Open Access

ARTICLE

Investigation of Inverter Temperature Prediction Model in Wind Farm Based on SCADA Data

by Qihui Ling^1,2,*, Wei Zhang², Qiancheng Zhao^1,2, Juchuan Dai^1,2

1 Engineering Research Center of Hunan Province for the Mining and Utilization of WTS Operation Data, Xiangtan, 411201, China
2 School of Mechanical Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China

* Corresponding Author: Qihui Ling. Email:

(This article belongs to the Special Issue: Advancements in Renewable Energy Systems with AI, Big Data, BlockChain, IoT, and Machine Learning Applications)

Energy Engineering 2022, 119(1), 287-300. https://doi.org/10.32604/EE.2022.014718

Received 23 October 2020; Accepted 20 April 2021; Issue published 22 November 2021

Abstract

The inverter is one of the key components of wind turbine, and it is a complex circuit composed of a series of components such as a variety of electronic components and power devices. Therefore, it is difficult to accurately identify the operation states of inverter and some problems regarding its own circuit, especially in the early stages of failure. However, if the inverter temperature prediction model can be established, the early states can be identified through the judgment of the output temperature. Accordingly, considering whether the inverter heats up normally from the perspective of heat dissipation, a method for the early operation state identification of the inverter is provided in this paper. A variable selection method based on fusion analysis of correlation and physical relationship is adopted to extract variables as input variables, which have high correlation with inverter temperature. Then multi-input and multi-output temperature prediction model of inverter is established based on a non-linear autoregressive exogenous model (NARX) network, and the prediction temperature residual is used as the real-time standard to evaluate the inverter states. For validating this, the validity and reliability of the established temperature prediction model are verified through case analysis, and the performance comparison with various models demonstrates that the proposed method has higher accuracy. The construction method of the prediction model can be used for reference to other aspects of wind turbine. All these bring huge benefits to wind energy industry.

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Keywords

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