This document discusses enabling technologies for energy routing monitoring, network installation, and predictive maintenance. It describes a system architecture using infrared thermography, augmented reality, thermal simulation software, and data mining algorithms. Thermography is used for installation testing and predictive maintenance, while augmented reality can be applied to training and verifying equipment status. Thermal simulation software models indoor and outdoor energy distribution to support design, verification, and predictive maintenance based on performance comparisons. Data mining algorithms like k-means clustering and neural networks further analyze sensor and image data for predictive maintenance.
Balancing Compression and Encryption of Satellite Imagery IJECEIAES
With the rapid developments in the remote sensing technologies and services, there is a necessity for combined compression and encryption of satellite imagery. The onboard satellite compression is used to minimize storage and communication bandwidth requirements of high data rate satellite applications. While encryption is employed to secure these resources and prevent illegal use of image sensitive information. In this paper, we propose an approach to address these challenges which raised in the highly dynamic satellite based networked environment. This approach combined compression algorithms (Huffman and SPIHT) and encryptions algorithms (RC4, blowfish and AES) into three complementary modes: (1) secure lossless compression, (2) secure lossy compression and (3) secure hybrid compression. The extensive experiments on the 126 satellite images dataset showed that our approach outperforms traditional and state of art approaches by saving approximately (53%) of computational resources. In addition, the interesting feature of this approach is these three options that mimic reality by imposing every time a different approach to deal with the problem of limited computing and communication resources.
Stereo vision-based obstacle avoidance module on 3D point cloud dataTELKOMNIKA JOURNAL
This paper deals in building a 3D vision-based obstacle avoidance and navigation. In order for an autonomous system to work in real life condition, a capability of gaining surrounding environment data, interpret the data and take appropriate action is needed. One of the required capability in this matter for an autonomous system is a capability to navigate cluttered, unorganized environment and avoiding collision with any present obstacle, defined as any data with vertical orientation and able to take decision when environment update exist. Proposed in this work are two-step strategy of extracting the obstacle position and orientation from point cloud data using plane based segmentation and the resultant segmentation are mapped based on obstacle point position relative to camera using occupancy grid map to acquire obstacle cluster position and recorded the occupancy grid map for future use and global navigation, obstacle position gained in grid map is used to plan the navigation path towards target goal without going through obstacle position and modify the navigation path to avoid collision when environment update is present or platform movement is not aligned with navigation path based on timed elastic band method.
Imaging and Image sensors is a field that is continuously evolving. There are new products
coming into the market every day. Some of these have very severe Size, Weight and Power
constraints whereas other devices have to handle very high computational loads. Some require
both these conditions to be met simultaneously. Current imaging architectures and digital image
processing solutions will not be able to meet these ever increasing demands. There is a need to
develop novel imaging architectures and image processing solutions to address these
requirements. In this work we propose analog signal processing as a solution to this problem.
The analog processor is not suggested as a replacement to a digital processor but it will be used
as an augmentation device which works in parallel with the digital processor, making the
system faster and more efficient. In order to show the merits of analog processing the highly
computational Normalized Cross Correlation algorithm is implemented. We propose two novel
modifications to the algorithm and a new imaging architecture which, significantly reduces the
computation time.
Balancing Compression and Encryption of Satellite Imagery IJECEIAES
With the rapid developments in the remote sensing technologies and services, there is a necessity for combined compression and encryption of satellite imagery. The onboard satellite compression is used to minimize storage and communication bandwidth requirements of high data rate satellite applications. While encryption is employed to secure these resources and prevent illegal use of image sensitive information. In this paper, we propose an approach to address these challenges which raised in the highly dynamic satellite based networked environment. This approach combined compression algorithms (Huffman and SPIHT) and encryptions algorithms (RC4, blowfish and AES) into three complementary modes: (1) secure lossless compression, (2) secure lossy compression and (3) secure hybrid compression. The extensive experiments on the 126 satellite images dataset showed that our approach outperforms traditional and state of art approaches by saving approximately (53%) of computational resources. In addition, the interesting feature of this approach is these three options that mimic reality by imposing every time a different approach to deal with the problem of limited computing and communication resources.
Stereo vision-based obstacle avoidance module on 3D point cloud dataTELKOMNIKA JOURNAL
This paper deals in building a 3D vision-based obstacle avoidance and navigation. In order for an autonomous system to work in real life condition, a capability of gaining surrounding environment data, interpret the data and take appropriate action is needed. One of the required capability in this matter for an autonomous system is a capability to navigate cluttered, unorganized environment and avoiding collision with any present obstacle, defined as any data with vertical orientation and able to take decision when environment update exist. Proposed in this work are two-step strategy of extracting the obstacle position and orientation from point cloud data using plane based segmentation and the resultant segmentation are mapped based on obstacle point position relative to camera using occupancy grid map to acquire obstacle cluster position and recorded the occupancy grid map for future use and global navigation, obstacle position gained in grid map is used to plan the navigation path towards target goal without going through obstacle position and modify the navigation path to avoid collision when environment update is present or platform movement is not aligned with navigation path based on timed elastic band method.
Imaging and Image sensors is a field that is continuously evolving. There are new products
coming into the market every day. Some of these have very severe Size, Weight and Power
constraints whereas other devices have to handle very high computational loads. Some require
both these conditions to be met simultaneously. Current imaging architectures and digital image
processing solutions will not be able to meet these ever increasing demands. There is a need to
develop novel imaging architectures and image processing solutions to address these
requirements. In this work we propose analog signal processing as a solution to this problem.
The analog processor is not suggested as a replacement to a digital processor but it will be used
as an augmentation device which works in parallel with the digital processor, making the
system faster and more efficient. In order to show the merits of analog processing the highly
computational Normalized Cross Correlation algorithm is implemented. We propose two novel
modifications to the algorithm and a new imaging architecture which, significantly reduces the
computation time.
Comparison of Articles on Content Based Image RetrievalElshalom Essay
Comparison between Content –Based Image Retrieval Based on Electromagnetism-Like Mechanism” on Hindawi Publishing Corporation, Mathematical Problems in Engineering Volume 2013 available on http://dx.doi.org/10.1155/2013/782519 and
Content Based Image Retrieval using Exact Legendre Moments and Support Vector Machine on The International Journal of Multimedia and Its Application Volume 2, No.2, May 2010.
Quaternion Based Omnidirectional Machine Condition Monitoring SystemCSCJournals
Thermal monitoring is useful for revealing some serious electrical problems in a factory that often go undetected until a serious breakdown occurs. In factories, there are various types of functioning machines to be monitored. When there is any malfunctioning of a machine, extra heat will be generated which can be picked up by thermal camera for image processing and identification purpose. In this paper, a new and effective omnidirectional machine condition monitoring system applying log-polar mapper, quaternion based thermal image correlator and max-product fuzzy neural network classifier is proposed for monitoring machine condition in an omnidirectional view. With this monitoring system, it is convenient to detect and monitor the conditions of (overheat or not) of more than one machines in an omnidirectional view captured by using a single thermal camera. Log-polar mapping technique is used to unwarp omnidirectional thermal image into panoramic form. Two classification characteristics namely: peak to sidelobe ratio (PSR) and real to complex ratio of the discrete quaternion correlation output (p-value) are applied in the proposed machine condition monitoring system. Large PSR and p-value observe in a good match among correlation of the input thermal image with a particular reference image, while small PSR and p-value observe in a bad/not match among correlation of the input thermal image with a particular reference image. Simulation results also show that the proposed system is an efficient omnidirectional machine monitoring system with accuracy more than 97%.
Background differencing algorithm for moving object detection using system ge...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Implementation of multiple 3D scans for error calculation on object digital r...IJERA Editor
Laser scanning is a widespread methodology of visualizing the natural environment and the manmade structures that exist in it. Laser scanners accomplish to digitalize our reality by making highly accurate measurements. Using these measurements they create a set of points in 3D space which is called point cloud and depicts an entire area or object or parts of them. Triangulation laser scanners use the triangle theories and they mainly are used to visualize handheld objects at a very close range from them. In many cases, users of such devices take for granted the accuracy specifications provided by laser scanner manufacturers and respective software and for many applications this is enough. In this paper we use point clouds, collected by a triangulation laser scanner under a repetition method, of two cubes that are geometrically similar to each other but differ in material. At first, the data of each repetition are being compared to each other to examine the consistency of the scanner under multiple measurements of the same scene. Then, the reconstruction of the objects‟ geometry is achieved and the results are being compared to the data derived by a digital caliper. The errors of calculated dimensions were estimated by the use of error propagation law.
A Hardware Model to Measure Motion Estimation with Bit Plane Matching AlgorithmTELKOMNIKA JOURNAL
The multistep approach involving combination of techniques is referred as motion estimation.
The proposed approach is an adaptive control system to measure the motion from starting point to limit of
search. The motion patterns are used to analyze and avoid stationary regions of image. The algorithm
proposed is robust efficient and the calculations justify its advantages. The motivation of the work is to
maximize the encoding speed and visual quality with the help of motion vector algorithm. In this work a
hardware model is developed in which a frame of pictures are captured and sent via serial port to the system.
MATLAB simulation tool is used to detect the motion among the picture frame. Once any motion is detected
that signal is sent to the hardware which will give the appropriate sign accordingly. This system is developed
on two platforms (hardware as well software) to estimate and measure the motion vectors
EMPLOYING MULTI CORE ARCHITECTURE TO OPTIMIZE ON PERFORMANCE, FOR APPROACH IN...IAEME Publication
Cloud detection is an important task in meteorological application. Cloud information is especially important for now-casting purposes [1] and as an input for different satellite based estimation of atmospheric and surface parameters [2 -4]. The solar energy is the principal source of energy in the solar system. Clouds have high reflectance and absorption property which is used to distinguish them with land, water or sea area. There is critical demand to develop application, which can calculate the presence of cloud by using the available satellite image processing data, so that prediction of radiated solar energy can be optimised and energy budget can be predicted more easily.
This is a straightforward image classification study to create and compare classifiers (KNN, Neural Networks and Adaboost) that decide the correct orientation of a given image i.e. 0°,90°,180° or 270°
The accurate prediction of solar irradiation has been
a leading problem for better energy scheduling approach.
Hence in this paper, an Artificial neural network based solar
irradiance is proposed for five days duration the data is
obtained from National Renewable Energy Laboratory, USA
and the simulation were performed using MATLAB 2013. It
was found that the neural model was able to predict the solar
irradiance with a mean square error of 0.0355.
In this deck from GTC 2019, Seongchan Kim, Ph.D. presents: How Deep Learning Could Predict Weather Events.
"How do meteorologists predict weather or weather events such as hurricanes, typhoons, and heavy rain? Predicting weather events were done based on supercomputer (HPC) simulations using numerical models such as WRF, UM, and MPAS. But recently, many deep learning-based researches have been showing various kinds of outstanding results. We'll introduce several case studies related to meteorological researches. We'll also describe how the meteorological tasks are different from general deep learning tasks, their detailed approaches, and their input data such as weather radar images and satellite images. We'll also cover typhoon detection and tracking, rainfall amount prediction, forecasting future cloud figure, and more."
Watch the video: https://wp.me/p3RLHQ-k2T
Learn more: http://en.kisti.re.kr/
and
https://www.nvidia.com/en-us/gtc/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Analog signal processing approach for coarse and fine depth estimationsipij
Imaging and Image sensors is a field that is continuously evolving. There are new products coming into the
market every day. Some of these have very severe Size, Weight and Power constraints whereas other
devices have to handle very high computational loads. Some require both these conditions to be met
simultaneously. Current imaging architectures and digital image processing solutions will not be able to
meet these ever increasing demands. There is a need to develop novel imaging architectures and image
processing solutions to address these requirements. In this work we propose analog signal processing as a
solution to this problem. The analog processor is not suggested as a replacement to a digital processor but
it will be used as an augmentation device which works in parallel with the digital processor, making the
system faster and more efficient. In order to show the merits of analog processing two stereo
correspondence algorithms are implemented. We propose novel modifications to the algorithms and new
imaging architectures which, significantly reduces the computation time
HSO: A Hybrid Swarm Optimization Algorithm for Reducing Energy Consumption in...TELKOMNIKA JOURNAL
Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers’ information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made.
Performance of low-cost solar radiation loggerIJECEIAES
In solar power systems, irradiance value data are among the most important parameters. Such data can be used in installing photovoltaic (PV) modules, such as determining the exact location, tilt angle, and required area, for optimal power efficiency. In this study, the comprehensive simulation and implementation of a solar radiation meter with a PV cell and temperature sensor are presented. The irradiance measurement value is based on the power reading generated by the small capacity of the PV cell at a specific load converted into a digital value in the microcontroller using the implicit Newton polynomial interpolation (NPI) equation as a low-cost alternative method. The effect of temperature is included in the conversion to obtain precise measurement results. Firstly, the structure and characteristics of the PV cell are discussed. Secondly, the parameters, measuring method, and conversion of the measurement reading data using the NPI equation are presented to assess the results. Finally, the simulation of the solar radiation meter using the PSIM and implementation of the hardware are conducted to validate the concepts and compare their results. The proposed hardware has an average error of 2.72% in the implementation of the measurement test.
Optimized design of an extreme low power datalogger for photovoltaic panels IJECEIAES
The paper focuses on the design and implementation of a low cost and compact data logger prototype using an extreme low power (XLP) and low pin count programmable interface controllers (PIC) microcontroller using its own flash memory for the periodic data acquisition storage, while many other works focus in the Arduino Eco-system. It is planned to pick four important analog measures from the photovoltaic system, and store them directly as 10-bit numerical counts, this yields to faster data acquisition and storage (no time consuming for mathematical computation to convert each numerical count of raw data to meaningful real-world data). Avoiding the use of any kind of display and keypad, and keeping the ratio run time over sleep time as low as possible, has a maximum impact on lowering the power consumption. This prototype can be serially linked to a personal computer (PC) to view the acquisition of measurements in real time, and to retrieve all collected data through a terminal application. The experimental results are stored in commaseparated values (CSV) files to ease post data analysis with any spread sheet software, for statistical calculations and graphs drawing, in order for instance, to find the faults of the photovoltaic system and optimize its management and its performance.
Comparison of Articles on Content Based Image RetrievalElshalom Essay
Comparison between Content –Based Image Retrieval Based on Electromagnetism-Like Mechanism” on Hindawi Publishing Corporation, Mathematical Problems in Engineering Volume 2013 available on http://dx.doi.org/10.1155/2013/782519 and
Content Based Image Retrieval using Exact Legendre Moments and Support Vector Machine on The International Journal of Multimedia and Its Application Volume 2, No.2, May 2010.
Quaternion Based Omnidirectional Machine Condition Monitoring SystemCSCJournals
Thermal monitoring is useful for revealing some serious electrical problems in a factory that often go undetected until a serious breakdown occurs. In factories, there are various types of functioning machines to be monitored. When there is any malfunctioning of a machine, extra heat will be generated which can be picked up by thermal camera for image processing and identification purpose. In this paper, a new and effective omnidirectional machine condition monitoring system applying log-polar mapper, quaternion based thermal image correlator and max-product fuzzy neural network classifier is proposed for monitoring machine condition in an omnidirectional view. With this monitoring system, it is convenient to detect and monitor the conditions of (overheat or not) of more than one machines in an omnidirectional view captured by using a single thermal camera. Log-polar mapping technique is used to unwarp omnidirectional thermal image into panoramic form. Two classification characteristics namely: peak to sidelobe ratio (PSR) and real to complex ratio of the discrete quaternion correlation output (p-value) are applied in the proposed machine condition monitoring system. Large PSR and p-value observe in a good match among correlation of the input thermal image with a particular reference image, while small PSR and p-value observe in a bad/not match among correlation of the input thermal image with a particular reference image. Simulation results also show that the proposed system is an efficient omnidirectional machine monitoring system with accuracy more than 97%.
Background differencing algorithm for moving object detection using system ge...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Implementation of multiple 3D scans for error calculation on object digital r...IJERA Editor
Laser scanning is a widespread methodology of visualizing the natural environment and the manmade structures that exist in it. Laser scanners accomplish to digitalize our reality by making highly accurate measurements. Using these measurements they create a set of points in 3D space which is called point cloud and depicts an entire area or object or parts of them. Triangulation laser scanners use the triangle theories and they mainly are used to visualize handheld objects at a very close range from them. In many cases, users of such devices take for granted the accuracy specifications provided by laser scanner manufacturers and respective software and for many applications this is enough. In this paper we use point clouds, collected by a triangulation laser scanner under a repetition method, of two cubes that are geometrically similar to each other but differ in material. At first, the data of each repetition are being compared to each other to examine the consistency of the scanner under multiple measurements of the same scene. Then, the reconstruction of the objects‟ geometry is achieved and the results are being compared to the data derived by a digital caliper. The errors of calculated dimensions were estimated by the use of error propagation law.
A Hardware Model to Measure Motion Estimation with Bit Plane Matching AlgorithmTELKOMNIKA JOURNAL
The multistep approach involving combination of techniques is referred as motion estimation.
The proposed approach is an adaptive control system to measure the motion from starting point to limit of
search. The motion patterns are used to analyze and avoid stationary regions of image. The algorithm
proposed is robust efficient and the calculations justify its advantages. The motivation of the work is to
maximize the encoding speed and visual quality with the help of motion vector algorithm. In this work a
hardware model is developed in which a frame of pictures are captured and sent via serial port to the system.
MATLAB simulation tool is used to detect the motion among the picture frame. Once any motion is detected
that signal is sent to the hardware which will give the appropriate sign accordingly. This system is developed
on two platforms (hardware as well software) to estimate and measure the motion vectors
EMPLOYING MULTI CORE ARCHITECTURE TO OPTIMIZE ON PERFORMANCE, FOR APPROACH IN...IAEME Publication
Cloud detection is an important task in meteorological application. Cloud information is especially important for now-casting purposes [1] and as an input for different satellite based estimation of atmospheric and surface parameters [2 -4]. The solar energy is the principal source of energy in the solar system. Clouds have high reflectance and absorption property which is used to distinguish them with land, water or sea area. There is critical demand to develop application, which can calculate the presence of cloud by using the available satellite image processing data, so that prediction of radiated solar energy can be optimised and energy budget can be predicted more easily.
This is a straightforward image classification study to create and compare classifiers (KNN, Neural Networks and Adaboost) that decide the correct orientation of a given image i.e. 0°,90°,180° or 270°
The accurate prediction of solar irradiation has been
a leading problem for better energy scheduling approach.
Hence in this paper, an Artificial neural network based solar
irradiance is proposed for five days duration the data is
obtained from National Renewable Energy Laboratory, USA
and the simulation were performed using MATLAB 2013. It
was found that the neural model was able to predict the solar
irradiance with a mean square error of 0.0355.
In this deck from GTC 2019, Seongchan Kim, Ph.D. presents: How Deep Learning Could Predict Weather Events.
"How do meteorologists predict weather or weather events such as hurricanes, typhoons, and heavy rain? Predicting weather events were done based on supercomputer (HPC) simulations using numerical models such as WRF, UM, and MPAS. But recently, many deep learning-based researches have been showing various kinds of outstanding results. We'll introduce several case studies related to meteorological researches. We'll also describe how the meteorological tasks are different from general deep learning tasks, their detailed approaches, and their input data such as weather radar images and satellite images. We'll also cover typhoon detection and tracking, rainfall amount prediction, forecasting future cloud figure, and more."
Watch the video: https://wp.me/p3RLHQ-k2T
Learn more: http://en.kisti.re.kr/
and
https://www.nvidia.com/en-us/gtc/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Analog signal processing approach for coarse and fine depth estimationsipij
Imaging and Image sensors is a field that is continuously evolving. There are new products coming into the
market every day. Some of these have very severe Size, Weight and Power constraints whereas other
devices have to handle very high computational loads. Some require both these conditions to be met
simultaneously. Current imaging architectures and digital image processing solutions will not be able to
meet these ever increasing demands. There is a need to develop novel imaging architectures and image
processing solutions to address these requirements. In this work we propose analog signal processing as a
solution to this problem. The analog processor is not suggested as a replacement to a digital processor but
it will be used as an augmentation device which works in parallel with the digital processor, making the
system faster and more efficient. In order to show the merits of analog processing two stereo
correspondence algorithms are implemented. We propose novel modifications to the algorithms and new
imaging architectures which, significantly reduces the computation time
HSO: A Hybrid Swarm Optimization Algorithm for Reducing Energy Consumption in...TELKOMNIKA JOURNAL
Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers’ information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made.
HSO: A Hybrid Swarm Optimization Algorithm for Reducing Energy Consumption in...
Similar to OVERVIEW AND APPLICATION OF ENABLING TECHNOLOGIES ORIENTED ON ENERGY ROUTING MONITORING, ON NETWORK INSTALLATION AND ON PREDICTIVE MAINTENANCE
Performance of low-cost solar radiation loggerIJECEIAES
In solar power systems, irradiance value data are among the most important parameters. Such data can be used in installing photovoltaic (PV) modules, such as determining the exact location, tilt angle, and required area, for optimal power efficiency. In this study, the comprehensive simulation and implementation of a solar radiation meter with a PV cell and temperature sensor are presented. The irradiance measurement value is based on the power reading generated by the small capacity of the PV cell at a specific load converted into a digital value in the microcontroller using the implicit Newton polynomial interpolation (NPI) equation as a low-cost alternative method. The effect of temperature is included in the conversion to obtain precise measurement results. Firstly, the structure and characteristics of the PV cell are discussed. Secondly, the parameters, measuring method, and conversion of the measurement reading data using the NPI equation are presented to assess the results. Finally, the simulation of the solar radiation meter using the PSIM and implementation of the hardware are conducted to validate the concepts and compare their results. The proposed hardware has an average error of 2.72% in the implementation of the measurement test.
Optimized design of an extreme low power datalogger for photovoltaic panels IJECEIAES
The paper focuses on the design and implementation of a low cost and compact data logger prototype using an extreme low power (XLP) and low pin count programmable interface controllers (PIC) microcontroller using its own flash memory for the periodic data acquisition storage, while many other works focus in the Arduino Eco-system. It is planned to pick four important analog measures from the photovoltaic system, and store them directly as 10-bit numerical counts, this yields to faster data acquisition and storage (no time consuming for mathematical computation to convert each numerical count of raw data to meaningful real-world data). Avoiding the use of any kind of display and keypad, and keeping the ratio run time over sleep time as low as possible, has a maximum impact on lowering the power consumption. This prototype can be serially linked to a personal computer (PC) to view the acquisition of measurements in real time, and to retrieve all collected data through a terminal application. The experimental results are stored in commaseparated values (CSV) files to ease post data analysis with any spread sheet software, for statistical calculations and graphs drawing, in order for instance, to find the faults of the photovoltaic system and optimize its management and its performance.
The Study of Smart Grid Knowledge Visualization Key TechnologiesNooria Sukmaningtyas
To solve the problems of knowledge visualization for smart grid, such as single manifestations,
low-efficient use and not high model reuse rate, a general three-dimensional smart grid knowledge
visualization model was presented in this paper. First, the construction method of grid device models
based on knowledge-based reasoning was given. Then, the grid visual scene building method was
proposed. This included rapid scene organization strategy, and exploration on the storage and re-use
mechanism of three-dimensional visualization scene. Moreover, collision detection using swarm
intelligence and bionic computing to solve its existing problems. Finally, The feasibility and practicality of
the method was verified by a developed intelligent power virtual drill platform based on JME (J Monkey
Engine).
This work deals with the integration of low-cost electronic devices that were integrated into constructing a dynamic maker that allows the triggering of augmented reality events. A hybrid structure was developed to combine the most favorable aspects of fiducial markers and dynamic markers. The lighting infrared patterns are effectively modifiable through the programming of an ESP8266 microcontroller card. To test the system, an infrared lighting pattern generated was detected through a digital camera, and an augmented reality application was implemented using a web page for displaying text. Electronic shift registers were used for the temporal storage of the infrared illumination pattern. The infrared illumination marker can’t be detected by human eyes, but it is easily recognized due to the inner black square shape embedded into a white wooden structure.
An efficient optical inspection of photovoltaic modules deploying edge detec...IJECEIAES
With the enhanced industrial and domestic energy needs, there is a great urge for renewable energy sources because of their eco-friendly nature. Solar energy is crucial among renewable energy sources and there is a great need to optimize and enhance the performance of solar energy usage that is mainly dependent on the system components. The current work has been aimed to discuss the fault detection of photovoltaic (PV) modules by evaluating an efficient, facile inspection algorithm electrical analysis for real-time applications. The paper presents a real-time experimental model for infrared thermography using a thermal imager mounted on a tripod at a suitable distance from the PV modules to capture the images in the best possible way. A novel hybrid algorithm has been proposed and the fault detection along with the electrical parameter analysis has been accurately performed on the PV modules to analyze and process various externally induced faults in the PV systems.
INTELLIGENT ELECTRICAL MULTI OUTLETS CONTROLLED AND ACTIVATED BY A DATA MININ...ijscai
In the proposed paper are discussed results of an industry project concerning energy management in building. Specifically the work analyses the improvement of electrical outlets controlled and activated by a logic unit and a data mining engine. The engine executes a Long Short-Terms Memory (LSTM) neural network algorithm able to control, to activate and to disable electrical loads connected to multiple outlets placed into a building and having defined priorities. The priority rules are grouped into two level: the first level is related to the outlet, the second one concerns the loads connected to a single outlet. This algorithm, together with the prediction processing of the logic unit connected to all the outlets, is suitable for alerting management for cases of threshold overcoming. In this direction is proposed a flow chart applied on three for three outlets and able to control load matching with defined thresholds. The goal of the paper is to provide the reading keys of the data mining outputs useful for the energy management and diagnostic of the electrical network in a building. Finally in the paper are analyzed the correlation between global active power, global reactive power and energy absorption of loads of the three intelligent outlet. The prediction and the correlation analyses provide information about load balancing, possible electrical faults and energy cost optimization.
Intelligent Electrical Multi Outlets Controlled and Activated by a Data Minin...IJSCAI Journal
In the proposed paper are discussed results of an industry project concerning energy management in
building. Specifically the work analyses the improvement of electrical outlets controlled and activated by a
logic unit and a data mining engine. The engine executes a Long Short-Terms Memory (LSTM) neural
network algorithm able to control, to activate and to disable electrical loads connected to multiple outlets
placed into a building and having defined priorities. The priority rules are grouped into two level: the first
level is related to the outlet, the second one concerns the loads connected to a single outlet. This algorithm,
together with the prediction processing of the logic unit connected to all the outlets, is suitable for alerting
management for cases of threshold overcoming. In this direction is proposed a flow chart applied on three
for three outlets and able to control load matching with defined thresholds. The goal of the paper is to
provide the reading keys of the data mining outputs useful for the energy management and diagnostic of the
electrical network in a building. Finally in the paper are analyzed the correlation between global active
power, global reactive power and energy absorption of loads of the three intelligent outlet. The prediction
and the correlation analyses provide information about load balancing, possible electrical faults and energy
cost optimization.
Using Image Processing Techniques for Automated Detection and Annotation of F...ijtsrd
With the increasingly growing number of solar energy sites, the need for better and faster fault detection techniques becomes more pressing. Using IR imaging is reliable and effective but scanning thousands or even hundreds of thousands of PV modules in mega sites quickly turns to be a time consuming and tedious task. To save on the spent effort and time digital image processing techniques can be introduced into the inspection process to help identify and annotate defects in the thermal footage of PV modules in an automated manner. The methodology used in the paper relies on analyzing the histogram to choose a suitable thresholding point that would help isolate the potential faulty areas in a grayscale IR image. Atilla Erg¼zen | Muhammet Sait "Using Image Processing Techniques for Automated Detection and Annotation of Faulty Regions in Thermal Infrared Images of PV Modules" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29749.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/29749/using-image-processing-techniques-for-automated-detection-and-annotation-of-faulty-regions-in-thermal-infrared-images-of-pv-modules/atilla-erg%C3%Bczen
Satellite image processing is an intricate task that requires vast computation and data processing, which cannot
be handled by a single computer. Furthermore, the processing of the massive amount of data accumulated by
the satellite is a huge challenge for the end user. Hence, grid computing is the essential platform to provide high
computing performance at the user end. This article reviews the grid services used for satellite image processing
and significant data processing.
Crane monitoring system based on internet of things using long range IJECEIAES
The four main causes of crane accidents are overturned, falls, mechanical failure, and contact with power lines. It is important to keep track of the crane’s health and condition as it is always too late when a failure of the crane was found. Any abrupt accidents will interrupt or delay the work progress and cause the operational costs to increase. Crane monitoring system is developed using long range (LoRa) technology due to its long range of detections making it suitable for monitoring machines that require large space including the dock area. It also consumes low power and is suitable for battery-operated systems. This paper discusses the design and development crane monitoring system using Arduino Uno together with NodeMCU ESP8266 as the hardware for this project. Temperature, power consumption, lifting activities, and total operating hours will be measured using appropriate sensors. The data will then be sent to the database where users can monitor each crane from a developed Android application using a mobile phone. This project allows users to view, monitor, and analyze real-time or past data in a graph or table view. Experimental results prove the proposed system is applicable and effective.
Solar energy is one of the most promising renewable sources that is currently being used worldwide to contribute for meeting rising demands. In this paper solar irradiance measurement will experimentally carried out in two different regions in Egypt; Cairo and Luxor cities. This paper proposes a simple solar lux measurement using a light dependent resistor (LDR) with an arduino kit. This technique is based on two approaches which are coarse and fine maximum sun lux determination. This is based on the predetermined 260 vertical slop of the LDR. Coarse tuning determines one of the reach sun lux quarter (900) of horizontal quad. The fine tuning allocates the optimized 100 in which; the maximum sun lux can be obtained. The optimal values of sun lux were found between the (90o–180o) quarter. This study confirms that the narrow ten degrees (95o-105o) are the optimized static sun lux extraction for the two site field measurements. This novel technique can be used for locating the angle of best installations for the solar cell at which maximum solar energy can be extracted.
To study thermal imaging technology for the protection of power system equipment and to interface FLIR thermal imaging camera with MATLAB for real time thermal imaging of electrical power equipment
Medical system based on thermal optical system and neural networkIJECEIAES
Military personnel in the training or operational phases always need constant medical examination, but the presence of efficient medical care is difficult to implement in real-time for such cases. A wireless system for thermal tracking of soldiers was proposed, as well as tracking their vital signs in real time. Thermal cameras are used with an optical system designed to increase the accuracy of the thermal images captured as the change in the electrocardiogram, heart rate, and temperature measurements are measured using a specially designed circuit. The results from both the thermal system and the biometric system are combined and sent to a computer for analysis using a model prepared with neural network technology. The proposed system was tested, and a database was created for 127 males and 110 females during training and rest times. The neural network model achieved a response time of 85 seconds until the release of the final analysis, and the accuracy of the proposed tracking system is 96%. The main contribution of this paper is the design of an integrated portable system for rapid, in-field, real-time military medical diagnostics.
Enhanced image reconstruction of electrical impedance tomography using simul...IJECEIAES
Electrical impedance tomography (EIT), as a non-ionizing tomography method, has been widely used in various fields of application, such as engineering and medical fields. This study applies an iterative process to reconstruct EIT images using the simultaneous algebraic reconstruction technique (SART) algorithm combined with K-means clustering. The reconstruction started with defining the finite element method (FEM) model and filtering the measurement data with a Butterworth low-pass filter. The next step is solving the inverse problem in the EIT case with the SART algorithm. The results of the SART algorithm approach were classified using the K-means clustering and thresholding. The reconstruction results were evaluated with the peak signal noise ratio (PSNR), structural similarity indices (SSIM), and normalized root mean square error (NRMSE). They were compared with the one-step gauss-newton (GN) and total variation regularization based on iteratively reweighted least-squares (TV-IRLS) methods. The evaluation shows that the average PSNR and SSIM of the proposed reconstruction method are the highest of the other methods, each being 24.24 and 0.94; meanwhile, the average NRMSE value is the lowest, which is 0.04. The performance evaluation also shows that the proposed method is faster than the other methods.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Thermal cameras are useful devices, Today, advanced thermal imaging devices with high sensitivity are being developed and used, especially in the medical field (Lahiri et al., 2012). Products from the research results of the author group towards compact size, easy to handle, convenient to carry, low cost of implementation, monitoring range in the measuring area of the sensor eye up to 7 meters, the monitoring temperature zone can be limited, the color displayed is equivalent to the temperature zone, and the resolution of the thermal pixels can also be adjusted simply through the buttons.
In order to expand the application range of research products, improve accuracy, reliability, increase resolution, etc., it is possible to use more measuring sensor eyes, use wireless communication networks, accurate image recognition and processing algorithms, large display screen to facilitate monitoring more clearly.
PROTECTION OF ELECTRICAL EQUIPMENT USING 3D THERMOGRAPHY AND IMAGE PROCESSINGEklavya Sharma
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OVERVIEW AND APPLICATION OF ENABLING TECHNOLOGIES ORIENTED ON ENERGY ROUTING MONITORING, ON NETWORK INSTALLATION AND ON PREDICTIVE MAINTENANCE
1. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.9, No.2, March 2018
DOI : 10.5121/ijaia.2018.9201 1
OVERVIEW AND APPLICATION OF ENABLING
TECHNOLOGIES ORIENTED ON ENERGY ROUTING
MONITORING, ON NETWORK INSTALLATION AND
ON PREDICTIVE MAINTENANCE
Alessandro Massaro, Angelo Galiano, Giacomo Meuli, Saverio Francesco Massari
Dyrecta Lab, IT research Laboratory, via Vescovo Simplicio,45, 70014
Conversano (BA), Italy
ABSTRACT
Energy routers are recent topics of interest for scientific community working on alternative energy.
Enabling technologies supporting installation and monitoring energy efficiency in building are discussed in
this paper, by focusing the attention on innovative aspects and on approaches to predict risks and failures
conditions of energy router devices. Infrared (IR) Thermography and Augmented Reality (AR) are
indicated in this work as potential technologies for the installation testing and tools for predictive
maintenance of energy networks, while thermal simulation, image post-processing and data mining
improve the analysis of the prediction process. Image post- processing has been applied on thermal images
and for WiFi AR. Concerning data mining we applied k-Means and Artificial Neural Network –ANN-
obtaining outputs based on measured data. The paper proposes some tools procedure and methods
supporting the Building Information Modeling- BIM- in smart grid applications. Finally we provide some
ISO standards matching with the enabling technologies by completing the overview of scenario
.
KEYWORDS
Energy Router, IR Thermography, Augmented Reality, Predictive Maintenance, Design Simulation, Data
Mining, Neural Network,Building Information Modeling, Failure Conditions.
1. INTRODUCTION
Smart Grid applications and energy management are interesting topics in the research field [1]-
[3]. A particular attention has been focused on Home Energy Management (HEMS) [3] and on
energy management by means of simulators [4]. As simulator useful for the study and the design
of energy system some authors have applied the open sources Energy 2D [5]-[7] and Energy 3D
tools [8]-[12]. The use of the simulator tools are suitable for the improvement of the Visual
Process Analytics (VPA) which includes data mining algorithms and multiple visualization
oriented on energy monitoring and predictions [8]. A good matching between Artificial
intelligence and computer-aided design (CAD) platforms support human designers for a high
performance energy models [9]-[11]. Approaches including infrared (IR) thermography post-
processed by data mining (K-Means clustering) algorithms, are able to support the planning of
procedures oriented on the monitoring of energy efficiency in building, by defining risk maps of
energy leakage with only a radiometric image [12]. The application of Artificial Neural Network
(ANNs) together with infrared thermography has been discussed in [13], where ANNs network
(Multilayered Perceptron –MLP-) provided predictive maintenance of electrical equipment by
classifying defects identified into thermal images. Also Augmented Reality (AR) technologies
have been applied on thermal visualizations [14]-[16] thus suggesting their application in
Building Information Modeling (BIM) [17]. In order to implement a decision support system
(DSS) based on predictive maintenance it is necessary to apply data mining algorithms. These
2. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.9, No.2, March 2018
2
algorithms can be executed by using open source tools such as R language, Orange Canvas,
Rapid Miner, KNIME and Weka (Waikato Environment for Knowledge Analysis), available as
Graphical User Interfaces (GUIs) using objects or libraries [18]-[19]. Advanced image processing
tools such as ImageJ can provide further typologies of advanced analyses such as 3D processing
and segmentation of images [20]-[21]. A typical tool for the design of hybrid renewable energy
systems is Homer [22]. This tools provides ideal load distribution during the time starting from
the energy power distribution of a smart grid. In this paper we discuss and apply the tools and the
approaches mentioned partially in the state of the art by highlighting some aspects concerning the
predictive maintenance. The paper is structured as follows:
• Description of the main architecture of enabling technologies utilized in a pilot research
project involving energy routing of renewable energy;
• Application of different tools considered the proposed architecture;
• Definition of algorithms for intelligent electric load management based on power
prediction;
• Discussion about ISO standard laws regarding the main topics developed in this work.
Figure 1. System architecture integrating enabling technologies for energy routing analysis.
2. ENABLING TECHNOLOGIES AND RESULTS
In this section we describe the architecture of Fig. 1 embedding different enabling technologies
oriented on the design, the installation, the monitoring and on the predictive maintenance of an
energy routing building network.
2.1. System architecture and main use cases
In Fig. 1 we illustrate the proposed system architecture containing the main use cases. We list
below the use case concerning the main actors of the system:
3. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.9, No.2, March 2018
3
• Case 1 (testing installation): the thermographic operator acquires thermal images by
means of a thermal camera or by a visor/mobile device supporting real time thermal
image processing (processing of augmented reality). The inspection of the energy router
components should be performed under good environment conditions (good thermal
excitation, absence of rain and humidity, etc.), and good operator position (good angle of
view). The thermal testing should executed by applying the electrical loads.
• Case 2 (testing and verification before to apply maintenance procedures): the operator
downloads thermal data and thermograms on the personal computer and verify better the
detected anomalies. After he compares the design specifications with the measured data
of the produced energy by observing possible mismatching. This verification it is
important to update the maintenance procedures (predictive maintenance of first level).
• Case 3 (data post-processing): the operator post-processes the radiometric data in order to
classify in details the defects and the anomalies by means of data mining algorithms (k-
Means for clustering and ANNs network for prediction) and of image processing tools.
The post-processing outputs provide further information about predictive maintenance
(predictive maintenance of second level). The post-processing phase includes the
improvement of the electric load management due to the analysis of data sensors
monitoring electrical power distribution in the building.
• Case 4 (training): the operator visualizes in a AR visor the post-processed image in order
to learn risk levels and maintenance procedures.
2.2. IR Thermography: basic principle and thermal camera specifications used in
the experimentation
Temperature measurement using IR Thermography measures the infrared radiation emitted by an
object and converts the energy detected due to particle vibrations into a temperature value [23].
For a good setting of a thermal camera radiation from other sources or from environment must be
removed in the conversion to temperature. This setting process is called compensation. As
reported in equation (1), the total radiation received by the thermal camera (WT) comes from three
sources: the emission of the target object (Eo), the emission of the surroundings and reflected by
the object (Er) and the emission of the environment (Ee):
T o r eW E E E= + + (1)
where
4
4
4
( )
(1 ) ( )
(1 ) ( )
o o e o
r o e r
e e e
E T
E T
E T
ε τ σ
ε τ σ
τ σ
= ⋅ ⋅ ⋅
= − ⋅ ⋅ ⋅
= − ⋅ ⋅
(2)
being σ is the Stefan–Boltzmann constant, ε is the emissivity, τ is the transmittance, and T is the
temperature. The transmittance of the environment is generally estimated using the distance from
the object to the camera and the relative humidity. In general, this value is very close to one. The
temperature of the environment can be obtained using a common thermometer. For the
experimental radiometric images used this work has been used a FLIR T1020 camera, having the
following main specifications: thermal sensitivity <0,02 °C at 30 °C, IR sensor resolution of 1024
4. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.9, No.2, March 2018
4
x 768 pixel, temperature range of -40°C to 2000°C, frame rate of 30 Hz, spectral range between
7.5 – 14 µm.
2.3. Augmented Reality Embedding IR Thermography
Augmented Reality embedding thermography could be improved in different ways such as: (i) by
constructing visor objects by means of development platform (Unity 3D [24] or Vuforia [25])
able to download them in a visor or in a smartphone inserted into a cardboard ; (ii) by integrating
a thermal sensor inside the visor [26]; (iii) by using smartphone or tablet in Multi-Spectral
Dynamic Imaging (MSX), thermal fusion and picture-in-picture modalities. The last approach is
the basic mode to improve augmented reality directly on a mobile device: a simple way to
produce a real time AR images is to use Flir Tools Mobile App which allows to view the thermal
images in real time by means of the WiFi connection. The ‘augmented’ reality is due to the
superposition of real photo contours (or part of the real image) on the thermal images, by viewing
in real time the composed images. Concerning this last approach it is possible to add in the
thermal image different information about inspected object such as parameters or comments, or
colour maps filtering different range of temperature values. In Fig. 2 we show an example of
importing assets elements in Unity 3D platform by considering a post-processed image of an
electrical cabinet. This approach could be useful for training procedures (case 4) by constructing
a virtual environment of a thermal post–processed image, in order highlight possible critical
aspects about fire risk or failure of electrical components. In Fig. 3 we show an image observed
on a smartphone through different modalities of an electrical cabinet: in this image it is possible
to distinguish better each element of the object thus facilitating the localization of the anomaly
characterized by a higher temperature. Therefore the Augmented reality can be adopted to:
• verify the energy equpment status by means ‘augmented’ viewing modalities, or by
means of a visor integrating IR thermal sensor, after the installation activity;
• transfer knowledge to workers enabled for installation and maintenance of elements
of the energy network by designing virtual environments;
• predict imminent electrical failures.
Figure 2. Case 4: thermal image loaded as assets in Unity 3D platform.
5. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.9, No.2, March 2018
5
Figure 3. Thermal image of a small electrical cabinet: case 1. (a) Original thermal image, (b) MSX image,
(c) thermal fusion image, (d) picture-in-picture image.
2.4. Software oriented on design, on verification, and on predictive maintenance
The design software can be used in order to test the correct work of the energy router network and
to optimize the energy router layout. We discuss in this paragraph different simulators applied for
the study of building thermal properties and for thermal characterization of some electrical
components. The simulator Energy 2D [5]-[7] has been applied for the thermal characterization of
building indoor heat transfer and for thermal characterization of the electrical cabinets, while
Energy 3D [8]-[12] provides mainly data about outdoor applications involving solar energy of
whole buildings and on photovoltaic (PV) panels. Both the simulators solve the follow
differential equation:
( ) [ ]
T
c T k T q
t
ρ
∂
+ ∇⋅ = ∇⋅ ∇ + ∂
υ (3)
being k the thermal conductivity, c the heat capacity, ρ the density, νννν field velocity, and q the heat
generation. The equation (3) is solved in the 3D (x,y,z) space by means of the Finite Difference
Time Domain (FDTD) approach. In Fig 4 (a) we illustrate an Energy 2D simulation of heat
indoor distribution by considering a model with two heat source (see circles) and three
“numerical” thermometers placed in the middle of the room. This model is useful in order to
compare measured indoor data with ideals ones provided by the simulation, and to allocate
efficiently sensors in the building structure (case 2). Furthermore the indoor simulation can be
analysed for the explanation of thermal phenomena observed by the IR thermography (location of
heat sources, thermal bridges, etc.): for example the predictive maintenance can be applied by
analysing results of thermal leakage of windows or some parts of the building to restructure. It
was observed from the simulations that the thermometer 1 (higher position), starting from 0 ° C,
reached the temperature of 20 °C after only 4 minutes and 30 seconds. This is an important aspect
about the monitoring of the real case, where is needed the knowledge of the time necessary to
heat a room. We report in same Fig. 4 (a) the superimposed graphs related to the temperature
6. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.9, No.2, March 2018
6
transitory which calculated the room heating time. We illustrate in Table 1 the parameters used
for the simulation.
Table 1. Energy 2D: parameters used in the simulation of Fig. 4 (a).
Element Physical Properties
Wall Thermal Conductivity= 0.001 W/m⋅°C
Specific Heat= 1300 J/(kg⋅°C)
Density = 25 kg/m3
Ceiling Thermal Conductivity= 1 W/m⋅°C
Specific Heat= 2000 J/(kg⋅°C)
Density = 25 kg/m3
Window Thermal Conductivity= 1 W/m⋅°C
Specific Heat= 1300 J/(kg⋅°C)
Density = 25 kg/m3
Ground Thermal Conductivity= 0.001 W/m⋅°C
Specific Heat= 1300 J/(kg⋅°C)
Density = 25 kg/m3
Roof Thermal Conductivity= 0.1 W/m⋅°C
Specific Heat= 1300 J/(kg⋅°C)
Density = 25 kg/m3
Thermal sources Thermal Conductivity= 1 W/m⋅°C
Specific Heat= 1300 J/(kg⋅°C)
Density = 25 kg/m3
Temperature Source = 75 °C
In Fig. 4 (b) we illustrate the time domain simulation of the coupled temperature inside an
electrical cabinet by setting each modules as a temperature source (for a total of six temperature
sources, where a critical one is at 50 °C ), and by placing three thermometers inside the cabinet.
In order to calculate the temperature distribution, we considered an air transmission medium
(yellow box). By executing simulation we observe that after one minute the estimated value of the
central thermometer is 43.6 °C which could represent a condition of overheating and therefore of
risk. So if only one element reaches high irregular temperatures, it can damage the other elements
and can origin an electrical malfunction. In Fig. 4 (c) and Fig. 4 (d) we illustrate some Energy 3D
simulations. Specifically in Fig. 4 (a) we show the thermal simulation of the building that takes
into account the solar heating due to geolocation, exposure and surrounding environment, besides
in Fig. 4 (c) we illustrate the heat distribution of an irradiated multiple solar racks calculating the
solar energy produced by 128 panels characterized by the following specification: average land
area occupied by panel of 6 m2
, total surface area of panels of 248.37 m2
, cost of solar panels $
82.20, output of maximum energy per hour 20.86 kWh. Simulation 3D is suitable for design, for
installation, and for verification of total energy produced (case 1 and case 2). If a defined alert
gap between energy simulated and energy measured is observed, will be updated the maintenance
programme by predicting the panels inefficiency (predictive maintenance of first level).
7. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.9, No.2, March 2018
7
Figure 4. (a) Energy 2D: thermal simulation of indoor heat propagation; in the plot are superimposed the
time evolution of the three temperatures. (b) Energy 2D: thermal simulation of temperature coupling in an
electrical cabinet; in the plot are superimposed the time evolution of the three temperatures. (c) Energy 3D:
simulation of solar impact on a building. (d) Energy 3D: simulation of solar impact and energy production
of four photovoltaic panels.
We illustrate in Table 2 an example of working and critical temperature values of elements of an electrical
cabinet (CEI EN 61439) useful for the comparison of numerical results.
Table 2. Element of an electrical cabinet: critical and operating temperatures.
Element Recommended operating
temperature
Maximum temperature
with risk of malfunction
Speed variators 35 °C 50 °C
PLC 35 °C 40 - 45 °C
Contactors 45 °C 50 °C
Switches 45 °C 50 °C
Fuses 50 °C 50 °C
Power supplies 35 °C 40 °C
PCB 30 °C 40 °C
Batteries 20 - 25 °C 30 °C
TLC devices 40 – 50 °C 55° C
PFC Capacitors 50 °C 55° C
We observe that online are available other open source tools oriented on the design and
temperature modelling of an electrical cabinet [27]. The design of a whole smart grid network
integrating different alternative energy sources can be achieved by Homer Simulator [22]. This
simulator is able to provide the main sizing of the smart grid and the template system
architecture. In the simulation of Fig. 5 is illustrated the schematic architecture of a network
characterized by a primary load of 10 kWh/d, 1.65 kW of peak, 1 kW wind turbine (module
XL1), 1 kW photovoltaic panels (module PV), 2.6 kW gasoline generator (module Gen), AC/DC
converter, and energy storage batteries (module P16P having nominal capacity 2kW/h). The
illustrated layout has been designed to handle small electrical loads lights only. In Fig. 5 we show
8. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.9, No.2, March 2018
8
the electric load characteristics of the simulated network: the curves refer to daily, monthly and
annual trends. By analysing these curves it is evident how there are three load peaks during the
day at the sixth hour, at the twelfth hour and at the eighteenth hour (most intense peak of 1.231
kW). The same simulator provides also information about load frequency (in the simulated case
0.35 kW is the power most used corresponding to a percentage of about 12% of the entire use of
the entire electric load), and about equipment characteristics (costs, replacement costs, life cycle).
Figure 5. Homer simulator: template of a smart grid architecture and load distribution.
2.5. Data post-processing
The data post-processing represent the real innovation of the BIM approach and provides
important information about predictive maintenance. In order to create an efficient predictive
model it is important to create the training dataset by experimental results. To do this different
sensors (meter analyser, data logger, meteorological sensors) have been installed on a PV
prototype demonstrator (having a peak power of 30 kWp). For the data post-processing we used
the java libraries using Eclipse platform, and ANN Weka libraries (Time Series Forecasting with
Multilayer Perceptron -MLP- classifier [28]). The training dataset has been created by 3690
measured data extracted from the experimental Sunguard platform [29], where each data sample
corresponds t the total energy measured at a time step of 5 minutes. The testing dataset has been
created by the measurements of the last 2.5 days (720 counts). In order to compare predicted
values with real ones, we have waited 2.5 days (from 00:01 on 23/June/2017 to 12:00 on
25/June/2017, equal to a count of other 720 measured data). A good data matching is observed in
the result of Fig. 6, thus validating of the neural networks predictive model. We observe that the
initial oscillation may be due to the initial calculation error which is successively attenuated. The
predictive maintenance should be obtained in this case by predicting long term efficiency of PV
panels, and by correlating other predicted data such as meteorological data. Data post-processing
can be applied also to radiometric data of thermal images. In Fig. 7 we shows an example of
measured thermal defect points by using the line measurement approach in Flir Tools software:
measured temperature are extracted from the written line crossing the defect point, and
successively measurement data are plotted from the exported csv file. We indicates in Table 3
some defect characterization useful for monitoring of PV panel efficiency and for predictive
maintenance (modification of standard maintenance planning). Concerning wind turbine defect
detection, thermography could provide information about abnormal overheating thus predicting
breakages. Wind turbines infact incorporate different electrical and mechanical components
which can break down, generating costly downtime and dangerous accidents. Inspections with
thermal imaging cameras can help prevent such accidents. Both for electrical and mechanical
components the general rule is that a component will become hot before it fails. Thermography
can be used to spot this rise in temperature before a failure occurs (hot spot detection of
9. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.9, No.2, March 2018
9
transformers, connectors, controllers yaw motors etc.). Another data post-processing process able
to facilitate the prediction of anomalies for electrical devices is data mining clustering.
Specifically the application of K-Means algorithm [30] provides clustering of radiometric values.
In Fig. 8 we illustrate the clustering calculation of measurements of a selection box embedding
power supplies. For this result we have adopted the KNIME K-Means module: the three clusters
(K=3) are characterized by increasing temperatures (cluster 0 = lower temperatures, cluster 1 =
average temperatures, cluster 2 = higher temperatures). The increase of measurements of cluster
2 dataset could mean a damage risk.
Table 3. PV panels: Defects detect by thermal images.
Error type Example Image representation
Production defect Impurity and gas inclusion Hot/could defect points
Cracks in cells Heating of cells with a
predominantly elongated
shape
Damages Cracks Heating of cells with a
predominantly elongated
shape
Cracks in cells a part of the cell looks
warmer
Temporary darkening Pollution Hot defect points
Bird excrements
Humidity
Defective bypass diode Short circuit and reduced circuit
protections
Patchwork conformation
Failed interconnections Modules or series of modules not
connected
Module or series of
modules continuously
warmer
Figure 6. Times series forecasting neural network (MLP) simulation: comparison between predicted and
measured results. Inset: geolocation of the PV prototype demonstrator.
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Figure 7. Defect point characterization. (a) Photo of PV panels of the prototype demonstrator. (b) MSX
thermal image with a marker indicating a thermal defect point. (c) Thermal image of a selected PV panel
obtained by changing color scale. (d) Thermal image of the region surrounding the defect point crossed by
the calculus line Li1 (A and B indicate the line extremes). (e) Data plotting of extracted csv file of the
radiometric measurements related to line plot.
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Figure 8. (a) Box selection of thermal image of power supplies of an electrical cabinet. (b) data mining
workflow implementing KNIME K-Means algorithm reading the exported csv file of the box selection (Flir
Tools data exporting). (c) Box plot of box selection measurements. (d) Scattering plot of measurements
grouped in clusters (plot of column values of Fig. 8 (c)).
In Fig. 9 (a) is shown a group of power supplies image post-processed by setting a filter
temperature range: this function highlights the regions where the temperature is between a
temperature range thus analyzing temperature coupling. The 3D image processed Fig. 9 (b)
highlights the pixel intensity information by observing the whole thermal environment of the
electrical cabinet. This processing could be adopted for training by AR technology (case 4). In
Fig. 9 (c) and Fig. 9 (d) are illustrated other measurements plotted by Flir ResearchIR software.
In Fig. 10 is illustrated in succession the post processing of a thermal image by means of K-
Means clustering [31] (ImageJ processing with K=10). This tool is a part of the trainable Weka
segmentation of ImageJ plugin that combines a collection of machine learning algorithms with a
set of selected image features to produce pixel-based segmentations. From Fig. 10 (d) it is
possible to observe an irregular heating for the panels located on middle and on the top right of
the detected row of photovoltaic panels. This could explain an inefficiency and suggest to
reformulate the maintenance planning. In table 4 are indicated the calculated centroid values and
the numerical errors related to the different calculus iteration of the K-Means processing (the
error decrease with the iteration number until value 0 related to the 12 th. iteration). The ImageJ
K-Means algorithm has been executed in about 2 second using a Intel core i3-403OU. 1.9GHz PC
CPU.
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Figure 9. (a) Temperature range filterning between 23.7 °C and 29.7 °C. (b) ImageJ 3D Surface Plot of a
post processed image. (c) Flir ResearchIR: histogram plot of pixel measurements enclosed in Box1, and
(d) oscilloscope plot.
Figure 10. (a) Photo of the PV panels related to the prototype demonstrator and (b) related thermal images.
(c) Flir ResearchIR: exported jpeg image containing pixel temperature information. (d) K-Means clustering
(K=10) of image of Fig. 10 (c).
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Table 4. Image J: K-Means image clustering results (It: iteration).
Cluster Centroid
Value
(it. 12)
Initial
Cluster
(it. 0)
It. 1 It. 2 It. 3 It. 4 It. 5 It. 6 It. 7 It. 8 It. 9 It. 10 It. 11
0 213.15 188 197.11 201.31 204.58 206.25 207.85 209.59 210.47 211.35 212.25 213.15 213.15
1 18.83 6 10.38 13.40 15.52 16.40 17.19 17.99 17.99 18.83 18.83 18.83 18.83
2 89.84 86 88.53 88.93 88.93 89.43 89.84 89.84 89.84 89.84 89.84 89.84 89.84
3 100.12 100 98.94 99.01 99.07 99.90 99.95 100 100 100.05 100.05 100.05 100.12
4 152.74 137 139.41 142.73 145.31 147.35 148.64 149.98 150.42 151.35 151.78 152.21 152.73
5 58.35 57 58.22 58.22 58.22 58.22 58.22 58.35 58.35 58.35 58.35 58.35 58.35
6 79.43 79 78.61 78.83 78.83 78.83 79.43 79.43 79.43 79.43 79.43 79.43 79.43
7 41.27 40 39.49 39.49 39.49 39.49 40.17 40.61 41.27 41.27 41.27 41.27 41.27
8 28.67 25 25.27 25.76 26.39 26.74 27.46 27.85 28.20 28.67 28.67 28.67 28.67
9 66.42 68 66.86 66.43 66.42 66.42 66.42 66.42 66.42 66.42 66.42 66.42 66.42
Error 0 // 118.89 38.31 22.28 8.83 6.33 5.81 1.53 2.57 0.98 0.99 0.28
2.6. Algorithms for electrical load management based on power consumption
prediction
Cloud monitoring and control of electric loads represent an important topic for energy routing
research. In Fig. 11 is illustrated an example of architecture oriented on fault tolerance (property
that enables the electric system to continue operating properly in the event of the failure, where
failure could mean that the total power overcomes a threshold). The smart building can be
monitored in cloud by different sensors (for example transmitting in Message Queuing Telemetry
Transport –MTTQ- or Advanced Message Queuing Protocol –AMQP- or other protocols [32]):
by means of dashboards an external user can activate or deactivate electric loads predicting the
total power behaviour. Different technologies could be implemented for the realization of electric
boards connecting sensors measuring electric power. Raspberry PI and Arduino are used in
research for smart home control [33], and are good candidates for programming fault tolerance
intelligence. Industrial components such as Controllino [34] are compatible with Arduino
technology and are versatile because can be installed directly on electrical cabinet and can be
easily interfaced with cloud. We report in Fig. 12 and Fig.13 the criteria of load management by
load lines, where segmented line indicates the measured electrical power and the red line
indicates the threshold line having a defined slope. Each power variation during the time is
characterized by a slope, if the predicted measurement of the total power (in this case one minute
prediction indicated by the dashed line of Fig. 13 (a) is considered) overcomes the threshold line,
will be deactivated a possible no priority load or groups of no priority loads for more accentuated
predicted slope (selecting from a list containing priority levels of each loads). Figure 12 (b)
exhibits the flowchart representing the load activation taking into account the priority order.
Figure 13 (b) indicates the deactivation procedure by considering the over threshold of the total
predicted (sum of power of each load which are classified by priority levels). The prediction is
estimated by hypothesizing the same slope behaviour of the last minute.
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Figure 11. Case Use 3: (a) schematic architecture of monitoring and control of power load switching; (b)
scheme of a case of fault tolerance managed by DSS.
Smart building
Sensor 1 Sensor 2
MTTQ
AMQP
Energy Router
(Energy
Switching)
Sensor n
Dashboard (Decision Support System -DSS)
Cloud
Control/
processing and
actuation
Lp1 Lp2 Lpn
(a) (b)
Priority loads
No priority loads
L1 L2
Ln
DSS
Threshold
Wh
Time
load
deactivation
Fault Tolerance Scheme
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0
100
200
300
400
500
600
700
800
900
1000
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Monitoring
Detected load Maximum load
Wh
minute
A(xC1,yC1)
B(xC2,yC2)
C(xmax1,ymax1)
D(xmax2,ymax2)
Transient Regime
Slope
Slope
Are all electric
loads active?
Sorting of non-active electric loads
in descending order of priority
diff= ymax2-yC2
Is list
empty?
Consider first electric
load from list
Available?
Remove electric load
from list
No operation
diff>size_load
diff=diff-size_load
Electric load activation
Remove electric load
from list
Remove electric load
from list
No operation
YES NO
NO
YES
NO
YES
YES
NO
(a)
(b)
Figure 12. (a) Case of power load threshold estimation and definition of parameters related the case of load
activation (total loads under threshold). (b) Flowchart of load activation.
16. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.9, No.2, March 2018
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0
100
200
300
400
500
600
700
800
900
1000
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
ymax2next-yC2next<0
Detected load Maximum load
E(xmax2next,ymax2next)
F(xC2next,yC2next)
D(xC2,yC2)
C(xmax2,ymax2)
B(xC1,yC1)
A(xmax1,ymax1)
Wh
minute
Transient Regime
Slope
(a)
ymax2-yC2>0
Sorting of non-active loads in
increasing order of priority
Is list
empty?
Consider first electric
load from list
diff<0
No operation
abs>size_lo
ad
diff=diff-size_load
Electric load
deactivation
Remove electric load
from list
Remove electric load
from list
Low priority electric load
deactivation
YES NO
NO
YES
YES
NO
yC2next and ymax2next estimation
diff=ymax2next -yC2next
abs=|diff|
diff=0
No operation
Electric load
deactivation
YES NO
NO YES
(b)
Figure 13. (a) Case of power load threshold estimation and definition of parameters related the case of load
deactivation (total loads over threshold). (b) Flowchart of load deactivation.
3. INTERNATIONAL STANDARDS
International standards are standards studied and developed by international standards
organizations. International standards are references for consideration and use worldwide in
different applications. The main organization is the International Organization for Standardization
(ISO). International standards in wind and solar energy generation are nowadays driving all the
17. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.9, No.2, March 2018
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most relevant aspects in engineering and construction of RES energy facilities. Standards and
Conformity Assessment issued by International bodies and Institutions (IEC, ISO) provide solid
bases for development of this sector with regard to site suitability and resource assessment,
design, engineering, modeling, measurement, test, operation and maintenance. Following some of
the most relevant standards in solar and wind:
• S+ IEC/TS 61836 Ed. 3.0 en:2016 (Redline version): solar photovoltaic energy systems -
Terms, definitions and symbols;
• IEC 60904-3 Ed. 3.0 b:2016: Photovoltaic devices - Part 3, measurement principles for
terrestrial photovoltaic (PV) solar devices with reference spectral irradiance data;
• ISO 9847:1992: Solar energy - Calibration of field pyranometers by comparison to a
reference pyranometer;
• IEC/TS 62727 Ed. 1.0 en:2012: Photovoltaic systems, specification for solar trackers;
• IEC 60050-415 Ed. 1.0 b:1999: International Electrotechnical Vocabulary - Part 415:
Wind turbine generator systems;
• ISO 12494:2017: Atmospheric icing of structures;
• ANSI/AGMA/AWEA 6006-A03 (R2016): design and specification of gearboxes for
wind turbines;
• IEC 60076-16 Ed. 1.0 b:2011: power transformers - Part 16: Transformers for wind
turbine applications;
• AS 4959-2010: Acoustics - Measurement, prediction and assessment of noise from wind
turbine generators (FOREIGN STANDARD), sets out a method for the measurement,
prediction and assessment of noise from wind turbine generators;
• BS EN 50308:2004: wind turbines; protective measures; requirements for design,
operation and maintenance (British Standard);
• IEC 61400-1 Ed. 3.0 b:2005: wind turbines- Part 1, design requirements;
• IEC 61400-2 Ed. 3.0 b:2013: wind turbines -Part 2, small wind turbines;
• IEC 61400-3 Ed. 1.0 b:2009: wind turbines - Part 3, design requirements for offshore
wind turbines;
• IEC 61400-4 Ed. 1.0 en:2012: wind turbines - Part 4, design requirements for wind
turbine gearboxe;
• IEC 61400-21 Ed. 2.0 b:2008: wind turbines - Part 21, measurement and assessment of
power quality characteristics of grid connected wind turbines
• IEC 61400-23 Ed. 1.0 en:2014: wind turbines - Part 23, full-scale structural testing of
rotor blades.
• Concerning thermography we list below some useful international standards:
• ISO 9712 – Third edition – 2005 “Non-destructive testing – Qualification and
certification of personnel”;
• ISO 18436-1 (2012) “Condition monitoring and diagnostics of machines - Requirements
for qualification and assessment of personnel - Part 1: Requirements for assessment
bodies and the assessment process”;
• ISO 18436-3 (2012) “Condition monitoring and diagnostics of machines - Requirements
for qualification and assessment of personnel - Part 3: Requirements for training bodies
and the training process”;
• ISO 18436-7 (2014) “Condition monitoring and diagnostics of machines - Requirements
for qualification and assessment of personnel - Part 7: Thermography”;
• ISO 18434-1:2008 Condition monitoring and diagnostics of machines –Thermography-
Part 1: General procedures.
• ISO 6781:1983 Thermal insulation—Qualitative detection of thermal irregularities in
building envelopes—Infrared method.
18. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.9, No.2, March 2018
18
• EN 13187 Thermal performance of buildings. Qualitative detection of thermal
irregularities in building envelopes. Infrared method.
• DIN 54190-1 Zerstörungsfreie Prüfung—Thermografische Prüfung—Teil 1: Allgemeine
Grundlagen.
• DIN 54190-2 Non-destructive testing—Thermographic testing—Part 2: Equipment.
• DIN 54190-3 Zerstörungsfreie Prüfung—Thermografische Prüfung—Teil 3: Begriffe.
• DIN 54191 Zerstörungsfreie Prüfung—Thermografische Prüfung elektrischer Anlagen.
• DIN 54192 Zerstörungsfreie Prüfung—Aktive Thermografie.
• VdS 2858en Thermography in electrical installations, a contribute to loss prevention and
operational reliability.
• ASTM E1934-99a (2014) Standard guide for examining electrical and mechanical
equipment with infrared thermography.
• CAN/CGSB 149-GP-2MP: Manual for Thermographic Analysis of Building Enclosures;
• ASTM C1060: Standard Practice for Thermographic Inspection of Insulation Installations
in Envelope Cavities of Frame Buildings;
• ASTM E1186: Standard Practice for Air Leakage Site Detection in Building Envelopes
and Air Barrier Systems;
• ASTM C1153: Standard Practice for Locating of Wet Insulation in Roofing System
Using Infrared Imaging;
• ASTM E1316: Terminology for Non Destructive Examinations;
• ASTM E1213: Standard Test Methods for Minimum Resolvable Difference for Thermal
Imaging System;
• ASTM E1311: Standard Test Methods for Minimum Detectable Temperature Difference
for Thermal Imaging System;
• ASTM E1862: Standard Test Methods for Measuring and Compensating for Reflected
Temperature Using Infrared Imaging Radiometers;
• ASTM E1897: Measuring and Compensating for Transmittance and Using Infrared
Imaging Radiometers;
• ASTM E1933: Standard Test Method for Measuring and Compensating for Emissivity
Using Infrared Imaging Radiometers;
• ASTM D4788: Standard Test Method for Detecting Delaminations in Bridge Decks
Using Infrared Thermography;
• Canada NMS Section 022713-2008: Thermographic Assessment: Building Envelope;
• Canada NMS Section 022719-2008: Thermographic Assessment: Mechanical
Equipment;
• Canada NMS Section 022723-2008: Thermographic Assessment: Electrical Systems.
4. CONCLUSION
The goal of the proposed paper is to describe the use of possible software and hardware
technologies enabling intelligent design, installation, monitoring and management of energy
router building equipements. All the proposed results are described in order to suggest some uses
of the proposed facilities the and to facilitate technology transfer. The discussed facilities are
related to IR Thermography, AR, image processing, load management and data mining. Al the
proposed technologies are oriented on Building Information Modeling and predictive
maintenance.
19. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.9, No.2, March 2018
19
ACKNOWLEDGEMENTS
The work has been developed in the frameworks of the Italian projects: “Energy Router e
Strumenti di Controllo Cloud per Smart Grid [Energy Router and Cloud-based control tools for
Smart Grids]”. The authors would like to thank the team of Garofoli S.p.A., EMI s.r.l. and of
Ginex Gaetano s.r.l. for their effort about requirements and study of the topics proposed in this
paper.
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Corresponding Author
Alessandro Massaro: Research & Development Chief of Dyrecta Lab s.r.l.