Real-time Ranking of Electrical Feeders using Expert Advice

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<ul><li><p>Real-time Ranking of Electrical Feeders using Expert Advice</p><p>Hila Becker 1,2, Marta Arias 11Center for Computational Learning Systems2Computer Science DepartmentColumbia University</p></li><li><p>OverviewThe problemThe electrical systemAvailable data ApproachChallengesOur solution using Online learningExperimental results</p></li><li><p>The Electrical System</p></li><li><p>The ProblemDistribution feeder failures result in automatic feeder shutdowncalled Open Autos or O/As O/As stress networks, control centers, and field crews O/As are expensive ($ millions annually)Proactive replacement is much cheaper and safer than reactive repairHow do we know which feeders to fix?</p></li><li><p>Some facts about feeders and failuresmostly 0-5 failures per daymore in the summerstrong seasonality effects</p></li><li><p>Feeder dataStatic dataCompositional/structuralElectricalDynamic dataOutage history (updated daily)Load measurements (updated every 15 minutes)Roughly 200 attributes for each feederNew ones are still being added.</p></li><li><p>OverviewThe problemThe electrical systemAvailable data ApproachChallengesOur solution using Online learningExperimental results</p></li><li><p>Machine Learning ApproachLeverage Con Edisons domain knowledge and resourcesLearn to rank feeders based on failure susceptibilityHow?Assemble dataTrain ranking model based on past dataRe-rank frequently using model on current data</p></li><li><p>Feeder Ranking ApplicationGoal: rank feeders according to failure susceptibility High risk placed near the topIntegrate different types of dataInterface that reflects the latest state of the systemUpdate feeder ranking every 15 min.</p></li><li><p>Application Structure</p></li><li><p>Decision Support GUI</p></li><li><p>OverviewThe problemThe electrical systemAvailable data ApproachChallengesOur solution using Online learningExperimental results</p></li><li><p>Simple SolutionSupervised batch-learning algorithmsUse past data to train a modelRe-rank frequently using this model on current dataUse the best performing learning algorithmHow do we measure performance?MartiRank - boosting algorithm by [Long &amp; Servedio, 2005]Use MartiRank for dynamic feeder ranking</p></li><li><p>Performance MetricNormalized average rank of failed feeders</p></li><li><p>Performance Metric Examplerankingoutages</p><p>1021314051607080</p></li><li><p>Real-time ranking with MartiRankMartiRank is a batch learning algorithmDeal with changing system by:generating new datasets with latest dataRe-training new model, replacing old modelUsing newest model to generate rankingMust implement training strategies</p></li><li><p>Real-time ranking with MartiRanktimetimetime</p></li><li><p>How to measure performance over timeEvery ~15 minutes, generate new ranking based on current model and latest dataWhenever there is a failure, look up its rank in the latest ranking before the failureAfter a whole day, compute normalized average rank</p></li><li><p>Using MartiRank for real-time ranking of feedersMartiRank seems to work well, but..User decides when to re-trainUser decides how much data to use for re-trainingPerformance degrades over timeWant to make system automaticDo not discard all old modelsLet the system decide which models to use</p></li><li><p>OverviewThe problemThe electrical systemAvailable data ApproachChallengesOur solution using Online learningExperimental results</p></li><li><p>Learning from expert adviceConsider each model as an expertEach expert has associated weightReward/penalize experts with good/bad predictionsWeight is a measure of confidence in experts predictionPredict using weighted average of top-scoring experts</p></li><li><p>Learning from expert adviceAdvantagesFully automaticAdaptiveCan use many types of underlying learning algorithmsGood performance guarantees from learning theory: performance never too far off from best expert in hindsightDisadvantagesComputational cost: need to track many models in parallel</p></li><li><p>Weighted Majority Algorithm [Littlestone &amp; Warmuth 88]Introduced for binary classificationExperts make predictions in [0,1]Obtain losses in [0,1]Pseudocode:Learning rate as main parameter, in (0,1]There are N experts, initially weight is 1 for allFor t=1,2,3, Predict using weighted average of experts predictionObtain true label; each expert incurs loss liUpdate experts weights using wi,t+1 = wi,t pow(,li)</p></li><li>Weighted Majority Algorithm e1. . .e2e3eNN Expertsw1w2w3wN100 1?w1*1 +w2*0 +w3*0 + . . . +wN*1&gt;0.5</li><li><p>In our case, cant use WM directlyUse ranking as opposed to binary classificationMore importantly, do not have a fixed set of experts</p></li><li><p>Dealing with ranking vs. binary classificationRanking loss as normalized average rank of failures as seen before, loss in [0,1]To combine rankings, use a weighted average of feeders ranks</p></li><li><p>Dealing with a moving set of expertsIntroduce new parametersB: budget (max number of models) set to 100p: new models weight percentile in [0,100]: age penalty in (0,1]If too many models (more than B), drop models with poor q-score, whereqi = wi pow(, agei)I.e., is rate of exponential decay</p></li><li><p>Online Ranking Algorithm e1. . .e2e3eBw1w2w3wB?F1F4F3F2F5F4F2F1F3F5F1F3F5F4F2F1F3F4F2F5F1F3F4F2F5F3F1F4F2F5eB+1eB+2wB+1wB+2</p></li><li><p>Other parametersHow often do we train and add new models?Hand-tuned over the course of the summerAlternatively, one could train when observed performance drops .. not used yetHow much data do we use to train models?Based on observed performance and early experiments1 week worth of data, and2 weeks worth of data</p></li><li><p>OverviewThe problemThe electrical systemAvailable data ApproachChallengesOur solution using Online learningExperimental results</p></li><li><p>Experimental ComparisonCompare our approach toUsing batch-trained modelsOther online learning methodsRanking PerceptronOnline versionHand Picked ModelTuned by humans with domain knowledge</p></li><li><p>Performance Summer 2005</p></li><li><p>Performance Winter 2006</p></li><li><p>Parameter Variation - Budget</p></li><li><p>Future WorkConcept drift detectionAdd new models only when change is detectedEnsemble diversity controlExploit re-occurring contexts</p><p>Generation: a prime mover, typically the force of water, steam, or hot gasses on a turbine, spins an electromagnet, generating large amounts of electrical current at a generating stationTransmission: the current is sent at very high voltage (hundreds of thousands of volts) from the generating station to substations closer to the customersPrimary Distribution: electricity is sent at mid-level voltage (tens of thousands of volts) from substations to local transformers, over cables called feeders, usually 10-20 km long, and with a few tens of transformers per feeder. Feeders are composed of many feeder sections connected by joints and splicesSecondary Distribution: sends electricity at normal household voltages from local transformers to individual customers Many occur in the summer when the load on the system increases, during heat waves When an O/A occurs, the load that had been carried by the failed feeder must shift to adjacent feeders, further stressing them. This can lead to a failure cascade in a distribution network(e.g. age and composition of each feeder section) and e.g. electrical load data for a feeder and its transformers, accumulating at a rate of several hundred megabytes per day) Software engineering challenges to manage data</p><p>with sufficient accuracy so that timely preventive maintenance can be taken on the right feeders at the right time.How to deal with impending failures that are corrected on the basis of our ranking Use data within a window, aggregate dynamic data within that period in various ways (quantiles, counts, sums, averages, etc.)Re-train daily, or weekly, or every 2 weeks, or monthly, or</p><p>Use data within a window, aggregate dynamic data within that period in various ways (quantiles, counts, sums, averages, etc.)Re-train daily, or weekly, or every 2 weeks, or monthly, or</p><p>No human intervention neededChanges in system are learned as it runsEvery 7 daysSeems to achieve balance of generating new models to adapt to changing conditions without overflowing system</p><p>if a model was successful in the previous summer but was retired during the winter, it should be rescued backin the upcoming summer if similar environmental conditions reappear.</p></li></ul>