Light-Emitting Diodes. (Monographs in Electrical and Electronic Engineering.) By A. A. Bergh and P. J. Dean. Pp. viii+591. (Clarendon: Oxford; Oxford University: London, 1976.) £22.

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Light-emitting diodes

Large amounts of data are increasingly accumulated in the energy sector with the continuous application of sensors, wireless transmission, network communication, and cloud computing technologies. To fulfill the potential of energy big data and obtain insights to achieve smart energy management, we present a comprehensive study of big data driven smart energy management. We first discuss the sources and characteristics of energy big data. Also, a process model of big data driven smart energy management is proposed. Then taking smart grid as the research background, we provide a systematic review of big data analytics for smart energy management. It is discussed from four major aspects, namely power generation side management, microgrid and renewable energy management, asset management and collaborative operation, as well as demand side management (DSM). Afterwards, the industrial development of big data-driven smart energy management is analyzed and discussed. Finally, we point out the challenges of big data-driven smart energy management in IT infrastructure, data collection and governance, data integration and sharing, processing and analysis, security and privacy, and professionals.

Big data driven smart energy management: From big data to big insights

The objective of this work is to determine and study, analyze and elaborate, classify and categorize the main risk analysis and risk-assessment methods and techniques by reviewing the scientific literature. The paper consists of two parts: a) the investigation, presentation and elaboration of the main risk-assessment methodologies and b) the statistical analysis, classification, and comparative study of the corresponding scientific papers published by six representative scientific journals of Elsevier B.V. covering the decade 2000–2009. The scientific literature reviewing showed that the risk analysis and assessment techniques are classified into three main categories: (a) the qualitative, (b) the quantitative, and (c) the hybrid techniques (qualitative–quantitative, semi-quantitative). The qualitative techniques are based both on analytical estimation processes, and on the safety managers–engineers ability. According to quantitative techniques, the risk can be considered as a quantity, which can be estimated and expressed by a mathematical relation, under the help of real accidents’ data recorded in a work site. The hybrid techniques, present a great complexity due to their ad hoc character that prevents a wide spreading. The statistical analysis shows that the quantitative methods present the highest relative frequency (65.63%) while the qualitative a lower one (27.68%). Furthermore the hybrid methods remain constantly at a very low level (6.70%) during the entire processing period.

http://risktr.com/dokumanlar/risk%20analysis%20and%20assessment%20methodologies%20in%20th%20work%20sites.pdf

Risk analysis and assessment methodologies in the work sites: On a review, classification and comparative study of the scientific literature of the period 2000–2009

Application of the fault tree analysis for assessment of power system reliability

Preserving the availability and integrity of the power grid critical infrastructures in the face of fast-spreading intrusions requires advances in detection techniques specialized for such large-scale cyber-physical systems. In this paper, we present a security-oriented cyber-physical state estimation (SCPSE) system, which, at each time instant, identifies the compromised set of hosts in the cyber network and the maliciously modified set of measurements obtained from power system sensors. SCPSE fuses uncertain information from different types of distributed sensors, such as power system meters and cyber-side intrusion detectors, to detect the malicious activities within the cyber-physical system. We implemented a working prototype of SCPSE and evaluated it using the IEEE 24-bus benchmark system. The experimental results show that SCPSE significantly improves on the scalability of traditional intrusion detection techniques by using information from both cyber and power sensors. Furthermore, SCPSE was able to detect all the attacks against the control network in our experiments.

Andrija Volkanovski

Papers

Application of the fault tree analysis for assessment of power system reliability

Genetic algorithm optimisation of the maintenance scheduling of generating units in a power system

Extension of station blackout coping capability and implications on nuclear safety

Implication of PSA uncertainties on risk-informed decision making

Multi-objective unit commitment with introduction of a methodology for probabilistic assessment of generating capacities availability