Power Performance ◽  
Power Normalization ◽  
Efficient Detection ◽  
Asset Value ◽  
Output Ratio ◽  
Wake Model

Abstract. Wind farm underperformance can lead to significant losses in revenues. The efficient detection of wind turbines operating below their expected power output and immediate corrections help maximize asset value. The method, presented in this paper, estimates the environmental conditions from turbine states and uses pre-calculated lookup tables from a numeric wake model to predict the expected power output. Deviations between the expected and the measured power output ratio between two turbines are an indication of underperformance. The confidence of detected underperformance is estimated by a detailed analysis of the uncertainties of the method. Power normalization with reference turbines and averaging several measures performed by devices of the same type can reduce uncertainties for estimating the expected power. A demonstration of the method's ability to detect underperformance in the form of degradation and curtailment is given. An underperformance of 8 % could be detected in a triple-wake condition.

scholarly journals Monitoring offshore wind farm power performance with SCADA data and advanced wake model

2016 ◽  
Author(s):  
Niko Mittelmeier ◽  
Tomas Blodau ◽  
Martin Kühn
Keyword(s):  
Power Output ◽  
Wind Farm ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Power Performance ◽  
Efficient Detection ◽  
Asset Value ◽  
Measurement Devices ◽  
Wake Model ◽  
Measured Power

Abstract. Wind farm underperformance can lead to significant losses in revenues. Efficient detection of wind turbines operating below their expected power output and immediate corrections help maximise asset value. The presented method estimates the environmental conditions from turbine states and uses pre-calculated power matrices from a numeric wake model to predict the expected power output. Deviations between the expected and the measured power output are an indication of underperformance. The confidence of detected underperformance is estimated by detailed analysis of uncertainties of the method. Power normalisation with reference turbines and averaging several measurement devices can reduce uncertainties for estimating the expected power. A demonstration of the method’s ability to detect underperformance in the form of degradation and curtailment is given. Underperformance of 8 % could be detected in a triple wake condition.

Short Circuit Ruggedness of 600 V SiC Trench JFETs

2020 ◽  
Vol 1004 ◽  
pp. 933-938
Author(s):  
Vinoth Sundaramoorthy ◽  
Lukas Kranz ◽  
Stephan Wirths ◽  
Marco Bellini ◽  
Gianpaolo Romano ◽  
...  
Keyword(s):  
Silicon Carbide ◽  
High Power ◽  
Operating Temperature ◽  
Short Circuit ◽  
Short Circuit Current ◽  
State Resistance ◽  
Resistance Value ◽  
A Current ◽  
Short Circuit Condition

Silicon Carbide JFETs with low on-state resistance are suitable for a number of high power applications. The static, dynamic and short circuit characterization of 600 V SiC Trench JFETs are reported in this paper. Typical JFETs fabricated with a 1.2 μm cell pitch had an on-resistance value around 40 mΩ and blocking voltages of ~600 V across the wafer. JFETs were successfully switched with a dc link voltage of 300 V, a current of 15 A and operating temperature of 125 °C. These JFETs were subjected to a short circuit condition with duration ranging from 10 μs to 45 μs at a dc link voltage of ~300 V, and operating temperatures of 25 °C and 125 °C. The device could withstand subsequent short circuit successfully without any failure at both 25 °C and 125 °C. The short circuit current showed consistent dependency on the applied gate voltage, when it was varied from 0 V to 15 V.

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