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International Journal of Application or Innovation in Engineering & Management (IJAIEM) Web Site: www.ijaiem.org Email: [email protected]

Volume 5, Issue 5, May 2016 ISSN 2319 - 4847

Volume 5, Issue 5, May 2016 Page 301

ABSTRACT Power system restoration following a massive blackout starts with re-energizing PRSs(Primary Restorative Systems) at first. As power systems have been gradually enlarged and become more complex, configuration of power systems has been changed, so PRSs are required to be reassigned and evaluated every year. So far it has been decided by knowledges and experiences of corresponding experts to analyze and evaluate them. This paper presents an expert system that finds feasible paths using analytic and heuristic knowledge from PSS/E data, and suggests an optimal PRS depending on the condition of Ferranti effect or a reactive capability margin. This system was tested in Korea Electric Power grid system, and showed a promising result Keywords: Expert System, Primary Restorative System, Restoration 1. Introduction POWER operating system is focused on remote monitoring and controlling under normal operation. After the wide area blackout, power system restoration has been achieved by the operator's expert and heuristic knowledge. The social impact including economic loss is so disastrous when a massive blackout occurs, and huge disturbances as well as damages to equipments under the restoration process can be caused if the blackout situation is not properly diagnosed by the operator or if there are some mistakes in the restoration process. Therefore quick and reliable diagnosis and restoration for the blackout are required. Restoration strategies are slightly different in each country as restoration methods are dependent on faults, voltage levels, structures of transmission grid, and equipment of its own power system. However, it is clear that during the first stage of restoration, Black-Start Generators (BSGs) that usually are hydro type supply Primary Supplied Generators(PSGs) having big capacity through primary restorative transmission lines in case of wide area blackout[1-6]. As power systems have been gradually enlarged and become more complex by economic growth, configuration of power systems has been changed, so PRSs are required to be reassigned and evaluated. So far it has been decided by the knowledges and experiences of corresponding experts to analyze and evaluate them, and it is a quite complex procedure. This paper presents an expert system that finds feasible paths using analytic and heuristic knowledge from PSS/E data, and suggests an optimal PRS depending on the condition of Ferranti effect or a reactive capability margin. This system consists of three modules that are a data conversion module, an expert system module, and a static analysis module. The data conversion module extracts necessary parameters from PSS/E data, and transfers them to the expert system and the static analysis modules. The expert system module finds a feasible path at the transmission grid, and transfers information about the path to the static analysis module. Then, it figures out the terminal voltage, transmission line charging capacity, and reactive capability limitation of blackstart generators, and returns the result to the rule base of the expert system. A BSG and a PSG should be chosen at first before configuring PRSs, and criteria for each choice are as follows. The BSG should have starting up equipment for supplying power to PRSs without external power. The PSG should not take too long time for starting up compared to BSGs.

But, PSS/E data do not have such information, therefore a BSG and a PSG should be manually chosen at first.

Application of an Expert System for Planning of Primary Restorative System

HeungJae Lee1, ChaNyeon Kim2 ,WonKun Yu3

1Kwangwoon University

2 Kwangwoon University

3 Dongyang Mirae University




2.DATA CONVERSION MODULE This data conversion module extracts data that are required for this system from PSS/E raw and dynamic data, and transfer them to the expert system and the static analysis modules. Information about power system topology and parameters of transformers and transmission lines are extracted from raw data, and generators’ synchronous reactance value and rotor type that is related to reactive capability limitation of the generator are done from dynamic data. Data flows between the modules are shown in Fig. 1.

Figure 1 Modules of the system

3. EXPERT SYSTEM FOR CONFIGURING A PRS

Figure 2 Structure of the expert system

The structure of the expert system aiding establishment of PRSs is shown in Fig. 2, and it consists of data and rule bases, inference engine, and MMI(Man Machine Interface). This study establishes data and rule bases from precise analysis of domain, also problem representation, predicate representation, and effective inference mechanism are developed to improve system’s performance. 3.1 Problem Representation A Problem about evaluation and reassignment of an optimally feasible PRS results in searching an optimal path from a BSG to a PSG. A constraint is reactive capability limitation to prevent self-excitation, and objective function to find optimal solutions is Ferranti effect or reactive capability margin. Topologically connected paths are 5-2-1, 5-3-2-1, and 5-4-2-1 at a simple network such as Fig. 3, and the state space representation of the expert system is shown in Fig. 4. Transmission line charging capacity is used as cost function of branches between nodes to minimize objective function. When each section between nodes is searched, searching for the path is stopped if the line charging capacity is larger than reactive capability limitation of the BSG. The line charging capacity and reactive capability limitation are analyzed at the static analysis module.

3.2 Data Base A representation method for the power system with the problem representation has influence on efficiency of the expert system. Predicates used in this system are as follows.



Volume 5, Issue 5, May 2016 03

Line : Tl_data(No, Name, From, To, Vrate, RateA, R, X, C) Bus : Bus_data(No, Name, Vrate) Transformer : Tr_data(No, Name, From, To, RateA) Generator : Gen(No, Name, To, Crank, RateA) Load : Load(No, Name, To, MVA)

where, No : number of the equipment Name : name of the equipment RateA : rated power of the equipment Vrate : rated voltage of the equipment From and To : both side numbers of the equipment Crank : starting up power for PSGs and reactive capability limitation for BSGs

Figure 3 Simple network of a PRS Figure 4 Representation of the state space

3.3 Rule Base

Figure 5 Flow chart of the PRS searching

The rule-base of this expert system module comprises basic rules for searching and inference as well as rules for preventing circulation and paths’ feasibility discrimination rules, so the optimal PRS can be searched. Discrimination rules are shown in table I, and searching can be kept if only compared values are bigger than standard values. The standard of reactive power has two values. One is the limitation by the steady state stability, and the other is the limitation by the stator end core heating. Two compared values and the former standard value of reactive power are found in the static analysis module, then they are transferred to the rule base. The latter standard and UVR (Under Voltage Relay) setting values can not be extracted from PSS/E data or the static analysis module, therefore these values are fed into the program when a BSG is chosen. Coordinate movements between the expert system and the static analysis modules are shown in Fig. 5.




Table 1 RULES OF THE RULE BASE Compared value Standard value Voltage Terminal voltage of BSGs UVR setting value of BSGs Reactive power Line charging capacity Reactive capability limitation of

BSGs

4. STATIC ANALYSIS MODULE Paths’ feasibility is checked by the static analysis module. Three factors are considered to check the paths’ feasibility using two port network and pole type of the rotor[7]. The terminal voltage of the BSG should be smaller than the UVR setting value, and the others are related to self-excitation. Terminal voltage of the BSG Charging capacity of the PRS Reactive capability limitation of the BSG

5. CASE STUDY The developed system for finding the optimal PRT was applied to Korean power system, and it used PSS/E data of KEPCO system. Korean power grid consists of seven subsystems for restoration, and this case study is focused on finding PRSs at a subsystem of them. The configuration of the subsystem is shown in Fig. 6. Bundang C/C and Pyungtaek T/P are selected as the BSG and the PSG respectively, and they are marked as the circle and the square each. These selections are identical to the restoration strategy of KPX. The searched result from the developed system is shown in Fig. 6 as a thick line. The terminal voltage of Bundang C/C is 0.9608[PU], and reactive capability margin of the BSGs is about 10[MVAR]. Those measures show that the path has the largest reactive power margin and highest terminal voltage of available paths.

Figure 6 A subsystem of KEPCO systems




6. CONCLUSION In this study, the expert system that can find the optimal PRS is developed. At this system, feasible paths are searched from PSS/E data automatically at first, then the optimal path is suggested depending on the condition of Ferranti effect or reactive capability margin. At a subsystem of Korean power system, feasible paths are found from PSS/E data of KEPCO system, and the optimal PRS is suggested. This system will be useful for aiding PRS planning. Acknowledgement

“The present Research has been conducted by the Research Grant of Kwangwoon University in 2014”

References [1] M. M. Adibi, J. N. Borkoski, R. J. Kafka, "Power System Restoration - A Task Force Report", IEEE Trans. on

PWRS, vol. 2, no. 2, May 1987. [2] Gaston Morin, "Service Restoration Following A Major Failure On The Hydro-Quebec Power System", IEEE

Trans. on PWRD, vol. 2, no. 2, pp. 454-462, Apr. 1987. [3] E. Mariani, F. Mastroianni, V. Romano, "Field Experiences In Reenerg-ization Of Electrical Networks From

Thermal And Hydro Units", IEEE Trans. on PAS, vol. 103, no. 7, pp. 1707-1713, Jul. 1984 [4] N. A. Fountas, N. D. Hatziargyriou, C. Orfanogiannis, A. Tasoulis, "Interactive Long-Term Simulation For Power

System Restoration Planning", IEEE Trans. on PWRS, vol. 12, no. 1, pp. 61-68, Feb. 1997. [5] Roger Kearsley, "Restoration In Sweden And Experience Gained From The Blackout Of 1983", IEEE Trans. on

PWRS, vol. 2, no. 2, pp. 422-428, May 1987. [6] Y. Hain, I. Schweitzer, "Analysis Of The Power Black-out Of June 8, 1995 In The Israel Electric Corporation",

IEEE Trans. on PWRS, vol. 12, no. 4, pp. 1752-1758, Nov. 1997. [7] H. J. Lee, S. M. Park, K. S. Lee, I. J. Song, N. H. Lee, J. C. Bae, B. W. Whang, "Analysis of The Primary

Restorative Transmission System", in Proc. 2003 IFAC Symposium on Power Plants & Power Systems Control 2003, pp. 1079-1082.

AUTHORS

Heung Jae Lee was born in Seoul in Korea, on January 27, 1958. He received the BS, MS and Ph. D. degrees from Seoul National University, in 1983, 1986 and 1990, respectively, all in electrical engineering. He was a visiting professor in the University of Washington from 1995 to 1996. His major research interests are the expert systems, the neural networks and the fuzzy systems application to power systems including the computer application. He is a full professor in the Kwangwoon University..

Cha Nyeon Kim received his B.S degrees from Kwangwoon University in Seoul Korea. He has been a Unified Master's and Doctor's Course student since 2014. Won Kun Yu received his BS, MS and Ph.D. degrees in electrical engineering from Kwangwoon University,in 2003, 2005 and 2013 respectively. Currently, he is an Assistant Professor in the Dongyang Mirae University..

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