Data centers are core to modern digital society, however their growth and increased end-use energy demand has brought them under focus from both the electrical energy providers and governments. This paper presents operational monitoring of a publicly funded energy and cost efficient pilot data center built in the north of Sweden, which has one pod populated with legacy Open Compute Project (OCP) information technology (IT) hardware, that is cooled and powered in a similar vein to the large scale OCP data centers that are operating today, using direct air with evaporative function for both cooling and humidification and no centralised Uninterruptable Power Supply (UPS). The function of the data center POD1 is achieved via the use of a chain of opensource software that monitors and orchestrates IT workload in a predefined approach to measure and deploy its operation. The first stage of the data center development effort has concentrated on a unified approach to environmental control that effectively combines both the direct fresh air with the IT level cooling control that demonstrates a consistently low Power Usage Effectiveness (PUE) of less than 1.04.

This paper proposes a passive Barkhausen noise sensor design suitable for low power applications. The sensor uses a permanent magnet and the relative motion between itself and a measured specimen instead of the conventional method that uses a fixed sensor and an alternating magnetic field. Since this novel design is passive, the sensor is well suited for low power applications and could potentially be used in e.g. A condition monitoring system integrated into a rolling element bearing. Proof of concept testing has been performed showing that the proposed sensor produces similar results as conventional Barkhausen noise sensors when applied to specimens being cyclically loaded until failure in a rotating bending rig. The results imply that material fatigue detection using the Barkhausen noise can be performed with the proposed sensor at a fraction of the energy cost compared to a conventional sensor. This warrants future research into the development of the proposed sensor, its advantages, disadvantages, and functionality

The circuit presented in this paper provides increased transfer efficiency for the electric charge generated by a piezoelectric element to the storage device, it also enables a simplified active control circuitry required for switched harvesting solutions as the control transistors are referenced to ground. This is achieved by inverting the voltage potential across the piezoelectric element and thereby maintaining a higher voltage while extracting energy. The increased potential allows vibration harvesters to operate over a broader frequency range. For weakly coupled piezoelectric harvesters, the increased potential allows the electrical power output to be increased. In this paper, the circuit is simulated in SPICE using existing models of discrete components in conjunction with a model of a piezoelectric vibration harvester. The power output and mechanical behavior of the harvester using the presented circuit are compared to two existing technologies: Synchronous Switch Harvesting on Inductor (SSHI) and a full-wave rectifying bridge. The comparison demonstrates that the novel Interleaved Switch Harvesting on Inductor (ISHI) circuit outperforms state-of-the-art active harvesting solutions, e.g., SSHI, as a result of the decreased energy losses from diodes. In addition, the ISHI circuit requires fewer components than does the SSHI circuit and solves the floating transistor source issue that arises in SSHI implementations. To achieve the maximum power conversion efficiency, impedance matching is required, which can be achieved by controlling the voltage of the storage device

The generation of electricity from vibrations has attracted considerable attention over the past decade. For this purpose, a piezoelectric-type harvester tuned to resonate at a specific frequency is typically used. This paper proposes a SPICE model capable of modeling piezoelectric energy harvesters. The model is parametrized enabling simple introduction of relevant physical parameters. Underlying theory based on Euler-Bernoulli beam theory is presented and modeling simplifications are justified. The SPICE model is verified by comparison to examples for which analytical and experimental solutions has been published. The SPICE model is valid close to the analyzed mode center frequency and delivers results within 1% compared to experimental and analytical data. Furthermore, we also show that the harvester can be electrically tuned to match the excitation frequency. This makes it possible to maximize the power output for both linear and non-linear loads. The here presented model enables efficient analysis of harvester design and changes of harvesting conditions

Embedded and Cyber-Physical Systems education faces several challenges as well as opportunities as every-"thing" becomes connected, and as technology development accelerates. Initiatives such as CDIO, as well as several other academic and industry initiatives to create new CPS programs illustrate strong interests and awareness of these challenges. We provide an overview of foreseen educational needs, existing state of the art in education and an analysis of the subject of CPS with the purpose of understanding the implications for education. The investigation points to key issues in curriculum design regarding balancing depth and breadth, theory and practices, academic and industrial needs, and core technical skills with complementary skills. Curricula in CPS could, if the right balance is achieved, educate CPS engineers of the future that are "ready to engineer". We conclude by synthesizing high level guidelines in terms of strategies and considerations for CPS curriculum development.

This chapter describes how district heating substations can be controlled in order to improve customer comfort and system performance, and how energy measurement data can be used to monitor the functionality of substations. We primarily consider indirectly connected district heating substations, where the primary energy supply system is separated from internal building heating and tap water systems. Different approaches to achieve higher efficacy of the district heating substation and the district heating system are introduced, as are methods for the detection of typical faults in district heating substations. These methods enable identification of faults that result in abnormal energy metering data, such as incorrectly configured or installed substations, or faults in the energy meter instrumentation, including the communication system and sensors.

Hardwired sensor installations using proprietary protocols found in today’s district heating substations limit the potential usability of the sensors in and around the substations. If sensor resources can be shared and re-used in a variety of applications, the cost of sensors and installation can be reduced, and their functionality and operability can be increased. In this paper, we present a new concept of district heating substation control and monitoring, where a service oriented architecture (SOA) is deployed in a wireless sensor network (WSN), which is integrated with the substation. IP-networking is exclusively used from sensor to server; hence, no middleware is needed for Internet integration. Further, by enabling thousands of sensors with SOA capabilities, a System of Systems approach can be applied. The results of this paper show that it is possible to utilize SOA solutions with heavily resource-constrained embedded devices in contexts where the real-time constrains are limited, such as in a district heating substation.

The IMC-AESOP architecture has been used to implemente a smart house demonstration. Six different systems has been integrated with local (802.11, 802.15.4) and global (telecom) communication. The six systems integrated are: Car arrival detection system, Garage door opening system, House security system, External house lightning system, External electrical outlet system, House energy control system. The SOA technologies used are CoAP and EXI using SenML to encode the services. Engineering tools have been used to simulate the usage scenario and provide prediction of system behaviour.

Using sensors to measure parameters of interest in rotating environments and communicating the measurements in real-time over wireless links, requires a reliable power source. In this paper, we have investigated the possibility to generate electric power locally by evaluating six different energy-harvesting technologies. The applicability of the technology is evaluated by several parameters that are important to the functionality in an industrial environment. All technologies are individually presented and evaluated, a concluding table is also summarizing the technologies strengths and weaknesses. To support the technology evaluation on a more theoretical level, simulations has been performed to strengthen our claims. Among the evaluated and simulated technologies, we found that the variable reluctancebased harvesting technology is the strongest candidate for further technology development for the considered use-case.

This project is motivated by the difficulties experienced by district energy utilities to detect faults in large-scale district energy systems. Faults that remain undetected can be costly and the industry loose credibility when customers detect faults and receive incorrect bills. Faults are common in district energy systems due to the high number of substations and instrumentation components. Also, the standard energy-metering instrumentation is designed for low cost and billing, not for automated fault detection. Large variations in building dynamics, building subsystems, human behaviour and the environment make the system complex to model and analyse. Therefore, conventional methods for fault detection are not applicable and the use of ad hoc methods for fault detection often result in numerous false alarms that are costly to analyse and manage. There is a growing interest among the utilities to develop services and functions that are based on data with high temporal resolution. Energy metering regulations are also expected to become more demanding in the future, which drives technology standards towards high-resolution data. This trend results in high rates of streaming data at the management level, which is more challenging to validate. Therefore, more efficient methods for fault detection are needed.This project deals with probabilistic methods for automated anomaly detection that are useful for the identification of faults in large-scale district energy systems. These methods are compatible with the information that is available in modern energy meter data management systems. We focus on methods and heuristics that can be applied automatically with a minimum of human assistance to enable cost-efficient analysis of data. With these methods, operators do not have to rely on ad hoc tests or manual inspection of graphs to detect anomalies in the data. Instead, operators can focus on the analysis of a subset of substations that are identified as abnormal. Intraday and intraweek variations in the thermal load are accounted for by automatically grouping hours of the week with similar thermal load characteristics. Alternatively, intraweek cycles can be accounted for by grouping days of the week with similar characteristics. Robust regression is used to model variable relationships with historical data. A robust outlier detection method is used to determine if variables deviate from the expectation defined by a regression model. Robust statistical methods are used to score outliers, so that outstanding substations can be identified automatically with a ranking procedure. The regression models can also be used for imputation of missing energy metering data, which is a common problem that is not always solved in an accurate way. We also present methods for the detection of long-term drift, which can be costly and otherwise difficult to detect, and the detection of poor precision in measurement data, which for example can result from oversized flow valves, misconfiguration and noise. In addition to fault detection, the proposed methods can be useful also for maintenance scheduling because substations that behave in a way that is consistent with the historical record can be given a lower maintenance priority compared to substations with abnormal behaviour.The proposed methods are studied using hourly data from a population of about one thousand district heating substations. Sample code of key functions is provided. We find that substations with documented faults, unknown faults and abnormal characteristics can be identified in about 5% of the substations. The lack of a well-defined dataset makes the development and evaluation of methods for fault detection challenging, and the fact that historical energy metering data includes abnormal data is often ignored in the literature. The proposed methods need to be implemented in a full-scale district energy management system under the supervision of experienced operators before the effects on the fault detection rate and cost efficiency can be properly evaluated. However, we are convinced that the proposed algorithms can be implemented in present data management systems and that they offer significant advantages over the methods that are commonly used today.