Topic Editors

Department of Computer Science and Software Engineering, Penn State Erie, The Behrend College, Erie, PA 16563, USA
1. Engineering College, University of South Australia, Adelaide, SA 5000, Australia
2. School of Energy and Power Engineering, Changsha University of Science and Technology, Changsha 410114, China
Dr. Lida Liao
School of Energy and Power Engineering, Changsha University of Science and Technology, Changsha 410114, China

Energy Equipment and Condition Monitoring

Abstract submission deadline
closed (15 June 2023)
Manuscript submission deadline
closed (15 September 2023)
Viewed by
21496

Topic Information

Dear Colleagues,

This topic has been developed to provide networking and collaborating opportunities for practitioners in the fields of energy equipment, fault diagnosis, status detection, as well as associated artificial intelligence (AI) applications. It is expected to engage the international participation of academics, researchers, and professional practitioners to share knowledge and experience from the frontiers of research and practices. The sharing of ideas could bridge gaps and enrich understanding from a systemic cross-disciplinary perspective. In this topic, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: 

  • Small wind turbines;
  • Small solar generation equipment;
  • Innovative wind turbine aerodynamic and structural designs;
  • Innovative solar generation equipment structural designs;
  • Hyperspectral imagery on solar panels or photovoltaic panels;
  • Hyperspectral imagery on wind energy;
  • The development of advanced measurement systems;
  • Improved numerical prediction tools for wind energy analysis;
  • Improved wind turbine maintenance, scheduling, lifetime assessment and health monitoring. We look forward to receiving your contributions.

Dr. Zhifeng Xiao
Dr. Bin Huang
Dr. Lida Liao
Topic Editors

Keywords

  • renewable energy
  • wind turbine
  • solar/photovoltaic
  • mechanical systems
  • health monitoring and lifetime assessment
  • image recognition
  • pattern recognition
  • fault diagnosis

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Machines
machines
2.6 2.1 2013 15.6 Days CHF 2400
Micromachines
micromachines
3.4 4.7 2010 16.1 Days CHF 2600
Remote Sensing
remotesensing
5.0 7.9 2009 23 Days CHF 2700
Sustainability
sustainability
3.9 5.8 2009 18.8 Days CHF 2400
Symmetry
symmetry
2.7 4.9 2009 16.2 Days CHF 2400
Wind
wind
- - 2021 24.8 Days CHF 1000

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Published Papers (9 papers)

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24 pages, 9915 KiB  
Article
Analysis of the Influence of Downhole Drill String Vibration on Wellbore Stability
by Yonggang Shan, Qilong Xue, Jin Wang, Yafeng Li and Chong Wang
Machines 2023, 11(7), 762; https://doi.org/10.3390/machines11070762 - 22 Jul 2023
Cited by 3 | Viewed by 1384
Abstract
Most studies related to aspects of wellbore stability, such as wellbore breakage, block dropping, and wellbore expansion, revolve around the physicochemical interaction between drilling fluid and surrounding rock, but relevant studies show that drill string vibration during drilling also has a crucial and [...] Read more.
Most studies related to aspects of wellbore stability, such as wellbore breakage, block dropping, and wellbore expansion, revolve around the physicochemical interaction between drilling fluid and surrounding rock, but relevant studies show that drill string vibration during drilling also has a crucial and even decisive influence on wellbore stability. In order to thoroughly explore the influence mechanism of drill string vibration on wellbore stability, our research group established a finite element flexible simulation model of drill string dynamics and used a storage downhole vibration measurement device to collect downhole real drilling vibration data to verify the correctness of the simulation model. Then, based on the critical conditions of wellbore breakage, a wellbore stability evaluation method was established, and the wellbore stability under different drilling parameters and drilling tool combination conditions was evaluated and analyzed. The research results play an important role in revealing the influence mechanism of drill string vibration on wellbore stability and can provide theoretical guidance for engineering problems such as wellbore instability risk assessment. Full article
(This article belongs to the Topic Energy Equipment and Condition Monitoring)
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23 pages, 11065 KiB  
Article
New Two-BWT Blade Aerodynamic Design and CFD Simulation
by Guo Li, Juchuan Dai, Fan Zhang and Chengming Zuo
Machines 2023, 11(3), 399; https://doi.org/10.3390/machines11030399 - 19 Mar 2023
Cited by 3 | Viewed by 1522
Abstract
Due to reduced manufacturing, transportation, and installation costs, the two-blade wind turbines (Two-BWT) are a viable option for offshore wind farms. So far, there is no mature design model for offshore Two-BWT. This paper proposes an aerodynamic design method for offshore Two-BWT blades [...] Read more.
Due to reduced manufacturing, transportation, and installation costs, the two-blade wind turbines (Two-BWT) are a viable option for offshore wind farms. So far, there is no mature design model for offshore Two-BWT. This paper proposes an aerodynamic design method for offshore Two-BWT blades using the blade element momentum (BEM) theory. This method calculates the power coefficient of the Two-BWT by analogy with the three-blade wind turbines (Three-BWT), and then determines the wind rotor diameter. Then, the airfoil, chord length, and twist angle are taken as the key design factors. Furthermore, the piecewise combination method (PCM) for airfoil distribution, the three-point sine method (Three-PSM) for chord length distribution, and the two-point sine method (Two-PSM) for torsion angle distribution are adopted, respectively. Subsequently, the minimum rotational speed, under the rated wind speed and rated power, is taken as the optimization objective to establish the optimization model. The global flow field of Two-BWT is constructed based on CFD technology, and the characteristics of wind speed distribution and blade pressure distribution in the flow field are investigated. Finally, the CFD results are compared with the results of the BEM theory, and the consistency of the results also shows the feasibility of the design method. Full article
(This article belongs to the Topic Energy Equipment and Condition Monitoring)
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13 pages, 1031 KiB  
Article
Switching Kalman Filtering-Based Corrosion Detection and Prognostics for Offshore Wind-Turbine Structures
by Robert Brijder, Stijn Helsen and Agusmian Partogi Ompusunggu
Wind 2023, 3(1), 1-13; https://doi.org/10.3390/wind3010001 - 5 Jan 2023
Viewed by 1546
Abstract
Since manual inspections of offshore wind turbines are costly, there is a need for remote monitoring of their health condition, including health prognostics. In this paper, we focus on corrosion detection and corrosion prognosis since corrosion is a major failure mode of offshore [...] Read more.
Since manual inspections of offshore wind turbines are costly, there is a need for remote monitoring of their health condition, including health prognostics. In this paper, we focus on corrosion detection and corrosion prognosis since corrosion is a major failure mode of offshore wind turbine structures. In particular, we propose an algorithm for corrosion detection and three algorithms for corrosion prognosis by using Bayesian filtering approaches, and quantitatively compare their accuracy against synthetic datasets having characteristics typical for wall thickness measurements using ultrasound sensors. We found that a corrosion prognosis algorithm based on the Pourbaix corrosion model using unscented Kalman filtering outperforms the algorithms based on a linear corrosion model and the bimodal corrosion model introduced by Melchers. Full article
(This article belongs to the Topic Energy Equipment and Condition Monitoring)
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17 pages, 1317 KiB  
Article
Pre-Charge Pressure Estimation of a Hydraulic Accumulator Using Surface Temperature Measurements
by Magnus F. Asmussen, Jesper Liniger, Nariman Sepehri and Henrik C. Pedersen
Wind 2022, 2(4), 784-800; https://doi.org/10.3390/wind2040041 - 13 Dec 2022
Cited by 1 | Viewed by 2586
Abstract
Pitch systems form an essential part of today’s wind turbines; they are used for power regulation and serve as part of a turbine’s safety system. Hydraulic pitch systems include hydraulic accumulators, which comprise a crucial part of the safety system, as they are [...] Read more.
Pitch systems form an essential part of today’s wind turbines; they are used for power regulation and serve as part of a turbine’s safety system. Hydraulic pitch systems include hydraulic accumulators, which comprise a crucial part of the safety system, as they are used to store energy for emergency shutdowns. However, accumulators may be subject to gas leakage, which is the primary failure mode. Gas leakage affects the performance of the accumulator and, in extreme cases, compromises the safety function of the pitch system. This paper deals with the development and experimental validation of an algorithm to detect gas leakage in piston-type accumulators. The innovation of the algorithm is the ability to generate estimates of the remaining amount of gas while solving the drift problem evidenced in previous research. Additionally, this method enables the ability to isolate gas leakage to a single accumulator out of a bank of accumulators. The approach is based on a State Augmented Extended Kalman Filter (SAEKF), which utilizes an extended thermal model of the accumulator, as well as temperature measurements along the accumulator surface to estimate the remaining gas in the accumulator. The method is experimentally validated and addresses the drift problem in estimating the gas leakage evidenced from previous research. Additionally, the method can identify and isolate gas leakage to a single accumulator from a bank of accumulators. Full article
(This article belongs to the Topic Energy Equipment and Condition Monitoring)
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20 pages, 3646 KiB  
Article
Reliable Design and Control Implementation of Parallel DC/DC Converter for High Power Charging System
by Qing Zhou, Yuelei Xu, Jarhinbek Rasol, Tian Hui, Chaofeng Yuan and Fan Li
Machines 2022, 10(12), 1162; https://doi.org/10.3390/machines10121162 - 4 Dec 2022
Cited by 3 | Viewed by 1419
Abstract
With the current popularity of Electric Vehicles (EV), especially in some critical EV applications such as hospital EV fleets, the demand for continuous and reliable power supply is increasing. However, most of the charging stations are powered in a centralized way, which causes [...] Read more.
With the current popularity of Electric Vehicles (EV), especially in some critical EV applications such as hospital EV fleets, the demand for continuous and reliable power supply is increasing. However, most of the charging stations are powered in a centralized way, which causes transistors and other components to be subjected to high voltage and current stresses that reduce reliability, and a single point of failure can cause the entire system to fail. Therefore, a significant effort is made in this paper to improve the reliability of the charging system. In terms of charging system structure design, a distributed charging structure with fault tolerance is designed and a mathematical model to evaluate the reliability of the structure is proposed. In terms of control, a current sharing control algorithm is designed that can achieve fault tolerance. To further improve the reliability of the system, a thermal sharing control method based on current sharing technology is also designed. This method can improve the reliability of the charging system by distributing the load more rationally according to the differences in component performance and operating environment; FPGA-based control techniques are provided, and innovative ideas of pipeline control and details of mathematical reasoning for key IP cores are presented. Experiments show that N + 1 redundancy fault tolerance can be achieved in both current sharing and thermal sharing modes. In the current sharing experiment, when module 3 failed, the total current only fluctuated 800 mA within 500 ms, which is satisfactory. In the thermal sharing experiment, after module 3 failed, modules 1, 2, and 4 adjusted the current reasonably under the correction of the thermal sharing loop, and the total current remained stable throughout the process. The experimental results prove that the charging system structure design and control method proposed in this paper are feasible and excellent. Full article
(This article belongs to the Topic Energy Equipment and Condition Monitoring)
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19 pages, 1122 KiB  
Article
Detection, Prognosis and Decision Support Tool for Offshore Wind Turbine Structures
by Sandra Vásquez, Joachim Verhelst, Robert Brijder and Agusmian Partogi Ompusunggu
Wind 2022, 2(4), 747-765; https://doi.org/10.3390/wind2040039 - 24 Nov 2022
Cited by 2 | Viewed by 1680
Abstract
Corrosion is the leading cause of failure for Offshore Wind Turbine (OWT) structures and it is characterized by a low probability of detection. With focus on uniform corrosion, we propose a corrosion detection and prognosis system coupled with a Decision Support Tool (DST) [...] Read more.
Corrosion is the leading cause of failure for Offshore Wind Turbine (OWT) structures and it is characterized by a low probability of detection. With focus on uniform corrosion, we propose a corrosion detection and prognosis system coupled with a Decision Support Tool (DST) and a Graphical User Interface (GUI). By considering wall thickness measurements at different critical points along the wind turbine tower, the proposed corrosion detection and prognosis system—based on Kalman filtering, empirical corrosion models and reliability theory—estimates the Remaining Useful Life of the structure with regard to uniform corrosion. The DST provides a systematic approach for evaluating the results of the prognosis module together with economical information, to assess the different possible actions and their optimal timing. Focus is placed on the optimization of the decommissioning time of OWTs. The case of decommissioning is relevant as corrosion—especially in the splash zone of the tower—makes maintenance difficult and very costly, and corrosion inevitably leads to the end of life of the OWT structure. The proposed algorithms are illustrated with examples. The custom GUI facilitates the interpretation of results of the prognosis module and the economical optimization, and the interaction with the user for setting the different parameters and costs involved. Full article
(This article belongs to the Topic Energy Equipment and Condition Monitoring)
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15 pages, 5908 KiB  
Article
Improving Energy Management through Demand Response Programs for Low-Rise University Buildings
by Akeratana Noppakant and Boonyang Plangklang
Sustainability 2022, 14(21), 14233; https://doi.org/10.3390/su142114233 - 31 Oct 2022
Cited by 3 | Viewed by 1653
Abstract
Recently, energy costs have increased significantly, and energy savings have become more important, leading to the use of different patterns to align with the characteristics of demand-side load. This paper focused on the energy management of low-rise university buildings, examining the demand response [...] Read more.
Recently, energy costs have increased significantly, and energy savings have become more important, leading to the use of different patterns to align with the characteristics of demand-side load. This paper focused on the energy management of low-rise university buildings, examining the demand response related to air conditioning and lighting by measuring the main parameters and characteristics and collecting and managing the data from these parameters and characteristics. This system seeks to control and communicate with the aim of reducing the amount of peak energy using a digital power meter installed inside the main distribution unit, with an RS-485 communication port connected to a data converter and then displayed on a computer screen. The demand response and time response were managed by power management software and an optimization model control algorithm based on using a split type of air conditioning unit. This unit had the highest energy consumption in the building as it works to provide a comfortable environment based on the temperatures inside and outside the building. There was a renewable energy source that compensated for energy usage to decrease the peak load curve when the demand was highest, mostly during business hours. An external power source providing 20 kWh of solar power was connected to an inverter and feeds power into each phase of the main distribution. This was controlled by an energy power management program using a demand response algorithm. After applying real-time intelligent control demand-side management, the efficient system presented in this research could generate energy savings of 25% based on AC control of the lighting system. A comparison of the key system parameters shows the decrease in power energy due to the use of renewable energy and the room temperature control using a combination of split-type air conditioning. Full article
(This article belongs to the Topic Energy Equipment and Condition Monitoring)
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21 pages, 3703 KiB  
Review
Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review
by Dawei Zhang, Chen Zhong, Peijuan Xu and Yiyang Tian
Machines 2022, 10(10), 912; https://doi.org/10.3390/machines10100912 - 9 Oct 2022
Cited by 26 | Viewed by 5179
Abstract
As one of the critical state parameters of the battery management system, the state of charge (SOC) of lithium batteries can provide an essential reference for battery safety management, charge/discharge control, and the energy management of electric vehicles (EVs). To analyze the application [...] Read more.
As one of the critical state parameters of the battery management system, the state of charge (SOC) of lithium batteries can provide an essential reference for battery safety management, charge/discharge control, and the energy management of electric vehicles (EVs). To analyze the application of deep learning in electric vehicles’ power battery SOC estimation, this study reviewed the technical process, common public datasets, and the neural networks used, as well as the structural characteristics and advantages and disadvantages of lithium battery SOC estimation in deep learning methods. First, the specific technical processes of the deep learning method for SOC estimation were analyzed, including data collection, data preprocessing, feature engineering, model training, and model evaluation. Second, the current commonly and publicly used lithium battery dataset was summarized. Then, the input variables, data sets, errors, and advantages and disadvantages of three types of deep learning methods were obtained using the structure of the neural network used for training as the classification criterion; further, the selection of the deep learning structure for SOC estimation was discussed. Finally, the challenges and future development directions of lithium battery SOC estimation using the deep learning method were explained. Over all, this review provides insights into deep learning for EVs’ Li-ion battery SOC estimation in the future. Full article
(This article belongs to the Topic Energy Equipment and Condition Monitoring)
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17 pages, 4646 KiB  
Article
State of Charge Estimation of Lithium-Ion Batteries Using Stacked Encoder–Decoder Bi-Directional LSTM for EV and HEV Applications
by Pranaya K. Terala, Ayodeji S. Ogundana, Simon Y. Foo, Migara Y. Amarasinghe and Huanyu Zang
Micromachines 2022, 13(9), 1397; https://doi.org/10.3390/mi13091397 - 26 Aug 2022
Cited by 9 | Viewed by 1787
Abstract
Energy storage technologies are being used excessively in industrial applications and in automobiles. Battery state of charge (SOC) is an important metric to be monitored in these applications to ensure proper and safe functionality. Since SOC cannot be measured directly, this paper puts [...] Read more.
Energy storage technologies are being used excessively in industrial applications and in automobiles. Battery state of charge (SOC) is an important metric to be monitored in these applications to ensure proper and safe functionality. Since SOC cannot be measured directly, this paper puts forth a novel machine learning architecture to improve on the existing methods of SOC estimation. This method consists of using combined stacked bi-directional LSTM and encoder–decoder bi-directional long short-term memory architecture. This architecture henceforth represented as SED is implemented to overcome the nonparallel functionality observed in traditional RNN algorithms. Estimations were made utilizing different open-source datasets such as urban dynamometer driving schedule (UDDS), highway fuel efficiency test (HWFET), LA92 and US06. The least Mean Absolute Error observed was 0.62% at 25 °C for the HWFET condition, which confirms the good functionality of the proposed architecture. Full article
(This article belongs to the Topic Energy Equipment and Condition Monitoring)
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