T.E. Dy-Liacco

Bio: T.E. Dy-Liacco is an academic researcher. The author has contributed to research in topics: Power system simulation & Computer security model. The author has an hindex of 1, co-authored 1 publications receiving 52 citations.

Enhancing power system security control

Abstract: This article presents a new concept for expanding the scope of power system security functions in an EMS control center, an approach which combines automatic learning applied to dynamic security analysis and a changeover of the conventional operator training simulator (OTS) into a dynamic OTS. Automatic learning is shown to be suitable for power system dynamic security analysis, provided training-set generation is effective and the output adds value to the decision-making process.

Catastrophe Predictors From Ensemble Decision-Tree Learning of Wide-Area Severity Indices

Abstract: Catastrophe precursors are essential prerequisites for response-based remedial action schemes, at both the protective and the operator levels In this paper, wide-area-severity indices (WASI) derived from PMU measurements serve as the basis for building fast catastrophe predictors using random-forest (RF) learning Given the randomness in the ensemble of decision trees (DTs) stacked in the RF model, it can provide at the recall stage not only an early assessment of the stable/unstable status of an ongoing contingency but also a probability outcome which quantifies the confidence level of the decision This methodology, which to the best of our knowledge is new to the dynamic security assessment (DSA) of power systems, is also very effective in evaluating the importance of and interaction among the various WASI input features Our research unexpectedly showed that the ensemble of trees in the RF is very robust in the presence of small changes in the training data and generalize across widely different network dynamics Thus, the same RF performed very well on a large database with more than 60 000 instances from a test system (10%) and an actual (90%) system combined One such a general RF (with 210 trees) boosted the reliability of a 9-cycle catastrophe predictor to 999%, compared to only 70% when a single conventionally trained DT is used

Efficient Database Generation for Decision Tree Based Power System Security Assessment

Abstract: Decision tree based planning tools provide operators with the most important system attributes that guide them in deciding as to what situation requires operator action. Key to this approach is the manner in which different operating conditions are sampled to form a database for training. This paper develops an efficient sampling strategy that maximizes database information content while minimizing computing requirements. The approach involves two stages: stage-I to find the high information content region in the multidimensional operating parameter state space and stage-II to bias the sampling towards that region using importance sampling. The proposed approach is applied for deriving operating rules against voltage stability issues on the Brittany region of the French EHV system. The results show that the decision trees produced by the proposed efficient sampling approach have significantly improved classification performance and offer economic benefits compared to conventional sampling strategies, all at greatly reduced computational requirements.

Digital twin framework and its application to power grid online analysis

Abstract: Digital twin (DT) framework is introduced in the context of application for power grid online analysis. In the development process of a new power grid real-time online analysis system, an online analysis digital twin (OADT) has been implemented to realize the new online analysis architecture. The OADT approach is presented and its prominent features are discussed. The presentation, discussion, and performance testing are based on a large-scale grid network model (40K+ buses), exported directly from the EMS system of an actual power grid. A plan to apply the OADT approach to digitize power grid dispatching rules is also outlined.

Synchrophasor Data Baselining and Mining for Online Monitoring of Dynamic Security Limits

Abstract: When the system is in normal state, actual SCADA measurements of power transfers across critical interfaces are continuously compared with limits determined offline and stored in look-up tables or nomograms in order to assess whether the network is secure or insecure and inform the dispatcher to take preventive action in the latter case. However, synchrophasors could change this paradigm by enabling new features, the phase-angle differences, which are well-known measures of system stress, with the added potential to increase system visibility. The paper develops a systematic approach to baseline the phase-angles versus actual transfer limits across system interfaces and enable synchrophasor-based situational awareness (SBSA). Statistical methods are first used to determine seasonal exceedance levels of angle shifts that can allow real-time scoring and detection of atypical conditions. Next, key buses suitable for SBSA are identified using correlation and partitioning around medoid (PAM) clustering. It is shown that angle shifts of this subset of 15% of the network backbone buses can be effectively used as features in ensemble decision tree-based forecasting of seasonal security margins across critical interfaces.

Overload Alleviation With Preventive-Corrective Static Security Using Fuzzy Logic

Abstract: This paper presents a concept overview of an automatic operator of electrical networks (AOEN) for real-time alleviation of component overloads and increase of system static loadability, based on state-estimator data only. The control used for this purpose is real-power generation rescheduling, although any other control input could fit the new framework. The key performance metrics are the vulnerability index of a generation unit (VIGS) and its sensitivity (SVIGS), accurately computed using a realistic ac power flow incorporating the AGC model (AGC-PF). Transmission overloads, vulnerability indices and their sensitivities with respect to generation control are translated into fuzzy-set notations to formulate, transparently, the relationships between incremental line flows and the active power output of each controllable generator. A fuzzy-rule-based system is formed to select the best controllers, their movement and step-size, so as to minimize the overall vulnerability of the generating system while eliminating overflows. The controller performance is illustrated on the IEEE 39-bus (New England) network and the three-area IEEE-RTS96 network subjected to severe line outage contingencies. A key result is that minimizing the proposed vulnerability metric in real-time results in increased substantial loadability (prevention) in addition to overload elimination (correction).