Uso das tecnologias de informação e comunicação no ensino – portal de ensino aprendizagem do projecto electrotécnico

As Tecnologias da Informação e Comunicação (TIC) permitem novas estratégias de difusão da informação e novos modelos de comunicação, com a proposta de modificar as atitudes e o comportamento humano em relação à Educação. Actualmente a internet é fundamental como meio de comunicação e partilha de informação. Ao ensino será exigido a utilização de TIC, suportadas na internet, para estabelecer a comunicação professor-aluno e aluno-aluno, para divulgação de conteúdos das unidades curriculares, e mesmo como meio de ensino/aprendizagem. O Portal de Ensino e Aprendizagem do Projecto Electrotécnico (PEAPE) pretende ser uma ferramenta de apoio ao ensino/aprendizagem, de unidades curriculares no Âmbito do projecto electrotécnico.

Características epidemiológicas do suicídio no Rio Grande do Sul

OBJETIVO: Descrever as características epidemiológicas de mortalidade por suicídio em uma série histórica de dez anos. MÉTODOS: Foram construídas séries históricas de mortalidade por suicídio no Rio Grande do Sul a partir de dados do Sistema de Notificação de Mortalidade do Ministério da Saúde, para o período 1980 a 1999. Os dados foram padronizados de acordo com os critérios da Organização Mundial de Saúde e analisados segundo variáveis demográficas clássicas. RESULTADOS: As taxas de suicídios durante todo o período estudado (coeficientes e mortalidade proporcional) configuraram-se como as maiores do País. Os coeficientes padronizados passaram de níveis em torno de 9/100.000 nos anos 80 para 11/100.000 em 1999. Esse alto nível de mortalidade deveu-se principalmente ao aumento da mortalidade masculina, cujos coeficientes passaram de 14/100.000 para os atuais 20/100.000. A razão homemmulher aumentou de três para cinco. Os maiores coeficientes correspondiam aos idosos, embora as taxas estejam aumentando na população de adultos jovens. Pessoas viúvas e aquelas ocupadas na agropecuária e pesca apresentaram coeficientes de mortalidade mais elevados. CONCLUSÕES: O estudo destaca o suicídio como um problema de saúde coletiva no Rio Grande do Sul e revela características que contribuem para ações preventivas.

Scheduling distributed energy resources in an isolated grid - an artificial neural network approach

Power Systems (PS), have been affected by substantial penetration of Distributed Generation (DG) and the operation in competitive environments. The future PS will have to deal with large-scale integration of DG and other distributed energy resources (DER), such as storage means, and provide to market agents the means to ensure a flexible and secure operation. Virtual power players (VPP) can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. This paper proposes an artificial neural network (ANN) based methodology to support VPP resource schedule. The trained network is able to achieve good schedule results requiring modest computational means. A real data test case is presented.

Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming

In the energy management of the isolated operation of small power system, the economic scheduling of the generation units is a crucial problem. Applying right timing can maximize the performance of the supply. The optimal operation of a wind turbine, a solar unit, a fuel cell and a storage battery is searched by a mixed-integer linear programming implemented in General Algebraic Modeling Systems (GAMS). A Virtual Power Producer (VPP) can optimal operate the generation units, assured the good functioning of equipment, including the maintenance, operation cost and the generation measurement and control. A central control at system allows a VPP to manage the optimal generation and their load control. The application of methodology to a real case study in Budapest Tech, demonstrates the effectiveness of this method to solve the optimal isolated dispatch of the DC micro-grid renewable energy park. The problem has been converged in 0.09 s and 30 iterations.

Intelligent training in control centres based on an ambient intelligence paradigm

This article describes a new approach in the Intelligent Training of Operators in Power Systems Control Centres, considering the new reality of Renewable Sources, Distributed Generation, and Electricity Markets, under the emerging paradigms of Cyber-Physical Systems and Ambient Intelligence. We propose Intelligent Tutoring Systems as the approach to deal with the intelligent training of operators in these new circumstances.

Intelligent SCADA for load control

A supervisory control and data acquisition (SCADA) system is an integrated platform that incorporates several components and it has been applied in the field of power systems and several engineering applications to monitor, operate and control a lot of processes. In the future electrical networks, SCADA systems are essential for an intelligent management of resources like distributed generation and demand response, implemented in the smart grid context. This paper presents a SCADA system for a typical residential house. The application is implemented on MOVICON™11 software. The main objective is to manage the residential consumption, reducing or curtailing loads to keep the power consumption in or below a specified setpoint, imposed by the costumer and the generation availability.

Information and communication technologies in education – a web-based intelligent tutoring system on teaching and learning electrical project

Electrical activity is extremely broad and distinct, requiring by one hand, a deep knowledge on rules, regulations, materials, equipments, technical solutions and technologies and assistance in several areas, as electrical equipment, telecommunications, security and efficiency and rational use of energy, on the other hand, also requires other skills, depending on the specific projects to be implemented, being this knowledge a characteristic that belongs to the professionals with relevant experience, in terms of complexity and specific projects that were made.

Energy resources management for more sustainable distribution systems: an intelligent approach

Energy Resources Management can play a very relevant role in future power systems in SmartGrid context, with high penetration of distributed generation and storage systems. This paper deals with the importance of resources management in incident situation. The system to consider a high penetration of distributed generation, demand response, storage units and network reconfiguration. A case study evidences the advantages of using a flexible SCADA to control the energy resources in incident situation.

Emergency energy resource management in SmartGrids

Energy resources management can play a very relevant role in future power systems in a SmartGrid context, with intensive penetration of distributed generation and storage systems. This paper deals with the importance of resource management in incident situations. The paper presents DemSi, an energy resources management simulator that has been developed by the authors to simulate electrical distribution networks with high distributed generation penetration, storage in network points and customers with demand response contracts. DemSi is used to undertake simulations for an incident scenario, evidencing the advantages of adequately using flexible contracts, storage, and reserve in order to limit incident consequences.

Distributed energy resources management with cyber-physical SCADA in the context of future smart grids

In the energy management of a small power system, the scheduling of the generation units is a crucial problem for which adequate methodologies can maximize the performance of the energy supply. This paper proposes an innovative methodology for distributed energy resources management. The optimal operation of distributed generation, demand response and storage resources is formulated as a mixed-integer linear programming model (MILP) and solved by a deterministic optimization technique CPLEX-based implemented in General Algebraic Modeling Systems (GAMS). The paper deals with a vision for the grids of the future, focusing on conceptual and operational aspects of electrical grids characterized by an intensive penetration of DG, in the scope of competitive environments and using artificial intelligence methodologies to attain the envisaged goals. These concepts are implemented in a computational framework which includes both grid and market simulation.

Demsi - a demand response simulator in the context of intensive use of distributed generation

Demand response can play a very relevant role in future power systems in which distributed generation can help to assure service continuity in some fault situations. This paper deals with the demand response concept and discusses its use in the context of competitive electricity markets and intensive use of distributed generation. The paper presents DemSi, a demand response simulator that allows studying demand response actions and schemes using a realistic network simulation based on PSCAD. Demand response opportunities are used in an optimized way considering flexible contracts between consumers and suppliers. A case study evidences the advantages of using flexible contracts and optimizing the available generation when there is a lack of supply.

Clinical Education - a fundamental curricular unit for all health practitioners

Clinical education is recognized as being crucial for the training of health professionals. This subject is debated amongst teachers, students and professionals. Besides the clinical and research skills, we look for other competencies such as oratory, creative thinking or leadership. We present the results of a study with 4th graders. It’s a exploratory study; the main purpose was to evaluate the outcomes of a unit of clinical education prepared according a new set of competencies and methodologies. The competencies were seen as valuable. Organization, leading or supporting a colleague, rethinking a program to serve client and family are equally important.

Comparison between deterministic and meta-heuristic methods applied to ancillary services dispatch

This paper proposes two meta-heuristics (Genetic Algorithm and Evolutionary Particle Swarm Optimization) for solving a 15 bid-based case of Ancillary Services Dispatch in an Electricity Market. A Linear Programming approach is also included for comparison purposes. A test case based on the dispatch of Regulation Down, Regulation Up, Spinning Reserve and Non-Spinning Reserve services is used to demonstrate that the use of meta-heuristics is suitable for solving this kind of optimization problem. Faster execution times and lower computational resources requirements are the most relevant advantages of the used meta-heuristics when compared with the Linear Programming approach.

Combining artificial intelligence techniques for the training of power system control centre operators

Control Centre operators are essential to assure a good performance of Power Systems. Operators’ actions are critical in dealing with incidents, especially severe faults, like blackouts. In this paper we present an Intelligent Tutoring approach for training Portuguese Control Centre operators in incident analysis and diagnosis, and service restoration of Power Systems, offering context awareness and an easy integration in the working environment.

ANN based day-ahead ancillary services forecast for electricity market simulation

Adequate decision support tools are required by electricity market players operating in a liberalized environment, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services (AS) represent a good negotiation opportunity that must be considered by market players. Based on the ancillary services forecasting, market participants can use strategic bidding for day-ahead ancillary services markets. For this reason, ancillary services market simulation is being included in MASCEM, a multi-agent based electricity market simulator that can be used by market players to test and enhance their bidding strategies. The paper presents the methodology used to undertake ancillary services forecasting, based on an Artificial Neural Network (ANN) approach. ANNs are used to day-ahead prediction of non-spinning reserve (NS), regulation-up (RU), and regulation down (RD). Spinning reserve (SR) is mentioned as past work for comparative analysis. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.