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This book constitutes the refereed proceedings of the 8th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2017, held in Costa de Caparica, Portugal, in May 2017.
The 46 revised full papers were carefully reviewed and selected from 95 submissions. The papers present selected results produced in engineering doctoral programs and focus on technological innovation for smart systems. Research results and ongoing work are presented, illustrated and discussed in the following areas: collaborative networks, computational intelligence, systems analysis, smart manufacturing systems, smart sensorial systems, embedded and real time systems, energy: management, energy: optimization, distributed infrastructure, solar energy, electrical machines, power electronics, and electronics.



Collaborative Networks


Supporting the Strategies Alignment Process in Collaborative Networks

The establishment of collaborative relationships with the network partners provides them important advantages, such as competitiveness and agility, when responding to the current rapid market evolutions. Nonetheless, the participation in collaborative networks becomes a complex process that starts with the alignment of all the enterprises’ objectives and strategies. Smart systems and approaches are needed in order support collaborative partners to deal with the strategies alignment challenge. The lack of alignment emerges because each enterprise defines its own objectives and strategies, to perform their business, and it could happen that non-compatible strategies are activated, involving the appearance of conflicts between strategies of different enterprises. To this regard, a decision support system is proposed, consisting of a mathematical model, a system dynamics method, a simulation tool and a guideline, with the main aim of supporting the process of identifying aligned strategies, among the enterprises of the collaborative network.

Beatriz Andres, Raul Poler

Service Personalization Requirements for Elderly Care in a Collaborative Environment

In diverse sectors companies collaborate to offer integrated user-centric services to obtain competitive advantage. However, regarding elderly care, services are typically isolated, and mostly provided by a single provider. Moreover, there is a need to personalize services in respect of the individuality of each senior and making these services evolve according to the lifestyle and necessities of the person. In this context, the concept of Elderly Care Ecosystem (ECE) emerges as a computer-based collaborative environment that promotes the integration of distinct services and providers. ECEs are complex systems that demand a clear description of their requirements, especially of those related to personalization of services. In this paper, we describe a set of core requirements and challenges for designing personalized services in an elderly care ecosystem. In particular, our contributions are: the identification of the main stakeholders of an ECE, the requirements to build a persona profile in ECE, and a reference requirements model based on i* for service personalization in ECE.

Thais Andrea Baldissera, Luis M. Camarinha-Matos, Cristiano De Faveri

A System Dynamics and Agent-Based Approach to Model Emotions in Collaborative Networks

A good amount of research within the last few decades has been focusing on computational models of emotion and the relationships they have with human emotional processes and how they affect the surrounding environments. The study of emotions is interdisciplinary and ranges from basic human emotion research, like in psychology, to the social sciences studies present in sociology. The interactions between those and the computational sciences are becoming a challenge. One particular challenge that is presented in this paper is the study of collaborative emotions within a Collaborative Network (CN) environment. A CN is composed of different participants with different interaction characteristics such as, expectations, will to cooperate and share, leadership, communication, and organizational abilities, among others. This paper presents an approach, based on system dynamics and agent-based modeling, to model the emotional state of an individual member of the network (via a non-intrusive way). Some simulation results illustrate the approach.

Filipa Ferrada, Luis M. Camarinha-Matos

Computational Intelligence


Efficient Fuzzy Controller to Increase Soybean Productivity

Soybean production has expanded intensively in South America over the last decades. As the second leading worldwide soybean producer, Brazil has in prospect to increase market share through production growth in soybean areas. Therefore, soybean fields ought to encompass a sizeable region among different types of soil and climate conditions, and so advanced irrigation methods should be advantageous. This present work aims to optimize soybean production through an irrigation system control based on environmental and plant requirements. It was developed an embedded fuzzy controller that is able to process soil moisture, air moisture, temperature, soil type and soybean growth stages and it returns the ideal amount of water. Also, to accomplish the data acquisition, a multiparameter sensor device establishes remote connection and provides continuous in-field measurement. The efficiency of the fuzzy controller and the monitoring unit was verified through simulations, and so, results reached the expected model.

Bruno S. Miranda, Gian M. Meira, Sidney J. Montebeller, Edinei P. Legaspe, Joel R. Pinto, Diolino J. Santos Filho, Paulo E. Miyagi

A Hybrid Expert Decision Support System Based on Artificial Neural Networks in Process Control of Plaster Production – An Industry 4.0 Perspective

Emerging technologies could affect future of factories and smartness is the main trend to receive that points. Quality was important and will be crucial in future but the question is how to build Smart Systems to guaranty quality in workshop level. This is an important challenge in Industry 4.0 paradigm. In this paper the main objective is to present practical solution under the light of Industry 4.0. The aim of this study is to presents propose a Hybrid Expert Decision Support System (EDSS) model, which integrates Neural Network (NN) and Expert System (ES) to detect unnatural CCPs and to estimate the corresponding parameters and starting point of the detected CCP. For this purpose, Learning Vector Quantization (LVQ) and Multi-Layer Perceptron (MLP) networks architecture have been designed to identify unnatural CCPs. Moreover, a rule based ES has been developed for diagnosing causes of process variations and subsequently recommending corrective action. The proposed model was successfully implemented in Construction Plaster producing company to demonstrate the capabilities and applicability of the model.

Javaneh Ramezani, Javad Jassbi

Flexibilizing Distribution Network Systems via Dynamic Reconfiguration to Support Large-Scale Integration of Variable Energy Sources Using a Genetic Algorithm

In recent years, the level of variable Renewable Energy Sources (vRESs) integrated in power systems has been increasing steadily. This is driven by a multitude of global and local concerns related to energy security and dependence, climate change, etc. The integration of such energy sources is expected to continue growing in the coming years. Despite their multifaceted benefits, variable energy sources introduce technical challenges mainly because of their intermittent nature, particularly at distribution levels. The flexibility of existing distribution systems should be significantly enhanced to partially reduce the side effects of vRESs. One way to do this is using a dynamic network reconfiguration. Framed in this context, this work presents an optimization problem to investigate the impacts of grid reconfiguration on the level of integration and utilization of vRES power in the system. The developed combinatorial model is solved using a genetic algorithm. A standard IEEE 33-node distribution system is employed in the analysis. Simulation results show the capability of network switching in supporting large-scale integration of vRESs in the system while alleviating their side effects. Moreover, the simultaneous consideration of vRES integration and network reconfiguration lead to a better voltage profile, reduced costs and losses in the system.

Marco R. M. Cruz, Desta Z. Fitiwi, Sérgio F. Santos, João P. S. Catalão

Data Fusion of Georeferenced Events for Detection of Hazardous Areas

When dealing with events in moving vehicles, which can occur over widespread areas, it is difficult to identify sources that do not derive from material fatigue, but from situations that occur in specific spots. Considering a railway system, problems could occur in trains, not because of train’s equipment failure, but because the train is crossing a specific location. This paper presents a new smart system being developed that is able to generate geo-located sensor-data; transmit it for smart processing and fusing to the inference engine being built to correlate the data, and drill-down the information. Using a statistical approach within the inference engine, it is possible to combine results collected over long periods of time in a “heat-map” of frequent fault areas, mapping faulty events to detect hazardous locations using georeferenced sensor data, collected from several trains that will be integrated in these maps to infer high probability risk areas.

Sérgio Onofre, João Gomes, João Paulo Pimentão, Pedro Alexandre Sousa

Systems Analysis


Student’s Attention Improvement Supported by Physiological Measurements Analysis

The focus of the most recent theories of emotional state analysis is the Autonomic Nervous System. Those theories propose that sympathetic and parasympathetic nervous systems interact antagonistically accordingly to each emotional state implying variations of interbeat intervals of consecutive heart beats. Emotional arousal and attention can be inferred based on the electrocardiogram (ECG) specifically through Heart Rate Variability (HRV) analysis, including the Low Frequency (LF), High Frequency (HF), and ratio LF/HF. The aim of this study is to analyze the impact of classic background music, in students’ emotional arousal and attention, and performance in the context of e-Learning training courses. As a result, it is foreseen the development of a system integrating wearables to smoothly gather the mentioned biosignals, which will be able to sense user’s emotions to further automatically propose recommendations for better learning approaches and contents, aiming student’s attention improvement.

Andreia Artífice, Fernando Ferreira, Elsa Marcelino-Jesus, João Sarraipa, Ricardo Jardim-Gonçalves

A System for Driver Analysis Using Smartphone as Smart Sensor

This work is focused on the development of system able to keep tracking driver’s behavior without a black box device mounted inside the car. Firstly, we intend to explore the data from GPS (Global Positioning System), accelerometer, gyroscope and magnetometer for a full characterization of the vehicle dynamics. Secondly, we develop an event detector that determines and classifies distinct kind of maneuvers, like turns, lane change, U-turns, among others. Finally, we developed a simple aggressiveness classifier using fuzzy logic. Experiments have been conducted and the initial results of the system were found to be encouraging on the implementation of a non-intrusive system for driver analysis.

Rui Daniel Vilaça, Rui Araújo, Rui Esteves Araújo

Multi-criteria Analysis and Decision Methodology for the Selection of Internet-of-Things Hardware Platforms

The Internet-of-Things (IoT) is today a reality, and Smart Systems have taken advantage of this to improve its own sense, act and control capabilities. IoT is a highly heterogeneous environment composed by a vast number of “things” (sensors, smart objects, etc.). These “things” are based on hardware platforms which can differ widely since manufactures are being capable of develop new devices every day to tackle different application domains. Consequently, a problem emerges regarding which will be the suitable, proper hardware solution for an IoT deployment. Make a right decision is probably one of the toughest challenges for science and technology managers. This work proposes a novel methodology to analyze a set of hardware alternatives based on user’s multi-criteria requirements, and advice on the more suitable hardware solution for a specific situation. For proof-of-concept it is used different Arduino boards as hardware alternatives, in which user requirements are based on hardware features. This methodology foresees its use during the development of Smart Systems (e.g.: Transportation, Healthcare) to optimize the selection of hardware platforms.

Edgar M. Silva, Ricardo Jardim-Goncalves

Smart Manufacturing Systems


Dynamic Simulation for MAS-Based Data Acquisition and Pre-processing in Manufacturing Using V-REP

With the advent of the Industry 4.0 movement, smart multiagent-based cyber-physical systems (CPS) are being more and more often proposed as a possible solution to tackle the requirements of intelligence, pluggability, scalability and connectivity of this paradigm. CPS have been suggested for a wide array of applications, including control, monitoring and optimization of manufacturing systems. However, there are several associated challenges in terms of validating and testing these systems due to their innate characteristics, emergent behavior, as well as the availability and cost of physical resources. Therefore, a dynamic simulation model constructed in V-REP is proposed as a way to test, validate and improve such systems, being applied to a data acquisition and pre-processing scenario as one of the key aspects of the interaction between a CPS and the shop-floor.

Ricardo Silva Peres, Andre Dionisio Rocha, Jose Barata

Enhancing Dependability and Security of Cyber-Physical Production Systems

Despite all its potentials, new industrial revolution enabled by cyber-physical systems (CPS), still has major concerns and obstacles to overcome with regards to dependability and security on its way to be fully appreciated. This study targets these concerns by proposing a generic model for intelligent distributed dependability and security supervision and control mechanisms to enable components to autonomously meet their own security and dependability objectives through real-time distributed improvement cycles, using multi-agent systems approach to enable full exploitation of the model’s evolution capabilities.

Hessamedin Bayanifar, Hermann Kühnle

Features Extraction from CAD as a Basis for Assembly Process Planning

This paper describes a novel approach to recognize product features, which are significant for Assembly Process Planning (APP). The work presented in this paper is a part of a larger effort to develop methods and tools for a more automated and bidirectional link between product CAD and the different processes and resources applied in APP. APP is the phase, in which the required assembly processes and resources are determined in order to convert a product to fully assembled or semi-assembled product. Product features will be extracted from the SolidWorks (SW) CAD file using SW - Application Programming Interface (API). SW-API is an interface that allows the exchange of data between CAD design and different software applications. The work includes automatic recognition for assembly knowledge, geometry and non-geometry knowledge (dimensions, geometrical tolerances, and kinematic constraints) in assembly design, which are relevant for assembly process and resources. Recognition algorithms have been developed by using visual basic. Net ( A case-study example is included for illustration of the proposed approach.

Baha Hasan, Jan Wikander

Safety Active Barriers Considering Different Scenarios of Faults in Modern Production Systems

Modern production systems, inserted in a context of high competitiveness, in accordance with policies of sustainability and people protection, as well as being integrated with other (smart) systems, makes complexity an inherent factor in any modern production system. Complexity is reflected in hardware, software and labour qualification for both the design and operation of such systems, resulting in the impossibility of (i) the prediction of all achievable states; (ii) the design of all integrated systems, (iii) non-existence of hardware faults and (iv) absence of human operating errors. Depending on the productive process under analysis, different scenarios, considering the combination of operational errors, faults in field components or even faults in system integration can lead to situations of serious risks for the environment, man and facilities. The bow-tie technique can elicit different scenarios of occurrence of faults and their dynamic evolution, by the results of other risk analysis techniques, such as FMEA, FTA and ETA. The concept of Safety Instrumented Systems, along with the concept of Safety Barriers could be a solution for these problems. This paper proposes the use of Petri nets for formal modeling and the generation of control algorithms, by the simplification of several scenarios of faults fault scenarios listed by a team in the process.

Jeferson A. L. de Souza, Diolino J. Santos Fo, Reinaldo Squillante, Fabricio Junqueira, Paulo E. Miyagi, Jose Reinaldo Silva

Smart Sensorial Systems


Image Analysis as a Tool to Age Estimations in Fishes: An Approach Using Blue Whiting on ImageJ

Otoliths are the fish bones that allow it to hear sounds and achieve balance. The otolith grows in size as fish grows; ring bands are formed in the otoliths’ surface registering periods of rapid and slow growth, opaque bands appear alternating with translucent bands. Age classification was made considering the number of translucent rings in the otolith; one translucent ring was equivalent to one year. The modeling of fish species abundance on the majority of fisheries assessment use age based models. The task of ring counting and ageing is time consuming and may introduce errors that can have a strong impact in stock assessment results. Thus, accurate and precise age estimates are crucial for the effective management and understanding of fisheries resources because recruitment dynamics, growth and mortality estimates relies on these data. The main goal of this study is to produce automatic reading procedures to help researchers, ageing blue whiting fish, minimize ring error count and improve accuracy and precision on age estimation.

Patrícia Gonçalves, Vitor Vaz da Silva, Alberto G. Murta, António Ávila de Melo, Henrique N. Cabral

Signal Processing Techniques for Accurate Screening of Wrist Fractures

The common way for doctors to differentiate wrist injury in to a sprain or a fracture, is to take radiographs (X-ray), which expose patients to radiation. The purpose of this study is to explore a non-invasive method to screen for potential fractures. A small, computer run, hand-held system has been developed which consists of a vibration induction mechanism and a piezoelectric sensor for capturing the vibration signals. Two analyzing techniques were considered. The first involves extraction of wavelet coefficients from decomposition of data and the second applies Fast Fourier Transform to the data. Results of both techniques were then cluster analyzed to partition between fracture and sprain. The data were acquired from both the injured and uninjured wrists of six adult patients. This study is currently being evaluated on children’s wrists.

Ridita Ali, Lyuba Alboul, Amaka Offiah

TRACEO3D Ray Tracing Model for Underwater Noise Predictions

Shipping noise is the main source of underwater noise raising concern among environmental protection organizations and the scientific community. Monitoring of noise generated by shipping traffic is a difficult challenge within the context of smart systems and solutions based on acoustic modeling are being progressively adopted to overcome it. A module of sound propagation stands as a key point for the development of a smart monitoring system since it can be used for the calculation of acoustic pressure, which can be combined with estimates of the source pressure level to produce noise predictions. This paper addresses the usage of the TRACEO3D model for application in such systems; the model validity is addressed through comparisons with results from an analytical solution and from a scale tank experiment. The comparisons show that the model is able to predict accurately the reference data, while a full-field model (normal mode-based, but adiabatic) is only accurate till a certain degree. The results show that TRACEO3D is robust enough to be used efficiently for predictions of sound propagation, to be included as a part of a smart system for underwater noise predictions.

Rogério M. Calazan, Orlando C. Rodríguez

Feature Transformation Based on Stacked Sparse Autoencoders for Sleep Stage Classification

In this paper a deep learning based dimension reduction, feature transformation and classification method is proposed for automatic sleep stage classification. In order to enhance the feature vector, before feeding it to the deep network, a discriminative feature selection method is applied for removing the features with minimum information. Two-layer Stacked Sparse Autoencoder together with Softmax classifier is selected as the deep network model. The performance of the proposed method is compared with Softmax and k-nearest neighbour classifiers. Simulation results show that proposed deep learning structure outperformed others in terms of classification accuracy.

Shirin Najdi, Ali Abdollahi Gharbali, José Manuel Fonseca

Embedded and Real Time Systems


Quality Evaluation Strategies for Approximate Computing in Embedded Systems

The quest for increased performance at lower energy consumption rates, especially in embedded systems used in smart systems, has reached physical limits that can no longer be exploited using traditional optimization techniques. One popular way to achieve additional gains is to intentionally perform inaccurate computations. Our framework eases evaluation, analysis and comparison of approximation techniques in terms of energy consumption, run time, quality and user-defined criteria. Applied to a set of benchmarks, we obtain valuable insights into related side effects, including increased file sizes indicating that a careless utilization of approximate computing threatens its usefulness.

Olaf Neugebauer, Peter Marwedel, Roland Kühn, Michael Engel

Configurable Reprogramming Methodology for Embedded Low-Power Devices

The embedded low-power devices are very important part of any smart system. With the large amounts of sensors and actuators used, it is a good practice to implement remote reprogramming capabilities into the firmwares of these devices. This paper presents a new configurable reprogramming methodology that can be applied to various platforms. It is built on the best reprogramming practices while giving developers more control over firmware outline, updated functions and modules. It also refers energy efficiency, as the data shared over the network and memory operations on the devices are minimal. The multiplatform capabilities make this scheme ideal for smart systems.

Ondrej Kachman, Marcel Balaz

Upper Bounds Prediction of the Execution Time of Programs Running on ARM Cortex-A Systems

This paper describes the application of statistical analysis of the timing behavior for a generic real-time task model. Using specific processor of ARM Cortex-A series and an empirical approach of time values retrieval, the algorithm to predict the upper bounds for the task of the time acquisition operation has been formulated. For the experimental verification of the algorithm, we have used the robust Measurement-Based Probabilistic Timing Analysis based on the Extreme value theory (EVT). In the ongoing work, we provide a systematic method for safe worst-case execution time estimations of programs running on the specific hardware. We focus here primary on the consequences of EVT assumptions and their correct interpretation for the upper bounds prediction.

Irina Fedotova, Bernd Krause, Eduard Siemens

Energy: Management


Assessment of Ancillary Service Demand Response and Time of Use in a Market-Based Power System Through a Stochastic Security Constrained Unit Commitment

In this paper, the impacts of an incentive-based Demand Response, i.e., Ancillary Service DR (ASDR), and a price-based DR, i.e., Time of Use (ToU), are revealed in a restructured power system which has some wind farms. This network is designed based on the pre-emptive market which is a day-ahead market with a balancing market prognosis. It is a proper mechanism to deal with the stochastic nature of non-dispatchable and outage of all units of the network. With Monte Carlo Simulation (MCS) method, several scenarios are generated in order to tackle the variability and uncertainties of the wind farms generation. The impacts of merging ASDR and ToU are investigated through running a two-stage stochastic security constrained unit commitment (SCUC), separately .

Saber Talari, Miadreza Shafie-khah, Neda Hajibandeh, João P. S. Catalão

Self-scheduling of Wind-Thermal Systems Using a Stochastic MILP Approach

In this work a stochastic (Stoc) mixed-integer linear programming (MILP) approach for the coordinated trading of a price-taker thermal (Ther) and wind power (WP) producer taking part in a day-ahead market (DAM) electricity market (EMar) is presented. Uncertainty (Uncer) on electricity price (EPr) and WP is considered through established scenarios. Thermal units (TU) are modelled by variable costs, start-up (ST-UP) technical operating constraints and costs, such as: forbidden operating zones, minimum (Min) up/down time limits and ramp up/down limits. The goal is to obtain the optimal bidding strategy (OBS) and the maximization of profit (MPro). The wind-Ther coordinated configuration (CoConf) is modelled and compared with the unCoConf. The CoConf and unCoConf are compared and relevant conclusions are drawn from a case study.

Rui Laia, Isaias L. R. Gomes, Hugo M. I. Pousinho, Rui Melício, Victor M. F. Mendes

Impact of Distributed Generation on the Thermal Ageing of Low Voltage Distribution Cables

The low voltage (LV) distribution cable networks were installed some decades ago but the new paradigm in electric power engineering generates new requirements from these old assets. The distributed generation, storage and new appliances can cause high variation of load and reverse power flow, nevertheless the LV cable grid was not designed to these new stresses. The aggregate load and generation can surpass the rated capacity of the cable lines causing short term temperature increasing. This temperature stress can decrease the expected lifetime of the cable lines. In this study the short term thermal overload of LV distribution cables was investigated. The experiments were executed on PVC insulated LV cable samples and electrical and mechanical properties of the cable jacket were investigated. The effect of these short-term overloads on the expected lifetime of cables is introduced and non-destructive measurement for tracking the effect of the short term thermal overloads on the cable is suggested.

Gergely Márk Csányi, Zoltán Ádám Tamus, Árpád Varga

A Hybrid Anti-islanding Method for Inverter-Based Distributed Generation

Nowadays, high penetration of Distributed Generations (DG)s in power systems caused some protection issues. One of these issues is unintentional islanding. As regards IEEE 1547 standard, this situation must be recognized immediately, and DG must be separated from the load in less than 2 s. In this paper, to detection of islanding in an inverter-based distributed generation, a new hybrid method with high performance is proposed. In the proposed method, a primary detection of islanding is conducted by measuring the voltage harmonic distortion at the Point of Common Coupling (PCC), as well as comparing the variations to a specified threshold level. After this primary detection, a temporary reactive current signal is injected to the PCC by the inverter of DG, and its terminal voltage and frequency are measured. In the case of deviation of voltage or frequency from permissible range, definitive detection of islanding is determined. Simulation results indicate the efficiency and accuracy of the proposed detection method in different circumstances, especially for loads with the different quality factors.

Ebrahim Rokrok, Miadreza Shafie-khah, Hamid Reza Karshenas, Esmail Rokrok, João P. S. Catalão

Energy: Optimization


A New DG Planning Approach to Maximize Renewable - Based DG Penetration Level and Minimize Annual Loss

Distributed Generation (DG) using renewable technologies is increasing due to their benefits including energy security and emission reduction. However, installing new DGs in distributed networks is limited due to network constraints such as feeder capacity and short circuit level, as well as higher investment costs. In this paper, network reconfiguration and reactive power planning are used to maximize DG penetration level and to minimize annual loss for DGs with biomass technologies. In order to model the problem uncertainties, 96 scenarios considering ten different network load levels are studied. A multi-objective method is applied for solving this optimization problem by using Pareto front. The numerical results indicate the positive impacts of the proposed approach on improving the network security.

Soroush Najafi, Miadreza Shafie-khah, Neda Hajibandeh, Gerardo J. Osório, João P. S. Catalão

Stochastic Optimization for the Daily Joint Operation of Wind/PV and Energy Storage

This paper deals with the problem of optimal bidding in a day-ahead market of electricity for a power producer having joint operation of wind with photovoltaic power systems and storage of energy. Uncertainty, not only on electricity market prices, but also on wind and photovoltaic powers, has to be faced in order to achieve optimal bidding. The problem is viewed as a sort of a two-stage stochastic optimization problem formulated by mix-integer linear programming. A case study with data from the Iberian Peninsula is presented and a comparison between joint and disjoint operations is discussed, allowing concluding that the joint operation attenuates the economic impact of disjoint operation volatility.

Isaías L. R. Gomes, Hugo M. I. Pousinho, Rui Melício, Vitor M. F. Mendes

The Impacts of Demand Response on the Efficiency of Energy Markets in the Presence of Wind Farms

In this paper, an optimal scheduling of thermal and wind power plants is presented by using a stochastic programming approach to cover the uncertainties of the forecasted generation of wind farms. Uncertainties related to wind forecast error, consequently wind generation outage power and also system load demand are modeled through scenario generation. Then, with regard to day-ahead and real-time energy markets and taking into account the relevant constraints, the thermal unit commitment problem is solved considering wind energy injection into the system. Besides, in order to assess impacts of Demand Response (DR) on the problem, a load reduction demand response model has been applied in the base model. In this approach, self and cross elasticity is used for modeling the customers’ behavior modeling. The results indicate that the DR Programs (DRPs) improves the market efficiency especially in peak hours when the thermal Gencos become critical suppliers and the combination of DRPs and wind farm can be so efficient.

Neda Hajibandeh, Miadreza Shafie-khah, Saber Talari, João P. S. Catalão

Implementing an Integer Linear Approach to Multi-objective Phasor Measurement Unit Placement

In this paper, an Integer Linear Programming (ILP) problem for a model of Multi-Objective Optimal PMU Placement (MOPP) is proposed. The proposed approach concurrently deals with two objectives. The first objective is the number of phasor measurement units (PMUs) which should be minimized. The second objective function is measurement redundancy which is the number of observable buses in the case of PMU outage. In fact, whatever the amount of second objective increases, the system would be more reliable. Furthermore, some linearized formulations are defined for each nonlinear formula. In fact, the nonlinear nature of formulation related to redundancy is substituted by linear inequality and so there is no nonlinear formula such that the calculation of the problem would be simplified. Finally, a modified 9-bus test system is implemented to show how the proposed method is effective.

Amir Baharvandi, Miadreza Shafie-khah, Saber Talari, João P. S. Catalão

Distributed Infrastructure


RELOAD/CoAP P2P Overlays for Network Coding Based Constrained Environments

The Internet of Things will bring into the Internet all kinds of smart systems, which will be able to interact with each other. Therefore, applications relying on data sharing for collaboration will increase, and effective distributed solutions for data storage become necessary. This need led to the proposal of a CoAP Usage for RELOAD, a generic P2P protocol that accepts pluggable application layers (Usages). This allows P2P overlay networks to be built where constrained systems store their data and clients are able to retrieve it. Since many smart systems rely on wireless networks to communicate, where network coding can be used to reduce packet error rate, P2P overlays should be prepared to store data from network coding based networks. More specifically, encoding vectors and encoded data must be stored, and a decoding service is required. In this article, we propose a CoAP Usage extension so that network coding based constrained networks can use RELOAD/CoAP P2P distributed storage.

Eman Al-Hawri, Noelia Correia, Alvaro Barradas

PVInGrid: A Distributed Infrastructure for Evaluating the Integration of Photovoltaic Systems in Smart Grid

Planning and developing the future Smart City is becoming mandatory due to the need of moving forward to a more sustainable society. To foster this transition an accurate simulation of energy production from renewable sources, such as Photovoltaic Panels (PV), is necessary to evaluate the impact on the grid. In this paper, we present a distributed infrastructure that simulates the PV production and evaluates the integration of such systems in the grid considering data provided by smart-meters. The proposed solution is able to model the behaviour of PV systems solution exploiting GIS representation of rooftops and real meteorological data. Finally, such information is used to feed a real-time distribution network simulator.

Lorenzo Bottaccioli, Enrico Macii, Edoardo Patti, Abouzar Estebsari, Enrico Pons, Andrea Acquaviva

MAP Estimator for Target Tracking in Wireless Sensor Networks for Unknown Transmit Power

This paper addresses the target tracking problem, by extracting received signal strength (RSS) and angle of arrival (AoA) information from the received radio signal, in the case where the target transmit power is considered unknown. By combining the radio observations with prior knowledge given by the target transition state model, we apply the maximum a posteriori (MAP) criterion to the marginal posterior distribution function (PDF). However, the derived MAP estimator cannot be solved directly, so we tightly approximate it for small noise power. The target state estimate is then easily obtained at any time step by employing a recursive approach, typical for Bayesian methods. Our simulations confirm the effectiveness of the proposed algorithm, offering good estimation accuracy in all considered scenarios.

Slavisa Tomic, Marko Beko, Rui Dinis, Milan Tuba, Nebojsa Bacanin

Solar Energy


Performance Assessment of Tank Fluid Purging and Night Cooling as Overheating Prevention Techniques for Photovoltaic-Thermal (PV-T) Solar Water Heating Systems

Tank fluid purging and night cooling are two overheating prevention techniques with potential to prevent photovoltaic-thermal collectors from experiencing temperatures capable of undermining their longevity and commercial appeal. Both techniques are readily available, inexpensive but inherently wasteful to use. Dynamic numerical simulations were conducted to determine the primary energy efficiency and the level of protection afforded by these techniques in active residential grid-connected solar domestic hot water systems. Also evaluated was the use of occupancy rate information, possible via so-called “smart systems”, to complement the techniques. The results revealed better performances for systems using stagnation control schemes relative to those not using them. Also, night cooling was shown to be unable to prevent overheating reliably while tank fluid purging proved to be more apt but resulted in substantial waste of water annually, which was slightly reduced by combining it with night cooling, which in turn proved to be the most energy efficient solution.

Pedro M. L. P. Magalhães, João F. A. Martins, António L. M. Joyce

Stochastic Optimal Operation of Concentrating Solar Power Plants Based on Conditional Value-at-Risk

This paper presents a stochastic programming approach, using a risk measure defined by conditional value-at-risk, for trading solar energy in a market environment under uncertainty. Uncertainties on electricity price and solar irradiation are considered through a set of scenarios computed by simulation and scenario-reduction. The short-term operation problem of a concentrating solar power plant is formulated as a mixed-integer linear program, which allows modelling the discrete status of the plant. To improve the operational productivity of the plant during the non-insulation periods, energy storage systems are considered. The goal is to obtain the optimal operation planning that maximizes the total expected profits while evaluating trading risks. For risk evaluation, the conditional value-at-risk is used to maximize the expected profits of the least profitable scenarios. A case study is used to illustrate the usefulness and the proficiency of the proposed approach.

João A. R. Esteves, Hugo M. I. Pousinho, Victor M. F. Mendes

Solar Thermoelectric System with Biomass Back-up

With the objective of having a solar thermoelectric system, running for 24 h a day along the different seasons of the year it is necessary to dimension the adequate storage and back-up systems. The choice of the back-up source of energy depends on how sustainable the power plant should be. In this study, the choice was the use of biomass in order to have a 100% renewable power plant. The selected site was the Alentejo region (Portugal). The local Direct Normal Irradiation (DNI) data was used to simulate with the System Advisor Model program (SAM) considering a solar system with north field and molten salt storage. The system needs no back-up during three months in a year. The use of biomass pellets is a viable alternative because it makes the power plant 100% renewable and dispatchable without loss of energy due to over-dimension of the expensive solar field and molten storage system.

José Teixeira Gonçalves, Cristina Inês Camus, Stanimir Stoyanov Valtchev

Electrical Machines


A Generalized Geometric Programming Sub-problem of Transformer Design Optimization

The first step in transformer design optimization is to solve a non-linear optimization task. Here, not only the physical and technological requirements, but the economic aspects are also considered. Large number of optimization algorithms have been developed to solve this task. These methods result the optimal electrical parameters and the shape of the core and winding geometry. Most of them model the windings by their copper filling factors. Therefore the transformer designer’s next task, to find out the detailed winding arrangement, which fits to the optimization results. However, in the case of large power transformers, the calculation of some parameters like: winding gradients, short-circuit stresses etc., needs the knowledge of the exact wire dimensions and winding arrangement. Therefore, an other optimization task should be solved. This paper shows how this sub-problem can be formulated and solved as a generalized geometric program.

Tamás Orosz, Tamás Nagy, Zoltán Ádám Tamus

Noise, Vibration and Harshness on a Permanent Magnet Synchronous Motor for a Remote Laboratory

The study of Noise, Vibration and Harshness (NVH) is becoming a key element when it comes to design and maintain a system that have rotatory elements. The understanding of the source of the vibrations can lead to a way of mitigating them, thus ensuring a better operation and expanding the life of the components. In this work, an analysis of the vibrations’ frequency spectra of an electric drive is developed, extracting some conclusions from it and deducing a source. The possibility of operating this system remotely makes it a perfect experiment for a remote lab, operating through Internet, being a novelty for this kind of studies.

Jaime Pando-Acedo, Enrique Romero-Cadaval, Consuelo Gragera-Peña, María Isabel Milanés-Montero

Levitating Bearings Using Superconductor Technology Under Smart Systems Scope

This paper presents a study about the cooling and leakage aspects of superconductor magnetic bearings towards their integration on a smart systems scope to keep the stability of the cooling system. The use of superconductor magnetic bearings in electric power generation, namely in wind power has been increased due to their weight, friction less, and volume reduction advantages. The novelty of this paper is the use of Zero-Field Cooling instead of the common Field Cooling technique. On the other hand, this new kind of bearings needs a constant flow of liquid nitrogen to keep the superconductivity properties. A prototype was modelled, simulated and implemented for experimental validation.

Martim V. Carvalho, António J. Arsenio, Carlos Cardeira, Paulo J. Costa Branco, Rui Melício

An Overview on Preisach and Jiles-Atherton Hysteresis Models for Soft Magnetic Materials

The design of efficient and high power density electrical machines needs an accurate characterization of magnetic phenomena. Core losses estimation is usually addressed by empirical models, where its lack of accuracy is well known. Hysteresis models are able to take an insight into the magnetic physical mechanisms. Compared to the empirical models, they contribute to a higher accuracy in modeling electromagnetic systems, including core losses estimation. At a macroscopic level, two models are often used: the Preisach and the Jiles–Atherton (J–A) models. This paper presents their basic formulation, as well the main limitations and scope of application. This is a first step to investigate the possible application of hysteresis models, in order to reach accurate core losses estimation in switched reluctance machines.

Pedro Melo, Rui Esteves Araújo

Power Electronics


Comparative Analysis of qZS-Based Bidirectional DC-DC Converter for Storage Energy Application

This paper presents a comparative analysis of the bidirectional qZS-based dc-dc converter for storage application with a traditional solution based on the boost dc-dc converter. The analysis estimates the energy stored in the capacitors and inductors, blocking voltage across semiconductors, and conduction losses. The comparison is based on the mathematical and simulation analysis.

Oleksandr Matiushkin, Oleksandr Husev, Kostiantyn Tytelmaier, Kaspars Kroics, Oleksandr Veligorskyi, Janis Zakis

Single-Phase Wireless Power Transfer System Controlled by Magnetic Core Reactors at Transmitter and Receiver

The applications of wireless power transmission have become widely increasing over the last decade, mainly in the battery charging systems for electric vehicles. This paper focuses on the single-phase wireless power transfer prototype controlled by magnetic core reactors in either side of the system: that of the transmitter, and that of the receiver. The described wireless power transfer system prototype employs a strong magnetic coupling technology to improve the power transmission efficiency. In the same time, a magnetic core reactor is used to control the “tuning” between the transmitter and the receiver frequencies, allowing for that increase of the system efficiency. Finally, practical results of the implemented prototype are presented.

Luis Romba, Stanimir S. Valtchev, Rui Melício

Soft-Switching Current-FED Flyback Converter with Natural Clamping for Low Voltage Battery Energy Storage Applications

This paper introduces a new galvanically isolated current-fed step-up dc-dc converter intended for high voltage gain applications. The converter have fully-controllable voltage doubler rectifier, with control signals synchronous to that of the inverter switches. The proposed converter can regulate output voltage within wide range of the power and input voltage variations. Proposed converter does not require snubbers or resonant switches and with proposed control sequence ensures switches operation under soft-switching conditions in all transient states. Soft-switching in semiconductors allows achieving high efficiency. Moreover, the input side current is continuous. The operating principle for the energy transfer from the current-fed to voltage-fed side is described, design guidelines along with experimental verification of the proposed converter are shown in this paper. The converter proposed can be used as a front-end converter for grid connected battery storage.

Roman Kosenko, Dmitri Vinnikov



Design Methodology for an All CMOS Bandgap Voltage Reference Circuit

The Internet of Thing (IoT) has given rise to the integration of smart systems in the industrial, healthcare, and social environments. These smart systems are often implemented by system-on-chip (SoC) solutions that require power management units, sensors, signal processors, and wireless interfaces. Hence, an independent voltage reference circuit is crucial for obtaining accurate measurements from the sensors and for the proper operation of the SoC. Making, bandgap circuits indispensable in these types of applications. Typically, bandgap circuits are implemented using bipolar transistors to generate two voltages with opposite temperature coefficients (Complementary To Absolute Temperature – CTAT; Proportional do Absolute Temperature - PTAT) which are added resulting in a temperature independent voltage reference. The disadvantage of using bipolar devices is that the power supply voltage must be larger than the ON base-emitter voltage, resulting in voltages larger than 0.7 V. The low power demands of IoT and of technology scaling, have forced lower values for the power supply voltage and thus new bandgap circuits using only CMOS transistors have gathered increased interest. In these, the MOS transistors operate in the weak inversion region where its current has an exponential relation with its gate-to-source voltage, as in the bipolar devices. Making it possible to generate both the PTAT and the CTAT voltages and thus produce a temperature independent voltage reference. This paper describes a design methodology of an all CMOS bandgap voltage reference circuit, in which one of the transistors works in the moderate region and the other in the weak inversion, to achieve the lowest possible voltage variation with temperature. The circuit produces a voltage reference of 0.45 V from a minimum power supply voltage of 0.6 V, with a total variation of 1.54 mV, over a temperature range of −40 to 100°C, resulting in a temperature coefficient of 24 ppm/°C, and a power supply rejection ratio (PSRR) of −40 dB.

Ricardo Madeira, Nuno Paulino

Reconfigurable Photonic Logic Architecture: An Overview

The photosensor studied in this document, is an amorphous silicon structure deposited on transparent glass allowing illumination on both sides. It responds to wavelengths from near infrared range to the ultra-violet. The front illumination surface is used for inputting light signal. The dynamic characteristics of the photosensor are altered by using optical bias on either surface of the sensor, thus the same input results in different outputs. Experimental studies made with the photosensor evaluate its applicability as a multiplexer, demultiplexer and logical operations device. A memory effect was observed. A programmable pattern emission system, and a validation and recovery system were built to illuminate the sensor with input light signals and to analyze the resulting output.

Vitor Silva, Manuel Barata, Manuela Vieira

Microneedle Based ECG – Glucose Painless MEMS Sensor with Analog Front End for Portable Devices

A portable microelectromechanical system (MEMS) for mobile phones, or other portable devices, that measures body electrical signals, as well as, extracts transdermal biological fluid for invivo analysis is proposed. This system integrates two sensing methods: three points finger electrocardiography (ECG) and glucose monitoring, through one electrode with a microneedle-array. This work presents the: (1) device modeling and microneedle-array’ fabrication method, (2) signal processing and biasing circuitry’ design and simulation, (3) Analog Front End (AFE) for measured signals, and (4) Glucose sensor characterization. Design parameters and geometries are obtained by solving the capillarity model inside the microneedles and running optimization numeric methods. The AFE consists in a differential band pass filter that provides amplification, filtering, and noise rejection. This work presents clear technological innovation, for its miniaturization and integration of known biological signals’ measurement methods in a portable Smart System, which points in the direction of Internet of Things’ goals.

Miguel Lima Teixeira, Camilo Velez, Dian Li, João Goes

Crystalline Silicon PV Module Under Effect of Shading Simulation of the Hot-Spot Condition

This paper centers on the silicon crystalline PV module technology subjected to operation conditions with some cells partially or fully shaded. A shaded cell under hot-spot condition operating at reverse bias are dissipating power instead of delivering power. A thermal model allows analyzing the temperature increase of the shaded cells of the module under hot-spot condition with or without protection by a bypass diode. A comparison of the simulation results for a crystalline PV module without shading and with partial or full shading is presented.

Ruben S. Anjos, Rui Melício, Victor M. F. Mendes, Hugo M. I. Pousinho


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Product Lifecycle Management im Konzernumfeld – Herausforderungen, Lösungsansätze und Handlungsempfehlungen

Für produzierende Unternehmen hat sich Product Lifecycle Management in den letzten Jahrzehnten in wachsendem Maße zu einem strategisch wichtigen Ansatz entwickelt. Forciert durch steigende Effektivitäts- und Effizienzanforderungen stellen viele Unternehmen ihre Product Lifecycle Management-Prozesse und -Informationssysteme auf den Prüfstand. Der vorliegende Beitrag beschreibt entlang eines etablierten Analyseframeworks Herausforderungen und Lösungsansätze im Product Lifecycle Management im Konzernumfeld.
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