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Small modular reactors (SMRs) are currently touted as the next evolution of nuclear energy. They are a type of nuclear reactor designed to be smaller in size than a traditional reactor. Nevertheless, they are based on the same fission technology that splits atoms to create heat, which can then be used to generate electricity. SMRs can offer clean, reliable power and heat at manageable costs for both on- and off-grid communities. Currently, there are more than 100 proposed SMR reactor designs worldwide participating in the race to commercialization. This article presents a comprehensive review of the different SMR technologies that are currently being developed. The technical characteristics of the SMRs have been evaluated to provide insights into their complementary role in a renewable energy system and highlight how they can accelerate the journey toward a deeply decarbonized world., QC 20240524

This paper investigates the droop control techniques used to ascertain the proper sharing of active and reactive power of multiple VSCs in an islanded microgrid. The dynamic model of the system control loops is performed in a dq rotating reference frame. The droop control loop is analyzed, taking into account the inductive nature of the output impedance of the microgrid due to the inductor of the filter and coupling inductor. To minimize the circulating current that flows between parallel VSC, a virtual complex impedance loop is included in the proposed controller. In order to avoid the stability problem occurring at the resonant frequency of the LCL filter, a parallel resistor is added to the LCL filter capacitor. For both equal and proportional power-sharing strategies, the proposed controller can achieve accurate power-sharing with an excellent dynamic performance of the system. The simulation results are presented to reveal the accurate performance of the proposed controller., QC 20240229Part of ISBN: 979-8-3503-9678-2

In the year 2022 Sweden registered close to 5 million vehicles. According to the Power Circle report of 2021, the growth rate that year for the usage of Electric vehicles (EVs) was a steady 82 percent. Sweden is on the brink of an energy crisis, the electricity demand is increasing by the day, and expanding the generation portfolio is a challenging task both logistically and from the perspective of the swiftness with which this energy crisis should be handled. This paper tries to analyze the potential of this Vehicle to Grid(V2G) technology by creating an analytical scenario to quantify the amount of energy that can be injected into the grid if all the power generation is shut down and the country has to survive on the power pushed back by the EVs into the grid assuming that reactive power compensation is present to maintain the stability of the grid. The paper presents analytical calculations on the number of hours the current EV fleet can help sustain the grid in case of a blackout. The results obtained can be utilized for the modeling of emergency plans in grid operation in case of a partial failure., QC 20240229 Part of ISBN 979-8-3503-9678-2

This paper presents a study that focuses on alleviating the impacts of grid outages in Ethiopia. To deal with grid outages, most industrial customers utilize backup diesel generators (DG) which are environmentally unfriendly and economically not viable. Grid integration of hybrid renewable energy systems (HRES) might be a possible solution to enhance grid reliability and reduce environmental and economic impacts of utilizing DG. In this study, an optimization of grid integrated HRES is carried out for different dispatch and control strategies. The optimal power supply option is determined by performing comparative analysis of the different configurations of grid integrated HRES. The result of the study shows that grid integrated HRES consisting of photovoltaic and wind turbine as renewable energy sources, and battery and hydrogen as hybrid energy storage systems is found to be the optimal system to supply the load demand. From the hydrogen produced on-site, the FC generator and FCEVs consume 143 620 kg/yr of hydrogen which is equivalent to 394 955 kg/yr gasoline fuel consumption. This corresponds to saving 1 184 865 kg/yr of CO2 emissions and 605 703 $/yr revenue. Besides, this system yields 547 035.4 $/yr revenue by injecting excess electricity to the grid. The study clearly shows the economic and environmental viability of this new technology for implementation.& COPY; 2023 The Author(s). Published by Elsevier Ltd on behalf of Hydrogen Energy Publications LLC. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/)., QC 20231009

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., QC 20221107Part of proceedings: ISBN 978-1-6654-1211-7

This paper shows the real-time operation model of Energy Management System (EMS) for control of Battery Energy Storage System (BESS). These models are needed to manage uncertain electricity loads, delivery of peak shaving and frequency regulation services and the BESS financial feasibility. The delivery of these services result in economic return for the resource owner and increased grid flexibility. The Energy Management System implemented in this work uses the electricity load and grid frequency data as inputs to generate BESS control commands that deliver the said services. The validation simulation performed on the School of Sinntorp in Sweden returns an annual return on investment of 22% for the BESS and inverter. Implementation of storage systems was shown to expedite the adoption of renewable energy while also solving the challenges that this adoption on scale poses. This active adoption is pivotal in achieving the UN sustainable developmental goals (SDGs) for energy., QC 20221108Part of proceedings: ISBN 978-1-6654-1211-7

Solar energy is considered as one of the most promising solutions to prevent climate change, and optimal utilization of solar energy contributes to reducing dependence on non-renewable energy sources. Distributed generation can help support the delivery of clean and reliable power to consumers. In this paper, an Energy Management System (EMS) is developed along with load and Photovoltaic (PV) generation forecasting to perform peak shaving of electricity demand. The results from the proposed method are implemented in a real case study, in Port of Gavle in Sweden. Total revenue generated from services of peak-shaving and frequency regulation is maximum in the scenario considering high PV generation and high consumption on a selected experimental day in July. The results show that PV-battery storage systems are effective options to increase the integration of renewable energies in the grid, flatten the load, and achieve stabilized operation., QC 20221021Part of proceedings: ISBN 978-166541211-7

Electric power grids, which form an essential part of the critical infrastructure, are evolving into highly distributed, dynamic networks in order to address the climate change. This fundamental transition relies on extensive automation solutions based on communications and information technologies. Thus, it also gives rise to new attack points for malicious actors and consequently, increases the vulnerability of the electric energy system. This study presents a qualitative assessment of power grid cybersecurity through expert interviews across countries in Europe and the U.S. to gain understanding of the latest developments and trends in the cybersecurity of future electric energy systems. The horizon of the assessment is 10 years spanning until the early 2030s. Thereafter, the study identifies how and to which extent the risks identified to be most significant are understood and addressed in the latest research and industry publications aiming at identifying areas deserving specific further attention. The most significant threats based on the assessment are False Data Injection (FDI), Denial of Service (DoS) supply chain, and ransomware and malware attacks., QC 20220531

This paper is about investigating the differences in fuel consumption of conventional and equivalent electricalsubsystems of passenger aircraft. The goal is to develop a framework that can help evaluate fuel consumptionof passenger aircraft, both conventional aircraft and More Electric Aircraft (MEA), for a given size of aircraftand a given stretch of flight.The work presented in this paper originates from pre-studies within MEA research performed by the ReliabilityCentered Asset Management (RCAM) research group at KTH in collaboration with SAAB that has been doneon a passenger aircraft with comparable size to the Airbus A320. The main difference to the prior study isthe addition of subsystem weight, passenger scaling effects, environmental dependencies and flight profile, alladded to increase the accuracy and diversity of the model. The fuel consumption is based on studies of existingtechnology for several passenger aircraft from Airbus and Boeing. The main focus was the EnvironmentalControl System (ECS).A numerical model of passenger aircraft including the ECS was constructed in MATLAB with different levelsof electrification, a conventional ECS or a fully electric ECS. A special case with an Airbus A320 with 180passengers doing a round trip between Copenhagen and Stockholm on a hot day was studied.The results show possible fuel savings in the magnitude of 4% to 8% when electrifying the ECS for the casestudied and for aircraft with 156 to 700 passengers., QC 20230608

In this paper, an Energy Management System (EMS) that manages a Battery Energy Storage System (BESS) is implemented. It performs peak shaving of a local load and provides frequency regulation services using Frequency Containment Reserve (FCR-N) in the Swedish reserve market. The EMS optimizes the approach of BESS resource dispatch distribution between two ancillary services, such that it maximizes profits and resource efficiency while enhancing overall system reliability of the electricity grid. The EMS consists of; a Prognosis Module running a day ahead to allow for identification of intervals for peak shaving and frequency regulation, and a Real-time Operation Module running on the day of operation creating BESS control commands. Implementing the Prognosis module on Öckerö ice-rink results in 12% cost saving on the grid tariff with 21% power peak reduction and three hours of daily frequency reserve. Multiple such systems can be aggregated to improve flexibility of the system., QC 20211209Part of proceeding: ISBN 978-166543597-0

The Introduction of Smart Meters (SMs) is one of the fundamental changes for the intelligent power grid. SMs provide input data from the electricity customers, which might also be a local electricity producer, also called prosumers. This article proposes novel strategies using an energy management system (EMS) to enhance economic value for the prosumers and for the network operators in terms of reliability performance and flexibility of the electricity power grid. These strategies, referred to as behind the meter strategies, could be influenced, e.g., using a battery energy storage system (BESS), plug-in electric vehicles (PEVs), and various alternatives of local electricity generation like solar photovoltaic (PV) or wind power. Using remote sensing and the reading functionality of SMs, the BESS can be intelligently managed by an EMS that uses the BESS resource for multiple ancillary services, thereby generating additional value from the present resource., QC 20210916

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., QC 20181107

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., Conference code: 138773; Export Date: 30 October 2018; Conference Paper; Funding details: CSC, China Scholarship Council; Funding text: This work is financed by Chinese Scholarship Council.QC 20190115

This paper proposes an approach for stress condition monitoring and maintenance assessment in wind turbines(WT) through large amounts of collected data from supervisory control and data acquisition (SCADA) system. Theobjectives of the proposed approach are: to provide a stress condition model for health monitoring, to assess the WT’smaintenance strategies, and to provide recommendations on current maintenance schemes for future operations ofthe wind farm. At first, several statistical techniques, namely Principal component analysis, Pearson, Spearman andKendall correlations, mutual information, regressional ReliefF and decision trees are used and compared to assessthe data for dimensionality reduction and parameter selection. Next, a normal behavior model is constructed by anartificial neural network which performs condition monitoring analysis. Then, a model based on mathematical form ofProportional hazards model is developed where it represents stress condition of the WT. Finally, those two modelsare jointly employed in order to analyze the overall performance of the WT over the study period. Several cases areanalyzed with a five-year SCADA data and maintenance information is utilized to develop and validate the proposedapproach., QC 20170614

This paper presents results from a study on the impact of smart grid solutions, which includes development of a generic framework for power system analyses. The study has been performed as one of several independent studies, part of a national governmental task on smart grid in Sweden. A large amount of weather data, along with electricity consumption and wind power generation data, have been analyzed. Achieved results from these initial analyses can be used as reference material and have also been used within case studies presented. The proposed framework is flexible and numerous combinations of scenarios are possible to define. Integration of wind and solar power, analyses of transfer limits using static or dynamic rating and energy storage can be considered as well as weather effects. Results show how power systems can handle more electricity consumption and generation. The study shows that Smart Grid solutions are beneficial for resource efficient electricity grids. Moreover, different risk levels with respect to increased load can be included. Case study results show that energy storages most of the time will be unused, but that they can be used to increase the system reliability., QC 20170206

In this paper, a data driven framework forperformance and maintenance evaluation (PAME) of windturbines (WT) is proposed. To develop the framework, SCADAdata of WTs are adopted and several parameters are carefullyselected to create a normal behavior model. This model which isbased on Neural Networks estimates operation of WT andaberrations are collected as deviations. Afterwards, in order tocapture patterns of deviations, self-organizing map is applied tocluster the deviations. From investigations on deviations andclustering results, a time-discrete finite state space Markov chainis built for mid-term operation and maintenance evaluation.With the purpose of performance and maintenance assessment,two anomaly indexes are defined and mathematically formulated.Moreover, Production Loss Profit is defined for PreventiveMaintenance efficiency assessment. By comparing the indexescalculated for 9 WTs, current performance and maintenancestrategies can be evaluated, and results demonstrate capabilityand effectiveness of the proposed framework., QC 20161026

This paper proposes an approach for stress condition monitoring and maintenance assessment in wind turbines (WTs) through large amounts of collected data from the supervisory control and data acquisition (SCADA) system. The objectives of the proposed approach are to provide a stress condition model for health monitoring, to assess the WT’s maintenance strategies, and to provide recommendations on current maintenance schemes for future operations of the wind farm. At first, several statistical techniques, namely principal component analysis, Pearson, Spearman and Kendall correlations, mutual information, regressional ReliefF and decision trees are used and compared to assess the data for dimensionality reduction and parameter selection. Next, a normal behavior model is constructed by an artificial neural network which performs condition monitoring analysis. Then, a model based on the mathematical form of a proportional hazards model is developed where it represents the stress condition of the WT. Finally, those two models are jointly employed in order to analyze the overall performance of the WT over the study period. Several cases are analyzed with five-year SCADA data and maintenance information is utilized to develop and validate the proposed approach., QC 20171215

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., QC 20170301

One of the main characteristics of a smart grid is the availability of large volumes of data, for example, gained from sensors. This data can be utilized as a tool to estimate the state of the system as a whole or any component within the system. In order to achieve actionable information from the variety of data that is available from smart grids, it is important to use the correct mathematical and signal processing tools. Furthermore, the future smart grid is expected to have high levels of reliability. This can be achieved by integrating the condition monitoring systems with maintenance management, wherein the focus is shifted from corrective maintenance to predictive condition-based maintenance. This chapter introduces the concept of reliability-centered asset management (RCAM). The RCAM approach provides the possibility of both qualitative and quantitative analysis toward optimal maintenance strategy. Furthermore, various issues with condition monitoring in smart grids have been discussed along with some literature that suggest possible solutions for these issues. Finally, a detailed case study of a data-based condition monitoring method based on artificial neural network is presented to demonstrate one of the many possibilities to use data from various measurement systems to reach actionable decisions., QC 20161213

In this paper, a data driven method for Wind Turbine system level anomaly detection and root sub-component identification is proposed. Supervisory control and data acquisition system (SCADA) data of WT is adopted and several parameters are selected based on physical knowledge in this domain and correlation coefficient analysis to build a normal behavior model. This model which is based on Self-organizing map (SOM) projects higher-dimensional SCADA data into a two-dimension-map. Afterwards, the Euclidean distance based indicator for system level anomalies is defined and a filter is created to screen out suspicious data points based on quantile function. Moreover, a failure data pattern based criterion is created for anomaly detection from system level. In order to track which sub-component should be responsible for an anomaly, a contribution proportion (CP) index is proposed. The method is tested with a two-month SCADA dataset with the measurement interval as 20 seconds. Results demonstrate capability and efficiency of the proposed method., QC 20210923

Analysis of field failure data collected from various wind farm databases indicates that the power converters are among the most critical components in offshore wind turbines, since they suffer from a high failure rate. One efficient approach to enhance the reliability and availability of the wind power systems is through using a redundant converter design, in which a set of power converters is placed together in parallel. The main advantage of a multiple parallel converter system is that the failure of one converter will not necessarily lead to the failure of the entire system. It may however increase the wind turbine’s acquisition cost, volume, and weight. In this paper, we propose an approach of joint redundancy and maintenance strategy optimization for offshore wind power converters, aiming to simultaneously determine the “optimal allocation of redundant converters” and the “optimal threshold number of converters that are allowed to fail before sending a maintenance crew to the offshore platform”. The optimal solution under various system- level constraints (such as reliability, weight, and the available space in nacelle) is derived and the conditions required to make using a redundant system beneficial are discussed. The proposed design is applied to an offshore wind turbine system and its performance is evaluated using a Monte-Carlo simulation technique. Finally, the results are compared with the conventional power converter system and a sensitivity analysis is conducted in order to make the proposed approach applicable for the next generation of wind turbines., QC 20150922

Smart Meters (SMs) offer remote switching functionalities to the Distribution System Operators (DSOs). The DSO can switch the breaker of the SM of any customer remotely whenever needed. The DSOs are currently applying this technique for customers typically one by one when customers are changing their addresses or when contracts are terminated. The breaker functionalities of the SMs could be used for multiple customers, thereby opening up new possibilities to balance electricity consumption and production. How the technology has been functioning in practice has, however, not been fully investigated with regard to multiple SM switching. In this paper, the experience from real tests on simultaneous multiple SM switching is presented. The tests have been conducted to observe the effect of SM switching on power quality (PQ). Power Quality Meters (PQMs) had been used both at the substation as well as the customer level to measure the PQ during SM switching. This paper presents the results by showing current measurements during switching ON and OFF of multiple SMs belonging to each substation., QC 20151211

This paper provides an approach to investigate the impacts of wind power (WP) and demand side management (DSM) on the day-ahead electricity market prices. A market model incorporating demand scheduling has been developed for the study, to assess whether there are any mutual economic benefits between WP and scheduling of electric heating demand. A case study performed on a representative Nordic system with a scenario for future WP development in the Nordic electricity market has been carried out. The study results show that DSM could reduce the revenue for the WP producers, up to 2%. On the other hand, WP could result in reduced heating cost, up to 10%, for the customers. However, with an objective of reducing the total system cost, customers in some areas could experience an increase in electricity price even after providing their flexible demand to the market., QC 20151211

Smart Meters (SMs) are undergoing an increasing deployment all over the world and have been introduced to several regions. The SMs generally have remotely accessible breakers which offer remote switching functionalities to the Distribution System Operators (DSOs). The DSOs have the possibility of using this unique functionality of SMs for various future applications, e.g., switching some loads during peak loads crisis or outage management during natural disasters. Under these conditions, the DSO can disconnect selected customers by using SM switching while continuing power supply to risk customers and emergency service providers. During multiple SM switching, the power quality (PQ) of the connected loads might be affected. This paper presents results on the impact of multiple SM switching at customer level. Data are used from a field test on multiple SM switching from a DSO in Sweden. The number of customers in this test was higher compared to the previous two tests of the work. Moreover, all customers in the selected test area were not switched in this test. Almost half of the customers in the test area were disconnected remotely using SMs while the other half of the customers remained connected with power supply to investigate the impact of SMs switching in a different way. This work observed some transient events during the SMs switching. Voltage quality was stable during the test except some flickering effects beyond the limit., QC 20160203

Global policy targets to reduce greenhouse gas emissions have led to increased interest in plug-in electric vehicles (PEV) and their integration into the electricity network. Existing electricity markets, however, are not well suited to encourage direct participation of flexible demand from small consumers such as PEV owners. The introduction of an aggregator agent with the functions of gathering, aggregating, controlling and representing the energy needs of PEV owners in the electricity market could prove useful in this regard. In this chapter, a mathematical model of PEV aggregator for participation in the day-ahead electricity market is described. The modeling is done by treating each of the individual vehicle batteries as a single large battery. The centralized charging and discharging of this battery is then scheduled based on the traveling needs of the PEV owners determined by an aggregated driving profile and the cumulative electrical energy needs of vehicles over the optimization horizon. Two methods for scheduling PEV demand named as joint scheduling method (JSM) and aggregator scheduling method (ASM) are presented. The two methods are subsequently used to observe the effects of introducing flexible scheduling of PEVs on the day-ahead market price in an IEEE test system and a Nordic test system. Results from the IEEE test system case studies will indicate that the scheduling of PEV energy through direct centralized control at high PEV penetration levels of 50 % or greater could lead to potential lowering of day-ahead market prices as compared to an indirect control method such as the use of fixed period charging. Results from the Nordic test system case study shows that controlled scheduling of PEV demand could lead to only a small increase in day-ahead market price of electricity., QC 20150529

QC 20160923

Electricity grids worldwide are continuing to be severely constrained and stretched to capacity. Certain grid conditions e.g., peak load crisis or loss of generation may lead to severe under frequency situations. Load shedding can then be useful to prevent the national grid from being overwhelmed. Typically, load shedding is done from medium voltage substation level and all customers including emergency service providers under that substation lose power. However, it is beneficial for a society to exclude emergency service providers from load shedding. Smart Meters (SMs) are generally equipped with remotely accessible switch which gives a unique opportunity to exclude prioritized customers during emergency load shedding. Distribution System Operators (DSOs) are currently using remote SM switching typically for one customer at a time. However, the SM switching technique has not been fully investigated with regard to multiple customers. In order to fulfill the gap, this paper presents results obtained from field tests on simultaneous multiple SMs switching with a main focus on the impact of multiple SMs switching on Power Quality (PQ). The results show that impact on PQ might be negligible for small number of SMs switching. Moreover, faster SMs switching and reliable communication system is found as an important prerequisite to use SMs for emergency load shedding., QC 20160907

In this paper, an extended optimal power-flow (OPF) model incorporating a detailed model of a voltage-source con- verter-based–multiterminal high-voltage direct current system (VSC–MTDC) is proposed, hereafter referred to as the mixed ac/dc OPF (M-OPF) model. A cost-benefit analysis approach using the M-OPF model as the calculation engine is proposed to determine the preferred VSC-MTDC alternative to be installed in an existing ac transmission system. In this approach, the op- erational benefits of VSC–MTDC systems are evaluated against their investment costs to derive the benefit-to-cost ratios (BCR) which reflect the cost-effectiveness of the alternatives. A case study has been carried out using a modified Nordic 32-bus system. The results of the study show that VSC–MTDC systems might lead to a reduction in total operation cost, and the reduction of the total system transmission loss depends to a large extent on the VSC–MTDC configuration. The results from sensitivity analyses show that if the VSC loss could be reduced to a third of the original level, the total benefit from the system would be increased by about 70%. A suggestion for the placement and configuration of a VSC–MTDC system is made based on calculated BCRs., QC 20150923

Maintenance costs for wind power plants are a significant part of the total life cycle cost, especially for offshore wind power plants, situated at remote sites. In order to decrease the cost of maintenance, monitoring systems have been used to estimate the condition of critical components in wind turbines. This paper proposes Life Cycle Cost analysis (LCC) approach for maintenance management of wind turbines. The LCC approach for maintenance management presented in this paper is an extension on previous work by J. Nilsson and L. Bertling, where a comparison has been made with this previous work and the same is extended with new data and models. Case studies are presented based on data from three different wind turbinesrated 3 and 6MW. Three different scenarios have been studied and the effect of condition monitoring system (CMS) has been analysed. For any chosen value the CMS proves to be a profitable option., QC 20140828

The development of a Smart Grid in Europe has a strong link to the climate goals for sustainable energy systems. Europe has been at the forefront of smart grid deployments, especially in the areas of managing large penetrations of renewable sources of energy, advanced metering infrastructure (AMI) and on advanced information technology., The article is Part of the IEEE Smart Grid Newsletter - August 2014QC 20140828

Asset management of wind turbines has gained increased importance in recent years. High maintenance cost and longer downtimes of wind turbines have led to research in methods to optimize maintenance activities. Condition monitoring systems have proven to be a useful tool towards aiding maintenance management of wind turbines. Methods using Supervisory Control and Data Acquisition (SCADA) system along with artificial intelligence (AI) methods have been developed to monitor the condition of wind turbine components. Various researchers have presented different artificial neural network (ANN) based models for condition monitoring of components in a wind turbine. This paper presents an application of the approach to decide and update the training data set needed to create an accurate ANN model. A case study with SCADA data from a real wind turbine has been presented. The results show that due to a major maintenance activity, like replacement of component, the ANN model has to be re-trained. The results show that application of the proposed approach makes it possible to update and re-train the ANN model., QC 20140901