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In the last years electric line users have detected an increasing number of drawbacks caused by electrical power quality variations. These variations already ...

TRANSIENTMETER: A Distributed Measurement System For Power Quality Monitoring (draft) P.Daponte, IEEE Senior Member M. Di Penta,


LESIM, Laboratory for Signal Processing and Measurement Information, University of Sannio, Palazzo Bosco, Piazza Roma, 82100 Benevento, Italy. Ph: +390824305817, Fax: +39082421866, E-mail: [email protected], http://lesim1.ing.unisannio.it.

Abstract: The paper deals with the design and implementation of TRANSIENTMETER, a monitoring system for the detection, classification and measurement of disturbances on electrical power systems. TRANSIENTMETER uses a CORBA architecture as communication interface, wavelet-based methods for automatic signal classification and characterization, and a smart trigger circuit for the detection of disturbances. Keywords: Distributed information systems, Feedforward neural networks, Power quality, Power systems monitoring, Wavelet transforms.

I. INTRODUCTION In the last years electric line users have detected an increasing number of drawbacks caused by electrical power quality variations. These variations already existed on electrical systems, but only recently they are causing serious problems. As a matter of fact: (i) devices used in electrical installations are more susceptible respect to oldest ones, because they contains control systems provided with microprocessors that can suffer of a large scale of disturbances; and (ii) the growing use of power electronics implies more precautions to limit harmonic distortion. To improve power quality with adequate solutions, it is necessary to know what kind of disturbance occurred. Therefore, a measurement system able automatically to detect, characterize and classify disturbances existing on electrical lines is required. This brings up advantages to both end users and utility companies [1]. The main advantages for end users are: (i) risk avoidance, a monitoring system can detect disturbances that can cause damages to his own equipments, (ii) manpower efficiency, an automatic monitoring system eliminates time loss due to examining large signal records and preparing reports, and (iii) process improvements, with a monitoring system it is possible to identify most sensitive equipments and so install power conditioning systems only where necessary. The main advantages for utilities are: (i) risk avoidance, an utility can show to customers the effective quality of power produced, to prove that are not responsible to any damages occurred on customer’s equipments, (ii) manpower efficiency, likewise the end-user, utility can rapidly detect a problem and recognize its causes, avoiding massive personnel scheduling, (iii) capital investment reductions, a continuous monitoring allows expensive power system improvements to be limited, and (iv) competitiveness, an efficient monitoring allows utilities, in a deregulated power market, to stipulate special power quality contracts, and to offer a better product. Finally, with a monitoring system

located in customer’s site, and allowing customer access to power quality database, utilities can offer an important service, differentiating themselves from its competitors. The paper proposes TRANSIENTMETER, a distributed measurement system for the automatic detection, classification and measurement of disturbances affecting an electrical power system. The software design philosophy is oriented to adopt standard and open technologies. Its components are completely software implemented, except for a trigger circuit for disturbances detection and a data acquisition board. A measurement algorithm, developed by using the wavelet transform and the wavelet networks, had been adopted for the automatic classification and measurement of disturbances. The paper is organized as follows: Section II highlights the state of the art of the monitoring systems; Section III describes the TRANSIENTMETER architecture; Section IV reports the wavelet-based algorithms for the disturbance classification and characterization; Section V reports numerical and experimental results. II. STATE OF THE ART OF POWER MONITORING SYSTEMS A. Monitoring Instrument Characteristics Monitoring instruments today available continuously digitize current and voltage signals, and evaluate RMS values of current and voltage, active, reactive and apparent power, and harmonic distortion. These measurements are stored in a large memory inside the instrument. Some more sophisticated equipments allow a transient event to be detected: when the voltage level or the current level goes below (or upper) user defined thresholds (this means that a sag, a swell, an interruption or an impulse has been occurred), then amplitude, duration and occurrence time are evaluated. At end of measurement, all information are printed in a report summarizing events occurred. The disadvantage of these kind of instruments is that their operation strongly depends of the correct calibration of thresholds. Furthermore, if a very low threshold is fixed and electrical lines are particularly affected from problems, instrument’s memory rapidly fills up. However, an high threshold could cause the loss of some information. So, the best way is an adaptive threshold that varies according to disturbance occurrence [3]. Some instruments can also increase their sampling rate when a disturbance has occurred. Another remarkable

characteristic is the possibility of compare events with CBEMA curves, opportunely personalized [3]. Finally, some instruments allow to transfer acquired data to remote computers for post-elaboration and storage. Major disadvantages of existing equipments are: - Cost (due to use of complex hardware solutions); - Low flexibility (hardware solutions are not very configurable and adaptable to particular operative conditions); - Configuration and operation from remote usually limited; - Total absence of disturbance classification: where present, the instrument generally distinguishes impulsive events from RMS variations; - No information about time-frequency signal analysis; - The need of large amount of memory to store samples of disturbances occurred, since, as seen, instruments cannot automatically classify the event and so users must examine manually all waveforms. B. Distributed Monitoring Systems In most case information produced by power monitoring system have to be used in places physically different from those where the monitoring instrument has been placed. Here are some typical situations that may occur: (i) technicians may have to analyze data acquired in an wind farm plant situated on the top of a mountain; (ii) technicians of an utility have to monitor power quality in a customer’s industrial site, to verify if problems claimed from customer depend on power produced, or instead depend on customer’s devices faults; and (iii) customers would access to power quality data, stored into utility’s database. First attempts of automatic data transfer used modems for downloading information directly from monitoring instruments and, in some cases, for driving them. Now, with the growing diffusion of computer networks and internetworking, and thanks to software architectures for developing distributed systems (CORBA, DCOM, RMI), it is possible to create monitoring systems more and more complex. Monitoring instruments are connected to network directly or by a modem or a serial line. Data coming from them flows in a database that users can access via web. In this way, it is not necessary to install a particular software for connecting user to remote database, but it is sufficient a common web browser. One or more control workstations are then used to drive and configure monitoring instruments and to manage database. In the following the main characteristics of all components of a distributed monitoring system will be analyzed: a) Measurement instruments: In a distributed monitoring system an instrument should be: - Remotely configurable and controllable; - Able to control other devices (ex. Starting UPS when a certain event occurs);


Able to temporarily retain acquired data, to avoid loss of information in case of communication breakdown. b) Control workstation: A control workstation has to be able to supply the following functions: - Configuration of monitoring instrument parameters; - Start/stop of a remote measurement procedure; - Gathering of data coming from remote monitoring instruments and storage of the same in the database; - Data analysis; - Data export, paste into the clipboard, export to spreadsheets (eg. Excel) and to scientific software (eg. Matlab); A good control software should also have: - Graphical functions (event plots, CBEMA curves, statistical graphs, trends); - Generation of customized reports; - Possibility of software extensibility without expensive maintenance operations. c) Database server: The DMBS (DataBase Management System) should have the following characteristics: - Fast and concurrent access from many users without critical performance degradation; - ODBC (Open DataBase Connectivity) support; - Any unauthorized access must be avoided; - Transaction support. d) Web-based data access software: The peculiarities of the software for Web data access are: - Restricted access: any user wishing to access data must login, typing his/her username and password; afterwards, he/she can only access to data of his/her own interest; - Advanced query: users may set filters based on various fields, and using any logical operator (AND, OR, NOT, etc.); - Graphics plot: users may want to see (and download) directly from the browser events plots, CBEMA curves, statistical graphs, temporal trends, etc. e) Communication channels and hierarchy: The selection of the communication channel strongly depends on monitoring instruments connectivity functions and on their physical locations. Some of possible channels are: - Fixed telephone lines by using a modem; - Mobile communication system by using a GSM modem; - Telephone line by using DTMF coding; - Radio communication: - Communication on the power distribution line: a typical example of this kind of transmission is the DLC (Distribution Line Carrier) developed by Cannon Technologies [5]; - Use of existing LAN or WAN: this is undoubtedly the most versatile way to build a distributed monitoring system. The major advantage is the use of widely diffused protocol (TCP/IP) and distributed object architectures that allow very flexible and maintainable systems to be realized.

Another very important aspect related to communication between monitoring instruments and control software is the possibility of design a hierarchic communication system. For example, if in a site there are several monitoring instruments, it is not convenient to supply each one with a GSM modem or with an independent telephone line. So, a computer could be used to gather all information coming from instruments and periodically send information to control workstation using a single telephone line. III. ARCHITECTURE OF TRANSIENTMETER A. Overview Let us now describe the main characteristics of the proposed monitoring system. Its components (including monitoring instruments) are completely software implemented, except that a trigger circuit for disturbances detection and a data acquisition board. TRANSIENTMETER uses pre-existing Internet/intranet as communication channel, and it is composed of the following components (Fig.1): a) Monitoring workstations, composed of: - A trigger circuit, able to detect transient events on the electrical signals; - A data acquisition board, driven by the trigger circuit, that acquires (using pre-triggering mode) all the disturbances detected: in the current version a National Instruments PCI 6024-E is used; - A software component, named TRANSIENTMETER Server (implemented using Borland C++ Builder 4.0), that processes the digitized signals and sends results to control workstation. b) A control software simply named TRANSIENTMETER ( also implemented using Borland C++ Builder 4.0), used to: - Configure measurement parameters; - Initialize remote monitoring workstations; - Start/stop measurement procedures, in manual or automatic mode; - Gather data coming from remote monitoring workstations and store them into the database; - Process signals coming from text files or wave files; - Query and manage database. c) A database, containing information about measurements made and events occurred. d) A web-based software, named TRANSIENTMETER Web (implemented with the server-side scripting language PHP3), that allows remote queries to database, shows results and allows downloads of event samples in text format and event plots in GIF format. Major advantages of the TRANSIENTMETER architecture are: - It contemporaneously detects and classifies disturbances; - Disturbances are extracted from electrical signal fundamental; - It is possible to select those disturbances the system has to detect (eg. disturbances of only a particular type, only disturbances having a certain amplitude and frequency);

Fig.1. Architecture of TRANSIENTMETER.

- It is possible to store on database only extracted disturbances or only disturbances parameters (without waveform recording, saving so much space); - The system is provided by a function that periodically calculates statistics on the current measurement, deleting from database event details and storing on it only these statistics, this is important for saving space in case of a very long monitoring session; - Automatic start/stop of measurements, programming a list of timers; - Distributed architecture able to support monitoring workstations that dynamically register themselves on the control workstation to signal their presence; - Advanced query functions present into control software and into web software. B. Database Architecture Database entity-relationship model is shown in Fig.2.

Fig.2. Database entity-relationship model.

Entities present on database are the following: 1) Monitoring site: it represents a generic monitoring site. Attributes are identifying code, description and state (On or Off); 2) Measurement: it represents a measurement, executed on a certain site, and composed of a certain number of events. Attributes are: identifying code, date and time of start and stop, configuration parameters, (sampling rate, monitored phases and quantities), number of disturbances detected and statistics; 3) Event: it represents a disturbance detected during a measurement. Attributes are: phase on which the disturbance has been detected, quantity measured (voltage or current), date and time of occurrence, type of event, amplitude, duration or frequency, samples of disturbances extracted from fundamental and added to fundamental (both stored in blob fields). Relationships between entities are: i) Executed on: relates the Monitoring Site entity with Measurement entity, specifying on which monitoring site a measurement has been made; ii) Detected: relates Measurement entity with Event entity, specifying events detected during a certain measurement. C. User Interfaces As it can be seen in Fig.3, TRANSIENTMETER has a Multiple Document Interface, composed of the following

When users access to TRANSIENTMETER Web, they can set up filters, submit the query and then results are shown on the browser. Clicking on each row of the query result page, it is possible to plot events (extracted or added to fundamental). D. Communication Interfaces Interface between control software (TRANSIENTMETER) and monitoring software (TRANSIENTMETER Server) has been built using a CORBA architecture. One of major advantages deriving from the use of CORBA is the great maintanability and extendibility of the structure. As it can be seen in Fig. 4, remote methods are called to configure monitoring instruments, to start/stop a measurement and to read data processed.

Fig.4. CORBA architecture.

The ORB (Object Request Broker) used is TAO (The ACE ORB) developed by St.Louis Washington University at the Component level of their ACE (Adaptive Communication Environment, it is an open-source framework for developing distributed architectures) [6,7]. This fully-compliant ORB is particularly suitable for high-performance and real-time applications. IV. MEASUREMENT ALGORITHMS In the following the measurement method implemented in TRANSIENTMETER [8-14] is described. As it can be seen in Fig.5, the instrument processes signal coming from data

Fig.3. TRANSIENTMETER user interface.

windows: - A window used to select sites where to start/stop a measurement; - A window containing a row for each measurement; - A window for each active measurement, containing a row for each event occurred in that measurement; - Event plot windows; - Statistics windows. Moreover, the instrument has a panel used to control the state of the system, and to watch number of events occurred on a certain site and to manually start/stop measurements.

Fig.5. Measurement algorithm.

Fig. 6 – Experiment setup.

acquisition board, driven by a trigger circuit. After the signal acquisition, there are some software modules oriented to: (i) classify the disturbance, (ii) calculate its duration or its frequency, and (iii) extract the disturbance from fundamental and calculate its amplitude. Let’s now examine main characteristics of each module: Trigger circuit It compares the original signal with a low pass version, filtered with a 70Hz frequency cut filter. If the difference goes beyond a certain threshold, a monostable multivibrator generates a TTL pulse that drives the data acquisition board. For monitoring several phases and quantities, many trigger circuits may be OR-connected: the output of the OR gate will drive the data acquisition board, and outputs of single circuits will be connected to board’s digital inputs to indicate on which phase the disturbance has been detected. Classification This phase is mainly based on the Wavelet Networks (WNs). Briefly, a WN is a feed-forward neural network whose first layer’s activation functions have been replaced by mother wavelet functions, and the training algorithm (back-propagation), modifies not only neural weights and thresholds, but also scale and translation parameters of wavelet nodes. This kind of network can extract from signal time-frequency information, very useful for transient signals analysis. A simple neural network can only extract wave shape information. Module for disturbance duration estimation It calculates the duration of a singularity of the signal. Initially, the module computes the CWT (Continuous Wavelet Transform) of the signal, and its local maxima. Then, starting from highest scale values (scale for CWT is the inverse of frequency), each CWT maximum is connected with the nearest one at the following scale. Each sequence of maxima is called “chain”. Subsequently, noise generated chains are eliminated, using appropriate thresholds, based on the fact that most noise maxima have amplitude growing with frequency, and all

others have a less amplitude than those generated by the singularity. Once only two chains remained, it is possible, at higher frequencies (where CWT ensures a good temporal resolution), to calculate distance between chains, corresponding to the disturbance duration e.g. in case of an interruption or an impulse, or the frequency disturbance e.g. in case of damped oscillation. Disturbance extraction and amplitude estimation This module decomposes the signal in sub-bands using DTWT (Discrete Time Wavelet Transform) implemented by a tree of QMFs (Quadrature Mirror Filters), and then each sub-band is reconstructed using another tree of QMF implementing an IDTWT (Inverse DTWT). The 50/60 Hz fundamental sinusoid is located at the center of the first subband. Selecting the sub-band containing the disturbance frequency previously calculated, and adding this sub-band with those adjacent, the extracted disturbance is now reconstructed. After the extraction, it is very easy to compute several disturbance characteristics like peak-to-peak amplitude, rise time, and so on. V. EXPERIMENTAL RESULTS Initially, TRANSIENTMETER was tested by means of simulated signals. This phase gave the possibility to opportunely set-up the several TRANSIENTMETER’s software components. Successively, experiments were performed by means of signals produced by an arbitrary waveform generator. These tests were aimed to adopt the better conditions for the trigger and acquisition phase. Finally, experiments are in progress for the monitoring of an industrial plant. Two monitoring workstations are used to monitor the disturbances produced by two motors (see fig. 6). The workstations are driven from a remote control workstation. The scope of the experience was to detect disturbances during the motor starting and

Fig.7. Damped oscillation occurred on motor stopping.

stopping operations. An example of disturbance detected during the monitoring phase (in particular, a damped oscillation occurred during motor stopping) is shown in Fig. 7. The figure reports the digitized signal, the extracted disturbance, and measurement results of the parameters characterizing the disturbance. VI. CONCLUSIONS TRANSIENTMETER has revealed itself, during all experiments, particularly effective in the automatic classification and characterization of transient disturbances and, with an appropriate calibration, it may be used on any operational environment. The use of a CORBA architecture to allow communication between control workstation and monitoring sites makes the software easily maintanable and extensible. In particular, the ACE-TAO platform has revealed itself appropriate for the scope, since it was specifically studied for real-time applications. Furthermore, TRANSIENTMETER is particularly cheap (it uses only a simple trigger circuit, a low-cost data acquisition board and some medium-level personal computers. Future improvements of the system are related to: i. Improving the developed measurement method, introducing adaptive thresholds for wavelet neural network and automatic calibration of thresholds used in the duration estimation; ii. Adding modules to detect, classify and characterize other kinds of disturbances such as harmonic disturbances, flickering, and so on; iii. Using dedicated hardware, eg. parallel DSPs for real-time signal processing; iv. Building an expert system able to detect disturbance causes and to propose adequate solutions. VII. ACKNOWLEDGMENTS The authors are grateful to Edison Energie Speciali S.pA and I.T.I.S. “Bosco Lucarelli” for the collaboration given during the experiments. VIII. REFERENCES [1] “Why monitoring power quality?”, Electrotek Inc., Tennessee, USA, http://www.pqmonitoring.com. [2] IEEE Working Group P1159, “Recommended practice for monitoring electric power quality - Draft 7”, Dec. 1994. [3] C.J.Melhorn, M.McGranaghan, “Interpretation and analysis of power quality measurements”, Electrotek Inc, Tennessee, USA. [4] D.Sabin, M.McGranaghan, “A systems approach to power quality monitoring for performance assessment”, Electrotek Inc, Tennessee, USA. [5] Cannon Technologies home page (http://www.cannontech.com).

[6] U.Syvid, “The adaptive communication environment: ACE”, Hughes Network Systems, Nov 1998. [7] N.Wang, “Overview of TAO’s ORB core”, http://www.cs.wustl.edu/∼nanbor. [8] T.B.Littler, D.J.Morrow, “Wavelets for the analysis and compression of power system disturbances”, IEEE Transactions on Power Delivery, vol. 14, no. 2, April 1999. [9] S.J.Huang, C.T.Hsieh, C.L.Huang, “Application of Morlet wavelets to supervise power system disturbances”, IEEE Transactions on Power Delivery, vol.14, no. 1, January 1999. [10] P.Pillary, P.Ribeiro, Q.Pan, “Power quality modeling using wavelets”, Proc. of the 1996 IEEE Int. Conf. on Harmonics and Quality of Power, Las Vegas, NV, USA, Oct. 16-18, 1996, pp. 625-631. [11] L.Angrisani, P.Daponte, M.D’Apuzzo, A.Testa, “A measurement method based on the wavelet transform for power quality analysis”, IEEE Trans. on Power Delivery, vol.13, no.4, Oct. 1998, pp.990-998. [12] L.Angrisani, P.Daponte, M.D’Apuzzo, “A virtual digital signal processing instrument for measuring superimposed power line disturbances”, Measurement, vol.24, no.1, 1998, pp.9-19. [13] S.Santoso, E.J.Powers, W.M.Grady, “Power quality disturbance identification using wavelet transforms and artificial neural networks”, Proc. of the 1996 IEEE Int. Conf. on Harmonics and Quality of Power, Las Vegas, NV, USA, October 16-18, 1996, pp. 615-618. [14] H.W.Furst, M.Pigl, J.Baier, “An analog method for detecting superimposed power line voltage disturbance”, IEEE Trans. on Instr. and Meas., vol. 43, no. 6, Dec. 1994, pp. 889-893. IX. BIOGRAPHIES Pasquale Daponte was born in Minori (SA), Italy, on March 7th, 1957. He is a Full Professor of Digital Signal Processing and Measurement Information at University of Sannio, and Senior Member of I.E.E.E. Instrumentation and Measurement Society. He is member of the: Editorial Board of the Measurement Journal, Elsevier Publisher; IMEKO Technical Committee TC-4 "Measurements of Electrical Quantities"; Working Group of the IEEE Instrumentation and Measurement Technical Committee N°10 Subcommittee of the Waveform Measurements and Analysis Committee for the definition of the new standard IEEE-1241 "Standard for Terminology and Test Methods for Analog-to-Digital Converters". He is coordinator of the IMEKO Working Group on ADC and DAC Metrology. He is deputy of the SOCRATES Program for the University of Sannio. He has published more than 140 scientific papers in journals and at national and international conferences on the following subjects: ADC Modeling and Testing, Digital Signal Processing, Distributed Measurement Systems, Sensors and Transducers. Massimiliano Di Penta was born in Campobasso (Italy) on 1973. He was graduated in information engineering from the University of Sannio (Benevento, Italy). From April 2000 he is Ph.D. student of information engineering at the University of Sannio. His interests are distributed systems, software testing and maintenance, design patterns. Gianpaolo Mercurio was born in Benevento (Italy) on 1969. He was graduated in electronics engineering from the University “Federico II” of Naples in 1998. From October 1998 he works at the L.E.S.I.M of the University of Sannio, dealing with research about time-frequency representations. From 1998 he is consultant of “Edison Energie Speciali S.p.A.”, a society of Montedison group dealing with wind power.