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Event-Driven Data Acquisition for Electricity Metering: A Tutorial

Ce document fournit un tutoriel sur les dernières avancées de la mesure événementielle (EDM) tout en indiquant les extensions potentielles pour améliorer ses performances. Nous avons revisité les effets sur la reconstruction du signal (i) d'une procédure affinée pour définir les événements de variation de puissance, (ii) d'un filtrage des mesures consécutives qui se réfère au même événement, (iii) d'un filtrage des pointes et (iv) d'un paramètre de temporisation. Nous avons illustré par de nombreux résultats numériques que l'EDM peut fournir une reconstruction de signal haute-fidélité tout en diminuant le nombre total de mesures acquises à transmettre. Son principal avantage est de ne stocker que des échantillons informatifs basés sur des événements prédéterminés, évitant la redondance et diminuant le trafic offert au réseau de communication sous-jacent. Ce tutoriel met en évidence les principaux avantages de l'EDM et indique des orientations de recherche prometteuses.
Este documento proporciona un tutorial sobre los avances más recientes de la medición impulsada por eventos (EDM) al tiempo que indica posibles extensiones para mejorar su rendimiento. Hemos revisado los efectos en la reconstrucción de señales de (i) un procedimiento ajustado para definir eventos de variación de potencia, (ii) filtrado de mediciones consecutivas que se refiere al mismo evento, (iii) filtrado de picos y (iv) parámetro de tiempo de espera. Hemos ilustrado a través de extensos resultados numéricos que la EDM puede proporcionar una reconstrucción de señal de alta fidelidad al tiempo que disminuye el número total de mediciones adquiridas a transmitir. Su principal ventaja es almacenar solo muestras informativas basadas en eventos predeterminados, evitando redundancias y disminuyendo el tráfico ofrecido a la red de comunicación subyacente. Este tutorial destaca las ventajas clave de la EDM y señala direcciones de investigación prometedoras.
This paper provides a tutorial on the most recent advances of event-driven metering (EDM) while indicating potential extensions to improve its performance. We have revisited the effects on signal reconstruction of (i) a fine-tuned procedure for defining power variation events, (ii) consecutive-measurements filtering that refers to the same event, (iii) spike filtering, and (iv) timeout parameter. We have illustrated via extensive numerical results that EDM can provide high-fidelity signal reconstruction while decreasing the overall number of acquired measurements to be transmitted. Its main advantage is to only store samples that are informative based on predetermined events, avoiding redundancy and decreasing the traffic offered to the underlying communication network. This tutorial highlights the key advantages of EDM and points out promising research directions.
تقدم هذه الورقة برنامجًا تعليميًا حول أحدث التطورات في القياس القائم على الأحداث مع الإشارة إلى التمديدات المحتملة لتحسين أدائها. لقد راجعنا التأثيرات على إعادة بناء الإشارة لـ (1) إجراء دقيق لتحديد أحداث تباين الطاقة، (2) تصفية القياسات المتتالية التي تشير إلى نفس الحدث، (3) تصفية الارتفاع، و (4) معلمة المهلة. لقد أوضحنا من خلال نتائج عددية واسعة أن إدارة البيانات البيئية يمكن أن توفر إعادة بناء إشارة عالية الدقة مع تقليل العدد الإجمالي للقياسات المكتسبة التي سيتم إرسالها. وتتمثل ميزته الرئيسية في تخزين العينات فقط التي تكون غنية بالمعلومات بناءً على أحداث محددة مسبقًا، وتجنب التكرار وتقليل حركة المرور المقدمة إلى شبكة الاتصالات الأساسية. يسلط هذا البرنامج التعليمي الضوء على المزايا الرئيسية للتنظيم الإداري ويشير إلى اتجاهات البحث الواعدة.
- State University of Campinas Brazil
- Lappeenranta-Lahti University of Technology LUT Finland
- Center for Wireless Communications United States
- Oulu University Hospital Finland
- Lappeenranta-Lahti University of Technology LUT Finland
Performance, FOS: Mechanical engineering, Redundancy (engineering), Advanced electricity metering, Electric power transmission networks, Engineering, Electricity, Computer engineering, Computer security, Smart power grids, smart grids, Event-driven data acquisition, Signal reconstruction, Computer network, Physics, Mechanical engineering, Metering mode, advanced electricity metering, Physical Sciences, Signal processing, Signals reconstruction, Computer Networks and Communications, Power variations, Decentralized Estimation, event-driven data acquisition, Electricity metering, Quantum mechanics, Electric power measurement, Real-time computing, Tutorial, FOS: Electrical engineering, electronic engineering, information engineering, Electric measuring instruments, Demand Response in Smart Grids, Power measurement, Electrical and Electronic Engineering, Event (particle physics), Key (lock), Data mining, Timeout, Electronic engineering, Data acquisition, Computer hardware, Security Challenges in Smart Grid Systems, Digital signal processing, Computer science, Distributed computing, Decentralized Inference in Wireless Sensor Networks, Operating system, Control and Systems Engineering, Power, Electrical engineering, Computer Science, Event-driven
Performance, FOS: Mechanical engineering, Redundancy (engineering), Advanced electricity metering, Electric power transmission networks, Engineering, Electricity, Computer engineering, Computer security, Smart power grids, smart grids, Event-driven data acquisition, Signal reconstruction, Computer network, Physics, Mechanical engineering, Metering mode, advanced electricity metering, Physical Sciences, Signal processing, Signals reconstruction, Computer Networks and Communications, Power variations, Decentralized Estimation, event-driven data acquisition, Electricity metering, Quantum mechanics, Electric power measurement, Real-time computing, Tutorial, FOS: Electrical engineering, electronic engineering, information engineering, Electric measuring instruments, Demand Response in Smart Grids, Power measurement, Electrical and Electronic Engineering, Event (particle physics), Key (lock), Data mining, Timeout, Electronic engineering, Data acquisition, Computer hardware, Security Challenges in Smart Grid Systems, Digital signal processing, Computer science, Distributed computing, Decentralized Inference in Wireless Sensor Networks, Operating system, Control and Systems Engineering, Power, Electrical engineering, Computer Science, Event-driven
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).2 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average visibility views 1 download downloads 3 - 1views3downloads
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