The energy system is in a transformation for a sustainable society. The overall targets are to meet climate goals and to reach energy independence. Wind turbines provide a main solution converting renewable energy resources into electricity with a resulting enormous global growth. A challenge is, however, to reduce the operation and maintenance costs for wind generation to ensure good investments. Asset management (AM) aims to handle assets in an optimal way in order to fulfil an organization’s goal whilst considering risk. This chapter proposes a novel method for AM and preventive maintenance using condition monitoring. The suggested model is an autoencoder-based anomaly detection method for tracking the condition of wind turbines. First, the technique uses supervisory control and data acquisition (SCADA) signals as its data input. The method then analyses the discrepancies between the acquired data from the SCADA system and the estimated values by the autoencoder models. The distribution of the output error is then calculated using the Kernel Density Estimation. Finally, a novel dynamic thresholding approach is employed to effectively extract anomalies in the data. The results show that the approach can identify significant irregularities before a breakdown happens. Additionally, it confirms that the method may notify operators of prospective changes in wind turbines even in the absence of alarm recordings.

This research presents an anomaly detection approach based on autoencoders for wind turbine condition monitoring. The overall goal is to develop a methodology for assessing wind turbine health in order to enable preventative maintenance programs. First, SCADA signals are used as data input in the approach. The approach then examines the differences between the estimated values by the autoencoder models and the measured signals from the SCADA system. Next, the Kernel Density Estimation is used to determine the distribution of the expected output's error. Finally, to efficiently extract anomalous activity in the data, a novel dynamic thresholding approach is used. The outcome reveals that the method is capable of detecting potential abnormalities prior to the onset of a breakdown. It also verifies that the approach can alert operators to potential changes within wind turbines even when the alarm records show no alerts.

With the industrial internet of things and SG technology developing, more and more operation data could be accessible, and condition-based maintenance shows promise for electrical equipment. This paper aims to develop a data-driven fault diagnosis utilizing operation data for high voltage equipment condition monitoring. To understand the asset management of power transformers, an interview is conducted as the expertise input for the study. The paper uses deep learning in an unsupervised way to model normal behaviors and identify underlying operational risks. The autoencoders are used to compress the raw data and extract the key features and the gated recurrent unit to model the dependencies between normal behaviors of power transformers. Finally, the method employs control charts to generate the alarm to indicate the underlying anomalies. The paper uses an online dataset to test the applications for sensor failures. The results show that the method can identify the operational risks before sensor failures.

The global energy system is experiencing a transition to a sustainable system with ambitious targets for increased use of renewable energy. One key trend for this transition has been the large introduction of wind power and integration into the electricity grid. In order to succeed in this transition, there is a need to develop efficient tools to support the handling of the assets. Asset management is a coordinated activity for the organization to get value from an asset. As the main part of asset management, maintenance includes all the technical and corresponding administrative actions to keep or restore the asset to the desired state in which it can perform its required functions. Traditional maintenance is usually based on scheduled monitoring and physical inspections. However, with new access to data and information about condition-based maintenance shows to be an efficient solution for asset management. This thesis explores data-driven solutions for electrical equipment to generate alerts towards potential operation risks, which targets digital, efficient, and cost-effective asset management. Specifically, the thesis investigates wind turbines.

This thesis proposes a fault detection framework for cost-effective preventive maintenance of wind turbines by using condition monitoring systems. The thesis utilizes the data from supervisory control and data acquisition systems as the main input. For log events, each event is mapped to corresponding components based on the Reliawind taxonomy. For operation data, recurrent neural networks are applied to model normal behaviors, which can learn the long-time temporal dependencies between various time series. Based on the estimation results, a two-stage threshold method is proposed as post-processing to determine operation conditions. The method evaluates the shift values deviating from the estimated behaviors and their duration time to attenuate minor fluctuations. A two-level condition monitoring system is constructed to apply the proposed fault detection framework, which targets to detect possible faults of components and conduct performance analysis of turbines. The fault detection framework is tested with the experience data from onshore wind farms. The results demonstrate that the framework can detect operational risks and reduce false alarms.

Energisystem i världen över genomgår en omställning till ett hållbart system med ambitiösa mål för ökad användning av förnybara energikällor. En central trend i denna omställning har varit en markant ökning av elektricitet från vindturbiner och integrering i elnätet. För att lyckas med denna omställning behöver effektiva verktyg utvecklas för förvaltning av tillgångar och underhållsstyrning. Asset Management är en samordnad aktivitet för en organisation för att få värde från en tillgång där underhåll utgör en stor del. Traditionellt underhåll baseras vanligtvis på schemalagd övervakning och fysiska inspektioner. Men med ny åtkomst av data och information om tillgångar tillståndsbaserat underhåll visar sig vara en effektiv lösning för kapitalförvaltning. Denna avhandling undersöker datadrivna lösningar för elektrisk utrustning att generera varnar för potentiella driftsrisker, som är inriktade på digital, effektiv och kostnadseffektiv underhållsstyrning. Specifikt studerar avhandlingen vindkraftverk.

Denna avhandling föreslår ett ramverk för feldetektering för kostnadseffektivt förebyggande underhåll av vindkraftverk med tillståndsövervakning. Avhandlingen använder data från övervakningskontroll och datainsamlingssystem. För logghändelser mappas varje händelse till motsvarande komponenter baserat på Reliawind taxonomin. För driftdata används återkommande neurala nätverk för att modellera normalt beteende, vilka lära sig de tidsmässiga beroendeförhållanden mellan olika tidsserier. Baserat på uppskattade resultat föreslås en tvåstegs tröskelmetod som efterbehandling för att bestämma driftsförhållandena. Metoden utvärderar skiftvärden som avviker från uppskattat beteende och dess varaktighet för att dämpa mindre fluktuationer. Ett tillståndsövervakningssystem med två nivåer är konstruerat för att tillämpa det föreslagna feldetekteringsramverket, som är inriktat på att upptäcka eventuella fel hos komponenter och genomföra prestandaanalys av turbiner. Ramverket för feldetektering testas med erfarenhetsdata från vindkraftparker på land. Resultaten visar att ramverket kan upptäcka operativarisker och minska falska larm.

This paper proposes a fault detection framework for the condition monitoring of wind turbines. The framework models and analyzes the data in supervisory control and data acquisition systems. For log information, each event is mapped to an assembly based on the IEA Reliawind taxonomy. For operation data, recurrent neural networks are applied to model normal behaviors, which can learn the long-time temporal dependencies between various time series. Based on the estimation results, a two-stage threshold method is proposed to determine the current operation status. The method evaluates the shift values deviating from the estimated behaviors and their duration time to attenuate the effect of minor fluctuations. The generated results from the framework can help to understand when the turbine deviates from normal operations. The framework is validated with the data from an onshore wind park. The numerical results show that the framework can detect operational risks and reduce false alarms.

This paper proposes a performance assessment framework to estimate operation status of wind turbines. The overall objective is to propose a method for health assessment to support preventive maintenance strategies for wind turbines. The framework uses the data in the supervisory control and data acquisition systems as input. The framework consists of three main stages: power curve prediction, sliding window method analysis and performance assessment. At the first stage, k-means and density-based clustering are applied to eliminate noisy measurements. Then both parametric and non-parametric methods are applied to estimate the ideal power curve, which is used as a reference value to assess the actual one. At the second stage, the sliding window method is used to calculate the deviation between actual power data and ideal values, which indicates the real time performance of wind turbines. At the third stage, different performance zones are defined to assess health conditions. The proposed approach has been applied with the experience data of six onshore wind turbines from a single wind farm. The results indicate that the introduced framework can monitor the operation conditions and evaluate the performance of wind turbines.

This paper presents an anomaly detection approach using machine learning to achieve condition monitoring for wind turbines. The approach applies the information in supervisory control and data acquisition systems as data input. First, machine learning is used to estimate the temperature signals of the gearbox component. Then the approach analyzes the deviations between the estimated values and the measurements of the signals. Finally, the information of alarm logs is integrated with the previous analysis to determine the operation states of wind turbines. The proposed approach has been tested with the data experience of a 2MW wind turbine in Sweden. The result demonstrates that the approach can detect possible anomalies before the failure occurrence. It also certifies that the approach can remind operators of the possible changes inside wind turbines even when the alarm logs do not report any alarms.

This paper presents an anomaly detection approach using artificial neural networks and the wavelet transform for the condition monitoring of wind turbines. The method aims to attain early anomaly detection and to prevent possible false alarms under healthy operations. In the approach, nonlinear autoregressive neural networks are used to estimate the temperature signals of the gearbox. The Mahalanobis distances are then calculated to measure the deviations between the current states and healthy operations. Next, the wavelet transform is applied to remove noisy signals in the distance values. Finally, the operation information is considered together with the refined distance values to detect potential anomalies. The proposed approach has been tested with the real data of three 2 MW wind turbines in Sweden. The results show that the approach can detect possible anomalies before failure events occur and avoid reporting alarms under healthy operations.

The issue of applying high performance computing (HPC) techniques to computation-intensive probabilistic optimal power flow has not been well discussed in literature. In this paper, the probabilistic convex AC OPF based on second order cone programming (P-SOCPF) is formulated. The application of P-SOCPF is demonstrated by accounting uncertainties of loads. To estimate the distributions of nodal prices calculated from PSOCPF, two point estimation method (2PEM) is deployed. By comparing with Monte Carlo (MC) method, the accuracy of 2PEM is proved numerically. The computation efficiency of 2PEM outperforms MC significantly. In the context of large scale estimation, we propose to apply high performance computing (HPC) to P-SOCPF. The HPC accelerated P-SOCPF is implemented in GAMS grid computing environment. A flexible parallel management algorithm is designed to assign execution threads to different CPUs and then collect completed solutions. Numerical results from IEEE 118-bus and modified 1354pegase case network demonstrate that grid computing is effective means to speed up large scale P-SOCPF computation. The speed up of P-SOCPF computation is approximately equal to the number of cores in the computation node.