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.

Energy Efficiency by Optimizing Annual Testing Schedules: Coordinating RATA Testing With Other Annual Test Requirements

2009 ◽  
Author(s):  
Tina Toburen ◽  
Allen Kephart ◽  
Rhonda Walker
Keyword(s):  
Fuel Consumption ◽  
Performance Test ◽  
Performance Testing ◽  
Heat Rate ◽  
Maximum Load ◽  
Performance Data ◽  
High Price ◽  
Monitoring Systems ◽  
Partial Load ◽  
Test Requirements

Nearly every power plant in the US must undergo annual Relative Accuracy Test Audits (RATA testing) to confirm the values reported by their continuous emission monitoring systems (CEMS). In order to perform a RATA test, the plant must operate at one or more stable loads for a number of hours. Depending on the type of unit and fuel, the required load levels for RATA testing can range from low, mid and high loads for coal-fired units to a single (normal) load for oil and gas fired units or four loads (from partial load to maximum load) for units utilizing 40 CFR Part 75 Appendix E alternative monitoring systems. Many plants operate in a dispatch environment where the plant is not in control of their load from hour to hour, and some even from minute to minute, such as those operating under Automatic Generation Control (AGC). Scheduling plant loads for the RATA testing must often be done far in advance and can come at a high price when factoring in fuel costs. Because it can be a significant undertaking to schedule the loads for a series of RATA tests, it makes economic sense to schedule other testing also requiring unit stability concurrently with the RATA tests. One of the most important tests that fits this category is performance testing for plant capacity and/or heat rate. Many plants are now required to perform capacity and/or heat rate demonstrations on a periodic basis to support their power purchase agreements or transmission reliability requirements. But even plants without performance test requirements can benefit from gathering performance related data during RATA testing. For plants dispatched based on demonstrated heat rates, understanding the heat rate impact of operating in AGC or at partial loads is essential. Awareness of expected heat rate is also vital for plants that must nominate their fuel consumption requirements in advance. If the RATA test loads are planned correctly, performance data collected during the RATA test periods can be used not only to fulfill required demonstrations for capacity and heat rate, but also to determine the actual annual degradation (recoverable and non-recoverable) observed for the plant equipment. Test data can also be used to build or update performance forecasting tools for dispatch purposes. Depending on the complexity of the RATA testing, multiple load points may be available (from minimum to maximum load) which can provide data on fuel consumption at various loads, supporting fuel purchasing and planning requirements for the plant. This paper intends to outline the value of coordinating annual performance tests with RATA tests in terms of manpower, load scheduling and fuel consumption. This paper will also discuss a number of issues that may arise when coordinating multiple tests — which could be performed by numerous independent parties, as well as the additional benefits which can be gained by collecting adequate performance data during RATA test periods.

Combined Measurement Uncertainty on Two Dependent Performance Tests

2005 ◽  
Author(s):  
Mehdi Soltani ◽  
Ron Dieck ◽  
Sang Ip
Keyword(s):  
Power Plant ◽  
Uncertainty Analysis ◽  
Measurement Uncertainty ◽  
Performance Test ◽  
Heat Rate ◽  
Combined Cycle ◽  
Combined Cycle Power Plant ◽  
Systematic Uncertainties ◽  
Electrical Output ◽  
Test Uncertainty

Measurement uncertainty analysis plays an important role in the evaluation process of the net electrical output and the net heat rate during a power plant performance test. Aside from the technical aspect of the test, it bears very significant commercial consequences for the test parties. The ASME Performance Test Codes (PTC19.1) provides elaborative guidelines for test uncertainty analysis. In a combined cycle power plant test, the test uncertainty is heavily influenced by the measuring devices, corrections to reference conditions, and the method by which the test is conducted. A rigorous measurement uncertainty analysis is required to document and minimize the potential gap between the test results and the “actual true” net electrical output and net heat rate of the plant. The purpose of this paper is to estimate the measurement uncertainty of a combined cycle power plant consisting of two power trains. It includes the consideration of correlated uncertainties. Each power train is comprised of a gas turbine, a Heat Recovery Steam Generator (HRSG) and a steam turbine. For the multiple train power plants, some of the measured parameters are not independent and therefore the systematic errors are partially correlated. In this paper, the correlated systematic uncertainties and their contribution to the total uncertainty are evaluated. The uncertainty results are compared with the case when the systematic uncertainties resulting from correlated errors are ignored. Not properly considering the correlated terms may under estimate the uncertainties by over 30%.

Uncertainty Analysis of Centrifugal Compressor Aero-Performance Test Data: Effects of Correlated Systematic Error

2006 ◽  
Author(s):  
Jose´ L. Gilarranz R.
Keyword(s):  
Uncertainty Analysis ◽  
Test Data ◽  
Centrifugal Compressor ◽  
Performance Test ◽  
Equations Of State ◽  
Uncertainty Propagation ◽  
Acceptance Test ◽  
Performance Measurements ◽  
Monte Carlo Simulation Technique ◽  
Real Gas Equations

This paper presents the continuation of the work performed during the development of an uncertainty analysis method for estimating error levels in data gathered during factory aero-performance acceptance tests of centrifugal compressors. The previous work incorporated the effects of the variation and uncertainty levels associated with every parameter used in the calculation of centrifugal compressor aero-thermal performance. The work discussed herein focuses on the effects of the variation and uncertainty levels associated with the key measured variables, which are the parameters identified as having the greatest effect on the uncertainty of the performance measurements. Also included in this work is an evaluation of the effects of the correlated bias uncertainty components associated with said key variables, as well as comments on how these effects can be harnessed to reduce the uncertainty of the test data. The evaluation is performed via parametric studies, which present the test uncertainty levels achievable as a function of different correlation levels between the systematic uncertainty components of the measured data. Two different methods are used for the analysis of data measured for several machines. The first method is based on the direct use of the Monte Carlo simulation technique combined with real gas equations of state. The second method employs uncertainty propagation equations and the methodology included in the ASME PTC-19.1(1998) test code. Both approaches use the polytropic compression model and equations for performance evaluation included in the ASME PTC 10 (1997) Power Test Code. Data gathered during an on-site acceptance test of a centrifugal gas compression package are used to illustrate the effects of the uncertainty in the knowledge of the gas composition handled by the compressor over the uncertainty levels that can be obtained with this type of tests.

Predicting Graceful Extensibility of Human-Machine Systems: A New Analysis Method for Evaluating Extensibility Plots to Anticipate Distributed System Performance

2020 ◽  
Vol 64 (1) ◽  
pp. 313-318
Author(s):  
Dane A. Morey ◽  
Jesse M. Marquisee ◽  
Ryan C. Gifford ◽  
Morgan C. Fitzgerald ◽  
Michael F. Rayo
Keyword(s):  
Artificial Intelligence ◽  
Distributed System ◽  
System Performance ◽  
Healthcare Setting ◽  
Evaluation Methods ◽  
Performance Data ◽  
Analysis Method ◽  
Machine System ◽  
Machine Systems

With all of the research and investment dedicated to artificial intelligence and other automation technologies, there is a paucity of evaluation methods for how these technologies integrate into effective joint human-machine teams. Current evaluation methods, which largely were designed to measure performance of discrete representative tasks, provide little information about how the system will perform when operating outside the bounds of the evaluation. We are exploring a method of generating Extensibility Plots, which predicts the ability of the human-machine system to respond to classes of challenges at intensities both within and outside of what was tested. In this paper we test and explore the method, using performance data collected from a healthcare setting in which a machine and nurse jointly detect signs of patient decompensation. We explore the validity and usefulness of these curves to predict the graceful extensibility of the system.

A Digital Computer Program for Processing Turbine-Generator Acceptance Test Data

1962 ◽  
Vol 84 (3) ◽  
pp. 295-304 ◽  
Author(s):  
G. A. Maneatis ◽  
W. H. Barr
Keyword(s):  
Thermodynamic Properties ◽  
Computer Program ◽  
Steam Turbine ◽  
Test Data ◽  
Digital Computer ◽  
Heat Rate ◽  
Acceptance Test ◽  
Turbine Generator

This paper describes a digital computer program which processes rapidly all of the data taken during a steam turbine-generator acceptance test. Specifically, it determines all thermodynamic properties of steam and water, computes corrected test heat rate, and finally develops a contract heat rate for purposes of comparison with manufacturer’s guarantees. The application of this program on two 330-megawatt units is discussed. The thinking leading to certain key decisions involving the ultimate approach taken is presented for the benefit of those contemplating a similar effort.

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