Physics-Based and Data-Driven Analytics for Enhanced Planning and Operations in Power Systems with Deep Renewable Penetration

This dissertation is motivated by the lack of combined physics-based and data-driven framework for solving power system challenges that are introduced by the integration of new devices and new system components. As increasing number of stochastic generation, responsive loads, and dynamic measurements are involved in the planning and operations of modern power systems, utilities and system operators are in great need of new analysis framework that could combine physical models and measuring data together for solving challenging planning and operational problems. In view of the above challenges, the high-level objective of this dissertation is to develop a framework for integrating measurement data into large physical systems modeled by dynamical equations. To this end, the dissertation first identifies four critical tasks for the planning and operations of the modern power systems: the data collection and pre-processing, the system situational awareness, the decision making process, as well as the post-event analysis. The dissertation then takes one concrete application in each of these critical tasks as the example, and proposes the physics-based/data-driven approach for solving the challenging problems faced by this specific application. To this end, this dissertation focuses on solving the following specific problems using physics-based/data-driven approaches. First, for the data collection and pre-processing platform, a purely data-driven approach is proposed to detect bad metering data in the phasor measurement unit (PMU) monitoring systems, and ensure the overall PMU data quality. Second, for the situational awareness platform, a physics-based voltage stability assessment method is presented to improve the situational awareness of system voltage instabilities. Third, for the decision making platform, a combined physics-based and data-driven framework is proposed to support the decision making process of PMU-based power plant model validation. Forth, for the post-event analysis platform, a physics-based post-event analysis is presented to identify the root causes of the sub-synchronous oscillations induced by the wind farm integration. The above problems and proposed solutions are discussed in detail in Section 2 through Section 5. The results of this work can be integrated to address practical problems in modern power system planning and operations

Government review of the Mod-2 wind turbine (as-built)

The findings and recommendations of the Government committee formed to conduct an as-built review of the three Mod-2 wind turbine units at Goldendale, Washington are given. The purpose of the review was to identify any critical deficiencies in machine components that could result in failure, and to recommend any necessary corrective action before resuming safe machine operation. The review concluded that one of the deficiencies identified would preclude planned attended or unattended operation, provided that certain corrective actions were implemented

Applications of machine learning in diagnostics and prognostics of wind turbine high speed generator failure

The cost of wind energy has decreased over the last decade as technology has matured and the industry has benefited greatly from economies of scale. That being said, operations and maintenance still make up a significant proportion of the overall costs and needs to be reduced over the coming years as sites, particularly offshore, get larger and more remote. One of the key tools to achieve this is through enhancements of both SCADA and condition monitoring system analytics, leading to more informed and optimised operational decisions. Specifically examining the wind turbine generator and highspeed assembly, this thesis aims to showcase how machine learning techniques can be utilised to enhance vibration spectral analysis and SCADA analysis for early and more automated fault detection. First this will be performed separately based on features extracted from the vibration spectra and performance data in isolation before a framework will be presented to combine data sources to create a single anomaly detection model for early fault diagnosis. Additionally by further utilising vibration based analysis, machine learning techniques and a synchronised database of failures, remaining useful life prediction will also be explored for generator bearing faults, a key component when it comes to increasing wind turbine generator reliability. It will be shown that through early diagnosis and accurate prognosis, component replacements can be planned and optimised before catastrophic failures and large downtimes occur. Moreover, results also indicate that this can have a significant impact on the costs of operation and maintenance over the lifetime of an offshore development.The cost of wind energy has decreased over the last decade as technology has matured and the industry has benefited greatly from economies of scale. That being said, operations and maintenance still make up a significant proportion of the overall costs and needs to be reduced over the coming years as sites, particularly offshore, get larger and more remote. One of the key tools to achieve this is through enhancements of both SCADA and condition monitoring system analytics, leading to more informed and optimised operational decisions. Specifically examining the wind turbine generator and highspeed assembly, this thesis aims to showcase how machine learning techniques can be utilised to enhance vibration spectral analysis and SCADA analysis for early and more automated fault detection. First this will be performed separately based on features extracted from the vibration spectra and performance data in isolation before a framework will be presented to combine data sources to create a single anomaly detection model for early fault diagnosis. Additionally by further utilising vibration based analysis, machine learning techniques and a synchronised database of failures, remaining useful life prediction will also be explored for generator bearing faults, a key component when it comes to increasing wind turbine generator reliability. It will be shown that through early diagnosis and accurate prognosis, component replacements can be planned and optimised before catastrophic failures and large downtimes occur. Moreover, results also indicate that this can have a significant impact on the costs of operation and maintenance over the lifetime of an offshore development

A Review of Classification Problems and Algorithms in Renewable Energy Applications

Classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy (RE) has gained significant attention in the last few years, contributing to the deployment, management and optimization of RE systems. The main objective of this paper is to review the most important classification algorithms applied to RE problems, including both classical and novel algorithms. The paper also provides a comprehensive literature review and discussion on different classification techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in RE systems, power quality disturbance classification and other applications in alternative RE systems. In this way, the paper describes classification techniques and metrics applied to RE problems, thus being useful both for researchers dealing with this kind of problem and for practitioners of the field