Compressor Performance Prediction Based on the Interpolation Method and Support Vector Machine

• Published in: Aerospace Vol. 10; no. 6; p. 558
• Main Authors: Zhong, Lingfeng, Liu, Rui, Miao, Xiaodong, Chen, Yufeng, Li, Songhong, Ji, Haocheng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2023
• Subjects: Accuracy; Algorithms; Analysis; Artificial intelligence; Artificial neural networks; Back propagation; Back propagation networks; Business metrics; compressor performance; Compressors; Data points; Data processing; Efficiency; Electric power systems; genetic algorithm; Genetic algorithms; Interpolation; interpolation method; Interpolation methods; Kernel functions; Machine learning; Mathematical analysis; Mathematical models; Methods; Neural networks; Optimization; Optimization algorithms; Parameter identification; Performance prediction; Simulation; support vector machine; Support vector machines; Training
• ISSN: 2226-4310; 2226-4310
• DOI: 10.3390/aerospace10060558

Compressors are important components in various power systems in the field of energy and power. In practical applications, compressors often operate under non-design conditions. Therefore, accurate calculation on performance under various operating conditions is of great significance for the development and application of certain power systems equipped with compressors. To calculate and predict the performance of a compressor under all operating conditions through limited data, the interpolation method was combined with a support vector machine (SVM). Based on the known data points of compressor design conditions, the interpolation method was adopted to obtain training samples of the SVM. In the calculation process, preliminary screening was conducted on the kernel functions of the SVM. Two interpolation methods, including linear interpolation and cubic spline interpolation, were used to obtain sample data. In the subsequent training process of the SVM, the genetic algorithm (GA) was used to optimize its parameters. After training, the available data were compared with the predicted data of the SVM. The results show that the SVM uses the Gaussian kernel function to achieve the highest prediction accuracy. The prediction accuracy of the SVM trained with the data obtained from linear interpolation was higher than that of cubic spline interpolation. Compared with the back propagation neural network optimized by the genetic algorithm (GA-BPNN), the genetic algorithm optimization of extreme learning machine neural network (GA-ELMNN), and the genetic algorithm optimization of generalized regression neural network (GA-GRNN), the support vector machine optimized by the genetic algorithm (GA-SVM) has a better generalization, and GA-SVM is more accurate in predicting boundary data than the GA-BPNN. In addition, reducing the number of original data points still enables the GA-SVM to maintain a high level of predictive accuracy.