Last-Meter Smart Grid Embedded in an Internet-of-Things Platform

The customer domain of the smart grid natu- rally blends with smart home and smart building systems, but typical proposed approaches are “distributor-centric” rather than “customer-centric,” undermining user acceptance, and are often poorly scalable. To solve this problem, we propose a detailed architecture and an implementation of a “last-meter” smart grid—the portion of the smart grid on customer premises—embedded in an internet-of-things (IoT) platform. Our approach has four aspects of novelty and advantages with respect to the state of the art: 1) seamless integration of smart grid with smart home applications in the same infrastructure; 2) data gathering from heterogeneous sensor communication protocols; 3) secure and customized data access; and 4) univocal sensor and actuator mapping to a common abstraction layer on which additional concurrent applications can be built. A demonstrator has been built and tested with purposely-developed ZigBee smart meters and gateways, a distributed IoT server, and a flexible user interface

Power systems automation, communication, and information technologies for smart grid: A technical aspects review

Smart grid (SG) introduced proven power system, based on modernized power delivery system with introduction of advanced data-information and communication technologies (ICT). SGs include improved quality of power transmission/distribution from power generation to end-users with optimized power flow and efficiency. In addition to above modern automation, two-way communications, advanced monitoring, and control to optimize power quality issues are the classic features of SGs. This ensures the efficiency and reliability of all its interconnected power system elements against potential threats and life time cycle. By integrating ICT into the power system SGs improved the working capabilities of the utility companies. Resultant of ICT with SG leads to better management of assets and ensure energy management for end users. This review article presents the different areas of communication and information technology areas involved in SG automation

A Review on IOT Techniques for Automating Devices

Nowadays world of Internet is changing towards Internet-of-Things simply called as IoT, where all things which we use in our day to day life connects to internet and can be monitor & can be operate remotely. IoT has many applications in all domains such as industrial wireless sensor network, smart homes, agriculture, etc. IoT uses standard protocols and predefined architecture for deployment using Smart technologies such as Radio Frequency Identification, Wireless Sensors, Actuators, Zigbee, etc. for communication. Applications of IoT are increasing day by day in many domains. This paper proposed an overview on architecture of IoT and technologies used in IoT. Applications of IoT, Problems in IoT and suitable solutions are also presented in this survey paper. DOI: 10.17762/ijritcc2321-8169.16041

Joint Power Control and Structural Health Monitoring in Industry 4.0 Scenarios using Eclipse Arrowhead and Web of Things

The integration of legacy IoT ecosystems in Industry 4.0 scenarios requires human effort to adapt single devices. This process would highly benefit from features like device lookup, loose coupling and late binding. In this paper, we tackle the issue of integrating legacy monitoring systems and actuation systems in an industrial scenario, by looking into the Web of Things (WoT) as a communication standard and the Eclipse Arrowhead Framework (AHF) as a service orchestrator. More specifically, we propose a general architectural approach to enable closed-loop automation between the above mentioned legacy systems by leveraging the adaptation of the WoT to the AHF. Then, we develop a rule-based engine that enables the control of the actuation based on sensor values. Finally, we present a proof-of-concept use case where we integrate a Structural Health Monitoring (SHM) scenario with a power control actuation subsystem using the developed component

Multi-Layered Clustering for Power Consumption Profiling in Smart Grids

Open access publicationSmart Grids (SGs) have many advantages over traditional power grids as they enhance the way electricity is generated, distributed, and consumed by adopting advanced sensing, communication and control functionalities that depend on power consumption profiles of consumers. Clustering algorithms (e.g., centralized clustering) are used for profiling individual’s power consumption. Due to the distributed nature and ever growing size of SGs, it is predicted that massive amounts of data will be created. However, conventional clustering algorithms neither efficient enough nor scalable enough to deal with such amount of data. In addition, the cost for transferring and analyzing large amounts of data is expensive high both computationally and communicationally. This paper thus proposes a power consumption profiling model based on two levels of clustering. At the first level, local power consumption profiles are derived, which are then used by the second level in order to create a global power consumption profile. The followed approach reduces the communication and computation complexity of the proposed two level model and improves the privacy of consumers. We point out that having good knowledge of the local power profiles leads to more effective prediction model and cost-effective power pricing scheme, especially in a heterogeneous grid topology. In addition, the correlations between the local and global profiles can be used to localize/identify power consumption outliers. Simulation results illustrate that the proposed model is effective in reducing the computational complexity without much affecting its accuracy. The reduction in computational complexity is about 52% and the reduction in the communicational complexity is about 95% when compared to the centralized clustering approach

Cross-correlation based classification of electrical appliances for non-intrusive load monitoring

This is the author accepted manuscript. The final version is available from the Institute of Electrical and Electronics Engineers via the DOI in this recordOver the last few decades, residential electrical load classification and identification have been one of the most challenging research in the area of non-intrusive load monitoring (NILM) for home energy management system. The application of NILM technique in the smart grid has gained enormous attention in recent years. Several methods, including information from the given domains into NILM, have been proposed. Recently, among these methods, machine learning techniques are shown to be significantly better based on large-scale data for load monitoring. In this paper, machine learning techniques are utilized for residential load classification on novel cross-correlation based features, which are extracted from the synthetic time series data. We also present a t-distributed stochastic neighbour embedding (t SNE) based dimensionality reduction from the high dimensional feature set so that the classification can be implemented on a general-purpose microcontroller for near real-time monitoring. Our experimental results show that the extracted features are suitable for reliable identification and classification of different and the combination of residential loads.Visvesvaraya PhD scheme, Government of Indi

Low-Power Wearable ECG Monitoring System for Multiple-Patient Remote Monitoring

Many devices and solutions for remote electrocardiogram (ECG) monitoring have been proposed in the literature. These solutions typically have a large marginal cost per added sensor and are not seamlessly integrated with other smart home solutions. Here, we propose an ECG remote monitoring system that is dedicated to non-technical users in need of long-term health monitoring in residential environments and is integrated in a broader Internet-of-Things (IoT) infrastructure. Our prototype consists of a complete vertical solution with a series of advantages with respect to the state of the art, considering both the prototypes with integrated front end and prototypes realized with off-the-shelf components: 1) ECG prototype sensors with record-low energy per effective number of quantized levels; 2) an architecture providing low marginal cost per added sensor/user; and 3) the possibility of seamless integration with other smart home systems through a single IoT infrastructure

PMU-based distribution system state estimation with adaptive accuracy exploiting local decision metrics and IoT paradigm

A novel adaptive distribution system state estimation (DSSE) solution is presented and discussed, which relies on distributed decision points and exploits the Cloud-based Internet of Things (IoT) paradigm. Up to now, DSSE procedures have been using fixed settings regardless of the actual values of measurement accuracy, which is instead affected by the actual operating conditions of the network. The proposed DSSE is innovative with respect to previous literature, because it is adaptive in the use of updated accuracies for the measurement devices. The information used in the estimation process along with the rate of the execution are updated, depending on the indications of appropriate local metrics aimed at detecting possible variations in the operating conditions of the distribution network. Specifically, the variations and the trend of variation of the rms voltage values obtained by phasor measurement units (PMUs) are used to trigger changes in the DSSE. In case dynamics are detected, the measurement data are sent to the DSSE at higher rates and the estimation process runs consequently, updating the accuracy values to be considered in the estimation. The proposed system relies on a Cloud-based IoT platform, which has been designed to incorporate heterogeneous measurement devices, such as PMUs and smart meters. The results obtained on a 13-bus system demonstrate the validity of the proposed methodology that is efficient both in the estimation process and in the use of the communication resources

Internet of Things Applications as Energy Internet in Smart Grids and Smart Environments

Energy Internet (EI) has been recently introduced as a new concept, which aims to evolve smart grids by integrating several energy forms into an extremely flexible and effective grid. In this paper, we have comprehensively analyzed Internet of Things (IoT) applications enabled for smart grids and smart environments, such as smart cities, smart homes, smart metering, and energy management infrastructures to investigate the development of the EI based IoT applications. These applications are promising key areas of the EI concept, since the IoT is considered one of the most important driving factors of the EI. Moreover, we discussed the challenges, open issues, and future research opportunities for the EI concept based on IoT applications and addressed some important research areas