A detection method for abnormal harmonic current monitoring data using three-parameter Weibull distribution

2016 ◽  
Author(s):  
Tian-Lei Zang ◽  
Yan Wang ◽  
Zhi-Yuan Ma
Keyword(s):  
Weibull Distribution ◽  
Detection Method ◽  
Monitoring Data ◽  
Harmonic Current ◽  
Current Monitoring

Anomaly detection of bridge health monitoring data based on KNN algorithm

2020 ◽  
Vol 39 (4) ◽  
pp. 5243-5252
Author(s):  
Zhen Lei ◽  
Liang Zhu ◽  
Youliang Fang ◽  
Xiaolei Li ◽  
Beizhan Liu
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Health Monitoring ◽  
Detection Method ◽  
Detection Algorithm ◽  
Monitoring Data ◽  
Data Detection ◽  
Multiple Time ◽  
Time Zone ◽  
Bridge Health Monitoring

Pattern recognition technology is applied to bridge health monitoring to solve abnormalities in bridge health monitoring data. Testing is of great significance. For abnormal data detection, this paper proposes a single variable pattern anomaly detection method based on KNN distance and a multivariate time series anomaly detection method based on the covariance matrix and singular value decomposition. This method first performs compression and segmentation on the original data sequence based on important points to obtain multiple time subsequences, then calculates the pattern distance between each time subsequence according to the similarity measure of the time series, and finally selects the abnormal mode according to the KNN method. In this paper, the reliability of the method is verified through experiments. The experimental results in this paper show that the 5/7/9 / 11-nearest neighbors point to a specific number of nodes. Combined with the original time series diagram corresponding to the time zone view, in this paragraph in the time, the value of the temperature sensor No. 6 stays at 32.5 degrees Celsius for up to one month. The detection algorithm controls the number of MTS subsequences through sliding windows and sliding intervals. The execution time is not large, and the value of K is different. Although the calculated results are different, most of the most obvious abnormal sequences can be detected. The results of this paper provide a certain reference value for the study of abnormal detection of bridge health monitoring data.

scholarly journals Improved p-q Harmonic Detection Method for Hybrid Active Power Filter

2018 ◽  
Vol 8 (5) ◽  
pp. 2910 ◽  
Author(s):  
Chau Minh Thuyen
Keyword(s):  
Dynamic Response ◽  
Reactive Power ◽  
Detection Method ◽  
Active Power Filter ◽  
Active Power ◽  
Harmonic Detection ◽  
Harmonic Current ◽  
Transient Time ◽  
Power Filter ◽  
Hybrid Active Power Filter

<p>The accuratedetermination of the load harmonic current is one of the important factors, it decides to effect of harmonic filtering and reactive power compensation for Hybrid Active Power Filter. The p-q harmonic detection method has been widely used in determining the harmonic currents of Hybrid Active Power Filter. However, when using this method, the dynamic response of Hybrid Active Power Filter in the transient period will have a large transient time and overshoot whenever the load changes abruptly. Therefore, in this paper an improved p-q harmonic current detection method based on fuzzy logic is proposed, which aims to reduce the overshoot and transient time in transient duration of Hybrid Active Power Filter. In order to compare the dynamic response of conventional and improved p-q harmonic detection methods, simulation results have demonstrated that: the proposed method has a shorter response time, the magnitude of the supply current in the transient time is smaller and the overshoot of the fundamental active and reactive power components is very small. This has a practical significance that contributes to the stability of the Hybrid Active Power Filter system</p>

A Novel Detection Method of Harmonic Current

2011 ◽  
Vol 403-408 ◽  
pp. 2539-2542
Author(s):  
Li Ming Wei ◽  
Shan Zhao
Keyword(s):  
Dynamic Response ◽  
Real Time ◽  
Simple Structure ◽  
Reactive Power ◽  
Detection Method ◽  
Harmonic Current ◽  
Power Theory ◽  
Instantaneous Reactive Power Theory ◽  
Instantaneous Reactive Power

Based on the instantaneous reactive power theory, an improving detection method of harmonic current is put forward. In this method, the harmonic current is detected by current average. The method has the characteristics of simple structure, strong real-time and good dynamic response. In the paper, the proposed method is used to carry out simulation of MATLAB and the results show the effectiveness of the method.

scholarly journals An Electro-Mechanical Actuator Motor Voltage Estimation Method with a Feature-Aided Kalman Filter

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4190 ◽  
Author(s):  
Yujie Zhang ◽  
Liansheng Liu ◽  
Yu Peng ◽  
Datong Liu
Keyword(s):  
Kalman Filter ◽  
Electromagnetic Interference ◽  
Estimation Method ◽  
State Equation ◽  
Monitoring Data ◽  
Published Data ◽  
Data Set ◽  
State Data ◽  
Current Voltage ◽  
Current Monitoring

Electro-Mechanical Actuators (EMA) have attracted growing attention with their increasing incorporation in More Electric Aircraft. The performance degradation assessment of EMA needs to be studied, in which EMA motor voltage is an essential parameter, to ensure its reliability and safety of EMA. However, deviation exists between motor voltage monitoring data and real motor voltage due to electromagnetic interference. To reduce the deviation, EMA motor voltage estimation generally requires an accurate voltage state equation which is difficult to obtain due to the complexity of EMA. To address this problem, a Feature-aided Kalman Filter (FAKF) method is proposed, in which the state equation is substituted by a physical model of current and voltage. Consequently, voltage state data can be obtained through current monitoring data and a current–voltage model. Furthermore, voltage estimation can be implemented by utilizing voltage state data and voltage monitoring data. To validate the effectiveness of the FAKF-based estimation method, experiments have been conducted based on the published data set from NASA’s Flyable Electro-Mechanical Actuator (FLEA) test stand. The experiment results demonstrate that the proposed method has good performance in EMA motor voltage estimation.

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