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Frontiers of Electrical and Electronic Engineering

ISSN 2095-2732

ISSN 2095-2740(Online)

CN 10-1028/TM

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Front Elect Electr Eng    2012, Vol. 7 Issue (4) : 399-415    https://doi.org/10.1007/s11460-012-0213-z
RESEARCH ARTICLE
Multi-objective allocation of measuring system based on binary particle swarm optimization
Khalil Gorgani FIROUZJAH(), Abdolreza SHEIKHOLESLAMI, Taghi BARFOROUSHI
Faculty of Electrical and Computer Engineering, Babol (Noshirvani) University of Technology, Babol, Iran
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Abstract

Due to the size and complexity of power network and the cost of monitoring and telecommunication equipment, it is unfeasible to monitor the whole system variables. All system analyzers use voltages and currents of the network. Thus, monitoring scheme plays a main role in system analysis, control, and protection. To monitor the whole system using distributed measurements, strategic placement of them is needed. This paper improves a topological circuit observation method to minimize essential monitors. Besides the observability under normal condition of power networks, the observability of abnormal network is considered. Consequently, a high level of system reliability is carried out. In terms of reliability constraint, identification of bad measurement data in a given measurement system by making theme sure to be detectable is well done. Furthermore, it is maintained by a certain level of reliability against the single-line outages. Thus, observability is satisfied if all possible single line outages are plausible. Consideration of these limitations clears the role of utilizing an optimization algorithm. Hence, particle swarm optimization (PSO) is used to minimize monitoring cost and removing unobservable states under abnormal condition, simultaneously. The algorithm is tested in IEEE 14 and 30-bus test systems and Iranian (Mazandaran) Regional Electric Company.
Keywords optimal allocation      phasor measurement units      observability      binary particle swarm optimization     
Corresponding Author(s): FIROUZJAH Khalil Gorgani,Email:kgorgani@stu.nit.ac.ir, khalilgorgani@gmail.com   
Issue Date: 05 December 2012
 Cite this article:   
Khalil Gorgani FIROUZJAH,Abdolreza SHEIKHOLESLAMI,Taghi BARFOROUSHI. Multi-objective allocation of measuring system based on binary particle swarm optimization[J]. Front Elect Electr Eng, 2012, 7(4): 399-415.
 URL:  
https://academic.hep.com.cn/fee/EN/10.1007/s11460-012-0213-z
https://academic.hep.com.cn/fee/EN/Y2012/V7/I4/399
Fig.1  Observability analyzer algorithm
Fig.1  Observability analyzer algorithm
Fig.1  Observability analyzer algorithm
Fig.1  Observability analyzer algorithm
Fig.1  Observability analyzer algorithm
Fig.2  Observability analyzer algorithm under single line outage
Fig.2  Observability analyzer algorithm under single line outage
Fig.2  Observability analyzer algorithm under single line outage
Fig.2  Observability analyzer algorithm under single line outage
Fig.2  Observability analyzer algorithm under single line outage
Fig.3  Observability analyzer algorithm under single current error
Fig.3  Observability analyzer algorithm under single current error
Fig.3  Observability analyzer algorithm under single current error
Fig.3  Observability analyzer algorithm under single current error
Fig.3  Observability analyzer algorithm under single current error
Fig.4  Observability assessment under normal and abnormal conditions
Fig.4  Observability assessment under normal and abnormal conditions
Fig.4  Observability assessment under normal and abnormal conditions
Fig.4  Observability assessment under normal and abnormal conditions
Fig.4  Observability assessment under normal and abnormal conditions
Fig.5  Mazandaran Regional Electric Company 24-bus (MREC 24-bus)
Fig.5  Mazandaran Regional Electric Company 24-bus (MREC 24-bus)
Fig.5  Mazandaran Regional Electric Company 24-bus (MREC 24-bus)
Fig.5  Mazandaran Regional Electric Company 24-bus (MREC 24-bus)
Fig.5  Mazandaran Regional Electric Company 24-bus (MREC 24-bus)
Bus number123456789101112131415161718192021222324
1421000200000000000000000
2220000002200000000000000
3100310000000000000000000
4003000000001002300000000
5001012000000000000000000
6000020000000000001100000
7200000240000000000000000
8000000410000000000003113
9020000001010000000010000
10020000000101010000000000
11000000001011000000000000
12000100000111110000000000
13000000000001200000000000
14000000000101010000000000
15000200000000001000000000
16000300000000000110000000
17000000000000000111000000
18000001000000000012000000
19000001000000000000200000
20000000001000000000011000
21000000030000000000011000
22000000010000000000000110
23000000010000000000000120
24000000030000000000000001
Tab.1  Line-bus incidence matrix of MREC 24-bus transmission network
No. of state variablesNo. of busses with zero injection currentNo. of injection currents in excess of 2 at bussesNo. of bussestest system
14+4–1=171414IEEE 14-bus
30+6–8=288630IEEE 30-bus
24+9–3=303924MREC 24-bus
Tab.2  Number of state variables in the test systems
Fig.6  Optimization under normal condition. (a) IEEE 14-bus test system; (b) IEEE 30-bus test system; (c) MREC 24-bus system
Fig.6  Optimization under normal condition. (a) IEEE 14-bus test system; (b) IEEE 30-bus test system; (c) MREC 24-bus system
Fig.6  Optimization under normal condition. (a) IEEE 14-bus test system; (b) IEEE 30-bus test system; (c) MREC 24-bus system
Fig.6  Optimization under normal condition. (a) IEEE 14-bus test system; (b) IEEE 30-bus test system; (c) MREC 24-bus system
Fig.6  Optimization under normal condition. (a) IEEE 14-bus test system; (b) IEEE 30-bus test system; (c) MREC 24-bus system
Fig.7  Optimization under single line outage. (a) IEEE 14-bus test system; (b) IEEE 30-bus test system; (c) MREC 24-bus system
Fig.7  Optimization under single line outage. (a) IEEE 14-bus test system; (b) IEEE 30-bus test system; (c) MREC 24-bus system
Fig.7  Optimization under single line outage. (a) IEEE 14-bus test system; (b) IEEE 30-bus test system; (c) MREC 24-bus system
Fig.7  Optimization under single line outage. (a) IEEE 14-bus test system; (b) IEEE 30-bus test system; (c) MREC 24-bus system
Fig.7  Optimization under single line outage. (a) IEEE 14-bus test system; (b) IEEE 30-bus test system; (c) MREC 24-bus system
Fig.8  Optimization under single current error. (a) IEEE 14-bus test system; (b) IEEE 30-bus test system; (c) MREC 24-bus system
Fig.8  Optimization under single current error. (a) IEEE 14-bus test system; (b) IEEE 30-bus test system; (c) MREC 24-bus system
Fig.8  Optimization under single current error. (a) IEEE 14-bus test system; (b) IEEE 30-bus test system; (c) MREC 24-bus system
Fig.8  Optimization under single current error. (a) IEEE 14-bus test system; (b) IEEE 30-bus test system; (c) MREC 24-bus system
Fig.8  Optimization under single current error. (a) IEEE 14-bus test system; (b) IEEE 30-bus test system; (c) MREC 24-bus system
Fig.9  Optimization under reliability constraints. (a) IEEE 14-bus test system; (b) IEEE 30-bus test system; (c) MREC 24-bus system
Fig.9  Optimization under reliability constraints. (a) IEEE 14-bus test system; (b) IEEE 30-bus test system; (c) MREC 24-bus system
Fig.9  Optimization under reliability constraints. (a) IEEE 14-bus test system; (b) IEEE 30-bus test system; (c) MREC 24-bus system
Fig.9  Optimization under reliability constraints. (a) IEEE 14-bus test system; (b) IEEE 30-bus test system; (c) MREC 24-bus system
Fig.9  Optimization under reliability constraints. (a) IEEE 14-bus test system; (b) IEEE 30-bus test system; (c) MREC 24-bus system
redundancy ratioNo. of current measurementNo. of voltage measurementNo. of monitoring bussesNo. of state variablesNo. of network bussesassessment typetest system
1.5823441714normalIEEE14-bus
2.2932771714line outage
2.2932771714current error
2.2932771714reliability
1.6038772830normalIEEE30-bus
2.355313132830line outage
2.505812122830current error
2.926616162830reliability
2.406111113024normalMREC24-bus
2.907314143024line outage
2.867313133024current error
2.907314143024reliability
Tab.3  Summarized optimization results in measurement system allocation
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