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基于人工神经网络（ANN）方法的联合循环发电厂热耗率预测

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

作者信息

Arferiandi Yondha Dwika, Caesarendra Wahyu, Nugraha Herry

机构信息

Engineering Department, Cilegon Combined Cycle Power Plant, PT Indonesia Power, Cilegon 42454, Indonesia.

Faculty of Integrated Technologies, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei.

出版信息

Sensors (Basel). 2021 Feb 3;21(4):1022. doi: 10.3390/s21041022.

DOI:10.3390/s21041022
PMID:33546103
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7913177/
Abstract

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), CO 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 R values of 0.925, 0.005, and 0.995 for P1, P2, and P3. Combining two parameters as inputs increased accuracy with regression R 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 R value of 0.995.

摘要

联合循环发电厂（CCPP）的热耗率是一个通常用于评估发电厂效率的参数。在本文中，使用人工神经网络（ANN）方法预测CCPP的热耗率，以帮助维护人员监测CCPP的效率。ANN方法使用燃气热输入（P1）、CO百分比（P2）和功率输出（P3）作为输入参数。一年中从数字控制系统（DCS）生成了大约4322个实际运行数据。这些数据用于ANN训练和预测。开发了七种参数变化以找到预测热耗率的最佳参数变化。具有一个输入参数的模型预测热耗率时，P1、P2和P3的回归R值分别为0.925、0.005和0.995。将两个参数作为输入组合可提高准确性，P1 + P2、P1 + P3和P2 + P3的回归R值分别为0.970、0.994和0.984。利用三个参数作为输入数据的ANN模型具有最佳的预测热耗率数据，回归R值为0.995。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/02fe/7913177/03c8250ff817/sensors-21-01022-g015.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/02fe/7913177/c606833e4fd5/sensors-21-01022-g009a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/02fe/7913177/25d32642f32c/sensors-21-01022-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/02fe/7913177/7dba5b22feba/sensors-21-01022-g011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/02fe/7913177/042bc6005812/sensors-21-01022-g013.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/02fe/7913177/b289e0114dfc/sensors-21-01022-g001a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/02fe/7913177/c9dbad3c8753/sensors-21-01022-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/02fe/7913177/9c7cbd7fa36d/sensors-21-01022-g003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/02fe/7913177/e4cc29b63242/sensors-21-01022-g007.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/02fe/7913177/c606833e4fd5/sensors-21-01022-g009a.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/02fe/7913177/03c8250ff817/sensors-21-01022-g015.jpg

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