Statistical model for power plant performance monitoring and analysis

Pan, Li; Flynn, Damian; Cregan, Michael

doi:10.1109/upec.2007.4468931

Cited by 9 publications

(5 citation statements)

References 7 publications

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“…All these reasons reveal the need for performance monitoring and evaluation systems. There are many novel on-line monitoring performance methods of thermal power unit [28][29][30].…”

Section: Determination Of Power Plant Performance Parametersmentioning

confidence: 99%

Developing A Process Parameter Based Performance Monitoring and Evaluation System for Power Plant

Bulut

2021

Sakarya University Journal of Science

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Today, there are many electricity production methods. Thermal power plants are still widely used for electricity production from hydrocarbon sources as one of the electricity generation options. Due to their complex structure, thermal power plants are composed of sections that interact with each other, and they are called units. A change in the performance of any unit or equipment affects the operation of the entire plant positively or negatively. Therefore, performance monitoring and evaluation systems are designed to support the measurement and monitoring of the performance of plant and equipment. In order to increase the availability of the plant, equipment-based maintenance plans should be prepared according to the maintenance needs of the equipment. In this study, a power plant performance monitoring and evaluation system has been developed and designed using process parameters approach for the parameters affecting the performance of plant. In order to monitor and evaluate the performance of thermal power plants, the requirements of the system were revealed and the efficiency and performance increases to be achieved by designing and using this system specific to a natural gas power plant were examined and results are given.

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“…All these reasons reveal the need for performance monitoring and evaluation systems. There are many novel on-line monitoring performance methods of thermal power unit [28][29][30].…”

Section: Determination Of Power Plant Performance Parametersmentioning

confidence: 99%

Developing A Process Parameter Based Performance Monitoring and Evaluation System for Power Plant

Bulut

2021

Sakarya University Journal of Science

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“…The use of statistical modelling techniques such as those with regression and multivariate models is required when the number of variables involved is large and they have to be simultaneously observed as multivariate variables. Several applications have been reported for monitoring and controlling engineering processes as in, e.g., [8,9].…”

Section: Modelling Engineering Systemsmentioning

confidence: 99%

An application of statistical modelling on power generating asset performance analysis at the Asam-asam steam power plant of South Kalimantan

Mursadin

2018

E3S Web Conf.

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Abstract.A power generating system in a steam power plant is a complex one. This system involves a large number of variables containing information concerning the operation conditions. It can also be seen as an important asset in both power generation and energy portfolios. At the Asam-asam power plant of South Kalimantan, the complexity of the system has led to difficulties in explaining the apparently steady decrease in the output power. The amount of data collected is simply too big and the dimension too high for analysis purposes based on conventional thermodynamics. This study was performed to tackle the problem using statistical modelling. This approach can accommodate empirical behaviors of the variables and the probabilistic nature of the system. Information obtained from this modelling can be valuable for various purposes. The method consists of literature review, model development, data collection and analysis, and model fitting. Generalized additive models were chosen. Data were available from the company as observed from more than 140 variables. The resulting model identifies variables significantly related to the output power and locate subsystems whose fluctuating behaviors are usually ignored in a conventional thermodynamic analysis. A direction for future research is recommended.

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“…Smrekar et al [ 12 ] predicted power generated by a coal-fired power plant. Thermal efficiency and power plant pollutant were also predicted with ANNs [ 13 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Heat Rate Prediction of Combined Cycle Power Plant Using an Artificial Neural Network (ANN) Method

Arferiandi¹,

Caesarendra

Nugraha³

2021

Sensors

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Heat rate of a combined cycle power plant (CCPP) is a parameter that is typically used to assess how efficient a power plant is. In this paper, the CCPP heat rate was predicted using an artificial neural network (ANN) method to support maintenance people in monitoring the efficiency of the CCPP. The ANN method used fuel gas heat input (P1), CO2 percentage (P2), and power output (P3) as input parameters. Approximately 4322 actual operation data are generated from the digital control system (DCS) in a year. These data were used for ANN training and prediction. Seven parameter variations were developed to find the best parameter variation to predict heat rate. The model with one input parameter predicted heat rate with regression R2 values of 0.925, 0.005, and 0.995 for P1, P2, and P3. Combining two parameters as inputs increased accuracy with regression R2 values of 0.970, 0.994, and 0.984 for P1 + P2, P1 + P3, and P2 + P3, respectively. The ANN model that utilized three parameters as input data had the best prediction heat rate data with a regression R2 value of 0.995.

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Statistical model for power plant performance monitoring and analysis

Cited by 9 publications

References 7 publications

Developing A Process Parameter Based Performance Monitoring and Evaluation System for Power Plant

Developing A Process Parameter Based Performance Monitoring and Evaluation System for Power Plant

An application of statistical modelling on power generating asset performance analysis at the Asam-asam steam power plant of South Kalimantan

Heat Rate Prediction of Combined Cycle Power Plant Using an Artificial Neural Network (ANN) Method

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