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高级复杂能源系统监测与控制：现有技术及其应用标准综述。

Advanced and Complex Energy Systems Monitoring and Control: A Review on Available Technologies and Their Application Criteria.

机构信息

Università LUM "Giuseppe Degennaro", S.S. 100-km 18, Casamassima, 70010 Bari, Italy.

LUM Enterprise S.r.l., S.S. 100-km 18, Casamassima, 70010 Bari, Italy.

出版信息

Sensors (Basel). 2022 Jun 29;22(13):4929. doi: 10.3390/s22134929.

DOI:10.3390/s22134929
PMID:35808429
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9269690/
Abstract

Complex energy monitoring and control systems have been widely studied as the related topics include different approaches, advanced sensors, and technologies applied to a strongly varying amount of application fields. This paper is a systematic review of what has been done regarding energy metering system issues about (i) sensors, (ii) the choice of their technology and their characterization depending on the application fields, (iii) advanced measurement approaches and methodologies, and (iv) the setup of energy Key Performance Indicators (KPIs). The paper provides models about KPI estimation, by highlighting design criteria of complex energy networks. The proposed study is carried out to give useful elements to build models and to simulate in detail energy systems for performance prediction purposes. Some examples of energy complex KPIs based on the integration of the Artificial Intelligence (AI) concept and on basic KPIs or variables are provided in order to define innovative formulation criteria depending on the application field. The proposed examples highlight how modeling a complex KPI as a function of basic variables or KPIs is possible, by means of graph models of architectures.

摘要

复杂能源监测和控制系统已被广泛研究，因为相关主题包括不同的方法、先进的传感器和应用于广泛应用领域的技术。本文是对能源计量系统问题的系统综述，涉及（i）传感器，（ii）根据应用领域选择其技术及其特性，（iii）先进的测量方法和方法，以及（iv）能源关键绩效指标（KPI）的设置。本文提供了 KPI 估计模型，突出了复杂能源网络的设计标准。拟议的研究旨在为构建模型和详细模拟能源系统以进行性能预测提供有用的要素。提供了一些基于人工智能（AI）概念和基本 KPI 或变量集成的能源复杂 KPI 示例，以便根据应用领域定义创新的公式标准。所提出的示例强调了如何通过架构的图形模型，将复杂 KPI 作为基本变量或 KPI 的函数进行建模。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bad0/9269690/9d8ae2045416/sensors-22-04929-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bad0/9269690/b61dd8761e5a/sensors-22-04929-g0A1.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bad0/9269690/4f3519da0c1c/sensors-22-04929-g0A5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bad0/9269690/0649163c8ad3/sensors-22-04929-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bad0/9269690/99e697bcece5/sensors-22-04929-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bad0/9269690/9d8ae2045416/sensors-22-04929-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bad0/9269690/b61dd8761e5a/sensors-22-04929-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bad0/9269690/7a7b1e80dfea/sensors-22-04929-g0A2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bad0/9269690/783a3e67ecec/sensors-22-04929-g0A3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bad0/9269690/30e165bc5337/sensors-22-04929-g0A4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bad0/9269690/4f3519da0c1c/sensors-22-04929-g0A5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bad0/9269690/0649163c8ad3/sensors-22-04929-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bad0/9269690/99e697bcece5/sensors-22-04929-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bad0/9269690/9d8ae2045416/sensors-22-04929-g003.jpg

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