Spatially Aware Ensemble-Based Learning to Predict Weather-Related Outages in Transmission

This paper describes the implementation of prediction model for real-time assessment of weather related outages in the electric transmission system. The network data and historical outages are correlated with variety of weather sources in order to construct the knowledge extraction platform for accurate outage probability prediction. An extension of logistic regression prediction model that embeds the spatial configuration of the network was used for prediction. The results show that developed algorithm has very high accuracy and is able to differentiate the outage area from the rest of the network in 1 to 3 hours before the outage. The prediction algorithm is integrated inside weather testbed for real-time mapping of network outage probabilities using incoming weather forecast

Applying Bayesian estimates of individual transmission line outage rates

Despite the important role transmission line outages play in power system reliability analysis, it remains a challenge to estimate individual line outage rates accurately enough from limited data. Recent work using a Bayesian hierarchical model shows how to combine together line outage data by exploiting how the lines partially share some common features in order to obtain more accurate estimates of line outage rates. Lower variance estimates from fewer years of data can be obtained. In this paper, we explore what can be achieved with this new Bayesian hierarchical approach using real utility data. In particular, we assess the capability to detect increases in line outage rates over time, quantify the influence of bad weather on outage rates, and discuss the effect of outage rate uncertainty on a simple availability calculation

Bayesian estimates of transmission line outage rates that consider line dependencies

Transmission line outage rates are fundamental to power system reliability analysis. Line outages are infrequent, occurring only about once a year, so outage data are limited. We propose a Bayesian hierarchical model that leverages line dependencies to better estimate outage rates of individual transmission lines from limited outage data. The Bayesian estimates have a lower standard deviation than estimating the outage rates simply by dividing the number of outages by the number of years of data, especially when the number of outages is small. The Bayesian model produces more accurate individual line outage rates, as well as estimates of the uncertainty of these rates. Better estimates of line outage rates can improve system risk assessment, outage prediction, and maintenance scheduling

Weather related fault prediction in minimally monitored distribution networks

Power distribution networks are increasingly challenged by ageing plant, environmental extremes and previously unforeseen operational factors. The combination of high loading and weather conditions is responsible for large numbers of recurring faults in legacy plants which have an impact on service quality. Owing to their scale and dispersed nature, it is prohibitively expensive to intensively monitor distribution networks to capture the electrical context these disruptions occur in, making it difficult to forestall recurring faults. In this paper, localised weather data are shown to support fault prediction on distribution networks. Operational data are temporally aligned with meteorological observations to identify recurring fault causes with the potentially complex relation between them learned from historical fault records. Five years of data from a UK Distribution Network Operator is used to demonstrate the approach at both HV and LV distribution network levels with results showing the ability to predict the occurrence of a weather related fault at a given substation considering only meteorological observations. Unifying a diverse range of previously identified fault relations in a single ensemble model and accompanying the predicted network conditions with an uncertainty measure would allow a network operator to manage their network more effectively in the long term and take evasive action for imminent events over shorter timescales

Koneoppimiseen perustuvat sään vaikutusennustukset

Defence is held on 2.11.2021 15:00 – 19:00 Remote connection link https://aalto.zoom.us/j/69735940472Natural disasters influenced over 4 billion people, required 1.23 million lives, and caused almost US$ 3 trillion economic losses between 2000 and 2019. The picture becomes even more deplorable when hazards, smaller-scale severe weather events not requiring casualties, are considered. For example, 78 percent of power outages in Finland were inflicted by extreme weather in 2017, and train delays, often caused by adverse weather, have been estimated to cost 1 billion pounds during 2006 and 2007 in the UK. To mitigate the effects of the adverse weather and increase the resilience of the societies, the World Meteorological Organisation (WMO) raised the consciousness of impact-based warnings along with impact forecasts. Such warnings and predictions can be used in various domains to prepare, alleviate and recuperate from adverse weather conditions.  This thesis studies how to preprocess data and use machine learning to create valuable impact forecasts for power grid and rail traffic operators. The thesis introduces a novel object-oriented method to predict power outages caused by convective storms. The method combines state-of-the-art storm identification, tracking, and nowcasting algorithms with modern machine learning methods. The proposed object-oriented method is also adapted to predict power outages caused by large-scale extratropical storms days ahead. In addition, the thesis studies the task of predicting weather-inflicted train delays. The method presented in the thesis hinges weather parameters on train delays to anticipate the delays days ahead. The thesis shows that the object-oriented approach is a vindicable method to predict power outages caused by convective storms and that a similar approach is feasible also in the context of extratropical storms. The introduced methods provide power grid operators increasingly accurate outage predictions. The thesis also demonstrates that the train delays related to adverse weather can be predicted with good quality training data. Such predictions offer cardinal information for rail traffic operators in preparing the challenging conditions. Presumably, similar approaches can be applied to any other domain with quantitative impacts produced by identifiable weather events, if sufficient impact data are available. Several advanced machine learning methods were evaluated in the tasks. The results corroborate with existing research: random forests provided a robust performance in all tasks, but also gradient boosting trees, Gaussian processes, and support vector machines proved useful.Luonnonkatastrofit vaikuttivat yli 4 miljardiin henkeen, vaativat 1,23 miljoonaa kuolonuhria ja tuottivat lähes 3 biljoonan dollarin taloudelliset tappiot vuosina 2000 -- 2019. Kuva heikkenee entisestään, mikäli huomioidaan myös pienemmän luokan vakavat säätapahtumat. Esimerkiksi 78 prosenttia Suomen vuoden 2017 sähkökatkoista oli sään aiheuttamia. Toisaalta -- usein säähän liittyvät -- junien myöhästymiset tuottivat arviolta miljardin punnan tappiot vuosina 2006 -- 2007 Isossa-Britanniassa. Maailman ilmatieteiden järjestö (WMO) onkin tähdentänyt vaikutusperusteisen varoitusten ja vaikutusennusteiden tärkeyttä vaaralliseen säähän varautumisessa. Vaikutusperusteiset varoitukset ja ennustukset ovat tärkeä apuväline useilla yhteiskunnan osa-alueilla varautuessa ääreviin sääilmiöihin sekä lievittäessä niiden vaikutuksia ja toipuessa niistä.  Tämä väitöskirja tutkii kuinka esiprosessoida dataa ja hyödyntää koneoppmimista sähköverkko- ja junaliikenneoperaattoreille tuotetuissa vaikutusennusteissa. Väitöskirja esittelee uuden oliopohjaisen metodin konvektiivisten rajuilmojen aiheuttamien sähkökatkojen ennustamiseksi. Metodi yhdistää ajantasaiset myrskyn tunnistus-, seuraus- ja lähihetkiennustusalgoritmit moderneihin koneoppimismenetelmiin. Ehdotettu oliopohjainen metodi on myös muokattu ennustamaan laaja-alaisten matalapainemyrskyjen aiheuttamia sähköatkoja. Lisäksi, väitöskirja tutkii sään aiheuttamien junien myöhästymisten ennustamista. Väitöskirjassa esitetty methodi yhdistää sääparametrit junien myöhästymisdataan, jotta myöhästymisiä voidaan ennakoida päiviä etukäteen.  Väitöskirja osoittaa, että oliopohjainen lähestymistapa toimii hyvin konvektiivisten myrskyjen aiheuttamien sähkökatkojen ennustamisessa, ja että vastaavaa metodia voidaan soveltaa myös matalapainemyrskyjen tapauksessa. Väitöskirjassa esitetyt metodit tarjoavat sähköverkko-operaattoreille entistä tarkempia sähkökatkoennusteita. Väitöskirja osoittaa myös, että sään aiheuttamien junien myöhästymisiä voidaan ennustaa mikäli hyvälaatuista koulutusdataa on saatavilla. Tällaiset ennustukset ovat hyvin tärkeitä junaliikenneoperaattoreille haasteellisiin olosuhteisiin varauduttaessa. Oletettavasti samoja lähestymistapoja voidaan hyödyntää myös muilla aloilla, joilla vaikutuksia ovat numeerisesti mallinnettavia ja tunnistettavan säätapahtuman tuottamia sekä kunnollista vaikutusdataa on saatavilla. Väitöskirja vertailee useiden koneoppmismetodeiden soveltuvuutta käsiteltäviin tähtäviin. Tulokset ovat linjassa edellisten tutkimusten kanssa: erityisesti satunnaismetsät ('random forests') tarjosivat toimitavarmoja ennusteita kaikissa tehtävissä, mutta gradienttivahvisteiset puut ('gradient boosting trees'), Gaussiset prosessit ('Gaussian processes') ja tukiverkkokoneet ('support vector machines') toimivat tehtävissä

Prediction of Solar Radiation Based on Spatial and Temporal Embeddings for Solar Generation Forecast

A novel method is proposed for real-time solar generation forecast using weather data, while exploiting both spatial and temporal structural dependencies. The network observed over time is projected to a lower-dimensional representation where a variety of weather measurements are used to train a structured regression model while weather forecast is used at the inference stage. Experiments were conducted at 288 locations in the San Antonio, TX area on obtained from the National Solar Radiation Database. The model predicts solar irradiance with a good accuracy (R2 0.91 for the summer, 0.85 for the winter, and 0.89 for the global model). The best accuracy was obtained by the Random Forest Regressor. Multiple additional experiments were conducted to characterize influence of missing data and different time horizons providing evidence that the new algorithm is robust for data missing not only completely at random but also when the mechanism is spatial, and temporal

Simulating Long-Term and Short-Term Community and Infrastructure Vulnerability and Response to Natural Hazards

Natural disasters can cause severe damage to infrastructure, the economy, and human lives. A better understanding of the vulnerability of individual households, critical infrastructure, and a community can be vital to in both short-term disaster response and long-term resilience planning. This dissertation develops methods to analyze the long-term vulnerability of communities when facing repeated hurricanes and heat waves in a changing climate, as well as the short-term vulnerability of power systems under different types of disasters. The approaches I develop are a combination of simulation modeling, predictive modeling, and network theory. The first chapter of this dissertation describes the vulnerability of communities when facing different hazardous events, such as hurricanes, heat waves, and power outages. The second chapter focuses on enhancing understanding of the long-term vulnerability of a community under repeated hurricanes. I discuss how learning, initial beliefs and memory decay effects influence individual decisions and change regional vulnerability under different hurricane climate scenarios. We found how different initial knowledge and the memory effect can result in different community vulnerability under different hurricane climate scenarios. The third chapter develops methods for estimating power system damage and power outages from extreme weather events. I use publicly available data to generate the layout of the distribution system which is not publicly available in most cases. I then use the synthetic distribution layout to simulate damage and power outages from weather events. This model can provide important information to understand regional and individual power outage risks. The fourth chapter studies how the long-term vulnerability of a community may change under repeated heat waves. I build an agent-based model to address the interplay of the vulnerability of community, climate change, individual mitigation, social networks, power outages, and government mitigation. The model shows how each component is triggering the evolution of community heat vulnerability with historical events and what-if scenarios. The fifth chapter summarizes this dissertation and discusses potential limitations and future directions of this study. Overall, this work develops new methods to study the vulnerability of communities under repeat natural disasters aiming to enhance better short-term response and long-term planning. These models can help decision-makers, policy-makers, and individuals to make better plans when facing unexpected extreme weather events.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167902/1/cwzhai_1.pd