Analysis of the Influence of Weather Factors on Outages in Spanish Distribution Networks

Transcription

1 1 Analysis of the Influence of Weather Factors on Outages in Spanish Distribution Networks V. Barrera, J. Meléndez, S. Herraiz, A. Ferreira, A. Muñoz Abstract The origin of outages occurring in a power distribution network are highly related with the weather conditions existing during its apparition. Wind, rain and lightnings usually are the trigger causes of power network outages, hence the analysis of the relationship between weather and network outage information plays an important role for forecasting the impact of future weather conditions in the power distribution network. In this paper, weather information and distribution-network outages collected during three years are studied and statistically analyzed. From the analysis some useful relationships between weather conditions and network outages were extracted. Index Terms Power quality assessment, statistical analysis, exploratory data analysis, weather conditions, distribution function, correlation analysis, wind, lightnings, rainfall. I. INTRODUCTION Traditionally, utilities collect information about the voltage disturbances and their associated consequences in the distribution network. The consequences go from self-extinguished disturbances to the most severe consequence well-known as interruption, outage or blackout as a result of the protection operation. When an interruption occurrs in the distribution network, a network outage or incident immediately starts in the network operation database of the electric facility, which is done in order to keep track of the evolution of the power supply restoration for future studies or answering costumer complaints. Once the power supply is restored the data collection related with the corresponding outage is finished in the operation database. One outage incident may contain information about its date of occurrence, duration, cause, power continuity indexes, the affected zone, number of affected costumers as well as the activities carried out to achieve the power restoration, among others [1]. There are several causes of outages but some of them are highly related with weather factors. Electric facilities do not collect weather data, but luckily several meteorological agencies around the world do it. From both, weather and outage data, the main goal of this paper is to analyze together the information related with This work is funded in part by the research project ENERGOS- CEN : Tecnologías para la gestión automatizada e inteligente de las redes de distribucion energética del futuro (PROGRAMA CENIT-2009), INDRA Sistemas S.A and the Ministry of Education and Science (Spanish acronym MEC) under the FPI grant BES V. Barrera, J. Meléndez and S. Herraiz are with (IIiA) Institute of Informatics and Applications of the University of Girona, Catalonia. V. Barrera is PhD student and member of C4.112 CIGRE working group ( vbarrera). J. Meléndez and S. Herraiz are lecturers of the Univesitat de Girona. [victor.barrera, joaquim.melendez, sergio.herraiz]@udg.edu. A. Ferreira and A. Muñoz are with INDRA in Department of Energy Technologies. Madrid, Spain. causes of outages and the information related with weather factors. After an independent exploratory data analysis of both kind of data, the influence of weather factors on network outages apparition is studied. This analysis is carried out as a preliminary effort to find useful statistical relationships for forecasting future outages triggered by weather factors. The analysis has been carried out by analyzing statistically seven weather variables and more than incidents reported in distribution networks. The weather variables that have been considered in the study are the number of lightning per day, wind speed, gust of wind and rainfall level. The outages have taken place in several distribution networks at the central and northwestern spanish regions. Both of them have been recorded during three years, from 2008 to II. DATABASE DESCRIPTION This section describes the database used to carried out the statistical analysis of weather factors influence on outage apparition in power distribution networks. A. Weather database The weather database contains information about the number of lightning daily recorded, wind speed, gust of wind and rainfall level during the analyzed time period in the central and northwestern spanish region. Central region includes the following cities: Madrid, Segovia, Guadalajara, Toledo and Ciudad Real, while the northwestern one includes: A Coruña, Lugo, Pontevedra, Ourense and León. The weather variables included in the database have been splitted in three groups as follows: 1) Two variables related with the number of lightning daily recorded: Lightning-MetOffice and Lightning-AEMET are two variables indicating the number of lightning detected in an area of km 2. MetOffice (UK) and AEMET (Spain) meteorological agencies have recorded both lightning-based variables, respectively. 2) One variable related with the rainfall level: In accordance with the American Meteorological Society [2], the rainfall is any liquid or solid particle falling from the sky on the earth s surface. This weather phenomenon includes rain, drizzle, snow, sleet and hail. In the provided database, the rainfall is measured in square millimeters of water. A rainfall level equal to 1 mm 2 indicates that a water layer with thickness equal to 1 mm 2 is formed over 1 m 2 flat surface of area because of the precipitation. 3) Four variables related with the wind: Two variables are associated with wind speed (km/h) and two of them with gust of wind (km/h). Wind speed is the velocity

2 2 of the wind, while the wind gust is an abrupt change in its speed that may or may not be accompanied by a change in wind direction [2]. Both wind related variables have been estimated through the 50% (Speed-50) and 90% (Speed-90) percentile from all recorded measures throughout the day by anemometer 1 set placed in the 10 analyzed spanish geographic zones. Authors unknow the number of installed anemometers and their exact location in each zone. The Speed-50 variable corresponds to the median value of all recorded wind speeds during a full day by anemometer set in a geographic zone, while Speed-90 corresponds about to the maximum recorded value of the wind speed throughout the day. Likewise, the Gust-50 and Gust-90 variables are similarly estimated than Speed-50 and Speed-90, respectively. In summary, the weather database has seven (7) weather variables for each of the above mentioned spanish zones during 2008, 2009 and 2010, except for Lightning-AEMET variable which is recorded only for 2009 and The behavior of weather variables in time for each zone is performed in Section III-A. Fig. 1. AEMET detected lightnings during 2008 to 2010 in A Coruña. B. Distribution network outage incident database This database contains the following information: Timestamp: It corresponds to the year, hour and minute when the outage has taken place. Outage cause: The cause leading the outage. Forty four (44) different causes of outages have been found in the database. Region where the network outage occurred: Information about the affected circuit or substation (name, region, etc) Other information no associated with weather factors: Such as affectation duration, power quality continuity indexes, non delivered power, number of affected costumers, among others. In accordance with the goals of this analysis, only the information described above in the three first items have been used to analyze the weather factor influence on network outages. There are a total of incidents leaded by the 44 reported causes from years 2008 to III. DESCRIPTIVE AND STATISTICAL ANALYSIS OF THE DATA The results of the exploratory data analysis for weather and incident data are presented in this section. A. Exploratoy data analysis of weather variables After an analysis of the behavior of each weather factor throughout the days, a correlation analysis as well as a normality test is carried out. Later, these results have been used to select the relevant weather variables so that redundancy information is excluded to the analysis. 1 General name for instruments designed to measure wind speed [2]. Fig. 2. MetOffice detected lightnings during 2008 to 2010 in A Coruña. 1) Initial descriptive analysis: The AEMET and MetOffice daily detected lightnings during 2008 to 2010 in the spanish northwestern region are depicted in Fig. 1 and Fig. 2, respectively. Notice that no data is available for AEMET variable for 2008 (red curve). Both figures indicate that in A Coruña there is a lightning activity throughout the year specially during months from March to September inclusive. Additionally, some days with a high lightning activity are evidenced during July and September in both AEMET and MetOffice variables in the same days. Fig. 3 contains the rainfall level in millimeters in A Coruña throughout the years 2008 to It is clearly evidenced that rainfall level significantly decreased during July and August because of the summer time. The wind gust (Fig. 4) and speed (Fig. 5) in A Coruña is around to km/h and km/h throughout the year, respectively. From figures 1 to 4 it is important to observe

3 3 Fig. 3. Rainfall in millimiters during 2008 to 2010 in A Coruña. Fig. 5. Wind speed (Speed-90) during 2008 to 2010 in A Coruña. rainfall levels during summer ending (July and August). The northwestern region presents higher gust wind values than central region, as it was expected, since northwestern region is a coastal region highly influenced by winds that come from the sea. Both central and northwestern regions present low wind speed levels from May to September. TABLE I OVERALL CONCLUSIONS ABOUT WEATHER FACTOR BEHAVIOR ON NORTHWESTERN AND CENTRAL REGIONS Variable Northwestern Central Detected lightnings Throughout the year specially from March to September. Only from April to September. Fig. 4. Gust of wind (Gust-90) during 2008 to 2010 in A Coruña. that there is a high wind activity when there is high rainfall levels and high lightning activity as well. It means that during rainy seasons the rainfall level and wind activity significantly increased. The months with low wind activity are May, June, July and August due to the summer season, when usually no strong winds are experienced. The Gust-50 and Speed-50 variables present similar behavior on time than 90%-percentile variables. Authors have shown only 90%-percentile variables because they correspond to the worst wind conditions. By visual inspection of the weather variable behavior on time in both analyzed spanish regions, the following characteristics listed in Table III-A.1 have been elucidated. The spanish central region experiences a low lightning activity during winter season. The central and northwestern regions experience low Rainfall Gust of wind Wind speed Almost naught during July and August. Average km/h during the year. Low from May to September. Almost naught during July. Average 30 km/h during the year. Low from May to September. 20 km/h km/h 2) Correlation analysis: This section assesses the correlation grade between the weather variables. The correlation indicates the strength and direction of the linear dependance between two variables. The Pearson coefficient (R) was used for measuring the linear correlation. R is defined in terms of the covariance and standard deviation of both studied variables as follows [3]: R = σ xy σ x σ y (1) Where σ xy is the covariance of (x,y) variables while σ x and σ y are the standard deviation of x and y distributions. R takes values between -1 and +1 [3]: If R is equal to 1, a perfect correlation exists. R indicates a direct relationship between both variables: when one of

4 4 Fig. 6. Pearson coefficients (R) for weather variables during year 2009 in A Coruña. Fig. 7. Normality test for weather variables during 2009 in A Coruña. them increases, the other does in a constant proportion as well. If 0 < R < 1, then a positive correlation exists, hence when one of them increases, the other variable as well. If R = 0, no linear relationship exists. It does not mean complete independence in the variables since it may exist no linear relationship between the variables. If 1 < R < 0, a negative correlation exists: when one variable increases, the other decreases. If R = 1, a perfect negative correlation exists. R indicates a inverse relationship between x and y variables: when one of them increases, the other decreases in a constant proportion as well. Fig. 6 depicts the R coefficients for the seven weather variables in A Coruña (2009). The blue-circles curve indicates the R values taken by Lightning-MetOffice with respect to the rest of variables. For instance, it indicates that during 2009 Lightning-MetOffice has a strong correlation with Lightning- AEMET variable (0,88), and a weak correlation with the rest of variables since R coefficient takes values lower than 0,4. Based on the above, three correlated set of variables can be identified from Fig. 6. First one, the variable set representing the number of lightnings (Lightning-MetOffice and Lightning- AEMET), the second one, the variable set related with the wind (Gust-50, Gust-90, Speed-50, Speed-90), and the third one, the rainfall variable, which is not significantly correlated with the rest of variables (R values lower than around 0,55). Pearson coefficients of Madrid zone have a similar behavior than A Coruña, since there are also the same three variable sets with weak relationship between the sets. A similar behavior is obtained for both A Coruña and Madrid during 2008 and 2010 as well as for the remaining 8 zones throughout time period in central and northwestern regions. 3) Statistical normality test: The performed normality test [4] suggests that any of the weather variables follow a normal distribution. In Fig. 7 the normality test results for all weather variables in A Coruña during 2009 are plotted. It can be seen that any variable follows the linear curve corresponding to its normal distribution. This fact indicates that any weather variable follows a gaussian distribution function in A Coruña. The variables recorded for the rest of years in A Coruña and also in the rest of the zones do not follow a gaussian function. The normality of the variables was also quantitatively verified using the Kolmogorov-Smirnov and Lilliefors tests [4]. 4) Selection of the weather variables: The correlation analysis results suggest to make only use of one variable representing each of the three variable sets identified during the analysis of Pearson coefficients (Fig. 6). From this fact, the following actions were carried out: Lightning-AEMET variable was excluded because it does not contain information for year 2008 and only Lightning- MetOffice was used as the variable representing the variable set related with the number of lightnings. An analysis of the mean, median, standard deviation, kurtosis and skewness coefficients for each wind-related variable has suggest the selection of Speed-90 as a weather variable representing the wind profile due to it is the most symmetrical, so it is the most centered around the mean and consequently has the lowest absolute difference between the mean and median statistics. The second statistical criterion suggests Speed-90 as windrelated weather variable, but an intuitively analysis suggests the selection of Gust-90 variable since abrupt changes in speed or direction of wind correspond to the worst environmental condition, where these suddenly changes can probably cause an outage in the power network. However, the overall statistical results suggest to continue the analysis only with Lightning-MetOffice, rainfall, Speed-90 variables. The remaining variables contain redundancy information. B. Exploratory data analysis of network outages 1) Exploratory data analysis: Throughout 2008 year, 44,869 network outages have taken place, which have been

5 5 Fig. 8. The ten most relevant causes of network outages and their corresponding number of outages from 2008 to Fig. 9. Mean value of the recorded lightnings for each outage cause (bars) throughout the year The dashed curve indicates the mean value of lightning during 2008 reported by MetOffice. generated by 44 outage causes. Only ten (10) outage causes are the relevant ones and they have caused 90% of all aforementioned outage incidents, see Fig. 8. From the plotted 10 outage causes, it is shown that not all of them depend on the weather factors, for instance, the cause Correct delivery without outage, which is used when a network outage do not cause an interruption in the power supply. In other words, it is unfeasible to know the rootcause of the network outage through the mentioned outage cause. Consequently, the following outage causes take special attention in the analysis of the effect of weather factors on network outage apparition, since from them the root-cause of the outage can be directly identified: Tree, Bypass to earth without outage, Wind, Melted fuse. The 10% of the outages are due to weather conditions, 40% are responsible of the electric facility, and the rest of them are due to reasons beyond the control of the electric utility. IV. INFLUENCE OF WEATHER FACTORS ON DISTRIBUTION NETWORK OUTAGES A. Analysis on the northwestern spanish region The effect of the weather variables on distribution-network outages apparition in the northwestern spanish region (A Coruña) is analyzed in this section, hence the effect of lightnings, the rainfall and the winds during year 2008 on distribution networks in A Coruña are statistically studied. 1) Analysis of the lightning effect: The mean value of recorded lightnings for the following outage causes is shown in Fig. 9: Animals, (2) Trees, (3) Excavations, (4) Fallen ground phase conductor, (5) Phase to ground in consumer, (6) Melted fuse, (7) Floods in thirds, (8) Floods and earthworks, (9) Overloads, (10) Third tree felling, (11) GNF tree felling and (12) Storm/Lightning. Additionally, the global mean of recorded lightning during 2008 is depicted (vertical dashed line). From Fig. 9 it can be seen that when the number of lightnings is greater than the annual average, the distribution network experiences outages caused by Storms as well as slight amount of outages caused by Floods. The most part of network outages occur during low lightning activity days (Ligthning-MetOffice < 100), which can be verified observing the cumulative distribution function (Fig. 10). All outage causes generate more than 90% of the network outages with a number of lightnings lower than 100 (dashed line), except for outages caused by Storms which 90% of the storm-caused outages occur with an amount lower than 2500 lightnings. Notice that the storm-caused curve is below the curve indicating all analyzed causes (dashed line). This also means that network outages caused by Storms are clearly distinguished with the number of lightning recorded during the day which the storm-caused outage has taken place. In the two following subsections similar figures as Fig. 9 and Fig. 10 are presented but they show results for rainfall (Rainfall-90) and wind (Speed-90) weather variables. 2) Analysis of the rainfall effect: From Fig. 11 can be seen that network outages generated by Trees, Storms and Fuse operation occur during days with rainfall levels over the 2008 annual average. It implies that during days with high rainfall levels will occur network outages caused by tree branches getting in contact with overhead lines, and consequently an increase in fuse operation disturbances due to the frequent contact between tree branches and line conductors. The rainfall level is able to discriminate the tree- and stormcaused network outages as well as slightly the fuse-caused outages. It can be graphically verified from Fig. 12 where the three causes are below the global cumulative distribution function (dashed line). 3) Analysis of the wind effect: As it was statistically demonstrated in the exploratory data analysis of weather variables, all wind-related variables contain approximately the

6 6 Fig. 10. Cumulative distribution function of the number of recorded lightnings during year 2008 reported by MetOffice. Fig Cumulative distribution function of the rainfall levels during year Fig. 11. Mean value of the recorded rainfall levels for each outage cause (bars) throughout the year The dashed curve indicates the mean value of rainfall level during 2008 reported by MetOffice. Fig. 13. Mean value of the recorded wind speed for each outage cause (bars) throughout the year The dashed curve indicates the mean value of wind speed during 2008 reported by MetOffice. same information (Section III-A.2). Taking advanced of this fact, the effect of the wind on distribution network outages is studied using the wind speed variable (Speed-90). Similarly, any other wind-related variable could also be used. Tree- and Flood-caused outages occur when the weather experiences high wind activity compared with the annual average (Fig. 13). Likewise, it may also become evident that high wind speed cause Overloads and Melted fuses, both type of outage presumably caused by the operation of protection relays and blown fuses since the tree branches and overhead lines recurrently getting in contact as consequence of the strong wind. On the other hand, the Floods may possibly be due to strong winds also take place during strong storms, thus the region experiencing a strong storm has high probability to suffer floods. Fig. 14 confirms that high wind speed increase the Treeand Flood-caused outages since their cumulative distribution function take values lower throughout the global cumulative function. V. DISCUSSION The results about the effect of lightnings, rainfall and wind on the apparition of outages in power distribution networks are summarized in Table V. The check symbol ( ) indicates that a weather factor influences to an outage cause, otherwise marker has been used as indicator. The Tree-caused outages are the most influenced by weather factors since the increase in the number of lightnings, rainfall

7 7 The influence of weather factors such as lightning, rainfall level and wind on outage in power distribution networks has been described and statistically analyzed. Four causes of outages on power distribution networks highly influenced by weather factors have been identified from a qualitative and statistical analysis. Outages resulting from tree contacts are the most influenced by the three aforementioned weather factors. The information extracted in this paper about the influence of weather factors on causes of outages in distribution networks can only be considered as an early attempt to cope the prediction of outage. Authors currently are working on an automatic methodology for forecasting outage from weather factors. ACKNOWLEDGEMENT The authors thank the utility Gas Natural Fenosa (GNF) for data and information support. Fig Cumulative distribution function of the wind speed during year REFERENCES [1] V. Barrera, J. Melndez, S. Herraiz, A Survey on Voltage Sag Events in Power Systems, IEEE/PES Transmission and Distribution Conference and Exposition: Latin America, Bogota, Colombia, August 13th to 15th, [2] Glossary of Meteorology. American Meteorological Society (AMS), [3] J. L. Rodgers and W. A. Nicewander. Thirteen ways to look at the correlation coefficient. The American Statistician, 42(1):5966, February [4] R. E. Walpole, R. H. Myers, S. L. Myers, K. E. Ye, Probability and Statistics for Engineers and Scientists (9th Edition), Prentice Hall, January, TABLE II EFFECT OF THE WEATHER FACTORS ON DISTRIBUTION NETWORK OUTAGES Number of lightnings Rainfall level Wind Tree Storm/Lightning Floods Melted fuse level and gust/speed of wind increase the occurrence of outages as a result of tree branches touching the overhead distribution lines. The remanning outage causes are influenced by one or two weather factors. As it is was expected, Storm-caused outages stored in the provided database take place during days with high lightning activity and high rainfall levels. Flood-caused outages curiously have not occurred during raining days despite floods should take place during days experiencing strong raining. However, Flood-caused outage apparition is influenced by high number of lightnings and strong winds, which are usually accompanied by non-negligible rainfall levels. Windy and raining days bring an increase in fuse operation apparently as consequence of the tree branch recurrently getting in contact with overhead power lines. VI. CONCLUSIONS