Đang tải... (xem toàn văn)
CÁC BÁO CÁO BAO GỒM: 1. Fault Distribution Modeling Using Stochastic Bivariate Models for Prediction of Voltage Sag in Distribution Systems (Thầy Bạch Quốc Khánh). 2. Voltage Sag Index Using Stochastic Method with Considering Protection Coordination and Sensitive Equipment (Lê Việt Tiến). 3. Subsequence Action to Eliminate Blackout after Detecting Islanding using Solid State Transfer Switch Implemented in PSCADEMTDC (Thầy Nguyễn Đức Tuyên).
ĐẠI HỌC BÁCH KHOA HÀ NỘI BỘ MÔN HỆ THỐNG ĐIỆN eBook for You PHҪNII CUNGCҨP ĈIӊN Tổng hợp các bài báo khoa học giai đoạn 2007-2012 Bộ môn Hệ thống điện - Đại học Bách Khoa Hà Nội 82 eBook for You IEEE TRANSACTIONS ON POWERDELIVERY, VOL. 23, NO. 1, JANUARY 2008 347 Fault Distribution Modeling Using Stochastic Bivariate Models for Prediction of Voltage Sag inDistribution Systems Bach Quoc Khanh, Dong-Jun Won, Member, IEEE, and Seung-Il Moon, Member, IEEE Abstract—This paper presents a new method regarding fault dis- tribution modeling for the stochasticprediction study of voltage sags in the distribution system. 2-D stochastic models for fault mod- eling make it possible to obtain the fault performance for the whole system of interest, which helps to obtain not only sag performance at individual locations but also system sag performance through system indices of voltagesag. By using the bivariate normal dis- tribution for fault distribu ti on modeling,this paper estimates the influence of model parameters on system voltagesag performance. The paper also develops the modified regarding phase loads that create better estimation for voltagesag performance for the distribution system. Index Terms—Bivariate normal distribution, distribution system, fault distribution modeling, phase loads, power quality (PQ), stochasticprediction, voltagesag frequency. I. INTRODUCTION A MONG power-quality (PQ) phenomena, the voltage sag (dip) is defined inIEEE1159, 1995 as a decrease in rms voltage to between 0.1 and 0.9 of the nominal voltage at the power frequency for the duration of 0.5 cycle to 1 min. There has been a greater interest in voltage sags recently due to problems caused by the performance of sensitive electronic equipment that iswidely used. Research about the voltage sag is usually related to a basic process known as a “compatibility assessment” [1], [2] which includes three steps. Step 1) Obtain the voltage sag performance of the system of interest. Step 2) Obtain equipment voltage tolerance. Step 3) Compare equipment voltage tolerance with the voltage sag performance and estimate the expected impacts of the voltage sag on the equipment. Current research has shown evidence that obtaining the v oltage sag performance still needs more improvement. The Manuscript received August 2, 2005; revised December 5, 2006. This work was supported by the Korea Foundation for Advanced Studies’ International Scholar Exchange Fellowship for the academic year of 2004–2005. Paper no. TPWRD-00456-2005. B. Q. Khanh iswith the Electric Power System Department, Faculty of Elec- trical Engineering, Hanoi University of Technology, Hanoi,Vietnam (e-mail: bq_khanh-htd@mail.hut.edu.vn). D J. Won iswith the School of Electrical Engineering, INHA Univ ersity, Incheon 402–751, Korea (e-mail: djwon@inha.ac.kr). S I. Moon iswith the School of Electrical Engineering and Computer Sci- ence, Seoul National University, Seoul 151-742, Korea (e-mail: moonsi@plaza. snu.ac.kr). Digital Object Identifier 10.1109/TPWRD.2007.905817 information about the voltage sag ismainly obtained by monitoring and stochastic prediction. With recently advanced computer-aided simulation tools, the stochastic prediction of voltage sag becomes the preferable approach that can obtain the results at required accuracy for various network topologies and operational conditions.“The method of fault positions” and “the method of critical distances” are known as the most widely used methods for stochastic prediction studies. It is notable that regardless of which method is used, a sto- chastic prediction study always has to solve two critical prob- lems : 1) the modeling of causes leading to voltage sags and 2) the simulation of the power system for computing voltage sag characteristics. Among important cause of voltage sags, short- circuit faults in the power system account for the largest part and the assessment of the voltage sag performance based on fault distribution modeling is a well-known approach. However, it is very difficult to build up “accurate” fault modeling because the data of faults can only obtained by monitoring and, thus, it has the same uncertainties as to what the monitoring of voltage sags can generate. This paper presents a new approach on fault distribution mod- eling for the stochastic prediction of voltage sags in the distri- bu ti on system using the method of fault positions. The simula- tion of the distribution system and fault distribution modeling are made on MATLAB for computing not only site indices, but also system indices of voltage sags. II.F AUL T DISTRIBUTION MODELING Modeling the fault distribution is to determine the short-cir- cuit fault frequency (i.e., fault rate or the number of short-circuit faults per year) for all fault types at all possible fault positions throughout the system of interest. It consists of the selection of fault position and fault type and the distribution of fault rate for the selected fault positions and fault types. Fault positions are generally chosen in a way that a fault po- sition should represent short-circuit faults leading to sags with similar characteristics [2].For the distribution system with typ- ical radial network topology, small line segments, and distribu- t ion transformers along the trunk feeders, it is possible to apply only one fault position for each distribution transformer and one fault position for each line segment. Different fault types should be applied to each fault position mainly depending on the number of phases available at the se- lected fault positions. The fault rate of each fault type is nor- mally referred from the observed historical data. 0885-8977/$25.00 © 2007 IEEE Tổng hợp các bài báo khoa học giai đoạn 2007-2012 Bộ môn Hệ thống điện - Đại học Bách Khoa Hà Nội 83 eBook for You 348 IEEE TRANSACTIONS ON POWERDELIVERY , VOL. 23, NO. 1, JANUARY 2008 The fault rate mainly depends on fault position, fault type, and fault cause. While two earlier factors have been discussed at length in past research, the distribution of the fault rate for the selected fault positions has received less interest. The most common assumption that has been argued so far is that because the fault can occur anywhere in the system, stochastically, it is possible to model the fault rate as the uniform distribution [3], [4]. In this sense, the fault rate at each position is identical to the component failure rate that is based on component relia- bility. However, in reality, many factors can lead to faults, not just the component failure, and fault rates at different positions in the system are rarely the same. Recently, a report [5] pro- posed some interesting 1-D models of fault distribution along individual line segments (between two nodes). However, this re- search could not consider the distribution of transformer faults. Furthermore, by using 1-D fault distribution, it is hard to ob- tain a system index about voltage sag performance since there are plenty of line segments in the distribution system. The new method of fault distribution modeling proposed by this paper carefully analyzes concerned fault causes and builds up a suit- able modeling of the fault distribution for the whole system of interest from which system i ndices can be obtained. III. N EW FAULT DISTRIBUTION MODELING BASED ON FAUL T CAUSES FOR DISTRIBUTION SYSTEMS Although there are a variety of causes that result in faults in distribution systems, it is possible to group them into two parts: namely 1) equipment failures and 2) external causes. Equipment failure is basically due to defects that are prob- ably created during manufacture, transportation, and installa- tion.Equipment failure depends on the time of being placed into operation, the aging period, and maintenance conditions. According to the reliability theory, it is often characterized by the component failure rate. There are several distribution func- tions to model this parameter but the most common one i s the exponential distribution which assumes the component failure rate to be constant. This value is equal to the average failure rate during the useful life of the “bathtub” curve [6]. Therefore, if the same type of equipment is used throughout the system (e.g., the same type of distribution transformers used in the dis- tribution system), it is possible to assume that the failure rate of equipment follows the uniform distribution depending on the equipment type although itstill may cause some errors (e.g., not all equipment is put into operation at the same time or has the same maintenance conditions). Besides equipment failure, there are many other causes from the ambient environment that also may lead to faults in power systems. This paper calls them the external causes. Some can in- fluence the fault performance of the power system in a large area such as severe weather (wind storms, lightning, etc.). Mean- while, others mainly have local impacts, such as trees and ani- mals (birds, mice, etc.). Human factors (scheduled interruption, human errors, mischief, and vandalism) can cause faults that only influence the power system in small parts as well as se- vere faults for a large power system. All of these causes occur randomly and they can be simulated by stochastic models. 1-D stochastic models seem to not be suitable as explained before. Fig. 1.Example of bivariate normal distribution. This paper proposes the idea of using 2-D stochastic models in- stead (e.g., the bivariate normal distribution model as illustrated in Fig. 1). For large power systems, it is hard to obtain a converged 2-D fault distribution model for various causes in a large area. How- ever, for small-to-medium-size networks, such as the section of distribution network fed from a bulk-point distribution substa- tion, of which the monitored historical data of fault performance shows that faults due to external causes occur concentratively on one location (e.g., some lines pass through a small area which isa thi gh risk for faults due to industrial pollution or trunk fall), it is the favorite condition to obtain a converged 2-D fault dis- tribution model. IV. P ROBLEM DEFINITION AND SOLUTION A. Case Study Definition To illustrate the new method of fault distribution modeling in the stochastic prediction of voltage sag in the distribution system, this paper uses the IEEE 123-bus radial distribution feeder [7] as the test system. It can be seen as the distribution system is fed from a bulk point. It does not narrow the scope of application of the study with the following assumptions. •Since line segments in the test system come in one, two, and three phases, distribution transformers at load nodes are the single phase type for separate single-phase loads. For three-phase loads, the connection of the distribu - t ion transformer is4.16-kV grounded wye—low-voltage grounded wye. • Voltage sags are only caused by faults in the test system. • If the test system is supposedly a section of a large distri- bution system, only faults occurring in it are considered. The faults in sections fed from other distribution substa- tions can be skipped as the transformer impedance indis- tribution substations, in reality, is rather high. Similarly, the faults in low-voltage networks are also ignored because of the large impedance of distribution transformers. This as- sumption only neglects voltage sags caused by faults in the transm ission system. It will be considered if the stochastic prediction of voltage sag in large transmission systems [4] is included. • In terms of reliability, the test system is modeled on two main components: lines and distribution transformers. The reliability of any other distribution equipment is suppos- edly included in the reliability of these two components. • The fault positions are selected as mentioned in Part II.For transformers, one fault position at each load node (i.e., the nodes connected with distribution transformers) is applied. Tổng hợp các bài báo khoa học giai đoạn 2007-2012 Bộ môn Hệ thống điện - Đại học Bách Khoa Hà Nội 84 eBook for You KHANH et al.:FAULT DISTRIBUTION MODELING USING STOCHASTIC BIVARIATE MODELS 349 For lines, one fault position is also applied for each line segment. Due to the short line segments, this paper selects the fault position at the end of each line segment (For the test system, there are 122 line segments and 87 load nodes. Therefore, 209 fault positions in total are selected). • Fault types (single phase to ground, phase to phase, two phases to ground, and three phases to ground) are applied to fault positions depending on the number of available phases. The fault impedance is assumed to be negligible. • The fault rate of a distribution transformer is a random variable depending on the position of the load node it is connected to. The fault rate of a line segment is also a random variable depending on the fault position and the length of thisline segment. Based on the previous definitions and assumptions, the com- putation of voltage sags at all load nodes on the primary side of distribution transformers throughout the test system is per- formed on MATLAB [8]. The voltage sag frequency at each load node is obtained when applying the fault rate to each fault posi- tion. The fault rates at the fault positions are calculated based on the new fault distribution modeling presented in Part B.Finally, related voltage sag indices are calculated. B. Fault-Rate Modeling Faults are random events and as previously indicated, they can be simulated by stochasticdistribution models. Accord ing to the analysis in Section III, the fault rate of each fault type at each fault position is equal to the sum of equipment failure rate and fault rate due to external causes. The equipment failure rate is supposed to follow the uniform distribution model. Therefore, for the fault position of the transformer , the failure rate is cal- culated as follows: (1) where number of transformer faults of the test system; total distribution transformers; contributory percentage of equipment failure. The line failure rate is normally expressed in the number of faults per year per foot (or meter) length. However, because of the short length of line segments, the line failure rate is calcu- lated for the whole line segment as follows: (2) where number of line faults of the test system; total line segments; length of the line segment (in feet). The distribution of the fault rate due to external causes de- pending on fault positions is supposedly in compliance with the 2-D stochastic model. This paper uses bivariate normal distribu- tion because it is the most common stochastic model which has such critical advantages as it accepts continuous variables and is easy to build up the distribution based on monitored historical data. Besides that, it is also simple to convert to other models using continuous variables. So the fault rate at each fault position is as follows. For the transformer (3) For the line segment (4) where contributory percentage of faults due to external causes ; , weighted factors of the fault rates of the transformer and the line segment that follow the bivariate normal distribution model depending on fault positions. The joint probability density function of bivariate normal dis- tribution is expressed as follows: where (5) , , , means and standard deviations of two variables , ; correlation coefficient. If the coordinates of fault positions are independent variables . The probability for a fault to occur at the fault position within an area can be calculated as follows: (6) If and is large enough, then the distribution is normalized as follows: (7) For the distribution system, geographically, if network nodes are disposed relatively uniform, itwill be possible to apply the Tổng hợp các bài báo khoa học giai đoạn 2007-2012 Bộ môn Hệ thống điện - Đại học Bách Khoa Hà Nội 85 eBook for You 350 IEEE TRANSACTIONS ON POWERDELIVERY , VOL. 23, NO. 1, JANUARY 2008 following approximation where and are the coordinates of the fault position . • Faults rate for the transformer (8) • Fault rate for the line segment (9) C. Development of Voltage Sag Indices PQ indices are used to estimate the quality of supplied elec- tric energy for the power system. To date, many PQ indices have been proposed for various PQ events. A well-known index of voltage sag is the system average rms voltage variation fre- quency index for voltage sag down to under X% of the nominal voltage value . It is often used for evaluating the PQ of a three-phase power system based on monitored limited seg- mentation [3]. The assessed system is segmented so that every point in the system is contained within a section monitored by an actual PQ measuring instrument. In distribution systems, because various phase loads (phase to neutral, phase to phase, three-phase loads) are available, asymmetrical faults, which account for most faults, never result in voltage sags to all single-phase loads (e.g, phase A-to-ground faults may not cause voltage sags to the loads connectedbetween phase B and neutral or phase C and neutral or loads connected between phase B and phase C). Therefore, using regardless of the number of phases involved, may not exactly reflect the voltage sag performance of the distribution system. From the demand sides, the indices are more interesting because they can estimate the voltage sag performance for phase loads. In order to take the availability of various phase loads in the distribution system into account, this paper newly develops in regard to phase loads as follows: (10) (11) (12) where , , number of sags down to under X% that phase-to-neutral (A,B,C), phase-to-phase (A-B, B-C, C-A), or three-phase load experiences; , , number of phase-to-neutral (A,B,C), phase-to-phase (A-B, B-C, C-A), or three-phase customers served from the system of interest. TABLE I S YSTEM FAUL T-RATE BREAKDOWN Fig. 2. Mapping of the IEEE—123-bus radial distribution test feeder. V. RESULT DEMONSTRATION AND ANALYSIS A. Procedures of StochasticPrediction The process of stochastic prediction study is performed through the following steps. First, the system fault rate (the total of faults occurring in the test system over a certain period of time) is assumed to be an arbitrary number, say 500 faults. This value is just for calcu- lation and easier graphic demonstration of the results. Besides that, contributory percentages of different fault types are also assumed as follows: •single phase to ground (N1): 80%; • two phase to ground (N11): 10%; • two phase together (N2): 8%; • three phase to ground (N3): 2% and the component fault rates are supposed to be • transformer: 50%; •line: 50%. The listed percentages shown are, in fact, based on actual survey data [9]. Based on the aforementioned assumptions, the system fault rates of transformers and lines for different fault types due to different fault causes (equipment failure or external causes) are calculated and shown in columns 2 and 3 of Table I. Param- eters ( , ) that are included make it possible to consider the influence of fault causes due to external factors. Second, the fault rate of each fault type is calculated for each fault position using the fault distribution models as stated in Tổng hợp các bài báo khoa học giai đoạn 2007-2012 Bộ môn Hệ thống điện - Đại học Bách Khoa Hà Nội 86 eBook for You KHANH et al.:FAULT DISTRIBUTION MODELING USING STOCHASTIC BIVARIATE MODELS 351 Fig. 3. Sag frequency spectrum and of different phase loads for the case the mean value is at node 13 and deviation . Table I. The test system with actual dimensions in feet is mapped out in Fig. 2. The fault positions are assigned with coordinates. Third, the voltage sag magnitude and phase shift at all load nodes are computed for all selected fault positions. With the ap- plication of fault rates to the selected fault positions, the voltage sag frequencies corresponding to different characteristics are obtained. The voltage sag frequency is calculated for the fol- lowing: • individual load nodes; • all possible phase loads, including phase-to-neutral, phase-to-phase, and three-phase loads; • the whole test system. B. Evaluation of Influences of the Fault Distributi on Modeling on the Voltage Sag Performance The fault distribution modeling uses several parameters. In practice, it is possible to adjust these parameters so that the re- sulting model issuitable for the fault performance of the distri- bution system of interest. However,the variation of these param- eters also makes the voltage sag performance change accord- ingly. In modeling fault distribution, this paper also considers the following options of fault distribution for estimating the in- fluences of fault distribution on voltage sag performance. • Change contributory percentages of the fault due to ex- ternal causes (change or ). In this paper, three op- tions , 50%, and 100% are considered. •Switch the position of the mean value ( , ) of the bi- variate normal distribution. This paper considers four op- tions of the mean value at nodes 13, 51, 67, and 85 as in- dicated in Fig. 2. • Vary the deviations , of the bivariate normal distri- bution. This paper also considers the options of the devi- ation that are equal to 0.2, 0.5, and 0.8 of the maximum value among deviations . C. Results Analysis Based on aforementioned procedures of stochastic prediction, the following are remarkable results. In Fig. 3, the indices of voltage sag for different phase loads, including voltage sag frequency spectrums, corresponding , , and for X ranging from 10% to 90% of the nominal voltage are shown. In this case study, , . Besides that, for the whole test system for dif- ferent mean values (at nodes 13, 51, 67, and 85) of the fault distribution models regardless of the number of involved phases are also depicted in Fig. 4. Obviously, there are big differences between of different phase loads or between of phase loads and of the whole system. of phases A, B, and C are different because the number of single-phase loads on each phase are different. are rather low as single-phase loads just experience sags due to single-phase-to-ground faults on the same phase. Generally, are greater because phase-to-phase loads are impacted by more faults (faults on two phases) than phase-to-neutral loads (faults on one phase). For phase-to-phase loads, there isalittle deep sag frequency; meanwhile, the shallow sag frequency rises greatly because al- most phase-to-ground faults (80% system fault rate) just cause shallow sags to phase-to-phase loads. for three phases is the greatest and for is equal to 500 sags per load because three-phase loads will experience voltage sag for any fault type. The aforementioned remarks also explainwhy , defined for phase loads, is for more useful indices for estimating the voltage sag performance in the distribution system where many single-phase loads exist. Fig. 4 also shows that different positions of the mean value of fault distribution models result indifferent spectrums of voltage sag frequency. It is notable that if the position of mean value gets closer to the bulk point of supply, the deep sag frequency will increase, that is, mainly because of the radial network topology of the distribution system. Tổng hợp các bài báo khoa học giai đoạn 2007-2012 Bộ môn Hệ thống điện - Đại học Bách Khoa Hà Nội 87 eBook for You 352 IEEE TRANSACTIONS ON POWERDELIVERY , VOL. 23, NO. 1, JANUARY 2008 Fig. 4. Sag frequency spectrum and of the whole system for different mean positions for the case that the deviation is . Fig. 5. Voltage sag frequency spectrum of the load-bus 63 on phase A for dif- ferent deviations. The mean value is at node 67 (upper) and node 13 (lower). Fig. 6. Voltage sag frequency spectrum for loads on phase A for different de- viations. The mean value is at node 67 (upper) and node 13 (lower). Figs. 5 and 6 plot the voltage sag frequency for load node 63 (see Fig. 2) on phase A and for all loads on phase A for Fig. 7. Voltage sag frequency spectrum and for the whole system for different deviations for the mean value at node 67. Fig. 8. Voltage sag frequency spectrum and for the whole system for different deviations for a mean value at node 13. different deviation values of fault distribution in the case the mean values are identical to the coordinates of node 13 and node 67. Similarly, Figs. 7 and 8 demonstrate the voltage sag frequency spectrum and Tổng hợp các bài báo khoa học giai đoạn 2007-2012 Bộ môn Hệ thống điện - Đại học Bách Khoa Hà Nội 88 eBook for You KHANH et al.:FAULT DISTRIBUTION MODELING USING STOCHASTIC BIVARIATE MODELS 353 Fig. 9. Voltage sag frequency distribution for sags lower than 10%, 40% to 50%, 60% to 70%, and 70% to 80%, , , mean at node 67. for the whole test system also for different deviation values and for the mean values at node 13 and node 67. Increasing the deviation values and will turn the normal distribution into the uniform distribu- tion. It causes shape variations to the voltage sag frequency spectrum. The clear increase of the frequency of deep sags is shown in all cases of the sag performance demonstration. If the mean position of the distribution model is located at node 13, which is very near the bulk point, the frequency of sags below 10% isevenraised by about 50% for the small deviation . That is also explained as the result of the radial network topology of the distribution system. The spectrum of the voltage sag frequency for different case studies (from Figs. 3–8) isquite similar inwhich deep sags ac- count for a large number mainly due to short feeders in the dis- tribution system. The frequency of 40% to 60% sags is also high as the network topology consists of one trunk line with many lat- eral taps in the middle. That means the point of common cou- pling of many load nodes is on the middle of the trunk line.Few load nodes connected to the trunk line near the bulk point of supply (the distribution substation) explain why the shallow sag frequency is very low.F ig . 9gives us a closer look at the voltage sag frequency distribution for different sag magnitudes. It is, without doubt, that deep sag frequencies appear at the nodes on branches connected close to the far end of the trunk line. Voltage sags 40% to 50% are distributed rather uniformly ex- cepting nodes near the bulk point. The shallow sag frequencies mainly occur at several nodes near the bulk point of supply. VI. C ONCLUSION This paper presented a new method of fault distribution mod- eling in the stochastic prediction of voltage sag for the distri- bution system using 2-D distribution models. When using 2-D distribution models for modeling fault distribution, parameters of the distribution model should be selected properly to match the monitored historical data of fault performance of the system of interest. By using the bivariate normal distribution for mod- eling fault distribution, this paper also analyzed the influences of its parameters on voltage sag performance. It i s notable that the alteration of the deviation value of the distribution has a much stronger impact on sag performance, especially for the deep sag frequencies pattern than switching the position of the mean value. The more concentrated occurrence of faults on one location in the distribution system of interest will increase the number of deep sags. The results are also evidence that the typ- ical radial network topology of the distribution system is also another important reason for the high frequency of deep sags. 2-D stochastic models, such as the bivariate normal distribu- tion used for modeling fault distribution, can provide a good o v erview of fault performance of the whole system of interest. Thus, it is possible not only to analyze the relation between faults and voltage sags at individual locations of the system, such as a specific load node or a segment of line, but also to compute system indices of voltage sags, such as . The application of 2-D stochastic models has some limits to the size of the system of interest.For the sections of the dis- Tổng hợp các bài báo khoa học giai đoạn 2007-2012 Bộ môn Hệ thống điện - Đại học Bách Khoa Hà Nội 89 eBook for You 354 IEEE TRANSACTIONS ON POWERDELIVERY , VOL. 23, NO. 1, JANUARY 2008 tribution system, of which the size is so large as the one sup- plied from a bulk distribution substation, it is practical to use this fault distribution modeling. The accuracy will be further improved for the distribution systems, of which the topology features the uniform arrangement of components. In addition, the stochastic prediction of the transmission system should be included if the influence of fault occurring in the transmission system on voltage sag performance in the distribution system of interest is considered. The presence of different phase loads in the distribution system indicated that for the whole system without considering the number of phase of the loads cannot reflect voltage sag performance properly. To have a better assessment of the voltage sag, this paper develops modified regarding phase loads. The results proved that there are bigdifferences between , , and for different phase loads and for the whole system. This modification of is more practical from the customer’spoint of view when power-supply contracts are set up. R EFERENCES [1] R. C. Dugan, M.F.McGranaghan, and H. W. Beaty, ElectricPower System Quality. New York: McGraw-Hill, 1996. [2] M. H. J. Bollen, Understanding Power Quality Problems—Voltage Sags and Interruptions. New York: IEEE Press, 2000. [3] D. L. Brooks, R. C. Dugan, M. Waclawiak, and A. Sundaram, “Indices for assessing utility distribution system RMS variation performance,” IEEE Trans. Power Del.,vol. 13, no. 1, pp. 254–259, Jan. 1998. [4] M. R. Qader, M. H. J. Bollen, and R. N. Allan, “Stochastic prediction of voltage sags in a large transmission system,” IEEE Trans. Ind. Appl., vol. 35, no. 1, pp. 152–162, Jan./Feb. 1999. [5] J. V. Milanovic, M. T. Aung, and C. P. Gupta, “The influence of fault distribution on stochastic prediction of voltage sags,” IEEE Trans. Power Del.,vol. 20, no. 1, pp. 278–285, Jan. 2005. [6] R.E.Brown, ElectricPower Distribution Reliability. New York: Marcel-Dekker, 2002. [7] IEEE Distribution Planning Working Group Report, “Radial distribu- tion test feeder,” IEEE Trans. Power Syst.,vol. 6, no. 3, pp. 975–985, Aug. 1991. [8] W. H. Kersting,Distribution System Modeling and Analysis. Boca Raton, FL: CRC, 2002. [9] T . A. Short, ElectricPower Distribution Handbook. Boca Raton, FL: CRC, 2004. [10] G. Olguin, “Voltage dip (sag) estimation in power system based on sto- chastic assessment and optimal monitoring,” Ph.D. dissertation, Dept. Energy Environ.,Div.Elect. Power Eng., Chalmers Univ. Technol., Gotteborg, Sweden, 2005. [11] M. R. Qader, M. H. J. Bollen, and R. N. Allan, “Stochastic prediction of voltage sags in reliability test system,” presented at the PQA-97 Eu- rope, Elforsk, Stockholm, Sweden, Jun. 1997. [12] J. A. Martinez-Velasco and J. Martin-Arnedo, “Stochastic prediction of voltage dips using an electromagnetic transient program,” presented at the 14th PSCC, Sevilla, Spain, Jun. 2002, Paper 4, Session 24. Bach Quoc Khanh received the B.S. and Ph.D. de- grees in power network and systems from Hanoi Uni- versity of Technology, Hanoi,Vietnam, in 1994 and 2001, respectively. He received the M.S. degree in system engineering from the Royal Melbourne Insti- tute of Technology (RMIT), Melbourne, Australia, in 1997. He is currently a Lecturer with the Faculty of Electrical Engineering, Electric Power System Department, Hanoi University of Technology. He was a Postdoctoral Fellow with the Power System Laboratory, School of Electrical Engineering and Computer Science, Seoul Nat ional University, Seoul, Korea. His special fields of interest include power distribution system analysis, DSM, and power quality. Dong-Jun Won (M’05) was born in Korea on Jan- uary 1, 1975. He received the B.S.,M.S., and Ph.D. degrees in electrical engineering from Seoul National University,Seoul, Korea, in 1998, 2000, and 2004, re- spectively. Currently, he isaFull-Time Lecturer with the School of Electrical Engineering with INHA Univer- sity, Incheon, Korea. He was a Postdoctoral Fellow with the Advanced Power Technologies Center, Department of Electrical Engineering, University of Washington, Seattle. His research i nterests include power quality, dispersed generation, renewable energy, and hydrogen economy. Seung-Il Moon (M’93) received the B.S. degree in electrical engineering from Seoul National Uni- versity, Seoul, Korea, in 1985 and the M.S. and Ph.D. degrees in electrical engineering from The Ohio State University, Columbus, in 1989 and 1993, respectively. Currently, he is an Associate Professor of the School of Electrical Engineering and Computer Science at Seoul National University. His special fields of interest include power quality, flexible ac transmission systems (FA CTS), renewable energy, and dispersed generation. Tổng hợp các bài báo khoa học giai đoạn 2007-2012 Bộ môn Hệ thống điện - Đại học Bách Khoa Hà Nội 90 eBook for You [...]... You Tổng hợp các bài báo khoa học giai đoạn 2007-2012 Bộ môn Hệ thống điện - Đại học Bách Khoa Hà Nội 107 eBook for You Tổng hợp các bài báo khoa học giai đoạn 2007-2012 Bộ môn Hệ thống điện - Đại học Bách Khoa Hà Nội 108 eBook for You Tổng hợp các bài báo khoa học giai đoạn 2007-2012 Bộ môn Hệ thống điện - Đại học Bách Khoa Hà Nội 109 eBook for You Tổng hợp các bài báo khoa học giai đoạn 2007-2012 Bộ. .. hợp các bài báo khoa học giai đoạn 2007-2012 Bộ môn Hệ thống điện - Đại học Bách Khoa Hà Nội 110 eBook for You Tổng hợp các bài báo khoa học giai đoạn 2007-2012 Bộ môn Hệ thống điện - Đại học Bách Khoa Hà Nội 111 eBook for You Tổng hợp các bài báo khoa học giai đoạn 2007-2012 Bộ môn Hệ thống điện - Đại học Bách Khoa Hà Nội 112 Tổng hợp các bài báo khoa học giai đoạn 2007-2012 A New Model Applied to... 9 Tổng sơ đồ phát triển Hệ thống điện Việt Nam, Bản IV, Viện Năng lượng, 2006 10 T A Short; Electric Power Distribution Handbook, CRC Press, 2004 Địa chỉ liên hệ : Bạch Quốc Khánh - Tel: 0904.698.900, email: bq_khanh-htd@mail.hut.edu.vn Bộ môn Hệ thống điện, Khoa Điện, Trường Đại học Bách khoa Hà Nội Số 1, Đại Cồ Việt, Hà Nội 76 Bộ môn Hệ thống điện - Đại học Bách Khoa Hà Nội 99 eBook for You 1 Tổng. .. 75 Bộ môn Hệ thống điện - Đại học Bách Khoa Hà Nội 98 Tổng hợp các bài báo khoa học giai đoạn 2007-2012 TẠP CHÍ KHOA HỌC & CÔNG NGHỆ CÁC TRƯỜNG ĐẠI HỌC KỸ THUẬT h SỐ 77 - 2010 lưới truyền tải điện cần được xét thêm các SANH do ngắn mạch ở phần nguồn và lưới truyền tải điện 500kV Hơn nữa, nghiên cứu cũng còn có thể phát triển việc xem xét các yếu tố ảnh hưởng đến phân bố sự cố khi lưới truyền tải điện. .. cách gián tiếp để xác định tình hình SANH trên HTĐ có thể dùng mô hình dự báo CLĐN dựa trên các nguyên nhân sinh ra nó Trong các nguyên nhân này, trên 90% 72 Bộ môn Hệ thống điện - Đại học Bách Khoa Hà Nội 95 eBook for You ABSTRACT Tổng hợp các bài báo khoa học giai đoạn 2007-2012 TẠP CHÍ KHOA HỌC & CÔNG NGHỆ CÁC TRƯỜNG ĐẠI HỌC KỸ THUẬT h SỐ 77 - 2010 SANH là do sự cố ngắn mạch trong HTĐ Do đó, có... limits that can be Bộ môn Hệ PS-1 Paper No .thống điện - Đại học Bách Khoa Hà Nội 94 eBook for You System voltage sag frequency point of common coupling of many load nodes is on the middle of the trunk line Tổng hợp các bài báo khoa học giai đoạn 2007-2012 TẠP CHÍ KHOA HỌC & CÔNG NGHỆ CÁC TRƯỜNG ĐẠI HỌC KỸ THUẬT h SỐ 77 - 2010 ĐÁNH GIÁ SỤT GIẢM ĐIỆN ÁP NGẮN HẠN TRÊN LƯỚI TRUYỀN TẢI ĐIỆN 220KV VIỆT NAM... 220kV của Việt Nam 73 Bộ môn Hệ thống điện - Đại học Bách Khoa Hà Nội 96 Tổng hợp các bài báo khoa học giai đoạn 2007-2012 TẠP CHÍ KHOA HỌC & CÔNG NGHỆ CÁC TRƯỜNG ĐẠI HỌC KỸ THUẬT h SỐ 77 - 2010 - Phân bố sự cố ngắn mạch : Sự cố ngắn mạch mang tính ngẫu nhiên phụ thuộc vào nhiều yếu tố [2] nên suất sự cố nhìn chung khác nhau đối với từng loại sự cố và vị trí sự cố Trong nghiên cứu này, do số liệu thống. .. cả 66 nút phụ tải do từng điểm và 74 Bộ môn Hệ thống điện - Đại học Bách Khoa Hà Nội 97 Tổng hợp các bài báo khoa học giai đoạn 2007-2012 TẠP CHÍ KHOA HỌC & CÔNG NGHỆ CÁC TRƯỜNG ĐẠI HỌC KỸ THUẬT h SỐ 77 - 2010 III PHÂN TÍCH KẾT QUẢ SARFIX Thực hiện trình tự tính toán như sơ đồ khối ở hình 3, sau đây là tóm tắt một số kết quả đáng lưu ý : - Tần suất SANH trung bình của một nút phụ tải bất kỳ : Hình 4... frequency spectrum (per year) for all fault events at 220kV Mai Dong Substation, Hanoi, Vietnam Bộ môn Hệ thống điện - Đại học Bách Khoa Hà Nội SARFISEMI-X Sag Magnitude (p.u) Sag Magnitude (p.u) SARFIITIC-X Fig 8 SARFIX and SARFICURVE-X of the transmission system of Vietnam 104 Tổng hợp các bài báo khoa học giai đoạn 2007-2012 D Result Analysis From the results, there’re some following remarks: - The... now CLĐN được cung cấp và đánh giá tác động của CLĐN đối với phụ tải Việc xác định yêu cầu CLĐN của các phụ tải thuộc về các nhà sản xuất thiết bị dùng điện mà điển hình là đặc tính chịu điện áp của phụ tải CBEMA, ITIC hoặc SEMI [1] (Hình 1) I ĐẶT VẤN ĐỀ Theo IEEE-1159, 1995, SANH (voltage sag) là hiện tượng CLĐN trong đó giá trị điện áp hiệu dụng của lưới điện sụt giảm còn từ 0,1 đến 0,9 điện áp định . HỌC BÁCH KHOA HÀ NỘI BỘ MÔN HỆ THỐNG ĐIỆN eBook for You PHҪN II CUNG CҨP ĈIӊN Tổng hợp các bài báo khoa học giai đoạn 2007-2012 Bộ môn Hệ thống điện - Đại học Bách Khoa Hà Nội 82 eBook. báo khoa học giai đoạn 2007-2012 Bộ môn Hệ thống điện - Đại học Bách Khoa Hà Nội 97 eBook for You TẠP CHÍ KHOA HỌC & CÔNG NGHỆ CÁC TRƯỜNG ĐẠI HỌC KỸ THUẬT SỐ 77 - 2010 75 III. PHÂN. là i. Nhận dạng tình hình CLĐN được cung cấp, ii. Xác định yêu cầu CLĐN của các phụ tải, iii. So sánh yêu cầu CLĐN của phụ tải với tình hình CLĐN được cung cấp và đánh giá tác đ ộng của CLĐN