Performance Test and System Uncertainty Analysis for Online Car-hailing Taxi Metering Terminal

2021 ◽  
Author(s):  
Rui Huang ◽  
Long Pei ◽  
Qinjuan Zhang ◽  
Lei Wang ◽  
Zhigang Yang ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
Performance Test ◽  
System Uncertainty

Application of Data Reconciliation Method to Increase CCPP Performance Test Result Accuracy

2015 ◽  
Author(s):  
Zengqian Wang ◽  
Jingjin Ji ◽  
Xinghao Wang ◽  
Bo Sun ◽  
Lei He ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
Power Output ◽  
Performance Test ◽  
Heat Rate ◽  
Performance Data ◽  
Data Reconciliation ◽  
Acceptance Test ◽  
Analysis Method ◽  
Reconciliation Method ◽  
Test Result

Performance acceptance test for gas-steam Combined Cycle Power Plant (CCPP) is of great significance for both equipment manufacturer and customer. The influence of measurement error on the calculation of guaranteed performance data as power output and heat rate can lead to unnecessary loss for either party. Commonly used uncertainty analysis method based on ASME PTC 19.1 would require all measuring instrumentation working at designed accuracy range. Meanwhile, due to the complexity of CCPP system and large number of measuring items, and as well the propagation of measurement and data reduction error, the uncertainty of corrected performance data could be significant. In this paper, process data reconciliation method based on VDI 2048 is introduced. With access to complete performance test data from a CCPP project, data reconciliation calculation is performed with an appropriate thermodynamic model. Several measurement values with gross error are identified and verified in heat balance calculation. Moreover, after recalculating with the reconciled data instead of raw data for the corrected power output and heat rate, comparison with the common uncertainty analysis method is also carried out. It is shown that with this reconciliation method, it is not only possible to find out gross errors such as instrumentation drift, but also able to dramatically increase the test result accuracy, which is of great value for both manufacturer and customer.

Verification and Validation in Computational Fluid Dynamics and Heat Transfer Using Experimental Uncertainty Analysis Concepts

2005 ◽  
Author(s):  
Hugh W. Coleman
Keyword(s):  
Heat Transfer ◽  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Uncertainty Analysis ◽  
Performance Test ◽  
Verification And Validation ◽  
Experimental Uncertainty ◽  
Uncertainty In Measurement ◽  
American Society ◽  
Expression Of Uncertainty

An approach to verification and validation (V&V) using experimental uncertainty analysis concepts to quantify the result of a validation effort is discussed. This is the approach to V&V being drafted by the American Society of Mechanical Engineers (ASME) Performance Test Code Committee, PTC 61: Verification and Validation in Computational Fluid Dynamics and Heat Transfer. The charter of the committee is “Provides procedures for quantifying the accuracy of modeling and simulation in computational fluid dynamics and heat transfer.” The committee is initially focusing its efforts on drafting a standard for V&V in computational fluid dynamics and heat transfer based on the concepts and methods of experimental uncertainty analysis. This will leverage the decades of effort in the community of experimentalists that resulted in the ASME Standard PTC 19.1 “Test Uncertainty” and the ISO international standard “Guide to the Expression of Uncertainty in Measurement.”

Integrating the ASME Performance Test Code 19.1 Approach of Calculating Measurement Uncertainty With the Regression Based Test Methods for Reporting Photovoltaic System Performance

2015 ◽  
Author(s):  
Cecil Lawrence
Keyword(s):  
Uncertainty Analysis ◽  
Measurement Uncertainty ◽  
High Performance ◽  
Power Plants ◽  
Performance Test ◽  
Performance Testing ◽  
Test Methods ◽  
Photovoltaic System ◽  
Section 8 ◽  
Rating Method

Solar Photovoltaic (PV) power plants have high performance test measurement uncertainty due to instrument precision limitations and spatial variations associated with irradiance and soiling measurement. Accurate prediction of the measurement uncertainty is critical for both the Owner and the EPC contractor to appropriately manage their risk. While there are several methods for testing the performance of PV plants, regression analysis based methods, like the PVUSA Method and the PPI rating method, are widely used. However, there is limited guidance on uncertainty analysis when using these methods. Most utilities and power producers have familiarity with the ASME PTC 19.1 code for measurement uncertainty analysis and often require the guidelines of PTC 19.1 be followed for evaluating the measurement uncertainty for the performance testing of PV plants. However there is lack of published literature on using the ASME PTC 19.1 approach with regression based PV performance test methods. This paper expands on the limited guidance provided by ASME PTC 19.1 Section 8-6 for regression based analysis and presents a detailed approach of calculating measurement uncertainty for PV power plants when using regression based testing methods. The paper also presents the importance of obtaining a good regression fit to the measurement uncertainty and elaborates on methods to reduce the measurement uncertainty. The overall approach discussed in this paper was applied on performance testing of two large utility-scale PV plants.

An Efficient Approach to Power System Uncertainty Analysis With High-Dimensional Dependencies

2018 ◽  
Vol 33 (3) ◽  
pp. 2984-2994 ◽  
Author(s):  
Yi Wang ◽  
Ning Zhang ◽  
Chongqing Kang ◽  
Miao Miao ◽  
Rui Shi ◽  
...  
Keyword(s):  
Power System ◽  
Uncertainty Analysis ◽  
High Dimensional ◽  
System Uncertainty ◽  
Efficient Approach

Instrumentation Selection and Uncertainty Analysis for Performance Test of Small Centrifugal Compressors

2004 ◽  
Author(s):  
Amro M. Al-Qutub ◽  
Fahad A. Al-Sulaiman
Keyword(s):  
Uncertainty Analysis ◽  
Performance Test ◽  
Correct Selection ◽  
Small Scale ◽  
Performance Parameters ◽  
Centrifugal Compressors ◽  
Accuracy Of Results ◽  
Lower Uncertainty ◽  
Selection Of

In the design phase of centrifugal compressors, it is essential to have some experimental results on performance. The extent of usefulness of the experiments depends on quality and accuracy of the results. Part of proper experimental procedure is the correct selection of instrumentation leading to lower uncertainty in the final results. ASME PTC 10 (Performance Test Code on Compressors and Exhausters) requires fluctuation limits on the measured performance parameters. This does not guarantee limits for accuracy of performance parameters. Also, different experimental setup will affect uncertainty of the results, even with similar instrumentation accuracy. The present research deals with uncertainty analysis for performance evaluation of small-scale centrifugal compressor. The instrumentation errors are accommodated in the relation to ASME PTC 19.1 (test uncertainty). The analysis takes into consideration the correlated bias limits. Selection of proper type of instruments for measuring associated parameters is based on literature review. A case study is included as an example to illustrate the selection on instrumentation accuracy and preferred bias correlations. The analysis is a useful tool in designing experiments for testing compressor and optimizing accuracy of results.

Uncertainty Analysis for One Direction Singular Rough Sets System

2013 ◽  
Vol 671-674 ◽  
pp. 3170-3173
Author(s):  
Zhi Lin Guo
Keyword(s):  
Uncertainty Analysis ◽  
Approximate Method ◽  
Rough Sets ◽  
Relative Precision ◽  
System Uncertainty

In view of the system uncertainty due to the variety of singular rough sets， boundary, the definitions of inside and outside boundary are given. Some properties of inside and outside boundary are discussed, and the measure of relative precision is provided. Furthermore, a approximate method which aims at dealing with system uncertainty is also obtained.

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