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IoT Applications in Wind Energy Conversion Systems

  • Lina Alhmoud EMAIL logo and Hussein Al-Zoubi
Published/Copyright: November 2, 2019
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Open Engineering
From the journal Open Engineering Volume 9 Issue 1

Abstract

Renewable energy reliability has been the main agenda nowadays, where the internet of things (IoT) is a crucial research direction with a lot of opportunities for improvement and challenging work. Data obtained from IoT is converted into actionable information to improve wind turbine performance, driving wind energy cost down and reducing risk. However, the implementation in IoT is a challenging task because the wind turbine system level and component level need real-time control. So, this paper is dedicated to investigating wind resource assessment and lifetime estimation of wind power modules using IoT. To illustrate this issue, a model is built with sub-models of an aerodynamic rotor connected directly to a multi-pole variable speed permanent magnet synchronous generator (PMSG) with variable speed control, pitch angle control and full-scale converter connected to the grid. Besides, a large number of various sensors for measurement of wind parameters are integrated with IoT. Simulations are constructed with Matlab/Simulink and IoT ’Thingspeak’ Mathworks web service. IoT has proved to increase the reliability of measurement strategies, monitoring accuracy, and quality assurance.

Keywords: Internet of things; Reliability; Sensors; Wind energy; Wind speed

1 Introduction

The Internet is a gigantic intercontinental network of networks. The nodes that comprise each one of these networks were classically used to be computers. Now, with the advent of the Internet of things (IoT), things of the IoT encompass any physical entity on earth. Therefore, the Internet is no longer a massive network of computers; but rather the Internet now interconnects heterogenous devices with the required interoperability. This makes these devices (or things) accessible from anywhere on the planet. The user can enjoy the services of these things remotely. Moreover, the operation of these devices can now be controlled remotely via the Internet. It is expected that in the near future, IoT will be so pervasive to cover every aspect of the human’s life including renewable energy generation and management.

A distinction between IoT and Cyber-Physical Systems (CPS) has been emphasized though by Lin et al. in [1]. According to Lin et al. [1], IoT is a horizontal architecture providing a networking infrastructure with the main job being interconnection. On the other hand, the CPS is a system consisting of three layers: the Sensor/Actuator layer at the bottom, the Communication Layer as the middle layer, and the application layer at the top. CPS integrates control centers, communication networks, sensors and actuators with the physical components [1].

The things in the IoT generate innumerable amounts of data. The nature of these data is in general unstructured data that need further processing painstakingly. Cloud services, provided by data centers, could be exploited to process such big data [1]. However, this imposes several challenges in case IoT and cloud computing are coupled directly to each other. An exigency shows up to provide a seamless integration, and here comes the role of fog (aka edge) computing. The main purpose of the fog is to move the burden of dealing with vast amounts of data from the cloud to the edge of the IoT, near the end devices. In addition, fog computing is hoped to offer ameliorated security and privacy.

The authors of [2] recounted the hitherto-known software and hardware IoT platforms and compared and contrasted between the various alternatives. The surveyed hardware items included SparkFun Blynk Board, Arduino Yun, Raspberry Pi 3 Board, cloudBit platform, Particle Photon, BeagleBone Black, Pinoccio, UDOO QUAD, and Samsung Artik 10. The basis that one should follow when choosing the right hardware is according to the available specifications, open API, and open hardware. The compared IoT software platforms encompassed Temboo, Kaa, Carriots, Cayenne, Ubidots, ThingSpeak, Leylan, the ThingBox, Smartliving, Artik Cloud, and Wyliodrin. The choice between the various software platforms should be based on programming IDE, available API, data management tools, and pricing [2].

It is meaningful to investigate wind energy conversion system (WECS) by digital solutions and cybersecurity. In digital renewable energy, data is collected and turned into action by condition monitoring process using machine learning [3]. Cyber-security and their impacts on overall WECS reliability is investigated [4, 5]. Wind turbine state of the art is analysed using big data for an accurate decisionmaking [6].

To the best of our knowledge, this is the first attempt to employ IoT to enhance the reliability of producing renewable energy from wind turbines. The remainder of this paper is organized as follows. Section 2 presents wind turbine reliability using IoT, followed by siting of wind resource assessment using IoT in Section 3. Reliability calculations and lifetime estimations are addressed in Section 4. This paper finishes with concluding remarks in Section 5.

2 Wind turbine reliability using IOT

At the close of 2016, the global cumulative installed wind turbines has reached around 487 GW as shown in Figure 1. USA is considered as the second largest market in terms of total installed capacity after China as shown in Figure 2. IoT has been extended to support applications in energy and power systems including monitoring and control of wind turbines and photovoltaic installations, among others [8]. The purpose of this research is to provide a grander reliability to the operation of wind turbines, as individuals, and to make IoT part of wind farms. Among the aforementioned software alternatives, we have chosen to work with ThinkSpeak in the first phase of our proposed work. ThingSpeak supports Arduino, Particle Photon and Electron, Raspberry Pi, and ESP8266Wifi Module. ThingSpeak can also update a Twitter status through the ThingTweet App [9].

Figure 1 Global Cumulative Installed Wind Capacity 2001-2016 in MW [7].
Figure 1

Global Cumulative Installed Wind Capacity 2001-2016 in MW [7].

Figure 2 Top 10 new installed capacity Jan-Dec 2016 [7].
Figure 2

Top 10 new installed capacity Jan-Dec 2016 [7].

To provide a real-time monitoring of wind turbine farms and to collect data wirelessly, each turbine within the array can be equipped with its own IoT device [10]. These IoT platforms could be used to report the measured voltage output to develop a power output model of the wind turbine, or what is called the power curve, in addition to the energy analysis for predicting the battery lifetime of the IoT nodes. With the help of IoT, various topics related to wind power generation could be tailored like the interactions across the harmonious wind turbines consituiting the wind farm, exploring various downwind topologies, as well as gingerly ameliorating yaw angles and pitch angles in a periodic fashion. For the purpose of sychronization and archistration between the IoT nodes of the wind farm, the Network Time Protocol (NTP) could be employed. The NTP runs at both the IoT nodes, as well as the web server [10].

Reliability of wind turbines supports a sustainable operation of such an important power source. This mandates real-time monitoring of the wind turbine condition [11] to reduce human intervention as much as possible. To provide an online monitoring and control of the operation of the sensors and actuators of the wind turbine, the IoT provides a promising solution. The proposed system utilizes IoT as shown in Figure 3.

Figure 3 IoT features.
Figure 3

IoT features.

There are several kits that can be utilized to interface wind turbine sensors and actuators at the side of the wind turbine and the Internet and the cloud from the other side. As mentioned earlier, examples include Beagle Bone, Rasberry Pie,... These provide wireless communications with the Internet. The proposed model to utilize IoT in wind energy generation for reliability enhancement is shown in Figure 4. Each wind turbine in the wind farm is equipped with an IoT platform, as the Particle Electron, a cellularconnected microcontroller, that can be directly connected to a cellular network (the operator). The Particle Electron complies with any cellular standard [12]. Then, the wind data is saved and processed on the cloud using ThingS-peak [9]. the wind data can be accessed from any where in the world via, say a regular computer or a smart mobile phone, where commands can be directed to the wind farm.

Figure 4 The proposed model to utilize IoT in wind energy generation.
Figure 4

The proposed model to utilize IoT in wind energy generation.

3 Siting of Wind Resource Assessment Using IoT

IoT plays a significant role in selecting the most suitable location for installing wind turbines. Meteorological sensors will respond to real-time weather conditions, and are designed to monitor specific environmental parameters such as wind speed, wind direction and ambient direction, which are essential for reliability studies as a graphical representation with time. The core of wind turbine reliability assessment depends on the accuracy of wind mission profile measurements [13]. Through IoT, automated historical data besides daily work reports can be obtained to enhance long-term reliability, optimize operations in long-term and increase annual energy production. Future wind turbines will be self organized owe to sensors and implementation control strategies using IoT, so they can optimize themselves at any point with any given condition thrown on them. The best wind resource is obtained when the weather is cold, but icing on the blades of wind turbines can reduce energy production by up to 20% [14]. In addition, it may cause spinning on the nacelle leading to mechanical fatigue which causes shaft cracks, tooth breakage, shattering, damage to the tower and reduction in reliability [15]. These undesirable events lead to smart de-icing systems to take place in freezing weather, hence IoT combines sensors and database. In case the output energy is too low, the more reliable decision will be made by IoT to continue running or shut down and throw the ice around using a heating system and an additional small fan built on the blades. Other techniques used to keep the turbine rotor running smoothly and to prevent ice from gradually increasing include acceleration or deceleration of the rotating blades to shake them, or using water resistant coatings.

Investigating the availability of wind resources is important for wind turbine installation and for predicting performance in order to quantify the overall reliability, performance and wind turbine energy output. Wind dialogue gives us an accurate, real time view of the operational status of our input wind mission profile. In addition to that it allows quick access to wind parameters reports. Wind turbines can be monitored through sensors to track air flow and mechanical power that will be extracted from wind. Where sensors highness, location, accuracy, duration, quality and quantity should be considered. Moreover, better IoT performance depends on trusted and identified measures including data sampling, recording, handling, recovery, quality, quantity and format, which should all be taken into account. We have carried out these steps using IoT to maintain reliability performance within a scheduled plan [16]. As sensor technology advances, it becomes possible to monitor wind turbines on wind farms, ensuring maximization of air flow and mechanical power. Wind turbines are highly optimized to their future needs through continuous, smooth collection of high quality data, which is measured using the proposed model in terms of representativeness, accuracy, and completeness. Using IoT, the operator can compare between ideal and actual performance of technical issues, and identify the root causes of the two cases to recommend individual and sustainable solutions for these technical issues of wind turbines by predicting components failures, and optimize maintenance schedules, which will drive down the cost of operating wind turbines.

Many important indicators of a wind farm operating environment such as wind speed and monthly average wind speed are plotted by IoT as shown in Figures 5 and 6. In addition, wind rose is an important graphical representation that gives information about how wind speed and direction are typically distributed as shown in Figure 7.Wind speed distribution shows a frequency of winds over time as shown in Figure 8. In wind turbine siting, the turbulence intensity (TI) indicator is a very important factor. TI normalizes the standard deviation (σ) of wind speed with the mean wind speed as shown in Figures 9 and 10. TI is defined as

Figure 5 Wind speed using IoT.
Figure 5

Wind speed using IoT.

Figure 6 Monthly average wind speed using IoT.
Figure 6

Monthly average wind speed using IoT.

Figure 7 Wind rose
Figure 7

Wind rose

Figure 8 Wind speed distribution
Figure 8

Wind speed distribution

Figure 9 Turbulence intensity using IoT.
Figure 9

Turbulence intensity using IoT.

Figure 10 Turbulence intensity
Figure 10

Turbulence intensity

(1)TI=σV

where σ is the standard deviation of wind speed; and V is the mean wind speed. When TI < 0.1 is considered as a low level, 0.1 < TI < 0.25 it is a moderate level of turbulence and TI > 0.25 is a high level.

4 Reliability Assessment

4.1 Model Integration

Similar to our previous work [16], the prediction of power cycling lifetime for machine side converter control in PMSG is examined. To handle this issue, A PMSG with a drive train model as shown in Figure 14 connected to a control strategy for the turbine pitch angle β as shown in Figure 15 are considered. A comprehensive thermal model for the power IGBT modules used in three-phase converter has been built to predict the dynamic junction temperature rise. To perform a real and complete thermal model as shown in Figure 11, a faster power modules loss prediction method was proposed based on lookup tables, and the required parameters were obtained from the Semikron IGBT module SKiiP2013 GB172-4DW. This data sheet is designed for wind turbine applications. Then an equivalent RC network model is built with parameters obtained from the same manufactures’ datasheet as shown in Figure 18 and Table 4. Hence,the Lifetime is estimated based on the behavior of the semiconductor devices under their mission profile.

Figure 11 Lifetime estimation model for power semiconductor devices.
Figure 11

Lifetime estimation model for power semiconductor devices.

Figure 12 System under study.
Figure 12

System under study.

Figure 13 Equivalent circuit of permanent magnet synchronous generator [19].
Figure 13

Equivalent circuit of permanent magnet synchronous generator [19].

Figure 14 Drive train model [20].
Figure 14

Drive train model [20].

Figure 15 Control strategy for the turbine pitch angle β [21].
Figure 15

Control strategy for the turbine pitch angle β [21].

Table 1

Aerodynamic Parameters

Nominal turbine power Pn1.5 MW
Turbine rotor radius R72 m
Rated wind speed16 m/s
Cut-in wind speed7 m/s
Cut-out wind speed22 m/s
Air density ρ1.1225 kg/m3
Table 2

PMSG Parameters

Generator typeMulti pole PMSG
Power P1.5 MW
Stator voltage Vsn575 V
No. of pole pairs48
Dynamic speed range nm7 − 22 rpm
Mechanical frequency fm0.1167 − 0.367 Hz
Electrical frequency fe5.6 − 17.6 Hz
Stator phase resistance Rs0.006 Ω
Armature inductance L0.000835 H
Flux linkage established by1.48 V · s
magnets λ
Generator rotor inertia JG35000 kg · m2
Generator rotor self-damping DG0.01 N · m · s
Rated grid voltage5.6 kV
Table 3

DC-link Parameters

dc-bus capacitor Cdc−link10000 * 10−6MW
Nominal dc-link voltage Vdc1150 V
Coupling inductor of GSC L0.3 pu
Coupling resistance of GSC R0.003 pu
Frequency of the GSC PWM carrier2700 Hz
Frequency of the RSC PWM carrier1620 Hz
DC bus voltage regulator gains [Kp[8 400]
KI]
Table 4

Component (type) Designation System.

Case formCurrent grade (& case size)Circuit abbreviationBlocking voltage (*100V)Chip
SKiiP1814GB17E4

A demonstration of the system under study is shown in Figure 12, which shows wind turbine aerodynamics, and topographic effects of wind speed, direction and ambient temperature that are combined with multi-pole space, direct driven, variable-speed permanent magnet synchronous generator (PMSG). The wind turbine is connected to the grid via frequency converter with a DC-link capacitor [17][18]. The main feature of PMSG is that there is no need to external excitation current [19]. The parameters of the system under study are listed in Tables 1, 2 and 3. A Steady state voltage in d−q rotor-oriented reference frame is shown in Figure 13, with:

(2)Vqsr=Rqiqsr+pλqsr+ωrλdsr
(3)Vdsr=Rdidsr+pλdsr−ωrλqsr

where Rd, Rq are stator d and q axis winding resistance, respectively, Ω. ids, iqs are stator d and q axis currents, respectively, A. λds,λqs are stator d and q axis flux linkage, respectively, V.s. ωr is the electrical rotor speed, rad/s. And p is the differential operator.

Equations (4) and (5) are the stator flux linkages:

(4)λqsr=Lsiqsr+Lmriqrr
(5)λdsr=Lsidsr+Lmridrr

where Ls is stator self inductance per phase, H. idr, iqr are rotor d and q axis currents, respectively, A. Lm is magnetization inductance per phase, H. And P is number of poles The electromagnetic torque is given by Equation (6)

(6)Te=32P2{λdsriqsr+λqsridsr}

And the active power Pgen and the reactive power Qgen of PMSG can be expressed as shown in Equations (7) and (8), respectively:

(7)Pgen=Teωm=32[Vdsridsr+Vqsriqsr]
(8)Qgen=32[Vqsridsr−Vdsriqsr]

The control of PMSG is done in rotor reference frame and the machine side converter is controlled through a PI controller,where minimum current is obtained through d-axis and current is held to zero to have a maximum torque. The grid side converter is explored through a hysteresis controller. A direct coupled PMSG in wind turbines eliminates the need of gear box as shown in Figure 14. The turbines are allowed to perform their best by adjusting their blade pitch angle as shown in Figure 15 by managing the impacts of unpredictable wind speed.

4.2 Converter Power Loss Estimation

PMSG is connected to fixed frequency grid via a variable frequency converter as shown in Figure 16. The rotational speed ωm varies between 7 − 22 rpm depending on the wind speed. Therefore, the corresponding mechanical frequency fm varies between 0.1167 Hz and 0.367 Hz. The electrical frequency generated by PMSG is determined by Equation (9)

Figure 16 Frequency converter.
Figure 16

Frequency converter.

(9)fe=fmP

where fm is the mechanical frequency and P is the number of pole pairs. The averaged model of three-phase back-to-back (PWM) converter is adopted in this work as shown in Figure 16. It is suitable for wind application and especially for computer simulation. It makes the simulation run faster with less complexity and acceptable accuracy for obtaining the converter losses. In the following, we show basic equations for calculating the conduction and switching losses based on Figure 17. Switching losses PSV of VSC is given by Equation (10) [30].

Figure 17 Typical energy losses E = f (Ic , Vcc)[30].
Figure 17

Typical energy losses E = f (Ic , Vcc)[30].

(10)Psw=6π.fs.EON.I+EOFF.I+EOFF.D.VdcVref.i^Liref

where fs is the switching frequency, EON.I and EOFF.I are the turn on and turn off energies of the IGBT, respectively. Eoff .D is the turn off energy in the power modules‘ diode due to reverse recovery current. Vdc is the dc link voltage. ˆ iL is the peack value of ac line current. Vref and iref are the reference values given in the data sheet [22]. Conduction losses of a single IGBT PCV.I is expressed by Equation (11) and conduction losses in diode PCV.D ares expressed by Equation (12).

(11)PCV.I=VCE.i^L2.π∫0πsin(ωt).1+M(t)2.dωt+rCE.i^L22.π∫0πsin2(ωt).1+M(t)2.dωt
(12)PCV.D=VF.i^L2.π∫0πsin(ωt).1−M(t)2.dωt+rF.i^L22.π∫0πsin2(ωt).1−M(t)2.dωt

where ω is the load current’s angular frequency, M(t) is the modulation function, VCE is the IGBT’s threshold voltage, rCE is the IGBT’s differential resistance, VF and rF are the diode’s threshold voltage and differential resistance, respectively. The total converter power losses is given by Equation (13).

(13)PCV=6.(PCU.I+PCU,D)

4.3 Electrothermal Model

Different techniques have been used to perform the electrothermal analysis; such as analytical, numerical, and behavioral [24]. Thus, numerical methods are used to solve the equations governing the conservative of mass and momentum as computational fluid dynamic (CFD) [25]. Besides, the finite element method (FEM) and finite difference method (FDM) are used for the discretization of the differential equation for heat conduction [26]. In this work, Analytical methods are selected to predict the operating temperature of semiconductor devices[27]. These methods provide better physical insight by use of a physical model. RC ladder networks are commonly adopted for thermal analysis since they are ready to be integrated into existing circuit simulator and are capable of simulating both electrical and thermal characteristics. Over that, RC thermal model can be implemented in the real-time simulation due to its easy implementation and simple computational [28]. Once the power dissipated in the IGBT power converters has been calculated, this power is used as excitation current source in a thermal model as shown in Equations (14-17) to estimate junction temperature of the operational IGBT’s.

(14)x˙=Ax+Bu[∘C/Δt]
(15)y=Cx+Du[∘C]
(16)Y/u=C(sI−A)−1B+D[∘C/W]
(17)Zjc(t)=Tj(t)−Tc(t)P=ΔTjc(t)P

where

A=−1R2C11R2C100−1R2C2−1R2C2−1R3C21R3C2001R3C3−1R3C3−1R4C31R4C300−1R4C4−1R4C4−1R5C4B=1C1000T,C=1000T,andD=R1.

The power losses and the operational temperatures are related by the thermal impedance as shown in Figure 18. A Semikron IGBT module SKiiP2013 GB172−4DW, which has the characteristics shown in Table 4, is designed for wind turbine applications. This IGBT module is used in this paper to construct the electrothermal model of the power devices. The values of the thermal impedances have been extracted from the dynamical thermal impedance curve from the manufacturer`s datasheet, as given in Table 5.

Table 5

Thermal Impedance of Cauer Network

Rth[K/W]123456
Zth(j − r)0.00110.00590.00630.00210.00210.0000
Zth(r − a)0.00020.00120.00420.00340.00020.0000
τ[s]123456
Zth(j − r)I0.00070.02680.21651.04532.24561.0000
Zth(r − a)0.13821.42548.307720.093189.771.0000
Figure 18 Cauer thermal impedance between the junction temperature and case layer.
Figure 18

Cauer thermal impedance between the junction temperature and case layer.

4.4 Lifetime Estimation

Lifetime is considered as an important statistical indicator. The estimation of lifetime is based on how long the product is expected to perform its function under certain electrical, mechanical and thermal conditions. In literature, there are many counting methods for lifetime estimation for power modules that are used to extract amplitude and number of thermal cycles from a mission profile [31, 32, 32]. Unfortunately, these methods cannot capture accurately all the characteristics needed for reliability assessment. In this work, the Rainflow cycle counting method, which was developed in 1968 by Endo and Matsuishi is applied as shown in Figure 19.Various failure modes for IGBT modules are essential to understand the failure mechanism to predict the power converter lifetime [29]. An improved lifetime model was obtained from [30] as shown in Equation (18) and the parameters are listed in Table 6.

Figure 19 Example of rainflow algorithm
Figure 19

Example of rainflow algorithm

Table 6

Parameters and limits for the calculation of power cycles using equation in (18)

ParametersSymbolUnitLimitsCoefficientValue
Technology factorA---9.34E+14
Temperature differenceΔTK45...150β1−4.416
Min. chip temperatureTj(min)◦C20 ...120β21285
Pulse durationtons1 ...15β3−0.463
Current per bond footIBA3 ...23β4−0.716
Voltage class/100VcV6 ...33β5−0.761
Bond wire diameterDμm75 ...500β6−0.5
(19)Nf=A.ΔTjβ1.exp(β2Tj.min+273).tonβ3.IBβ4.VCβ5.Dβ6

Figure 20 shows the frequency distribution of temperature cycles defined by their mean temperature value Tm extracted from the rainflow counting algorithm with 1697.5 cycles. IGBT junction temperature variations is uploaded with IoT ’Thingspeak’ Mathwork web service and shown in Figure 21. The analysis shows that the lifetime is heavily influenced by thermal cycling, and these thermal cycles lead to a degradation of the wire bonds. Lifetime of IGBT power module is estimated around 14521 hours.

Figure 20 Mean temperature value Tm extracted from the rainflow counting algorithm.
Figure 20

Mean temperature value Tm extracted from the rainflow counting algorithm.

Figure 21 Junction temperature (◦C)
Figure 21

Junction temperature (◦C)

5 Conclusions

Wind energy resource assessment and reliability performance have been estimated using IoT, where mission profile should be monitored, analyzed and controlled in real time. Then, a comprehensive thermal model is developed in three steps: the power losses are calculated; then the junction temperatures are evaluated and the lifetime is estimated.

Therefore, the main purpose of this paper is to estimate a reliability model of PMSG using ’Thingspeak’ Mathworks web service. So, due to long operation hours under challenging environments, wind turbines can meet with their main technological advancement through IoT. This will grant field operators the tools to ensure maintenance strategy development, reduce uncertainty, maintain systemreliability and availability and enhance annual energy production. In addition, wind farm operators expect industrial strength with global scalability and significant benefits from IoT implementation and evaluation such as gathering data to seek performance optimization, instrumenting wind farm with sensors, identifying predictive and preventive maintenance and connecting system level and component level together through IoT. On the other hand, lack of budget and lack of skills are considered main barriers for releasing IoT in wind farms, besides security concerns, communication protocols, limiting devices access to internet, time pressure and lack of priority. We plan to address these issues in our future work. Furthermore, we plan to use the Particle Electron for data acquisition.

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Received: 2019-05-15
Accepted: 2019-09-27
Published Online: 2019-11-02

© 2019 L. Alhmoud and H. Al-Zoubi, published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/eng-2019-0061
Keywords for this article
Internet of things; Reliability; Sensors; Wind energy; Wind speed
Creative Commons
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Articles in the same Issue

  1. Regular Article
  2. Exploring conditions and usefulness of UAVs in the BRAIN Massive Inspections Protocol
  3. A hybrid approach for solving multi-mode resource-constrained project scheduling problem in construction
  4. Identification of geodetic risk factors occurring at the construction project preparation stage
  5. Multicriteria comparative analysis of pillars strengthening of the historic building
  6. Methods of habitat reports’ evaluation
  7. Effect of material and technological factors on the properties of cement-lime mortars and mortars with plasticizing admixture
  8. Management of Innovation Ecosystems Based on Six Sigma Business Scorecard
  9. On a Stochastic Regularization Technique for Ill-Conditioned Linear Systems
  10. Dynamic safety system for collaboration of operators and industrial robots
  11. Assessment of Decentralized Electricity Production from Hybrid Renewable Energy Sources for Sustainable Energy Development in Nigeria
  12. Seasonal evaluation of surface water quality at the Tamanduá stream watershed (Aparecida de Goiânia, Goiás, Brazil) using the Water Quality Index
  13. EFQM model implementation in a Portuguese Higher Education Institution
  14. Assessment of direct and indirect effects of building developments on the environment
  15. Accelerated Aging of WPCs Based on Polypropylene and Plywood Production Residues
  16. Analysis of the Cost of a Building’s Life Cycle in a Probabilistic Approach
  17. Implementation of Web Services for Data Integration to Improve Performance in The Processing Loan Approval
  18. Rehabilitation of buildings as an alternative to sustainability in Brazilian constructions
  19. Synthesis Conditions for LPV Controller with Input Covariance Constraints
  20. Procurement management in construction: study of Czech municipalities
  21. Contractor’s bid pricing strategy: a model with correlation among competitors’ prices
  22. Control of construction projects using the Earned Value Method - case study
  23. Model supporting decisions on renovation and modernization of public utility buildings
  24. Cements with calcareous fly ash as component of low clinker eco-self compacting concrete
  25. Failure Analysis of Super Hard End Mill HSS-Co
  26. Simulation model for resource-constrained construction project
  27. Getting efficient choices in buildings by using Genetic Algorithms: Assessment & validation
  28. Analysis of renewable energy use in single-family housing
  29. Modeling of the harmonization method for executing a multi-unit construction project
  30. Effect of foam glass granules fillers modification of lime-sand products on their microstructure
  31. Volume Optimization of Solid Waste Landfill Using Voronoi Diagram Geometry
  32. Analysis of occupational accidents in the construction industry with regards to selected time parameters
  33. Bill of quantities and quantity survey of construction works of renovated buildings - case study
  34. Cooperation of the PTFE sealing ring with the steel ball of the valve subjected to durability test
  35. Analytical model assessing the effect of increased traffic flow intensities on the road administration, maintenance and lifetime
  36. Quartz bentonite sandmix in sand-lime products
  37. The Issue of a Transport Mode Choice from the Perspective of Enterprise Logistics
  38. Analysis of workplace injuries in Slovakian state forestry enterprises
  39. Research into Customer Preferences of Potential Buyers of Simple Wood-based Houses for the Purpose of Using the Target Costing
  40. Proposal of the Inventory Management Automatic Identification System in the Manufacturing Enterprise Applying the Multi-criteria Analysis Methods
  41. Hyperboloid offset surface in the architecture and construction industry
  42. Analysis of the preparatory phase of a construction investment in the area covered by revitalization
  43. The selection of sealing technologies of the subsoil and hydrotechnical structures and quality assurance
  44. Impact of high temperature drying process on beech wood containing tension wood
  45. Prediction of Strength of Remixed Concrete by Application of Orthogonal Decomposition, Neural Analysis and Regression Analysis
  46. Modelling a production process using a Sankey diagram and Computerized Relative Allocation of Facilities Technique (CRAFT)
  47. The feasibility of using a low-cost depth camera for 3D scanning in mass customization
  48. Urban Water Infrastructure Asset Management Plan: Case Study
  49. Evaluation the effect of lime on the plastic and hardened properties of cement mortar and quantified using Vipulanandan model
  50. Uplift and Settlement Prediction Model of Marine Clay Soil e Integrated with Polyurethane Foam
  51. IoT Applications in Wind Energy Conversion Systems
  52. A new method for graph stream summarization based on both the structure and concepts
  53. “Zhores” — Petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in Skolkovo Institute of Science and Technology
  54. Economic Disposal Quantity of Leftovers kept in storage: a Monte Carlo simulation method
  55. Computer technology of the thermal stress state and fatigue life analysis of turbine engine exhaust support frames
  56. Statistical model used to assessment the sulphate resistance of mortars with fly ashes
  57. Application of organization goal-oriented requirement engineering (OGORE) methods in erp-based company business processes
  58. Influence of Sand Size on Mechanical Properties of Fiber Reinforced Polymer Concrete
  59. Architecture For Automation System Metrics Collection, Visualization and Data Engineering – HAMK Sheet Metal Center Building Automation Case Study
  60. Optimization of shape memory alloy braces for concentrically braced steel braced frames
  61. Topical Issue Modern Manufacturing Technologies
  62. Feasibility Study of Microneedle Fabrication from a thin Nitinol Wire Using a CW Single-Mode Fiber Laser
  63. Topical Issue: Progress in area of the flow machines and devices
  64. Analysis of the influence of a stator type modification on the performance of a pump with a hole impeller
  65. Investigations of drilled and multi-piped impellers cavitation performance
  66. The novel solution of ball valve with replaceable orifice. Numerical and field tests
  67. The flow deteriorations in course of the partial load operation of the middle specific speed Francis turbine
  68. Numerical analysis of temperature distribution in a brush seal with thermo-regulating bimetal elements
  69. A new solution of the semi-metallic gasket increasing tightness level
  70. Design and analysis of the flange-bolted joint with respect to required tightness and strength
  71. Special Issue: Actual trends in logistics and industrial engineering
  72. Intelligent programming of robotic flange production by means of CAM programming
  73. Static testing evaluation of pipe conveyor belt for different tensioning forces
  74. Design of clamping structure for material flow monitor of pipe conveyors
  75. Risk Minimisation in Integrated Supply Chains
  76. Use of simulation model for measurement of MilkRun system performance
  77. A simulation model for the need for intra-plant transport operation planning by AGV
  78. Operative production planning utilising quantitative forecasting and Monte Carlo simulations
  79. Monitoring bulk material pressure on bottom of storage using DEM
  80. Calibration of Transducers and of a Coil Compression Spring Constant on the Testing Equipment Simulating the Process of a Pallet Positioning in a Rack Cell
  81. Design of evaluation tool used to improve the production process
  82. Planning of Optimal Capacity for the Middle-Sized Storage Using a Mathematical Model
  83. Experimental assessment of the static stiffness of machine parts and structures by changing the magnitude of the hysteresis as a function of loading
  84. The evaluation of the production of the shaped part using the workshop programming method on the two-spindle multi-axis CTX alpha 500 lathe
  85. Numerical Modeling of p-v-T Rheological Equation Coefficients for Polypropylene with Variable Chalk Content
  86. Current options in the life cycle assessment of additive manufacturing products
  87. Ideal mathematical model of shock compression and shock expansion
  88. Use of simulation by modelling of conveyor belt contact forces
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