Wind energy has seen great development during the past decade. However, wind turbine availability and reliability, especially for offshore sites, still need to be improved, which strongly affect the cost of wind energy. Wind turbine operational cost is closely depending on component failure and repair rate, while fault detection and isolation will be very helpful to improve the availability and reliability factors. In this paper, an efficient machine learning method, random forests (RFs) in combination with extreme gradient boosting (XGBoost), is used to establish the data-driven wind turbine fault detection framework. In the proposed design, RF is used to rank the features by importance, which are either direct sensor signals or constructed variables from prior knowledge. Then, based on the top-ranking features, XGBoost trains the ensemble classifier for each specific fault. In order to verify the effectiveness of the proposed approach, numerical simulations using the state-of-the-art wind turbine simulator FAST are conducted for three different types of wind turbines in both the below and above rated conditions. It is shown that the proposed approach is robust to various wind turbine models including offshore ones in different working conditions. Besides, the proposed ensemble classifier is able to protect against overfitting, and it achieves better wind turbine fault detection results than the support vector machine method when dealing with multidimensional data.

A Data-Driven Design for Fault Detection of Wind Turbines Using Random Forests and XGboost

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020AV000284

Carbon-Neutral Pathways for the United States

/pdf/cost-projections-for-utility-scale-battery-storage-2x8hlwbv07.pdf

Cost Projections for Utility-Scale Battery Storage

Trends in tools and approaches for modelling the energy transition

/pdf/cost-projections-for-utility-scale-battery-storage-2021-1i933tumr7.pdf

Cost Projections for Utility-Scale Battery Storage: 2021 Update

We propose a stochastic programming-based tool to support adaptive transmission planning under market and regulatory uncertainties. We model investments in two stages, differentiating between commitments that must be made now and corrective actions that can be undertaken as new information becomes available. The objective is to minimize expected transmission and generation costs over the time horizon. Nonlinear constraints resulting from Kirchhoff's voltage law are included. We apply the tool to a 240-bus representation of the Western Electricity Coordinating Council and model uncertainty using three scenarios with distinct renewable electricity mandates, emissions policies, and fossil fuel prices. We conclude that the cost of ignoring uncertainty (the cost of using naive deterministic planning methods relative to explicitly modeling uncertainty) is of the same order of magnitude as the cost of first-stage transmission investments. Furthermore, we conclude that heuristic rules for constructing transmission plans based on scenario planning can be as suboptimal as deterministic plans.

An Engineering-Economic Approach to Transmission Planning Under Market and Regulatory Uncertainties: WECC Case Study

The recognition of transmission’s interaction with other resources has motivated the development of co-optimization methods to optimize transmission investment while simultaneously considering tradeoffs with investments in electricity supply, demand, and storage resources. For a given set of constraints, co-optimized planning models provide solutions that have lower costs than solutions obtained from decoupled optimization (transmission-only, generation-only, or iterations between them). This paper describes co-optimization and provides an overview of approaches to co-optimizing transmission options, supply-side resources, demand-side resources, and natural gas pipelines. In particular, the paper provides an up-to-date assessment of the present and potential capabilities of existing co-optimization tools, and it discusses needs and challenges for developing advanced co-optimization models.

Co-optimization of electricity transmission and generation resources for planning and policy analysis: review of concepts and modeling approaches

/pdf/regional-energy-deployment-system-reeds-model-documentation-5cw858mrle.pdf

Regional Energy Deployment System (ReEDS) Model Documentation: Version 2020

/pdf/regional-energy-deployment-system-reeds-model-documentation-3195nhyhqg.pdf

Regional Energy Deployment System (ReEDS) Model Documentation: Version 2016

/pdf/regional-energy-deployment-system-reeds-model-documentation-hz1ynljksp.pdf

Regional Energy Deployment System (ReEDS) Model Documentation: Version 2018

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