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25 pages, 6403 KiB

Open AccessArticle

A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks

by Annalisa Santolamazza, Daniele Dadi and Vito Introna

Energies 2021, 14(7), 1845; https://doi.org/10.3390/en14071845 - 26 Mar 2021

Cited by 53 | Viewed by 5242

Abstract

Wind energy has shown significant growth in terms of installed power in the last decade. However, one of the most critical problems for a wind farm is represented by Operation and Maintenance (O&M) costs, which can represent 20–30% of the total costs related [...] Read more.

Wind energy has shown significant growth in terms of installed power in the last decade. However, one of the most critical problems for a wind farm is represented by Operation and Maintenance (O&M) costs, which can represent 20–30% of the total costs related to power generation. Various monitoring methodologies targeted to the identification of faults, such as vibration analysis or analysis of oils, are often used. However, they have the main disadvantage of involving additional costs as they usually entail the installation of other sensors to provide real-time control of the system. In this paper, we propose a methodology based on machine learning techniques using data from SCADA systems (Supervisory Control and Data Acquisition). Since these systems are generally already implemented on most wind turbines, they provide a large amount of data without requiring extra sensors. In particular, we developed models using Artificial Neural Networks (ANN) to characterize the behavior of some of the main components of the wind turbine, such as gearbox and generator, and predict operating anomalies. The proposed method is tested on real wind turbines in Italy to verify its effectiveness and applicability, and it was demonstrated to be able to provide significant help for the maintenance of a wind farm. Full article

(This article belongs to the Special Issue Future Maintenance Management in Renewable Energies)

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18 pages, 1211 KiB

Open AccessArticle

An Unsupervised Learning Approach to Condition Assessment on a Wound-Rotor Induction Generator

by Elsie Swana and Wesley Doorsamy

Energies 2021, 14(3), 602; https://doi.org/10.3390/en14030602 - 25 Jan 2021

Cited by 17 | Viewed by 2855

Abstract

Accurate online diagnosis of incipient faults and condition assessment on generators is especially challenging to automate through supervised learning techniques, because of data imbalance. Fault-condition training and test data are either not available or are experimentally emulated, and therefore do not precisely account [...] Read more.

Accurate online diagnosis of incipient faults and condition assessment on generators is especially challenging to automate through supervised learning techniques, because of data imbalance. Fault-condition training and test data are either not available or are experimentally emulated, and therefore do not precisely account for all the eventualities and nuances of practical operating conditions. Thus, it would be more convenient to harness the ability of unsupervised learning in these applications. An investigation into the use of unsupervised learning as a means of recognizing incipient fault patterns and assessing the condition of a wound-rotor induction generator is presented. High-dimension clustering is performed using stator and rotor current and voltage signatures measured under healthy and varying fault conditions on an experimental wound-rotor induction generator. An analysis and validation of the clustering results are carried out to determine the performance and suitability of the technique. Results indicate that the presented technique can accurately distinguish the different incipient faults investigated in an unsupervised manner. This research will contribute to the ongoing development of unsupervised learning frameworks in data-driven diagnostic systems for WRIGs and similar electrical machines. Full article

(This article belongs to the Special Issue Future Maintenance Management in Renewable Energies)

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16 pages, 6551 KiB

Open AccessArticle

A Data-Driven Approach to Extend Failure Analysis: A Framework Development and a Case Study on a Hydroelectric Power Plant

by Sara Antomarioni, Marjorie Maria Bellinello, Maurizio Bevilacqua, Filippo Emanuele Ciarapica, Renan Favarão da Silva and Gilberto Francisco Martha de Souza

Energies 2020, 13(23), 6400; https://doi.org/10.3390/en13236400 - 3 Dec 2020

Cited by 10 | Viewed by 2574

Abstract

Power plants are required to supply the electric demand efficiently, and appropriate failure analysis is necessary for ensuring their reliability. This paper proposes a framework to extend the failure analysis: indeed, the outcomes traditionally carried out through techniques such as the Failure Mode [...] Read more.

Power plants are required to supply the electric demand efficiently, and appropriate failure analysis is necessary for ensuring their reliability. This paper proposes a framework to extend the failure analysis: indeed, the outcomes traditionally carried out through techniques such as the Failure Mode and Effects Analysis (FMEA) are elaborated through data-driven methods. In detail, the Association Rule Mining (ARM) is applied in order to define the relationships among failure modes and related characteristics that are likely to occur concurrently. The Social Network Analysis (SNA) is then used to represent and analyze these relationships. The main novelty of this work is represented by support in the maintenance management process based not only on the traditional failure analysis but also on a data-driven approach. Moreover, the visual representation of the results provides valuable support in terms of comprehension of the context to implement appropriate actions. The proposed approach is applied to the case study of a hydroelectric power plant, using real-life data. Full article

(This article belongs to the Special Issue Future Maintenance Management in Renewable Energies)

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23 pages, 7169 KiB

Open AccessArticle

Remote Management Architecture of UAV Fleets for Maintenance, Surveillance, and Security Tasks in Solar Power Plants

by Sergio Bemposta Rosende, Javier Sánchez-Soriano, Carlos Quiterio Gómez Muñoz and Javier Fernández Andrés

Energies 2020, 13(21), 5712; https://doi.org/10.3390/en13215712 - 1 Nov 2020

Cited by 18 | Viewed by 4305

Abstract

This article presents a remote management architecture of an unmanned aerial vehicles (UAVs) fleet to aid in the management of solar power plants and object tracking. The proposed system is a competitive advantage for sola r energy production plants, due to the reduction [...] Read more.

This article presents a remote management architecture of an unmanned aerial vehicles (UAVs) fleet to aid in the management of solar power plants and object tracking. The proposed system is a competitive advantage for sola r energy production plants, due to the reduction in costs for maintenance, surveillance, and security tasks, especially in large solar farms. This new approach consists of creating a hardware and software architecture that allows for performing different tasks automatically, as well as remotely using fleets of UAVs. The entire system, composed of the aircraft, the servers, communication networks, and the processing center, as well as the interfaces for accessing the services via the web, has been designed for this specific purpose. Image processing and automated remote control of the UAV allow generating autonomous missions for the inspection of defects in solar panels, saving costs compared to traditional manual inspection. Another application of this architecture related to security is the detection and tracking of pedestrians and vehicles, both for road safety and for surveillance and security issues of solar plants. The novelty of this system with respect to current systems is summarized in that all the software and hardware elements that allow the inspection of solar panels, surveillance, and people counting, as well as traffic management tasks, have been defined and detailed. The modular system presented allows the exchange of different specific vision modules for each task to be carried out. Finally, unlike other systems, calibrated fixed cameras are used in addition to the cameras embedded in the drones of the fleet, which complement the system with vision algorithms based on deep learning for identification, surveillance, and inspection. Full article

(This article belongs to the Special Issue Future Maintenance Management in Renewable Energies)

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12 pages, 3734 KiB

Open AccessArticle

Investigation of the K-Mg-Ca Sulfate System as Part of Monitoring Problematic Phase Formations in Renewable-Energy Power Plants

by Fiseha Tesfaye, Daniel Lindberg, Mykola Moroz and Leena Hupa

Energies 2020, 13(20), 5366; https://doi.org/10.3390/en13205366 - 15 Oct 2020

Cited by 13 | Viewed by 2804

Abstract

Besides the widely applied hydropower, wind farms and solar energy, biomass and municipal and industrial waste are increasingly becoming important sources of renewable energy. Nevertheless, fouling, slagging and corrosion associated with the combustion processes of these renewable sources are costly and threaten the [...] Read more.

Besides the widely applied hydropower, wind farms and solar energy, biomass and municipal and industrial waste are increasingly becoming important sources of renewable energy. Nevertheless, fouling, slagging and corrosion associated with the combustion processes of these renewable sources are costly and threaten the long-term operation of power plants. During a high-temperature biomass combustion, alkali metals in the biomass fuel and the ash fusion behavior are the two major contributors to slagging. Ash deposits on superheater tubes that reduce thermal efficiency are often composed of complex combinations of sulfates and chlorides of Ca, Mg, Na, and K. However, thermodynamic databases involving all the sulfates and chlorides that would favor a better understanding and control of the problems in combustion processes related to fouling, slagging and corrosion are not complete. In the present work, thermodynamic properties including solubility limits of some phases and phase mixtures in the K₂SO₄-(Mg,Ca)SO₄ system were reviewed and experimentally investigated. Based on the new and revised thermochemical data, binary phase diagrams of the K₂SO₄-CaSO₄ and K₂SO₄-MgSO₄ systems above 400 °C, which are of interest in the combustion processes of renewable-energy power plants, were optimized. Full article

(This article belongs to the Special Issue Future Maintenance Management in Renewable Energies)

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26 pages, 7244 KiB

Open AccessArticle

Renewable Energy Curtailment: Prediction Using a Logic-Based Forecasting Method and Mitigation Measures in Kyushu, Japan

by Alex Bunodiere and Han Soo Lee

Energies 2020, 13(18), 4703; https://doi.org/10.3390/en13184703 - 9 Sep 2020

Cited by 14 | Viewed by 5070

Abstract

High variable renewable energy (VRE) penetration led to the first-ever VRE curtailment in Japan, occurring in Kyushu in October 2018. Since then, there has been an average of 3% solar curtailment, with a peak of 13.7% in April 2019, resulting in approximately ¥9.6 [...] Read more.

High variable renewable energy (VRE) penetration led to the first-ever VRE curtailment in Japan, occurring in Kyushu in October 2018. Since then, there has been an average of 3% solar curtailment, with a peak of 13.7% in April 2019, resulting in approximately ¥9.6 billion of wasted energy. The VRE curtailment is expected to worsen as VRE penetration continues to increase along with nuclear energy increment in line with Japan’s 2030 energy goals. To prevent this curtailment and increase energy stability, a novel, logic-based forecasting method using hourly supply/demand data was developed. Initially, inaccurate results were returned; however, after several rounds of calibration that adjusted the quartile value of the max/min operating windows, the overall accuracy of this method was increased to 97% of real curtailment. This calibrated model was then used to test several curtailment mitigation scenarios. Some scenarios increased curtailment, while the two most successful scenarios, which reduced the installed nuclear capacity either seasonally or totally, limited curtailment by 95% and 97%, respectively. Another scenario with increased grid interconnection between regions reduced curtailment by 79%. Moreover, it would provide other benefits by unifying the national grid thereby increasing disaster resistance, reducing curtailment, improving grid flexibility and allowing for higher VRE penetrations. Currently, the situation is worsening, and some actions are required to reduce the curtailment and to achieve its 2030 energy goals in Japan. The mitigation measures studied by the logic method could be recommended to be referred to. Full article

(This article belongs to the Special Issue Future Maintenance Management in Renewable Energies)

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23 pages, 7984 KiB

Open AccessArticle

Smart Energy Management of Residential Microgrid System by a Novel Hybrid MGWOSCACSA Algorithm

by Bishwajit Dey, Fausto Pedro García Márquez and Sourav Kr. Basak

Energies 2020, 13(13), 3500; https://doi.org/10.3390/en13133500 - 7 Jul 2020

Cited by 43 | Viewed by 3879

Abstract

Optimal scheduling of distributed energy resources (DERs) of a low-voltage utility-connected microgrid system is studied in this paper. DERs include both dispatchable fossil-fueled generators and non-dispatchable renewable energy resources. Various real constraints associated with adjustable loads, charging/discharging limitations of battery, and the start-up/shut-down [...] Read more.

Optimal scheduling of distributed energy resources (DERs) of a low-voltage utility-connected microgrid system is studied in this paper. DERs include both dispatchable fossil-fueled generators and non-dispatchable renewable energy resources. Various real constraints associated with adjustable loads, charging/discharging limitations of battery, and the start-up/shut-down time of the dispatchable DERs are considered during the scheduling process. Adjustable loads are assumed to the residential loads which either operates throughout the day or for a particular period during the day. The impact of these loads on the generation cost of the microgrid system is studied. A novel hybrid approach considers the grey wolf optimizer (GWO), sine cosine algorithm (SCA), and crow search algorithm (CSA) to minimize the overall generation cost of the microgrid system. It has been found that the generation costs rise 50% when the residential loads were included along with the fixed loads. Active participation of the utility incurred 9–17% savings in the system generation cost compared to the cases when the microgrid was operating in islanded mode. Finally, statistical analysis has been employed to validate the proposed hybrid Modified Grey Wolf Optimization-Sine Cosine Algorithm-Crow Search Algorithm (MGWOSCACSA) over other algorithms used. Full article

(This article belongs to the Special Issue Future Maintenance Management in Renewable Energies)

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32 pages, 5500 KiB

Open AccessArticle

A Holistic Framework for Supporting Maintenance and Asset Management Life Cycle Decisions for Power Systems

by Kiki Ayu and Akilu Yunusa-Kaltungo

Energies 2020, 13(8), 1937; https://doi.org/10.3390/en13081937 - 15 Apr 2020

Cited by 28 | Viewed by 4748

Abstract

The outburst of population as well as increasing industrialisation have triggered a very prominent imbalance between electricity demand and supply in emerging economies such as Indonesia. Based on this premise, electricity generation and distribution firms such as Perusahaan Listrik Negara (PLN) are faced [...] Read more.

The outburst of population as well as increasing industrialisation have triggered a very prominent imbalance between electricity demand and supply in emerging economies such as Indonesia. Based on this premise, electricity generation and distribution firms such as Perusahaan Listrik Negara (PLN) are faced with an urgent need to enhance availability and reliability through capacity expansion as well as the institutionalisation of cost-effective maintenance and asset management (MAM) principles. Some of the principles recommended here involve embedding customised overall health index (OHI) and total life cycle cost (LCC) estimation principles into engineering decisions that relate to asset renewal and/or replacement. While discussions about the fundamental theories and estimation approaches for OHI and LCC for power transformers (PTs) already exist in the current body of literature, however, they are mostly in a generic form which has somewhat limited proper implementation of these valuable principles in practice. This study is unique because it provides a very systematic framework towards achieving cost-effective MAM through a case study. Additionally, the proposed framework is all-encompassing, as it also assesses the impacts of human unreliability through the application or proven risk assessment techniques. The proposed framework commences with the evaluation of existing decision support system at PLN through a MAM audit, whereby the performance of the West Java arm of PLN with regards to critical MAM elements was examined. Full article

(This article belongs to the Special Issue Future Maintenance Management in Renewable Energies)

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24 pages, 3016 KiB

Open AccessArticle

Assessment of Barriers to Knowledge and Experience Transfer in Major Maintenance Activities

by Lilian. O. Iheukwumere-Esotu and Akilu Yunusa Kaltungo

Energies 2020, 13(7), 1721; https://doi.org/10.3390/en13071721 - 4 Apr 2020

Cited by 31 | Viewed by 4602

Abstract

Systematic failure analysis generally enhances the ability of engineering decision-makers to obtain a holistic view of the causal relationships that often exist within the systems they manage. Such analyses are made more difficult by uncertainties and organisational complexities associated with critical and inevitable [...] Read more.

Systematic failure analysis generally enhances the ability of engineering decision-makers to obtain a holistic view of the causal relationships that often exist within the systems they manage. Such analyses are made more difficult by uncertainties and organisational complexities associated with critical and inevitable industrial maintenance activities such as major overhauls, outages, shutdowns, and turnarounds (MoOSTs). This is perhaps due to the ratio of tasks-to-duration typically permitted. While core themes of MoOSTs including planning, contracts, costing, execution, etc., have been the focus of most research activities, it is worth noting that the ability to successfully transfer and retain MoOSTs knowledge is still under-investigated. Effectively implementing a case study-based approach for data collection, the current study explores the harmonisation of various risk assessments (i.e., fault tree analysis and reliability block diagrams) and multicriteria decision analysis (MCDA) tools to investigate perceived barriers to MoOSTs knowledge management and experience transfer. The case study selected for this study is a dual process line all-integrated cement manufacturing plant (the largest of such process configuration in its region). The justification for this choice of industry was driven by the volume and frequency of MoOSTs executed each year (typically 4–1 per process line), thereby providing a good opportunity to interact with industrial experts with immense experience in the management/execution of MoOSTs within their industry. A multilayered methodology was adopted for information gathering, whereby baseline knowledge from an earlier conducted systematic review of MoOSTs practices/approaches provided fundamental theoretical trends, which was then complemented by field-based data (from face-to-face interviews, focus group sessions, questionnaires, and secondary information from company MoOSTs documentation). During the analysis, fault tree analysis (FTA) and reliability block diagrams (RBDs) were simultaneously used to generate the causal relationships and criticality that exist between identified barriers, while the MCDA (in this case analytical hierarchy process) was used to identify and prioritise barriers to MoOSTs knowledge management and experience transfer, based on sensitivity analysis and consistency of approach. The primary aim of this study is to logically conceptualise core barriers/limiters to knowledge in temporary industrial project environments such as MoOSTs, as well as enhance the ability of decision-makers to prioritise learning efforts. The results obtained from analysis of data identify three major main criteria (barriers) and 23 subcriteria ranked according to level of importance as indicated from expert opinions. Full article

(This article belongs to the Special Issue Future Maintenance Management in Renewable Energies)

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15 pages, 2548 KiB

Open AccessArticle

A Novel Risk-Based Prioritization Approach for Wireless Sensor Network Deployment in Pipeline Networks

by Xiaojian Yi, Peng Hou and Haiping Dong

Energies 2020, 13(6), 1512; https://doi.org/10.3390/en13061512 - 22 Mar 2020

Viewed by 2542

Abstract

In the face of increased spatial distribution and a limited budget, monitoring critical regions of pipeline network is looked upon as an important part of condition monitoring through wireless sensor networks. To achieve this aim, it is necessary to target critical deployed regions [...] Read more.

In the face of increased spatial distribution and a limited budget, monitoring critical regions of pipeline network is looked upon as an important part of condition monitoring through wireless sensor networks. To achieve this aim, it is necessary to target critical deployed regions rather than the available deployed ones. Unfortunately, the existing approaches face grave challenges due to the vulnerability of identification to human biases and errors. Here, we have proposed a novel approach to determine the criticality of different deployed regions by ranking them based on risk. The probability of occurrence of the failure event in each deployed region is estimated by spatial statistics to measure the uncertainty of risk. The severity of risk consequence is measured for each deployed region based on the total cost caused by failure events. At the same time, hypothesis testing is used before the application of the proposed approach. By validating the availability of the proposed approach, it provides a strong credible basis and the falsifiability for the analytical conclusion. Finally, a case study is used to validate the feasibility of our approach to identify the critical regions. The results of the case study have implications for understanding the spatial heterogeneity of the occurrence of failure in a pipeline network. Meanwhile, the spatial distribution of risk uncertainty is a useful priori knowledge on how to guide the random deployment of wireless sensors, rather than adopting the simple assumption that each sensor has an equal likelihood of being deployed at any location. Full article

(This article belongs to the Special Issue Future Maintenance Management in Renewable Energies)

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Review

Jump to: Research

37 pages, 6168 KiB

Open AccessReview

An Overview on Energy and Development of Energy Integration in Major South Asian Countries: The Building Sector

by Rashiqa Abdul Salam, Khuram Pervez Amber, Naeem Iqbal Ratyal, Mehboob Alam, Naveed Akram, Carlos Quiterio Gómez Muñoz and Fausto Pedro García Márquez

Energies 2020, 13(21), 5776; https://doi.org/10.3390/en13215776 - 4 Nov 2020

Cited by 54 | Viewed by 6040

Abstract

India, Pakistan, and Bangladesh (IPB) are the largest South Asian countries in terms of land area, gross domestic product (GDP), and population. The growth in these countries is impacted by inadequate renewable energy policy and implementation over the years, resulting in slow progress [...] Read more.

India, Pakistan, and Bangladesh (IPB) are the largest South Asian countries in terms of land area, gross domestic product (GDP), and population. The growth in these countries is impacted by inadequate renewable energy policy and implementation over the years, resulting in slow progress towards human development and economic sustainability. These developing countries are blessed with huge potential for renewable energy resources; however, they still heavily rely on fossil fuels (93%). IPB is a major contributor to the total energy consumption of the world and its most energy-intensive building sector (India 47%, Pakistan 55% and Bangladesh 55%) displays inadequate energy performance. This paper comprehensively reviews the energy mix and consumption in IPB with special emphasis on current policies and its impact on economic and human development. The main performance indicators have been critically analyzed for the period 1970–2017. The strength of this paper is a broad overview on energy and development of energy integration in major South Asian countries. Furthermore, it presents a broad deepening on the main sector of energy consumption, i.e., the building sector. The paper also particularly analyzes the existing buildings energy efficiency codes and policies, with specific long-term recommendations to improve average energy consumption per person. The study also examines the technical and regulatory barriers and recommends specific measures to adapt renewable technologies, with special attention to policies affecting energy consumption. The analysis and results are general and can be applied to other developing countries of the world. Full article

(This article belongs to the Special Issue Future Maintenance Management in Renewable Energies)

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