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Web based Management System for Power Quality Assessment and Detection of Critical Zones Miguel Romero, Ricardo Pardo, Luis Gallego, Andr´es Pavas
Abstract—The implementation of a web system information management for assessment of power quality indices and detection of critical zones on a distribution system is proposed in this paper. The proposed system was implemented on a APACHE server platform, consisting of three main parts. The first part registers, pre-processes and stores power quality measurements on an open source data base using MySQL. In the second part power quality indices are calculated for each substation, such indices allow the evaluation of disturbances like voltage sags, voltage swells, voltage flicker (Pst) and voltage unbalance. Finally, in the third part the indices are displayed as power quality maps by means (Google Maps) tools, where critical zones are identified for each disturbance according to disturbance activity and severity criteria. This is a low cost, useful and easy to implement tool by the network operator, also can be implemented by the regulatory body for end user consultations.
on all buses with a voltage higher than 1kV since October 2007. These measurement systems can record steady-state disturbances as voltage flicker (PST) and voltage unbalance every 10 minutes, as well as temporary events such as voltage interruptions, voltage sags and swells each time they occur. According to the above, the network operator face new tasks like: - Managing of large amount of power quality information. - Systematic computing of statistical indices for measured disturbances. - Applying of new methodologies for power quality assessment and - Detection of critical zones of power quality.
Index Terms—Power Quality management, Sags Activity indice, critical zones of power quality.
Although the implementation of smart schemes in the Colombian grids has barely begun, the efficient and automated management of power quality data represents a first step towards that purpose.
I. I NTRODUCTION He currently available distribution systems are moving to the structure of smart grids. This movement is mainly caused by all advances in electronic measurement and control devices, communication system and customer’s needs. A smart grid integrates advanced sensing technologies, control methods, and integrates them by means of communications into the electricity grid [1]. These characteristics permit to improve of electric energy exchange efficiency, reliability and power quality, responding to several needs of customers and utilities. Furthermore, a smart grid allows the customization of electric power by using distributed energy resources, compensating facilities and managing the grid’s topology.
T
The operation of all devices composing a smart grid requires the processing of a large amount of data. These data regard to power flow, voltage and current levels, power quality indicators, among others. If a fast response is expected from the smart grid as a effective and economically suitable response to the customers and utilities needs, such data must be analysed efficiently and at the lowest possible cost. On the other hand, in Colombia the regulatory body (CREG) demanded from the colombian electricity utilities the implementation of measurement systems for power quality disturbances. The systems comprise measurement devices installed M. Romero (
[email protected]), R. Pardo (
[email protected]), L. Gallego (
[email protected]) and A. Pavas (
[email protected]) work at the research group PAAS-UN of the National University of Colombia. L. Gallego and A. Pavas are professors with the Electrical and Electronic Engineering Department of the National University of Colombia
In this paper a web management system is proposed to provide the necessary tools to centralize power quality information, store it in a practical and efficient way, and compute statistical indices. The network operator can apply different methodologies and systematically perform power quality assessment of measured buses. When indices are calculated, the results can be observed through tables and profiles graphs for a given time interval, allowing the user to identify the severity of the problems in a detailed form. The severity must be evaluated according to technical standards and power quality disturbance’s references values. In Colombia several efforts have been made to provide technical procedures and reference values for power quality indices [2] [3][4]. The results also include indicators showed geographically, where zones with power quality problems can be identified. Due to the features of the application, information management and consultation can be done from any computer with Internet access and related privileges without using any additional software, which means easy implementation of the tool. This paper is organised as follows. Section II presents a description of the measurement system. Section III shows the characteristics and structure of the database for power quality information and its implementation on an open source data base MySQL. Section IV presents the proposal for power quality indices calculation, statistical analysis and displaying of results. Finally the procedure to determine and display
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critical zones of power quality on google Maps is presented in section V.
II. M EASUREMENT S YSTEM D ESCRIPTION The web management system has been implemented for the major electricity utility of Bogot´a - Colombia (CODENSA). CODENSA’s system has 290 power quality meters which are installed in urban and rural zones nearby. On urban zones the power quality meters are connected to communication system at each substation by means of optic fiber networks, then the information is carried through LAN networks to the control center. Bogota City Urban Area
- PHP5. General-purpose scripting language for dynamic web pages development. - GNU-Octave. Open source High-level interpreted language for numerical computations. - GoogleMaps API. Application programming interface for Google Maps. The proposed system has a server, which collects the information from the power quality meters. The information on the server can be managed in Internet by different users. User can get detail information about errors on the measurements registering, power quality indices, statistics, power quality profiles, critical bus bars and geographical zones with power quality problems as shown in Fig. 2.
Optic Fiber Web Management System for Power Quality
PQ register
Users of electricity
LAN PQ register
Control Center
Errors of information Power Quality indices
PQ register
PQ Statistics
Modem GPRS
PQ register
Critical Bus bars
PQ register
Critical Geograrphical Zones
PQ register PQ register
Regulatory entities.
PQ Profiles
PQ register PQ register
PQ register
Modem GPRS
PQ register
Celular Operator
Figure 2.
INTERNET PQ Electric Distribution Company Department
PQ information available to users on Internet.
PQ register
Rural Areas
Figure 1.
Structure of power quality measurement system.
On rural zones around, the power quality meters are interconnected by means GPRS systems. The information is carried to the databases in the control center as shown in Fig. 1. Steady state disturbances are recorded by the power quality meters every 10 minutes and voltage events are recorded each time they occur, as prescribed in the IEC 61000-4-30 standard [5] for Class A equipment. Steady-state disturbances as voltage flicker, voltage unbalance, voltage harmonics, and voltage sudden events as interrupts, sags, swells and voltage deviations are measured. Nevertheless, the proposed system is focused on the management of the information required by regulatory entity CREG [6], therefore the analysed disturbances are steady-state disturbances (voltage flicker and voltage unbalance) and voltage events (voltage Sags, voltage swells and voltage deviations). In order to manage and assess the Bogota’s power quality information, a Web management system is proposed using the following software tools: - APACHE. Extensible open source HTTP server software. - MySQL. Open source database.
For management and consulting power quality information, the web application has three html modules: 1) Information Management. Users can manage the data entries to the database, review misinformation and obtain detailed information of each observed bus. 2) Power quality indices. Users can see indices calculated regarding the entire system or regarding a specific group of buses of the distribution system for a selected time interval. 3) Power quality maps and critical zones detection. Users can see indices of each disturbance on a geographical map, where critical zones are identified. In the following sections the characteristics of each module are explained in detail. III. I NFORMATION M ANAGEMENT MODULE The information management module has the necessary tools to receive, debug and upload information from the measurement system into the proposed web based management system. In addition, uploaded information is organized on a database, where power quality indices are calculated for subsequent consultations. According Fig. 2 Different type of users can consult or manage the web system according their role. Electricity customers can consult local power quality conditions.
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Regulatory body can consult global indices for control purposes and the electricity utility can consult and manage the information in order to identify problems and propose suitable solution strategies. According to the above described roles, some characteristics for power quality consultations are defined: 1) Power quality Indices. The appropriate indices for power quality assessment are established taking in account the technical standards.The colombian regulatory body proposed to register two steady state disturbances indices, voltage flicker (Pst) and voltage unbalance (V2/V1); these indices are recorded every 10 min. On the other hand, voltage events like voltage sags, swells, interruptions and voltage deviations must be recorded as well. In addition, some indices for voltage sags an voltage swells are proposed: - Number harmful voltage sags, which are labeled according to the lower section of the ITIC curve. - Number harmful voltage swells, located on the upper section of the ITIC curve. - Sags Activity indice, SAI, is proposed in [4] for evaluating voltage sags according to their severity. 2) Time intervals. Time intervals for power quality analysis are established according to several resolution levels (day, week, month, year), which can help for identifying possible patterns in the occurrence of disturbances. 3) Grouping levels. Grouping levels are defined in order to get global indices that allow the analysis of general system conditions, as well as individual cases (System level, Voltage level and Bus bar level). Besides the above mentioned items, some descriptive characteristics of the recorded power quality information are: -
The amount and size in MB of the information. How often information is stored. Type and size of data. Amount of calculated indicators.
According to above, the database structure in Fig. 3 is proposed. Each box in Fig. 3 is a relational table of the database. The raw data are organized according to dates and bus bars, then power quality indices are calculated for different time intervals and grouping levels. The database is implemented on open source database MySQL. The size of database is determined according to data registered and time of occurrence of power quality disturbances. Voltage events do not occur permanently. On the other hand, steady state disturbances are measured and registered all the time, their values are recorded every 10 minutes. Moreover, different indicators calculated for several time intervals (day, week, month and year) are recorded as well. In preliminary tests, size of database for 290 registers is about 600MB per year. An annual partition of the database is
proposed in order to maintain a high and efficient performance in the application. Users with administrative privileges can access the application from any computer to introduce information into the database, then authentication is performed as shown in Fig. 4. After that, users can select the input from the database using the measurement directly registered from the system or through information generated by the control center.
Start Authentication Define source
Resquest PQ acquisition system
Resquest data file Data debugging
Power quality indices
Report of errors
Power quality data
Indices calculation
Figure 4. Flowchart input information to the database and indices calculation.
Subsequently, the information is preprocessed in order to identify and report any possible errors regarding format, communications or measurements. Raw data of power quality are stored in a MySQL database. For each time interval and grouping level, power quality indices are calculated and updated periodically. The errors are used to generate reports aimed to identify potential faults in the communication system. Finally, the module contains the necessary tools to perform custom queries of any observed bus in a selected time interval. The electricity utility can check the error reports, to carry out detailed monitoring consultations of recorded values and analyse information of the whole system or individuals cases.
IV. P OWER QUALITY I NDICES CALCULATION MODULE Power Quality parameters are recorded by the measuring devices according to the specifications of the IEC 610004-30 standard for Class A, as mentioned above. Power quality indices have different perspectives, global indices are calculated for each disturbance to obtain a general perspective of the system. indices for different grouping levels (voltage levels) offer a intermediate view between the global and the individual perspective. indices for bus bars are calculated to obtain a detail information of specific cases. For that, several statistical indices are proposed to aggregate power quality information for the previous three possibilities. The proposed statistical indices are percentile (95, 70 and 50), min, max
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Dates
Raw Data
Steady state disturbances
Voltage events
Grooping Levels
Bus Bars
{
Indices of Bus bars per day
Indices of Bus bars per Week
Indices of Bus bars per Month
Indices of Bus bars per Year
Indices of the system per day
Indices of the system per Week
Indices of the system per Month
Indices of the system per Year
Critical Bus bars per day
Critical Bus bars per Week
Critical Bus bars per Month
Critical Bus bars per Year
Time intervals Figure 3.
structure of proposed database on MySQL
and average values. indices calculated for steady state disturbances and voltage events are described in the following paragraphs.
A. Indices for steady state disturbances Voltage unbalance (V2/V1) and voltage flicker (Pst) are recorded every 10 minutes. From these measurements, percentiles, average, max and min values for different time intervals (day, week, month or year) are calculated. Power quality indices for grouped bus bars are calculated differently that indices for a single bus bar. Global bus bars indices are presented initially by the module in order to identify generalized problems. Then, indices for critical bus bars are presented with the aim to analyse specific cases. In case of global indices or indices for grouped bars, power quality indices are calculated in two steps. First, the grouping level (Global or voltage level) and time interval are selected. The Percentile 95% indice is calculated for each bus bar of the grouping level. The result is a vector with percentile 95 values for every bus bar. Second, all proposed statistical indices are calculated on the previous vector. The Table I shows an example of voltage unbalance indices calculated for voltage level 115kV on April, 2010. An interpretation of a percentile indice in Table II is:
TABLE I (V2/V1) % VOLTAGE UNBALANCE FOR 115 K V LEVEL B US BARS Month April- 2010
P95 5,99
P70 0,00
P50 0,00
Min 100
Max 0,00
Average 4,16
95% of bus bars have a value of voltage unbalance at most 5,99 for 95% of the time. Similarly, Bus bars have an average value of voltage unbalance of 4,16 for 95% of the time. In addition, in Fig. 5 a daily profile of voltage unbalance is presented. When grouped levels indices are calculated, critical bus bars are identified by the module. In Fig. 6 some bus bars with critical voltage unbalance indices of 115kV voltage level are identified. In a single bus bar case, statistical indices are calculated for a time interval, then the results are displayed through charts and indices profiles in the web management system. TABLE II P ST INDICES FOR VOLTAGE FLICKER IN A BUS BAR IN B OGOT A´ Month April- 2010
P95 0,66
P70 0,45
P50 0,37
Min 0,22
Max 2,4
Average 0,41
Table II shows an example of voltage flicker indices (Pst) for a bus bar in April 2010. A possible interpretation of a
9,0
Similar consultations can be made for voltage unbalance.
8,0 7,0
B. indices for voltage events
6,0
Voltage sags, voltage swells, interruptions and voltage deviations are events that not occur permanently, therefore, indices for these disturbances are calculated as the sum of the occurred events in a time interval. In order to take into account the severity of the voltage sags and voltage swells, harmful voltage sags and harmful voltage swells indices are proposed.
5,0 4,0 3,0 2,0 1,0 29-04-10
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(V2/V1) % Voltage unbalance values
5
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(V2/V1) % Voltage unbalance values
Figure 5.
% Voltage unbalance profile for 115kV bus bars on April, 2010.
120
Harmful voltage sags are voltage sags that are under the lower curve ITI. On the other hand, harmful voltage swells are voltage swells that are above the upper curve ITI. In Table III an example of number of occurred voltage sags in a single bus bar is presented.
100
TABLE III N UMBER OF VOLTAGE SAGS IN A BUS BAR IN B OGOT A´
80
Number of voltage sags 72
Month April- 2010
60
Number of harmful voltage sags 23
40
Although in Table III a lot of voltage sag ocurred (72), only (23) are below the curve ITI, therefore those are considered harmful voltage sags.
20 0 TZB
SKB
Figure 6.
VTB TOB Critical Bus bars
M3B
% Voltage unbalance profile for 115kV bus bars on April, 2010.
percentile indice in Table II is: Pst value for 95% of the time is at most 0.66. At the same time, the module shows detailed information of the (Pst) voltage flicker profile. Fig. 7 shows the daily behaviour of the indicator Pst on April, 2010.
Number of events
30
15
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0,6 Pst Values
05-04-10
0,7
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0,8
03-04-10
0
0,9
Voltage Sags
Harmful Voltage Sags
0,5
Figure 8.
0,4 0,3
Fig. 8 shows the daily occurrence of voltage sags and harmful voltage sag in the bus bar for the same month. According to Fig. 8 the most of the voltage sags occur on the same day.
0,2 0,1
Dates
Pst Voltage flicker Profile for April 2010.
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0
Figure 7.
Number of voltage sags and harmful voltage sags.
In case of global indices, voltage events indices are calculated in two steps. The sum of events for each bus bar is calculated. The result is a vector with the number of events for every bus bar. After that, all proposed statistical indices
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are calculated on the previous vector. The Table IV shows an example of voltage swells indices calculated for whole system on September, 2010. TABLE IV N UMBER OF VOLTAGE SWELLS FOR THE SYSTEM Month September- 2010
P95 96
P70 4
P50 0
Min 0
Max 118
Average 0
An interpretation of a percentile indice in Table IV is: 95% of bus bars have at most 96 voltage swells on September. Similarly, 70% of bus bars have at most 4 voltage swells. It means that only a few bus bars have voltage swells problems.
categories Moderate, Severe and Critical sags proposed
Where: - SAI i : Sags Activity indice in the measured bar i. - Aix : Rating of Sags Category X in the measured bar i (between 0 and 1). - wx : Weighting of Sags Category X (between 0 and 1). - X: Sags Categories, X = {M, C, S} .
25 Number of events
Figure 10.
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The weights of each category are a percentage of total. The weights are defined considering that a critical sag has a more harmful effect than a severe sag or than a moderate one, a severe sag has a more harmful effect than a moderate one and moderate sags could be tolerated. According to the reference [4], the weights for MV and AV are respectively:
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0
SAI i = 0.04AiMM V + 0.23AiSM V + 0.73AiCM V
(2)
Number of voltage swells and harmful voltage swells.
SAI i = 0.01AiMHV + 0.09AiSHV + 0.90AiCHV
(3)
Fig. 9 shows the daily occurrence of voltage swells in the system. The not occurrence of harmful voltage swells is observed.
For normalization of each attribute or category, the proposed expression in [4] is taken as reference: # " 1 1 i AX = 1− i A0 e2 ∆−kXmeas + 1 (4) e2∆ A0 = 2∆ e +1
Dates Harmful Voltage Swells
Voltage Swells Figure 9.
C. Sags Activity indice (SAI) In order to take into account various characteristics such as depth, duration and amount of voltage sags, Sags Activity indice is implemented. According to [4] voltage sags are classified as moderate, severe or critical event using the immunity curves of electrical equipment as shown in Fig. 10. In this way sags are evaluated according to its impact on user’s equipment. The SAI indice (Sags Activity indice) is defined as a value between zero and one, where “ 1” represents the best quality scenery and 0 represents the worst one. X SAI i = wx Aix (1) x∈I
Where: - k, β and ∆ = kXref : Fitting parameters in the normalization function. - Xref : Reference level or critical value for sags X ∈ {M, C, S} (Reference value per year or per quarter). i : Level of disturbance in substation i, in other - Xmeas words, number of sags belonging to a category X registered during the quarter or year in the substation i. - AiX : Normalized attribute in substation i for the category X. The expression gives a curve yielding values between 0 and 1, the amount of events in each category depends on the
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TABLE V E VALUATION OF SAI INDICE IN FOUR MV SUBSTATIONS DURING THE FIRST QUARTER OF 2009 Category
Reference Level
Pond [%]
M S C
309 44 14 SAI
4.0 23.0 73.0
Sub 1 Sags AX 36 0.967 96 0.001 51 0.000 0.039
Sub 2 Sags AX 162 0.686 41 0.253 7 0.711 0.604
Sub 3 Sags AX 154 0.713 37 0.336 3 0.926 0.782
Sub 4 Sags AX 71 0.918 18 0.796 0 1.000 0.950
reference values presented in [4]. Each measured bus will have a SAI indicator calculated on a quarterly basis, which may be used to compare different bus bars at the same period of time. 0,8
Table V presents an example of the assessment of SAI indice for four substations during the first quarter of 2009. Based on these results and with the purpose of providing a qualitative interpretation of SAI indice, it might be said that values over 0.7 are considered acceptable because these value (0.75) represents the minimum SAI value when (i) there are no critical events and (ii) there are severe and/or moderate events. On the other side, the maximum value that SAI might take is 0.25, corresponding to a case when there are only critical events and therefore any equipment is disturbed which is unacceptable. Finally, in the range between 0.25 and 0.7 a meaningful amount of equipment might be affected by sag events which might be also be named as a customer conflict range.
0,7
0,6
0,5
0,4
V. P OWER QUALITY MAPS AND CRITICAL ZONES DETECTION MODULE
The power quality maps and critical zones detection module shows the calculated indices for each disturbance for a period of time on a geographical map. By means of this tool, the utility can identify zones with differentiated power quality conditions. In this module, a map with geo-referenced information of each bus on the distribution system is used. A specific map from google-maps is used to show the location of all buses and to graph the indices using contours. The user can select a disturbance to be seen and the assessment time. A query is performed by the web system and indices calculated are assigned to each geographical point. After that, every contour is calculated by an algorithm programmed on Octave (Linux). The procedure for calculating contour is explained in detail: - Disturbance, time interval, and voltage level are selected. - indices are calculated for bus bars of the corresponding voltage level. - Geographical coordinates are obtained from selected bus bars. - Coordinates and the indices are used for calculating contours by octave software. - The resulting vectors are processed by Application programming interface (API) Google maps.
0,3
Figure 11.
Critical zones detection for Pst Voltage flicker indice
- Zones with different power quality conditions are enclosed by the mentioned contours. Fig. 11 shows an example of critical zones for Pst voltage flicker indice in 11,4kV bus bars in Bogota. Geographical maps and detection of critical zones can be performed for any disturbance. The electricity utility can perform maintenance plans focused on Critical zones of the distribution system, which are displayed in red. VI. C ONCLUSIONS The implementation of a web based management system for the analysis of power quality disturbances was presented. The system was designed and developed to provide suitable power quality information to perform assessment and comparing tasks in distribution systems of any possible size. The system was developed using free licensed software tools as MySQL, APACHE, PHP and Googlemaps. The management system can be used by electricity utilities at distribution or transmission
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levels. The proposed system allows the identification of critical areas, where the utilities may carry out preventive or corrective maintenance tasks, switch on and off compensating facilities or activate any available distributed energy resource. This kind of application can support the electricity customers and utilities in improving the power quality and reliability according to their particular needs. The presented web management system can be used before a smart grid’s implementation, providing decisive information to locate energy resources, network operating devices and any available compensating facility at a suitable and optimal performing location in the grid. For existing smart grids, the application can be integrated to the main grid’s control system to deliver data about power quality conditions, indicators and tendencies. Although the web management system’s version presented in this paper includes a few disturbances, it can be updated to consider any other as harmonics, interharmonics, transients or any disturbance registered by the power quality measuring devices. Such update is being developed and will be presented soon. VII. ACKNOWLEDGEMENTS This work has been carried out within the research project “Technological Innovation on Integrated Power Quality Management”, co-funded by the Colombia research council COLCIENCIAS, CODENSA S.A. E.S.P and the Universidad Nacional de Colombia. The authors would like to acknowledge the funding and cooperation of all the supporting entities. R EFERENCES [1] Momoh, J.A. Smart grid design for efficient and flexible power networks operation and control. IEEE/PES PSCE’09 Power Systems Conference and Exposition. Seattle, Washington - USA. March 15-18. 2009. [2] Romero, M. and Murillo, O.J. and Luna, L. and Gallego, L. and Parra, E. and Torres, H. Determination of sag disturbing and sag vulnerable zones in a distribution network using stochastic fault simulation. 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century. July, 2008. [3] Luna, L. and Gallego, L. and Romero, M. Evaluation and identification of critical zones due to sag activity. 2010 14th International Conference on Harmonics and Quality of Power (ICHQP). Bergamo - Italy. Sept. 2010. [4] Romero, M. and Pavas, A. and Cajamarca, G. and Gallego, L. A new methodology for the comparative analysis of sags among substations in a distribution network in Colombia. 2010 14th International Conference on Harmonics and Quality of Power (ICHQP). Bergamo - Italy. Sept, 2010. [5] International Electrotechnical Commission. Electromagnetic compatibility (EMC) - Part 4-30: Testing and measurement techniques - Power quality measurement methods. IEC Standard 61000-4-30. 2008. [6] Comisi´on Reguladora de Energ´ıa y Gas. Resoluci´on CREG 024 abril 26 de 2005. Modificaci´on de las normas de calidad de la potencia el´ectrica aplicables a los servicios de Distribuci´on de Energ´ıa El´ectrica. 2005. [7] Gallego L, Torres, H.,Pavas A, Urrutia D., Cajamarca G., Rond´on, D. A methodological proposal for monitoring, analyzing and estimating power quality indices: The case of Bogot´a - Colombia. IEEE Power Tech 2005. St Peterburg, Russia. July, 2005. [8] Andres Pavas, M. Romero, D. Urrutia, G. Cajamarca, L. Gallego, H. Torres, E. Parra. Nueva Metodolog´ıa de An´alisis comparativo de Sags entre Subestaciones de una Red de Distribuci´on - Caso ColombianoSimposio Internacional sobre la Calidad de la Energ´ıa El´ectrica - SICEL, 2007,http://www.revistas.unal.edu.co/indice.php/SICEL/article/view/1125 [9] Mueller, D., McGranaghan, M. Electrotek Concepts. Effects of voltage sags in process industry applications. Invited paper spt IS 01-2, presented at the IEEE/ KTH Stockholm power tech conference, Stockholm Sweden, pages (6), June 18 - 22, 1995. Inc Knoxville, Tennessee.
[10] Richard P. Bingham. Sags and swells. Manager of Technology and Products Dranetz-BMI 1994,Original Draft September 1994 Revised February 16, 1998, New Durham Road Edison, NJ 08818-4019 USA [11] Bollen, M. Understanding Power Quality Problems: Voltage Sags and Interruptions, The Institute of Electrical and Electronics Engineers, IEEE Press,INC. New York, NY 10016-5997, 2000. 543 pages, NY-USA. [12] Torres, H. Calidad de la energ´ıa el´ectrica. Asociaci´on Colombiana de Ingenieros ACIEM Cundinamarca 2001. paginas 320, Universidad Nacional de Colombia, Biblioteca central. Bogot´a Colombia. [13] McGranaghan, M., Mueller, D. Effects of voltage sags in process industry applications. IEEE transactions on industry aplications, vol 29, No 2, March / April 1993. Tennessee. [14] Conrad, L., Little, K., Grigg, C. Predicting and preventing problems associated with remote fault clearing voltage dips. Industry applications, IEEE transactions on publication date Jun / Feb 1991, vol 27, issuel on page(s) 167 - 172, Tennessee.
Miguel Romero Eng. Miguel F. Romero was born in Bogota, Colombia in 1982. He finished his undergraduate (2006) and MSc.(2010) studies in the Department of Electrical Engineering of the National University of Colombia, at present, Eng. Romero is developing a PhD in Electrical Engineering at the same institution. Eng. Romero has been research assistant in the Research Program of Acquisition and Analysis of Electromagnetic Signals of the National University of Colombia - PAAS-UN Group since 2004. His research interests are power quality, power systems analysis and network distribution design.
Ricardo A. Pardo Eng. Ricardo A. Pardo was born in Bogota, Colombia in 1983. He finished his un-dergraduate studies in the Department of Electrical Engineering of the National University of Colombia in 2010. At present is developing a MSc in Electrical Engineering at the same institution. Eng. Pardo has been research assistant in the Research Program of Acquisition and Analysis of Electromagnetic Signals of the National University of Colombia PAAS-UN Group since 2010. His research interests are power quality, power systems analysis, network distribution design and power markets.
Luis Gallego Luis Eduardo Gallego. MSc Eng. Luis Eduardo Gallego Vega was born I Bogota, Colombia, in 1976. He finished his undergraduate and Master studies in the Department of Electrical Engineering of the National University of Colombia, actually is candidate to obtain a PhD in Electrical Engineering in the same institution. Eng. Gallego has been researcher in the Research Program of Acquisition and Analysis of Electromagnetic Signals of the National University of Colombia - PAAS-UN since 2000, working in research projects mainly related to power quality and lightning protection. Eng. Gallego has been enrolled in teaching activities related to power quality and computational intelligence. His research interests are power quality analysis, power markets and computational intelligence applied to power systems modelling.
Andr´es Pavas 1978, received the BSc.(2003) and MSc.(2005) degrees in Electrical Engineering from the National University of Colombia, at present is developing doctoral studies at the same institution. Eng. Pavas has been researcher in the Program of Acquisition and Analysis of Electromagnetic Signals of the National University of Colombia-PAAS-UN Group since 2001, where has proposed and executed research projects related to power quality and lightning protection. Eng. Pavas is lecturer in the National University of Colombia in the areas of electric machinery and power quality. Eng. Pavas has been awarded with the Scholarship for Outstanding Posgraduate Students of the National University of Colombia and a Sandwich Scholarship of the German Academic Exchange Service (DAAD) to develope his dissertation at Ruhr University of Bochum-Germany. His research interests are power quality, electromagnetic compatibility and probabilistic aspects of power quality.
