Influence of the Characteristics of Weather Information in a Thunderstorm-Related Power Outage Prediction System

Watson, Peter L.; Koukoula, Marika; Anagnostou, Emmanouil N.

doi:10.3390/forecast3030034

Cited by 19 publications

(6 citation statements)

References 60 publications

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“…Watson et al presented a weather ensemble impact outage model that is able to predict the potential impacts of thunderstorms, such as the location of storm impacts and the total damage by an event using weather variables, WRF (Weather Research and Forecasting Model) simulation and NOAA (National Oceanic and Atmospheric Administration) analysis data sets. This model found applications in preparing for thunderstorms by decision makers [27]. Yang et al used learning curves and evaluation metrics to develop a model to quantify the uncertainty in outage prediction.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Assessment of Conventional and Artificial Neural Networks Methods for Electricity Outage Forecasting

et al. 2022

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The reliability of the power supply depends on the reliability of the structure of the grid. Grid networks are exposed to varying weather events, which makes them prone to faults. There is a growing concern that climate change will lead to increasing numbers and severity of weather events, which will adversely affect grid reliability and electricity supply. Predictive models of electricity reliability have been used which utilize computational intelligence techniques. These techniques have not been adequately explored in forecasting problems related to electricity outages due to weather factors. A model for predicting electricity outages caused by weather events is presented in this study. This uses the back-propagation algorithm as related to the concept of artificial neural networks (ANNs). The performance of the ANN model is evaluated using real-life data sets from Pietermaritzburg, South Africa, and compared with some conventional models. These are the exponential smoothing (ES) and multiple linear regression (MLR) models. The results obtained from the ANN model are found to be satisfactory when compared to those obtained from MLR and ES. The results demonstrate that artificial neural networks are robust and can be used to predict electricity outages with regards to faults caused by severe weather conditions.

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Section: Introductionmentioning

confidence: 99%

A Comparative Assessment of Conventional and Artificial Neural Networks Methods for Electricity Outage Forecasting

et al. 2022

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“…However [47], The RTMA Real-Time Mesoscale Analysis, WRF Weather Research, and Forecasting model have been proposed using (NOAA)National Oceanic and Atmospheric Administration analysis dataset, (WRF) Weather Research and Forecasting. While [48] The Cb-Fusion has been proposed using weather radar data from the German Meteorological Service (DWD), NWP output from the DWD open dataset, which was from 1743 hours of observations of more than 1000 km2.…”

Section: 3mentioning

confidence: 99%

Thunderstorms Prediction Using Satellite Images

Amjad¹,

Malik²,

Ghous³

et al. 2023

ijisct

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Human life and property are put at risk by lightning, heavy hail, and strong winds caused by thunderstorms. Accurate prediction of thunderstorms has long been a difficult problem. In difference from conventional methods, the modern methods for predicting thunderstorms utilize a combination of computer models, observation (via the use of radars and satellite photos), and modern knowledge and patterns employed by thunderstorms stations. Observations: Using these methods, it is possible to make reasonably accurate predictions. Prediction models based on complex formulas run the majority of forecasting computer models. Artificial neural networks, machine learning, deep learning, and statistics are all examples of adaptive systems that alter their structure in response to information flowing through the network. In this reviewed paper, different Artificial intelligence techniques are used such as machine learning, deep learning, and ANN (Artificial neural network). Different statistical models were presented to predict thunderstorm systems. Various researchers achieved technical milestones by presenting statistical methods on thunderstorm systems using satellite data.

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“…Reference [7] uses a Bayesian-based method to predict power outages using radar measurements data. Reference [8] uses a comparative analysis of numeric weather prediction of different layers to predict thunderstorm-related power outages. In this work, the line outage probability is considered as input to the algorithm; however, the uncertainty of the weather prediction is considered as well.…”

Section: Introductionmentioning

confidence: 99%

Series FACTS Devices for Increasing Resiliency in Severe Weather Conditions

Beikbabaei,

Mehrizi-Sani

2023

Energies

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Severe weather conditions are low-probability, high-impact events that affect grid operations. The majority of power outages are caused by severe weather conditions. Grid resiliency to weather events can be enhanced by decreasing the reliance on its affected sections. One way to do this is to reduce the power flow through lines vulnerable to severe weather. If a line is disconnected, its initial power flow is distributed through the neighbor lines, which may cause congestion in the grid. FACTS devices can be used to control the power flow of lines that have a higher chance of power outages. Most previous works do not consider weather events in power flow control. In this work, a linearized optimal power flow (OPF)–based algorithm is developed to minimize the real power flow of vulnerable lines considering the thermal limits of lines to prevent infeasible solutions; the simulation is fast, making it suitable for large-scale systems. The proposed optimization problem is presented as a mixed-integer linear program (MILP), making it capable of using short-term load forecasting due to its high solution speed. The proposed optimization problem considers multiple lines with different outage probabilities and the uncertainties of the weather forecast. Moreover, it estimates the power reduction in vulnerable lines due to changes in the series FACTS devices. The performance of the proposed optimization problem is tested on IEEE 14-, 30-, and 118-bus systems for several scenarios. The results are validated with the AC power flow results from MATPOWER.

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Influence of the Characteristics of Weather Information in a Thunderstorm-Related Power Outage Prediction System

Cited by 19 publications

References 60 publications

A Comparative Assessment of Conventional and Artificial Neural Networks Methods for Electricity Outage Forecasting

A Comparative Assessment of Conventional and Artificial Neural Networks Methods for Electricity Outage Forecasting

Thunderstorms Prediction Using Satellite Images

Series FACTS Devices for Increasing Resiliency in Severe Weather Conditions

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