Online Dynamic Security Assessment using Ensemble Decision Tree Learning

Vijay VittalArizona State University

July, 2014

Outline

• Decision tree-based dynamic security assessment using synchrophasor measurements – basic idea and examples

• Enhanced online dynamic security assessment via ensemble decision tree learning– improved robustness to changes in operating conditions and

system topology– handling missing synchrophasor measurements

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Synchrophasor and Applications

• Synchrophasor measurements provide information on dynamic system state – voltage/current magnitudes and phase angles– instantaneous frequency

• Synchrophasor has been utilized by many onlineapplications [PSRC][DOE]:– wide-area monitoring and visualization– dynamic security assessment – islanding and load shedding– …

focus of this talk!

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Dynamic Security Assessment

• Dynamic security assessment (DSA) determines whether a power system maintains stability after a disturbance and in the transition to a new operating condition

• Online DSA is challenging due to a vast number of credible N-k contingencies and massive state variables of power systems

• Cost-effective online DSA schemes have been proposed by utilizing decision trees (DTs) and phasor measurements [Sun et al. 07].

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DT-based Online DSA• Exhaustive offline study

– a knowledge base– measurement as attribute

• Data mining– a DT-based classifier– decision regions

• Online DSA– a new case of the present OC mapped into a decision region

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Decision Trees• DT: a tree-structured predictive model that maps an

observation on attributes to a decision• Decision regions are characterized by critical attributes and

thresholds - nomograms

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Attributes for DT-based DSA• Numerical attributes:

– voltage/current magnitude – active/reactive power flows – voltage phase angle difference

• For example: IEEE 39-bus system with 9 PMUs– 9 voltage magnitudes– 26 active power flows– 26 reactive power flows– 26 line current magnitudes– 36 voltage phase angle differences

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DT for DSA: An Example• A DT for voltage stability of SRP system under six N-2 critical

contingencies [Diao 09].

• V: voltage magnitude• P: active power flow• Q: reactive power flow• A: voltage phase angle • CNTO$: contingency index• N: number of cases

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Robust Online DSA

• For accurate and robust DSA, the knowledge base and DTs have to be updated timely to track system dynamics:– operating condition (OC) variations– forced topology change of power systems

• There has been limited effort directed to handling these dynamics– [Diao et al. 10] handle OC variations by building a DT from scratch,

whenever existing DT misclassifies a new case– [Jensen et al. 07][Xu et al. 12] suggest creating an “overall” knowledge

base that covers all possible system topologies and operating conditions

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Proposed Robust Online DSA• Offline training

(hours ahead)

• Near real-time update

• Online DSA

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Ensemble DT learning• An ensemble of small DTs (DT with a smaller height) are utilized for

efficient update with new cases.

• Ensemble DT learning for DSA– build (in offline) or update (in

near real-time) multiple small DTs using adaptive data weights

– calculate or update the voting weight for each small DT

– classification decision is then obtained via weighted voting among the small DTs

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Case Study (1)

• Test system– Western Electricity Coordinating Council (WECC) system

• 17,252 buses, 3115 generators and 14,989 transmission lines

– a regional grid of WECC is monitored• 33 buses with PMUs

• Test data– 15-minite data of load and generation on July 21, 2009– A contingency list comprised of 181 N-k contingencies, provided

by the region grid operator

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Case Study (2)• Size and number of DTs

– V-fold cross validation (V=10) to test the generalization capability– DT height J=2, DT number L=35

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Test Results (1)• Benchmarks

1. a single DT updated by re-building2. boosting DTs updated by re-building

• Proposed approach requires much less computational time

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Test Results (2)• Proposed approach has least miss-detection rate, and competitive

overall performance compared to Boosting (rebuilding) that has highest computational complexity

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Missing Measurements in Online DSA

• The performance of synchrophasor-based online DSA depends on reliable synchrophasor measurements provided by phasor measurement units (PMUs) and phasor data concentrators (PDC)

• Real-time synchrophasor measurements can become unavailable due to

– failures of PMUs or PDCs– Communication failures

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Handling Missing Data in DT

• DTs handles missing data using “surrogate”[CART]– A surrogate attribute is the one that can produce the most similar

splitting results to the primary attribute, when used for building a node of DT

• Surrogate-based approach is found ineffective for online DSA– Top surrogate is usually “co-located” with the primary attribute

(i.e., measured by the same PMU), and thus will be missing together when the PMU fails

– Using non-top surrogate attributes can compromise the classification accuracy of DTs

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A Case Study using Surrogate (1)• Surrogate attributes found by the CART algorithm [CART] are mostly

co-located with the primary attributes

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A Case Study using Surrogate (2)• Non-co-located attributes have relatively low associations with the

primary attributes• Low association can result in low prediction accuracy

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Proposed Approach

1. Offline ensemble DT learning: an ensemble of small DTs are trained by using attribute subsets

2. In near real-time, new cases are used to re-assess the performance of small DTs

3. In online DSA, security classification decisions are obtained via a weighted voting of viable small DTs

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Attribute Subset (1)• Each area of power systems typically has a PDC concentrating all PMU

measurements from this area [Phadke 08]

• Within an attribute subset, all the attributes are chosen from the same area, so as to contain the impact of a PDC failure on all DTs

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Attribute Subset (2)• Among co-located attributes, only one is chosen by an

attribute subset– the redundancy within an attribute subset is reduced, and thus

generalization capability of small DTs is improved [Ho 98]

• All phase angle differences within an area are included in an attribute subset– Voltage phase angle differences contain important information

regarding the level of stress in OCs, and thus are more likely to be critical attributes for DSA

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Case Study• Test system: IEEE 39-bus system in three areas, with 9 PMUs

• 96 measurements are selected as numerical attributes

• 30 N-2 contingencies with “3-phase to ground” fault are picked via exhaustive search

• Rotor-angle-based stability criteria

• OCs which are secure under all N-1 contingencies are selectedfor knowledge base

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Test Results

• average misclassification error over all possible missing data scenarios• b is the availability of the PDCs and PMUs of an area

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Impact of Measurement Noise• When measurements comply with IEEE C37.118, i.e., when total vector error (TVE)

is below 1%, the impact of noise is negligible.• Measurement noise is generated from a truncated complex Gaussian distribution

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Remarks

• Synchrophasor-based DSA should meet the following requirements:– Accuracy and robustness– Scalability: synchrophasor data from newly-deployed PMUs

should be easily incorporated as attributes– Distributed implementation and parallel computing

• Ensemble DT learning techniques (including boosting, random subspace methods used in this work) would be invaluable to achieving these goals

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References• [PSRC] Power System Relaying Committee, “Use of Synchrophasor Measurements

in Protective Relaying Applications,” Aug, 2013.• [DOE] Department of Energy, “Synchrophasor Technologies and Deployment in

the Recovery Act Smart Grid Programs,” Aug, 2013.• [Sun et al. 07] K. Sun, S. Likhate, V. Vittal, V. Kolluri, and S. Mandal, “An online

dynamic security assessment scheme using phasor measurements and decision trees,” IEEE Trans. Power Syst., vol. 22, no. 4, pp. 1935–1943, Nov 2007.

• [Diao 09] Ruisheng Diao, “Power System Online Security Assessment Using Synchronized Phasor Measurements And Decision Trees,” Dissertation, Aug, 2009.

• [He et al. 13] Miao He, Junshan Zhang and Vijay Vittal, “Robust Online Dynamic Security Assessment using Adaptive Ensemble Decision-Tree Learning,” IEEE Trans. Power Syst., vol. 28, no. 4, pp. 4089-4098, Nov. 2013.

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References• [Diao et al. 10] R. Diao, V. Vittal, and N. Logic, “Design of a real-time security

assessment tool for situational awareness enhancement in modern power systems,” IEEE Trans. Power Syst., vol. 25, no. 2, pp. 957–965, May 2010.

• [He et al. 13] Miao He, Vijay Vittal and Junshan Zhang, “Online Dynamic Security Assessment With Missing PMU Measurements,” IEEE Trans. Power Syst., vol. 28, no. 2, pp. 1969 - 1977, May 2013.

• [CART] L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees. Chapman and Hall/CRC, 1984

• [Ho 98] T. K. Ho, “Random decision forests,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 8, pp. 832–844, Aug. 1998.

• [Phadke 08] A. Phadke and J. Thorp, Synchronized Phasor Measurements and Their Applications. New York: Springer, 2008.

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