Health Management Systems for Power Engineering Applications ...

Health Management Systems for Power Engineering Applications: Power Distribution Systems and Rotating Machinery Systems Le Xu, Bulent Ayhan and Mo-Yuen Chow Department of Electrical & Computer Engineering North Carolina State University Raleigh, NC 27695, U.S.A. {lxu, bayhan, chow}@ncsu.edu

Abstract--Health management systems have been gaining substantial attentions in power engineering areas in recent years due to the reliability, equipment downtime, and outage cost concerns. This paper will describe a health management system developed in Advanced Diagnosis, Automation, and Control Lab at North Carolina State University. The health management system utilizes several data analysis and decision making techniques including statistical analysis, data visualizations, principal component analysis, and discriminant analysis, in order to perform data mining, feature extraction, fault detection and diagnosis. A power distribution system and a rotating machinery system will be used to illustrate the features of the proposed health management systems. Index Terms—health management systems, power distribution systems, rotating machinery systems, graphic user interface.

I. INTRODUCTION

H

ealth management systems have been gaining substantial attentions in power engineering areas in recent years due to the reliability, equipment downtime, and outage cost concerns. A health management system is a common framework connecting fault detection, fault diagnosis, fault prognosis and mitigation control. Fault detection is the process of observing the measured system data and system status information and comparing them with normal range of observed attributes to judge whether some measurements fall outside the range representing the normal operation. Fault diagnosis refers to the process of determining the state of failing components and identifying the cause(s) of the anomalous situation of the managed system or its subsystems. Fault prognosis predicts impending component failures or abnormal system states before they actually occur, and estimates their remaining useful life. Mitigation control aims to eliminate the risk or damages to the systems and to reduce further system degradation. Health management systems have various benefits in terms of operational efficiency, safety, reliability and availability. Correct predictions of system status and time to failures can decrease the possibility of the system malfunction; effective identification of root causes and troubleshooting can greatly reduce the system downtime. Furthermore, the system working conditions tracked and logged by health management

This work is supported in part by National Science Foundation Grant ECS-0245383

system always provide essential information to make proper decisions. For health management, knowledge of the managed system is the most crucial aspect [1]. Data mining, also called Knowledge Discovery in Databases (KDD), is one direction to go in order to extract useful knowledge and substantial system information from available data. [1]-[5] This paper will describe HMS-ADAC, a Health Management System developed in the Advanced Diagnosis, Automation, and Control Laboratory at North Carolina State University. The system utilizes several data analysis and decision making techniques including statistical analysis, data visualizations, principal component analysis, and discriminant analysis, in order to perform data mining, feature extraction, fault detection and diagnosis. A power distribution system and a rotating machinery system will be used to illustrate the features of the proposed health management systems for power engineering applications. II. APPLICATION At the very beginning, some critical systems such as aerospace industry and nuclear power plant showed great interest in health management systems since the failures of these critical systems will result in severe consequences. Later on, many other areas such as utility systems, electric machinery systems, transportation systems start to gain interest in health management systems as well. A. Power distribution systems application The first illustration of the HMS-ADAC is the power distribution systems. Power systems are the backbone of modern society; their proper running keeps the production on track and people’s lives in order. “Blackout 2003” affected 50 millions people in northeast of the United States and led to $7-10 billions loss [6]. Power distribution systems are the retail parts of utilities, aiming at providing reliable, economical and safe supply of electricity to their customers. However, power distribution systems are vulnerable to the natural disturbances and sensitive to the system configurations, since they are geographically dispersed and under various dynamic operating environments. Power distribution systems are significantly affected by many different events, such as equipment failures, animal activities and trees. A lot of utility companies experience a large number of distribution faults every year. Nevertheless, their characteristics of large-scale, nonlinear and time-varying as

well as the uncertainly due to distribution make them difficult to analyze and model. Many power companies log their system data including outage information. The recorded outage data are valuable resources for the health management systems to extract necessary knowledge in order to implement efficient fault detection and fault diagnosis schemes. HMS-ADAC uses four statistical measures to present useful information about the fault patterns with respect to different influential factors to unfold the inherent information hidden in the large amount of data from different angle of views [7]-[9]. HMS-ADAC then provides data visualization on the extracted information via a GUI to provide qualitative information to the users in addition to the quantitative data and measures. B. Rotating Machinery Systems The second illustration of HMS-ADAC is electric motor systems. Electric motor systems have dominated in the field of electromechanical energy conversion, and are known to be the workhorses of industry [10]. The applications of electric motor systems are widespread. Some are key elements in assuring the continuity of the process and production chains of many industries. The list of the industries and applications that they take place in is rather long. Electric motors are often used in hostile environments such as for oil field pumps and critical applications such as nuclear plants and military applications, where the reliability must be at high standards. The failure of electric motor systems can result in a total loss of the motor itself, in addition to a likely costly downtime of the entire plant. Thus, health management systems to prevent electric motor failures are of great concern in industry and are gaining increasing attention [11]-[15]. The most common failures to electric motors are bearing and winding related failures. We have developed a health management system for induction motors with the focus on the bearing condition monitoring. A GUI has been developed to utilize several technologies for feature extraction and analysis, as will be discussed briefly in the following sections.

distribution faults, is used as a prototype to illustrate the proposed health management system. The developed GUI is called HMS-PDS, of which the statistic analysis and data visualization parts are introduced next. 1) Data Display In the Duke Energy outage database, some of the information fields are recorded based on coding systems. For example, weather code 1 stands for fair weather condition and cause code 3 means trees. So we firstly transform the coded data back to their actual meanings to provide a concrete sense of the recorded outage data, such that users will be able to understand the data more easily. As shown in Fig.1, the user can pick out any outage record from the selected data file by entering the index number, and then the actual meanings of the seven selected influential factors and the outage cause of the selected record are shown in the display panel. 2) Statistical Measures We have developed four statistical measures: actual measure, normalized measure, relative measure and likelihood measure to examine the data from different perspectives [7], [8]. The actual measure shows the actual number of treecaused faults in different regions. This measure can be made use of by utility companies to optimally deploy equipments and personnel, such as allocating tree trimming resources among different regions according to the number of treecaused faults in each area. The normalized measure indicates the percentage of tree-caused faults among all distribution faults in different regions. This measure takes into account of the regional characteristics considering that different regions have their own geographical features, for instance, some are cities and some are wooded areas, so the patterns of treecaused faults are different. This measure suggests the relative importance of tree-caused faults, which can help the companies to make decisions on the fault prevention priority. For instance, if this measure shows that some region has high percentage of tree caused faults, the company may place higher priority on tree trimming program than treatments for faults with lower percentage.

III. GRAPHIC USER INTERFACE A Graphic User Interface (GUI) is developed as a workbench to effectively represent the techniques applied in the proposed health management systems. It not only can unfold the essences of the applied techniques intuitively, but also integrate relative information in a compact way and takes advantage of human’s visual and spatial cues. The capabilities of presented GUI can be extended to include other advanced techniques for data analysis. A. Power Distribution Systems (PDS) In the power distribution systems application, Duke Energy’s outage data, which consist of more than 5 millions records of all its service areas from 1994 to 2002, are used. With data preprocessing, the outage data from six representative Duke Energy’s service areas are selected, and seven potential influential factors are selected in each single record [7], [8]. Tree, one of the major causes of power

Fig.1. Data display window

The relative measure presents the proportion of tree-caused fault under certain event among all the possible events. For example, this measure can show the percentage of the treecaused faults occurred under fair weather out of all the weather conditions including lightning, storm and so on. This measure shows detailed information in terms of different

events for fault analysis. The likelihood measure calculates the conditional probability of tree-caused faults given a specific event. For example, it can indicate how likely the outage is caused by tree if we know the outage occur in summer. This measure is helpful to fault diagnosis; when the exact cause of a fault is unknown, this measure gives useful information for root cause identification [7]. These four measures provide different perspectives of the tree-caused fault features. We represented the data in graphic form based on these four measures in HMS-PDS as well. With the quantities being visually displayed, the information released by these measures can be more easily understood. As shown in Fig. 2, the user can choose different measures with respect to different factors if applicable; the user can also pick several regions of interest or all of the six selected regions 3) Reliability Indices The HMS-PDS is also able to calculate and to plot several reliability indices (SAIFI, CAIDI and SAFI), considering that distribution reliability indices are the primary benchmark used by utilities and regulators to identify service quality and to measure performance [16], as shown in Fig.3. B. Rotating Machinery Systems The rotating machinery systems application focuses on the motor bearing condition monitoring. The developed GUI is called HMS-Motor. The main part of the HMS-Motor consists of the feature extraction and data plotting parts. Fig. 4 depicts a snapshot of the main part of the HMS-Motor. The user can compute the bearing fault specific frequencies by entering the bearing parameters with respect to the geometry and the motor speed [13]. The bearing fault specific frequencies include 6 inner-race fault frequencies, 12 outerrace fault frequencies, three ball fault frequencies, and three user defined frequencies. The user can also plot and analyze the spectrum amplitudes of these frequencies individually or in groups by selecting the ones of interest.

Fig.3. Reliability indices window

The HMS-Motor can compute Power Spectral Density (PSD) spectrum of the time domain bearing vibration data based on the FFT length and the sampling time of the time domain motor bearing data provided by the user. The HMSMotor can plot Time domain data, Frequency domain data, and Spectrum amplitudes as well. The feature data sets generated by the HMS-Motor can be further analyzed using the data analysis module, which provides two data analysis methods: Principal Component Analysis and Multiple Discriminant Analysis. 1) Principal Component Analysis: Principal Component Analysis (PCA) is a well-known and widely used data representation and dimension reduction technique [17]. In PCA, the objective is to find a projection that best represents the data in a least squares sense. There are several features extracted from the motor bearing vibration data. Some of these features can be redundant; thus, they do not have any considerable effect to the fault detection or diagnosis processes. PCA allows reducing the dimension by combining features and forming a new set of components. Among the new set of components, the user can choose the significant components that best represent the data. Fig.5 depicts a general snapshot of the HMS-Motor-PCA. After loading the training and testing feature data sets created by the HMS-Motor, the user can apply PCA on them and choose the PCA components according to the eigenvalue analysis to generate 3D plots of both the raw data and the data after PCA.

Fig.2. Statistical measures window

Fig.4. HMS-Motor window

component analysis, and discriminant analysis. A power distribution system and a rotating machinery system are used to illustrate the features of the proposed health management systems. V. REFERENCES [1] [2] [3] [4]

Fig.5. HMS-Motor-PCA window

2) Multiple Discriminant Analysis (MDA): Multiple discriminant analysis is a widely used statistical technique for classification purposes. The objective in multiple discriminant analysis is to find a set of linear combinations of the variables whose values are as close as possible within groups and as far apart as possible between groups [18]. The module HMS-Motor-MDA utilizes multiple discriminant analysis method for diagnosing the motor bearing failures. The feature data generated by HMS-Motor can then be saved in data files together with their corresponding class labels. The data set with the known class labels can be used as the training data set to find the related discriminant functions. The generated discriminant functions are then used to find the class information of the unlabeled test data set. The user has the ability to choose the discriminant function among the following discriminant functions: linear, quadratic, and mahalanobis. The performance statistics of the executed discriminant analysis are provided both graphically and numerically, illustrating the percentage accuracy and the percentage error. A partial view of the HMS-Motor-MDA is depicted in Fig.6.

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Fig.6. A partial view from the HMS-Motor-MDA

IV. CONCLUSION In this paper, a health management system that utilizes several data analysis and decision making techniques in order to perform data mining, feature and knowledge extraction, fault detection and diagnosis is introduced. These techniques include statistical analysis, data visualizations, principal

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