Model-Based Data Reconciliation to Improve Accuracy and Reliability of Performance Evaluation of Thermal Power Plants

2005 ◽  
Author(s):  
Peter Hartner ◽  
Josef Petek ◽  
Peter Pechtl ◽  
Patrick Hamilton
Keyword(s):  
Performance Evaluation ◽  
Evaluation System ◽  
Power Plants ◽  
Performance Monitoring ◽  
Thermal Power ◽  
Least Square ◽  
Data Reconciliation ◽  
Sensor Calibration ◽  
Plant Optimization ◽  
On Line

Accurate on-line plant and equipment performance evaluation is becoming critical in the power generation industry as operators seek to optimize their plants, particularly in competitive power markets. The analysis accuracy of an on-line performance monitoring system is directly dependent on the quality of the input data and usually suffers because installed plant sensors are not high-precision instruments. The inherent measurement uncertainties can be overcome by using a readily available heat balance program in combination with a least square solver. This data reconciliation system will provide the performance evaluation system with data that better reflects the plant’s current operating point, thus improving the performance evaluation system’s output and allowing for better plant optimization. Additionally, the reconciliation system can identify broken, biased or highly noisy sensors. These improvements can be obtained without installing additional precision sensors or putting unreasonable efforts into sensor calibration.

Variability in Thermal Performance and Measured Heat Rate in Fossil-Fuelled Power Plants

1992 ◽  
Vol 206 (2) ◽  
pp. 109-123 ◽  
Author(s):  
F L Carvalho ◽  
F H D Conradie ◽  
H Kuerten ◽  
F J McDyer
Keyword(s):  
Thermal Performance ◽  
Power Plants ◽  
Performance Monitoring ◽  
Unit Commitment ◽  
Thermal Power ◽  
Heat Rate ◽  
Plant Performance ◽  
Plant Management ◽  
Fuel Quality ◽  
Stable Operating

The paper examines the variability of key parameters in the operation of ten thermal power plants in various commercial grid environments with a view to assessing the viability of ‘on-demand’ plant performance monitoring for heat rate declaration. The plants of various types are limited to coal- and oil-fired units in the capacity range of 305–690 MW generated output. The paper illustrates the influence of control system configuration on effective and flexible power plant management. The analysis of variability indicates that there is a reasonable probability of achieving adequately stable operating periods within the normal operating envelope of grid dispatch instructions when thermal performance monitoring and display can be undertaken with a high confidence level. The levels of variability in fuel quality, which were measured during nominally constant levels of fuel input and generated output, range from about +1 per cent for oil-fired plants to about ±5 per cent for coal-fired power plants. The implications of adopting on-line monitoring of unit heat rate as an input to the generation ordering and unit commitment process are potentially significant cost and energy conservation benefits for utilities having a high proportion of coal- and oil-fired generation.

A Case Study of Optimizing Combined Cycle Performance at Kalaeloa

2009 ◽  
Author(s):  
Helmer Andersen
Keyword(s):  
Power Plants ◽  
Performance Monitoring ◽  
Combined Cycle ◽  
Plant Performance ◽  
Operational Performance ◽  
Cycle Performance ◽  
Monitoring Tools ◽  
Online Performance Monitoring ◽  
On Line

Fuel is by far the largest expenditure for energy production for most power plants. New tools for on-line performance monitoring have been developed for reducing fuel consumption while at the same time optimizing operational performance. This paper highlights a case study where an online performance-monitoring tool was employed to continually evaluate plant performance at the Kalaeloa Combined Cycle Power Plant. Justification for investment in performance monitoring tools is presented. Additionally the influence of various loss parameters on the cycle performance is analyzed with examples. Thus, demonstrating the potential savings achieved by identifying and correcting the losses typically occurring from deficiencies in high impact component performance.

scholarly journals Selection Ideal Coal Suppliers of Thermal Power Plants Using the Matter-Element Extension Model with Integrated Empowerment Method for Sustainability

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Zhongfu Tan ◽  
Liwei Ju ◽  
Xiaobao Yu ◽  
Huijuan Zhang ◽  
Chao Yu
Keyword(s):  
Evaluation System ◽  
Supplier Selection ◽  
Power Plants ◽  
Thermal Power ◽  
Fuel Quality ◽  
Extension Model ◽  
Development Capacity ◽  
Generation Cost ◽  
Promethee Method ◽  
The Ideal

In order to reduce thermal power generation cost and improve its market competitiveness, considering fuel quality, cost, creditworthiness, and sustainable development capacity factors, this paper established the evaluation system for coal supplier selection of thermal power and put forward the coal supplier selection strategies for thermal power based on integrated empowering and ideal matter-element extension models. On the one hand, the integrated empowering model can overcome the limitations of subjective and objective methods to determine weights, better balance subjective, and objective information. On the other hand, since the evaluation results of the traditional element extension model may fall into the same class and only get part of the order results, in order to overcome this shortcoming, the idealistic matter-element extension model is constructed. It selects the ideal positive and negative matter-elements classical field and uses the closeness degree to replace traditional maximum degree of membership criterion and calculates the positive or negative distance between the matter-element to be evaluated and the ideal matter-element; then it can get the full order results of the evaluation schemes. Simulated and compared with the TOPSIS method, Romania selection method, and PROMETHEE method, numerical example results show that the method put forward by this paper is effective and reliable.

The Exhaust Steam Enthalpy of Steam Turbine Cognitive Modeling Based on Simplify Evidential Regression Multi-Model

2012 ◽  
Vol 614-615 ◽  
pp. 83-88
Author(s):  
Sha Liu ◽  
Pei Hong Wang ◽  
Zhi Gang Su
Keyword(s):  
Steam Turbine ◽  
Cognitive Modeling ◽  
Power Plants ◽  
System Analysis ◽  
Thermal Power ◽  
Evidence Theory ◽  
Cognitive Model ◽  
Measurement Model ◽  
Analysis Model ◽  
On Line

The calculation of exhaust steam enthalpy for steam turbine units is an important parameter in the on-line monitoring and system analysis for thermal power plants. The cognitive modeling method for exhaust steam enthalpy based on evidence theory was studied in this paper. Take 330MW steam turbine for example, exhaust steam enthalpy samples are obtained from steam turbine variable condition analysis model, then exhaust steam enthalpy cognitive model based on simplify evidential regression multi-model is established. The error analysis shows that the accuracy of this model has higher prediction accuracy than the SVM and NW soft measurement model.

A New Method of On-Line ELD for Thermal Power Plants

1988 ◽  
Vol 21 (11) ◽  
pp. 395-401
Author(s):  
Y. Tamura ◽  
Y. Fukuyama ◽  
S. Yazawa ◽  
J. Hosaka ◽  
N. Joho ◽  
...  
Keyword(s):  
Power Plants ◽  
Thermal Power ◽  
New Method ◽  
Thermal Power Plants ◽  
On Line

Research on the Low-Carbon Development Evaluation System for the Thermal Power Plants

2013 ◽  
Vol 295-298 ◽  
pp. 730-733
Author(s):  
Dong Xiao Niu ◽  
Tong Liu ◽  
Qiong Wang ◽  
Peng Wang
Keyword(s):  
Evaluation System ◽  
Power Plants ◽  
Thermal Power ◽  
Practical Significance ◽  
Thermal Power Plants ◽  
Low Carbon ◽  
Low Carbon Development ◽  
Development Evaluation ◽  
Development Assessment ◽  
Installed Capacity

The environment problem becoming increasingly prominent in nowadays, people pay more and more attention to low-carbon. The low-carbon status of power plants has a significant impact on the low-carbon development of the whole country. And the proportion of thermal power installed capacity of total installed capacity is more than 70%. So there’s necessity and practical significance to study low-carbon development evaluation system for thermal power plants. This paper discusses the principles, indexes selection and evaluation methods for thermal power plants’ low-carbon development assessment. And its case study done shows the effectiveness of the methods.

Study and Application of Two-Level Optimization of Coal Blending for Power Plant Based on On-Line Coal Identification

2011 ◽  
Author(s):  
Ji Xia ◽  
Peng Peng ◽  
Cheng Zhang ◽  
Tao Yang ◽  
Gang Chen
Keyword(s):  
Power Plant ◽  
Power Plants ◽  
Thermal Power ◽  
Time Lag ◽  
Utilization Rate ◽  
Coal Blending ◽  
Sensing Model ◽  
On Line ◽  
Blended Coals ◽  
The Heat Balance

In china, many thermal power plants have to burn blended coals forced by the complexity of coal type and market tension and transportation pressure of coal purchasing. As a engineering implementation method of coal blending, “different coals grinding in different mills and then mixed burning in the furnace” has many advantages such as low investment, easy to control milling system parameters and can be optimized online, etc, compared with traditional coal blending methods. But it is limited by the number of mills and cannot achieve high-precision ratio of blending. To remedy this shortcoming, a model of two-level optimization of coal blending for the thermal power plant with direct blowing pulverizing system was established in this paper. The tradional coal blending was regarded as first step of optimization. The secondary optimization was implemented by adjusting the outputs of different mills, then the blend was changed to accurate ratio. Furthermore, since the existence of coal bunker, it made a time lag from coal discharge to combustion, meanwhile, the real-time load was unpredictable and the coal utilization rate was inconsistent of each bunker. The three reasons make it uncertain of the current coal of bunker. To identify each coal in the mill(equivalent to bunker) correctly was the basis of achieving the second blending optimization. Therefore, a soft-sensing model of coal moisture based on the heat balance equation was used to take this work. At last, a intelligent coal blending system by the two-level optimization model was developed for a power plant and achieved good results.

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