Power network observability: the assessment of the measurement system strength

Crainic, E.D.; Horisberger, H.; Do, X.D.; Mukhedkar, D.

doi:10.1109/59.99378

Cited by 23 publications

(13 citation statements)

References 18 publications

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“…where Hfid = (Hiid Hhid)-lWhid (16) The network training is finished at step (vii), i.e. the matrices of interconnection weights W,,, and W, , are optimally calculated.…”

Section: )mentioning

confidence: 99%

“…P+Qpowerflow T V vollagempgnitudc x rero injection, virtual measurement One-line diagram for IEEE 24-busbar test system P + Q power injection Tap ualues a Transloma tap ti, representation b nquivdent circuit of a transformer5.1 ANN test results16 network structures based on the RTS-24 are used for training the topology associative ANN. Each of these configurations is represented by a set of 120 samples, totallising 1920 input/output patterns in the training set.…”

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confidence: 99%

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Solving data acquisition and processing problems in power systems using a pattern analysis approach

Silva

Quintana

Pang

1991

IEE Proc. C Gener. Transm. Distrib. UK

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The paper proposes a new methodology for the combined solution of the topological identification observability analysis and bad data processing problems in power systems. The solution is based on a pattern analysis approach. An efficient framework is suggested for solving data acquisition and processing problems, as well as joining pattern analysis and analytical procedures. Two Werent techniques of pattern analysis are combined to produce a classifier and an estimator with unique characteristics to deal with noisy environments. Unobservable network areas, multiple interacting bad data and bad critical measurements can be efficiently treated. The patterns required for the training process can be acquired from the SCADA system and/or from load-flow simulations. Test results are presented for the IEEE 24-busbar reliability test system. IntroductionNowadays, in the operation of power systems, the performance of application programs used for either security or economical purposes is highly dependent on the quality of data being used. The role of power system state estimation (PSSE) is to supply a reliable real-time data base.The general problem of PSSE can be divided into four parts. The first is concerned with the network topology identification. The second is related to the observability analysis; the set of measurements that allows the estimation of all busbar complex voltages is determined in this part. Bad data detection and identification are performed in the third part. In the fourth part, the system state is estimated from the network structure and parameters, and the prefiltered set of measurements. The topology of the system can be determined if the status of the switches is available error free. However, the SCADA (supervisory control and data acquisition) system does not monitor all switches. Even the status of the monitored ones can not be trusted because of noisecorrupted telemetered information. Solutions for the problem of converting busbar-section-circuit breaker topology into busbar-branch topology have been proPaper 8045C (C4, m), lirst posed using logical procedures. The first real-time methods [l, 23 were designed to be implemented in a central computer. Decentralised processing schemes have been proposed [3, 41 to speedup the connectivity determination. The inclusion of the linked Tables of adjacent network elements into the database is avoided in Reference 5. This is possible by considering the network connectivity determination problem as a special case of an abstract graph connectivity problem. More recently, a tracking topology processor [6] has been suggested to avoid reordering and/or refactorising the whole state estimation matrix from the previous solution. Besides these logical procedures, indirect methods for the detection of configuration changes have been developed to correct erroneous information on the switching equipment status. Based on the mathematical model for a known

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“…where Hfid = (Hiid Hhid)-lWhid (16) The network training is finished at step (vii), i.e. the matrices of interconnection weights W,,, and W, , are optimally calculated.…”

Section: )mentioning

confidence: 99%

mentioning

confidence: 99%

Solving data acquisition and processing problems in power systems using a pattern analysis approach

Silva

Quintana

Pang

1991

IEE Proc. C Gener. Transm. Distrib. UK

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“…Measurements which may expose observability/data validation routines to risk are known as critical data for SE. Observability means the aptitude for estimating the system state in its entirety from data currently available [3].…”

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confidence: 99%

Low-cardinality critical k-tuples in measurement sets for state estimation

Augusto

Filho

Souza

2013

2013 IEEE Grenoble Conference

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Measurement redundancy is the fulcrum of the state estimation process. During the real-time operation of power networks, data redundancy may suffer reductions of different degrees, owing to: loss of communication channels; rejection of spurious measurements; unavailability of metering devices; etc. Criticality analysis consists in verifying if a measurement set is deficient in redundancy, for the state estimation attainment. This scrutiny of data is intimately related to the observability analysis and bad data processing functions of state estimators. This paper concentrates on the identification of critical groups of elements (k-tuples) pertaining to a given measurement set. The potential/practical application of the proposed methodology is illustrated by numerical results obtained with the IEEE 14-bus test system. Index Terms--state estimation, redundancy, observability, power system measurements, power systems.

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“…Step 2) The injection measurement M1 relates only nodes of the OMS [1,2,3], so it is classified as redundant. …”

Section: Examplementioning

confidence: 99%

A topological approach to the identification of critical measurements in power-system state estimation

Bretas

London

Alberto

et al. 2005

IEEE Trans. Circuits Syst. I

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Abstract-This paper presents a new topological methodology for critical measurements identification in observable networks. A measurement is said to be critical, in an observability sense, if its removal from the measurement set makes the associated system lose observability. The proposed methodology is based on the properties of both, observable measurement subnetworks (OMS) and redundant branch sets (RBS), for the first time proposed. To reduce the combinatorial bluster, the proposed method divides the measurements into two groups and classifies them into two phases. It allows identifying the critical measurements without any numerical calculation. Indeed, it is simple and fast. To clarify the proposed method and to demonstrate its simplicity, two examples are provided. The proposed method is successfully tested in the IEEE-14 bus system as well as in two realistic systems of Brazilian utilities. The first is a 121-bus system by ELETROSUL, and the other is a 383-bus system by Companhia Hidroelétrica do São Francisco (CHESF).

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Power network observability: the assessment of the measurement system strength

Cited by 23 publications

References 18 publications

Solving data acquisition and processing problems in power systems using a pattern analysis approach

Solving data acquisition and processing problems in power systems using a pattern analysis approach

Low-cardinality critical k-tuples in measurement sets for state estimation

A topological approach to the identification of critical measurements in power-system state estimation

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