Download Research on WebGIS Real-time Reactive Power Optimization of Distribution Network

Research on WebGIS Real-time Reactive Power Optimization of
Distribution Network
LIU Weina, HUANG Lihua
Mechanical and electric engineering college Agriculture University of Hebei, P.R.China, 071001
[email protected]
Abstract: The high-efficient and advanced niche adaptive genetic algorithm has been recommended for
reactive power optimization of electrical distribution system in electricity power market for the purpose
that electric power system ran safely and stably; and line loss kept lowly; the quality of electricity power
improved. The examples tested indicate that the performance of seeking excellent point, computation
speed and early inhibition of procedures of the reactive power optimization based on niche adaptive
genetic algorithm is better than the other improved genetic algorithms. The program as one calculation
module is integrated with WebGIS system, so the results of optimization become clearer and the
system's utility is increased significantly.
Keywords: WebGIS; Distribution network; Real-time; reactive power optimization.
1 Introduction
In electricity power market the choice of an efficient, advanced reactive power optimization method is
very important. Reactive power optimization is the effective measures of keeping the operation of
electricity power system secure and economical. Through reasonable deployment of adjustable-tap
transformer, the voltage amplitude, the compensation capacity of the input device, line loss can be
controlled to the minimum [1,2]. Traditional reactive power optimization was based on the majority of
the power industry in the state-run monopoly and used of data offline experience, so compensation of
reactive power is imprecise and reducing loss is not distinct. In this paper, the real-time reactive power
optimization software based on WebGIS is developed on the basis of security and economy. When the
software runs, it calls real-time parameters from FTU constantly, so that the results of reactive power
optimization are more realistic, and loss is lower, which causes the power companies with better
efficiency.
2 Application of WebGIS in reactive power optimization
Geographic Information System (GIS) is a practical means in geographic information system and a
multidisciplinary, integrated technology platform. It is a comprehensive and integrated information
science and technology, which represents various modern approach to collect, measure, analyze, store,
manage, display, disseminate and apply geographical data, and GIS is an organic integration of mapping,
remote sensing, computer, applied mathematics and a variety of applications disciplines. GIS composed
of computer systems, geographical data and user generates and outputs various geographic information,
which can provides new knowledge for land use, resource evaluation and management, environmental
monitoring, transportation, economic development, urban planning and the administration of
government departments, therefore serves engineering design and planning, administration decision,
[4]
through integrating, storing, searching, handling and analyzing geographical data .
Internet-based geographic information system, often called WebGIS, is the combination of a Web
technology and GIS technology, and it is a new technology, which makes use of Web technology to
expand and improve geographic information system. WebGlS does not only involve a majority or even
all of the function of the traditional GIS software, but also utilizes the unique advantages and functions
of Internet. Namely users can access remote GIS data and applications, analyze GIS and provide
interactive maps and data on the Internet. Even, they can have no their own local computer GIS software
installed. On the Internet arbitrary nodes, users can browse spatial data of GIS in the Website, produce
335
thematic maps, search and analyze space information [5].
WebGIS is object-oriented, distributed and interoperable. Any GIS data and functions can be targets,
which is deployed in the different Internet servers. When these objects are needed, it is necessary to
assemble and integrate them. On the Internet any other systems can exchange and cooperate with these
objects.
The tasks of Distribution network Geographic Information System are to draw the feed wire diagram by
a certain proportion on geographical background map of the urban and rural streets. The symbols,
models and specifications of electrical equipment including breaker, trolly wire, telegraph pole, cable
and distribution transformer are labeled on the map. Operators can inquire, form the statistics and
maintain the equipment, and put on records, but also calculate the theoretical line loss of every feed wire
and finish power flow computation and the short circuit calculation [6]. The system integrates
distribution network graphics, data and calculation, which can manage the distribution network
scientifically. The basic functions include: Map show, Hawkeye function, layer management, attribute
editing, etc.
3 Mathematical model of reactive power system optimization
Reactive power system optimization is one of the most important issues in the course of operating
system, adopting the appropriate capacitor compensation and adjusting transformer tap to upgrade
[7]
voltage quality and decrease loss . The method of optimization should be used to determine the
capacity of reactive power compensation, compensation location, transformer tap and the cooperation of
them [8]. Mathematical model of the method includes the power constraint equations, the variable
constraint equations and objective function.
In this paper the objective function of reactive power optimization designed involves three aspects:
keeping voltage amplitude of each node not to exceed the limit; lowering loss of power system; avoiding
adjusting frequently transformer tap and compensation device. There are some constraint conditions,
including: voltage generator, reactive power, voltage of load nodes, ratio of transformer, capacity of
compensation and power equation, etc.
4 Niche adaptive Genetic Algorithm based on the sharing function
"Birds of a feather flock together" --niche phenomenon in nature is introduced into the genetic algorithm.
It is the fact that various biological are inclined to get along with the biological whose characteristics
[9]
and shape are similar to each other, and mate and raise up seed .
In this paper, niche adaptive genetic algorithm [10] based on the penalty function is adopted. It is the
basic idea of algorithm that compares distance between the individual in the groups firstly, if the
distance is less than L the distance specified in advance, compares their fitness and then exert a
strong penalty function on the individual whose fitness is smaller than the other, so its fitness was
reduced greatly. Therefore fitness of the individual dealt with penalty function becomes worse. In the
latter evolutionary process this individual is likely to be eliminated. In other words, the better individual
will only exist, so as to safeguard the diversity of the groups and maintain a certain distance between
each individual. And the individual can be dispersed over the whole constraint space, thereby the niche
genetic algorithm is achieved.
In the process of programming, the program aimed to keep objective function smallest, and adopted real
number coding, adaptive crossover and rate of mutation to improve the conventional algorithms, which
speeded up the searching speed, avoided premature convergence of data and improved operating
efficiency.
—
5 Process of procedure of reactive power optimization and Integrated with GIS
Diagram flow of reactive power optimization adopting niche adaptive genetic algorithm (NAGA) was
336
shown in figure 1:
Write original parameters &
GA parameters into database
Calculate power adopting
Back/Forw ard Sweep Method
Chose c ompensation location
w ith improved sensitivity
algorithm
Generate the initial stocks satisfying constraint
conditions at random
Calculate power
Calculate values of individual fitness
Choose ,Cross , Mutate
Calculate adopting Niche adaptive
Genetic Algorithm
Did it satisfy
termination
conditions?
N
Y
Output optimal solution
Fig.1 Flow chart of reactive power optimization based on NAGA
Users can choose lines with software of reactive power optimization, or click the feeder directly on the
interface of GIS for optimization. The result of optimization was preserved into database as backup, at
the same time shown on the interface of GIS [11]. In Fig. 2, 80kvar was compensated in distribution
transformer of line. The software is flexible and user-friendly, which makes the optimal result clear and
makes the system be more humanizing.
Fig.2 Show of the result of reactive power compensation
6 Examples certification
6.1 Analysis of system of IEEE30
Select data of IEEE30 to certificate the software [12]. The network has 43 bypass, six generators nodes
and 21 load nodes. Node 1, 2, 5, 8, 11, 13 are qua balance nodes or PU nodes, and the other nodes are
337
PQ nodes. Overall load: PLoad = 2.834, QLoad = 1.262. The voltage of PU nodes and balance nodes
ranges between 0.9 and 1.1, namely Ppu . min = 0.9, U pu. max = 1.1. The voltage of PQ nodes ranges
between 0.95 and 1.05, namely U pq. min = 0.95, U pu. max = 1.05. The variable is a per-unit value. The
comparison of results obtained through adopting the basic genetic algorithm (SGA) and niche adaptive
genetic algorithm (NAGA) is shown in Table 1, in which nu / nq denotes the number of voltage
exceeded / reactive power exceeded nodes respectively, Q represents the amount of reactive power
compensation.
Tab.1 The Comparison of calculative results
Ploss
Qloss
Q
Algorithm
nu nq
/Mvar
/MW
/Mvar
SGA
0
0
0.04862
-0.32768
20.528
NAGA
0
0
0.04253
-0.29742
18.367
6.2 Analysis of the actual example
Use Visual C# to do reactive optimization procedures adopting Niche Adaptive genetic algorithm
optimization. The ratio of adjustable-tap transformer (T) and capacity of compensation (Qc) are control
variables; node voltage (U) and reactive power of generators (Qg) are state variables. Put up
optimization calculation on 10kv distribution line of a practical distribution network, and parameters of
the system are as follows: 103 nodes, 32 distribution transformers, 4 OLTC. The voltage of PQ nodes is
set between 0.95 and 1.05; the ratio of adjustable-tap transformer ranges between 0.90 and 1.10;
regulating step is 0.025; and the maximum of compensation capacity should not exceed capacity of
reactive load. Compensation is expected to five points. The results are shown in Table 2, in which the
unit of Qc is KVAR.
Tab.2 The Comparison of calculative results
Parameter
Initial Power
SGA
NAGA
T1
1.10
1.075
1.05
T2
1.05
1.025
0.975
T3
1.075
1.05
1.00
T4
1.075
1.05
1.05
Variable
QC1
0
90
90
QC2
0
80
75
QC3
0
60
90
QC4
0
16
16
QC5
0
75
50
Loss %
7.74
7.26
6.45
Time(s)
86
58
fee yuan
13085
12563
（ ）
（ ）
7 Conclusions
Through the design and operation of software of WebGIS Real-time Reactive Power Optimization of
Distribution Network, the following conclusions has been drawn:
1) Object-oriented Visual C # programming language and SQL Sever2000 database were adopted to
store data, so the software is easy to be maintained and practical and its interface was designed friendly.
2) The module of reactive optimization is joined into WebGIS system, so users can access remote
Internet GIS data, analyze data, provide interactive maps and data on the Internet, enable optimization
results to show in the GIS map clearly and improve efficiency.
3) The software enabled the reactive power compensation equipment and adjustable-tap transformer to
be utilized fully, improved quality of voltage, reduced the loss and operating expenses, and raised
economic benefits of the power companies.
338
References
[1] Zhong Hongmei, Ren Zhen, Zhang Yongjun. Development and application of real-time ORP
software . [J] Relay,2004,32(11):55-57(in Chinese)
[2] Liu Qingsong. Reactive Power Optimization of Radical Distribution System on Improved Genetic
Algorithm. [J] Journal of Electric Power, 2005,20(1):17-20(in Chinese)
[3] Zhao Fengying. Research of Application of Geographic Information System in Distribution Network
Automation.[D]QingDao University 2006(in Chinese)
[4] Allan B Cox An Overview to Geographic Information Systems [J] The Journal of Academic
Librarianship, 1995 (5): 237-249.
[5] Sun Haozeng. Reactive Power Optimization Based on Improved Genetic Algorithm and Its
Application in the System of Power SCADA.[D]Northeastern University, 2005(in Chinese)
[6] Cai Changchun. The Method Study of Power System Dynamic Reactive Power Optimization and
Its Implement [D], HeHai University, 2007(in Chinese)
[7] Kwang Y. Lee, Xiaomin Bai, Young-Moon Park Optimization Method for Reactive Power Planning
by Using a Modified Simple Genetic Algorithm [J]. IEEE Transaction on Power System, 1995, 10
(4): 1843-1850.
[8] Huang Wei, Xu Chunli, Zhang Jianhua and Hu Shan’ang. Study of reactive power optimization
based on immune genetic algorithm. IEEE transactions on transmission and distribution conference
and exposition, 2003.9.Vol.1:186-191
[9] Gallardo, A.; Lowther, D.A. Some aspects of niching genetic algorithms applied to electromagnetic
device optimization. IEEE transaction on magnetics, 2000.7. Vol.36:1076-1079
[10] Joseph Carnahan, Rahul Simha. Nature’s algorithms. IEEE transactions on potentials,
2001.4.Vol.20(2) :21-25
[11] Wang Jianjie, Research and Application of Reactive Power Compensation for Distribution Network
Based on GIS [D], Agriculture University of Hebei, 2007(in Chinese)
[12] Sheng Zhaojun, Liu Han. Reactive power optimization of integrative power system based on
improved genetic algorithm[J].Electric Power Automation Equipment,2004,24(4):27-29(in
Chinese)
．
，
．
．
Author introduction:
Liu Wei-na (1980-),female, Baoding Hebei, Agricultural University of Hebei , assistant teacher,
Research field: Electrical power system analysis movement and control.
E-Mail: [email protected]
Huang Lihua (1963-),female, Rongcheng Shangdong, Agricultural University of Hebei , associate
professor, Research field: Electrical power system analysis.
Address 071001, Mechanical and electric engineering college, Agriculture University of Hebei
Phone 0312-7526482 13070576568
、
：
：
339