Artificial-Intelligence-Based Open-Circuit Fault Diagnosis in VSI-Fed PMSMs and a Novel Fault Recovery Method

断层(地质) 计算机科学 控制理论(社会学) 电压 支持向量机 补偿(心理学) 逆变器 定子 决策树 工程类 人工智能 电气工程 地质学 地震学 心理学 控制(管理) 精神分析
作者
Khaled A. Mahafzah,Mohammad A. Obeidat,Ayman M. Mansour,Ali Q. Al-Shetwi,Taha Selim Ustun
出处
期刊:Sustainability [MDPI AG]
卷期号:14 (24): 16504-16504 被引量:2
标识
DOI:10.3390/su142416504
摘要

Artificial intelligence (AI) techniques are widely used in fault diagnosis because they are superior in detection and prediction. The detection of faults in power systems containing electronic components is critical. The switch faults of the voltage source inverter (VSI) have a severe impact on the driving system. Short-circuit switches increase the thermal stress due to their fast and high stator currents. Additionally, open-circuit switches cause unstable motor operation. However, these issues are not sufficiently addressed or accurately predicted for VSI switch faults in the literature. Thus, this paper investigates the use of different AI classifiers for three-phase VSI fault diagnosis. Various AI methods are used, such as naïve Bayes, support vector machine (SVM), artificial neural network (ANN), and decision tree (DT) techniques. These methods are applied to a VSI-fed permanent magnet synchronous motor (PMSM) to detect the faults in the inverter switches. These methods use the drain–source voltage and PWM signals to decide whether the switch is healthy or unhealthy. In addition, they are compared in terms of their detection accuracy. In this regard, the comparative results show that the DT method has the highest accuracy as compared to other methods in the fault diagnosis process. Moreover, this paper proposes a novel and universal voltage compensation loop to compensate for the absence of the voltage portion due to the open switch fault. Thus, the driving system is assisted in operating under its normal operating conditions. The universal term is used because the proposed voltage compensation loop can be implemented in any type of inverter. To validate the results, the proposed system is implemented using two software programs, LTSPICE XVII-USA, WEKA 3.9-New Zealand.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
劲秉应助谦让友绿采纳,获得10
刚刚
122发布了新的文献求助50
1秒前
北风歌发布了新的文献求助10
2秒前
2秒前
4秒前
4秒前
粱忆寒完成签到,获得积分10
6秒前
6秒前
yls完成签到,获得积分10
6秒前
7秒前
7秒前
请叫我风吹麦浪应助mint采纳,获得10
7秒前
7秒前
Lotus发布了新的文献求助10
7秒前
静心完成签到,获得积分10
9秒前
lierking应助传统的芷容采纳,获得30
10秒前
10秒前
11秒前
11秒前
xuxu发布了新的文献求助10
11秒前
11秒前
乾明少侠完成签到 ,获得积分10
12秒前
CipherSage应助hh采纳,获得10
12秒前
卡萨卡萨完成签到,获得积分10
13秒前
15秒前
16秒前
17秒前
Noldor发布了新的文献求助10
17秒前
所所应助xuulanni采纳,获得10
18秒前
18秒前
三十完成签到 ,获得积分10
19秒前
精明人达完成签到,获得积分10
19秒前
善学以致用应助北风歌采纳,获得10
21秒前
清风发布了新的文献求助10
22秒前
活泼菠萝发布了新的文献求助10
24秒前
夜王发布了新的文献求助10
26秒前
26秒前
云游归尘完成签到 ,获得积分0
28秒前
俞定尚心才可心完成签到 ,获得积分10
29秒前
31秒前
高分求助中
Востребованный временем 2500
Agaricales of New Zealand 1: Pluteaceae - Entolomataceae 1040
지식생태학: 생태학, 죽은 지식을 깨우다 600
海南省蛇咬伤流行病学特征与预后影响因素分析 500
Neuromuscular and Electrodiagnostic Medicine Board Review 500
ランス多機能化技術による溶鋼脱ガス処理の高効率化の研究 500
Relativism, Conceptual Schemes, and Categorical Frameworks 500
热门求助领域 (近24小时)
化学 医学 材料科学 生物 工程类 有机化学 生物化学 纳米技术 内科学 物理 化学工程 计算机科学 复合材料 基因 遗传学 物理化学 催化作用 细胞生物学 免疫学 电极
热门帖子
关注 科研通微信公众号,转发送积分 3462526
求助须知:如何正确求助?哪些是违规求助? 3056054
关于积分的说明 9050624
捐赠科研通 2745705
什么是DOI,文献DOI怎么找? 1506521
科研通“疑难数据库(出版商)”最低求助积分说明 696165
邀请新用户注册赠送积分活动 695677