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Advanced Machine Learning Functionalities in the Medium Voltage distributed monitoring system QuEEN: A Macro-Regional Voltage Dips Severity Analysis

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ENERGIES 2021年第23期14卷 7949页

作者： Zanoni, Michele Chiumeo, Riccardo Tenti, Liliana Volta, Massimo Ric Sistema Energet RSE SpA Via Rubattino 54 I-20134 Milan Italy

This paper presents the integration of advanced machine learning techniques in the medium voltage distributed monitoring system QuEEN. This system is aimed to monitor voltage dips in the Italian distribution network mainly for survey and research purposes. For each recorded event it is able to automatically evaluate its residual voltage and duration from the corresponding voltage rms values and provide its "validity" (invalidating any false events caused by voltage transformers saturation) and its "origin"(upstream or downstream from the measurement point) by proper procedures and algorithms (current techniques). On the other hand, in the last years new solutions have been proposed by RSE to improve the assessment of the validity and origin of the event: the DELFI classifier (DEep Learning for False voltage dips Identification) and the FExWaveS + SVM classifier (Features Extraction from Waveform Segmentation + Support Vector Machine classifier). These advanced functionalities have been recently integrated in the monitoring system thanks to the automated software tool called QuEEN PyService. In this work, intensive use of these advanced techniques has been carried out for the first time on a significant number of monitored sites (150) starting from the data recorded from 2018 to 2021. Besides, the comparison between the results of the innovative technique (validity and origin of severe voltage dips) with respect to the current ones has been performed at the macro-regional level too. The new techniques are shown to have a not negligible impact on the severe voltage dips number and confirm a non-homogenous condition among the Italian macro-regional areas.

关键词： power quality voltage dips distributed monitoring system machine learning deep learning

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Architectural Criteria for a distributed Energy monitoring system 17

Architectural Criteria for a Distributed Energy Monitoring S...

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17th IEEE International Conference on Environment and Electrical Engineering (IEEE EEEIC) / 1st IEEE Industrial and Commercial Power systems Europe Conference (IEEE I and CPS Europe)

作者： Mongiovi, Luca Guido Cristaldi, Loredana Tironi, Enrico Bua, Franco Liziero, Michele Frattini, Giulia Borghi, Luigi Aurelio Lavecchia, Cristina Grigis, Giacomo Martirano, Luigi Energy & ICT Consultant Expert Energy Management Milan Italy Politecn Milan DEIB Dept Milan Italy Engn Consulting & Design Pavia Italy EnergyTeam SpA Milan Italy Didelme Sistemi Srl Castellanza VA Italy Fdn Osservatorio Meteorol Milano Duomo Milan Italy Schneider Elect SpA Stezzano Italy Sapienza Univ DIAEE Dept Rome Italy

ISBN: (纸本)9781538639177

The standards related to energy management emphasize the role of the measurement activity but no exhaustive guidelines have been published for outlining requirements and architecture of a comprehensive energy efficiency monitoring system that goes beyond a simple measuring system. A monitoring system for Energy Efficiency should transform several kinds of energetic and context data into actionable pieces of information for controlling energy performance, improving operations and maintenance as well as verifying the effectiveness and the continuity of corrective actions. Eventually the energy efficiency monitoring system should be modular to be upgraded over the time and to fit the needs of any organization. This paper aims at contributing to the work of CEN-CENELEC JWG9 "Energy measurement plan for organizations" by providing background information for the development of its standards and for future work

关键词： Energy Management distributed monitoring system Achitecture Susteinability Technical standardization

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Architectural Criteria for a distributed Energy monitoring system

Architectural Criteria for a Distributed Energy Monitoring S...

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IEEE International Conference on Environment and Electrical Engineering

作者： Luca Guido Mongiovi Loredana Cristaldi Enrico Tironi Franco Bua Michele Liziero Giulia Frattini Luigi Aurelio Borghi Cristina Lavecchia Giacomo Grigis Luigi Martirano Energy & ICT Consultant - Expert in Energy Management Milan Italy Politecnico di Milano - DEIB Department Milan Italy ECD - Engineering Consulting and Design Pavia Italy EnergyTeam SpA Milan Italy Didelme Sistemi srl Castellanza (VA) Italy Fondazione Osservatorio Meteorologico Milano Duomo Milan Italy Schneider Electric S.p.A. Stezzano Italy Sapienza University - DIAEE Department Rome Italy

ISBN: (纸本)9781538639184

关键词： Energy Management distributed monitoring system Achitecture Susteinability Technical standardization

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Tendon Anomaly Identification in Prestressed Concrete Beams Based on an Advanced monitoring MEMS and Data-Driven Detection of Structural Damage

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SENSORS 2025年第1期25卷 289-289页

作者： de Alteriis, Giorgio Mariniello, Giulio Pastore, Tommaso Silvestri, Alessia Teresa Augugliaro, Giuseppe Papallo, Ida Mennuti, Canio Bilotta, Antonio Lo Moriello, Rosario Schiano Asprone, Domenico Univ Naples Federico II Dept Ind Engn Piazzale Tecchio 80 I-80125 Naples Italy Univ Naples Federico II Dept Struct Engn & Architecture Via Claudio 21 I-80125 Naples Italy Univ Naples Federico II Dept Chem Mat & Prod Engn Piazzale Tecchio 80 I-80125 Naples Italy Natl Inst Accid Insurance Work INAIL I-00040 Monte Porzio Catone Rome Italy Univ Naples Federico II Dept Elect Engn & Informat Technol Via Claudio 21 I-80125 Naples Italy

The growing importance of state assessments in civil engineering has led to intensive research into the development of damage identification methods based on vibrations. Natural frequencies and modal shapes have garnered great interest because modal parameters are invariant of structure. Moreover, thanks to the global nature of modal parameters, their variations are not limited to the location of the damage. This is an important advantage that offers the opportunity to identify damage with sensors whose position does not have to coincide with the damaged area. The integration of MEMS sensors into structural health monitoring (SHM) systems offers a promising approach to long-term structural maintenance, especially in large-scale infrastructure. This paper presents an anomaly detection technique that analyzes raw sequential data within a statistical framework to detect damage that causes prestress loss of the tendon by exploiting a distributed monitoring system composed of six high-performance MEMS sensors. The proposed system is preliminarily evaluated to identify the frequency of the first mode, and then the proposed methodology is validated on acceleration data collected on a 240 cm beam in three different damage configurations, achieving a high detection accuracy and showing that its output can also evaluate the damage localization.

关键词： distributed monitoring system structural health monitoring MEMS frequency domain decomposition anomaly detection

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Multilinear Regression Analysis applied to Voltage Dips classified by a Deep Learning -based Validity Algorithm 116

Multilinear Regression Analysis applied to Voltage Dips clas...

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2024 International Annual Conference of the Italian Association of Electrotechnics

作者： Zanoni, Michele Chiumco, Riccardo Gandolfi, Chiara Tenti, Liliana Volta, Massimo Ric Sistema Energet RSE SpA Transmiss & Distribut Technol Milan Italy

ISBN: (纸本)9798350378498;9788887237627

In the last years, the QuEEN monitoring system enhanced its functionalities integrating some innovative criteria to evaluate both the validity and origin of voltage dips by means of Deep Learning and Machine Learning techniques. In particular, the DELFI (Deep Learning for False voltage dip Identification) classifier obtained more promising results in the events validity assessment than those pursued by a first criterion based on the estimate of the 2(nd) harmonic. In previous works of the authors, the influence of some networks characteristics on voltage dips performance have been evaluated by means of Multiple Linear Regression analysis (MLR) applied on events statistics whose validity were checked with the 2(nd) Harmonic Criterion. In this work, the MLR analyses have been performed on statistics validated with DELFI, i.e., with the data recorded by QuEEN in the periods 2018-2020 and 2021-2023. In the MLR analysis the neutral operation, the geographical area and the overhead lines length were used as explicative variables, while three voltage dips performance indexes were adopted as response variables (i.e., total number of events in the period and total number of events occurred respectively under class 2 and class 3 immunity curve). Considering only the true events for DELFI, a greater influence of specific geographical area was observed on the counting indices, with a higher impact for the indices associated with greater severity.

关键词： power quality voltage dip deep learning distributed monitoring system multiple regression analysis network characteristics

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Edge AI Deployment and Stream-Based Time Series Classification Within ARTHUR 12th

Edge AI Deployment and Stream-Based Time Series Classificati...

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13th International Conference on Cloud Computing and Services Science (CLOSER)

作者： Schneider, Niels Ruf, Philipp Lermer, Matthias Reich, Christoph Furtwangen Univ Appl Sci Inst Data Sci Cloud Comp & IT Secur Robert Gerwig Pl 1 D-78120 Furtwangen Germany

ISBN: (纸本)9783031681646;9783031681653

The convergence of Artificial Intelligence (AI) and manufacturing has given rise to transformative opportunities in enhancing operational efficiency and productivity. This study delves into the concept of edge computing, where AI algorithms are deployed closer to data sources within a distributed architecture. The ARTHUR framework, a versatile Data Acquisition system with distributed Sensors, developed under the slogan "test before invest", is introduced as a platform to realise this approach. By employing low-cost hardware, event-based communication, and in-database AI operations, ARTHUR enables real-time data processing and analysis for research, education and industrial applications. A case study demonstrates the deployment of a machine learning model for time series classification using vibration data from CNC machines. The results underscore the potential of edge-based AI in manufacturing, highlighting its capability to empower adaptive and responsive production systems.

关键词： distributed monitoring system Machine learning Cloud Data acquisition IoT-cloud integration in-Database operations

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ARTHUR: Machine Learning Data Acquisition system with distributed Data Sensors 13

ARTHUR: Machine Learning Data Acquisition System with Distri...

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13th International Conference on Cloud Computing and Services Science (CLOSER)

作者： Schneider, Niels Ruf, Philipp Lermer, Matthias Reich, Christoph Furtwangen Univ Appl Sci Inst Data Sci Cloud Comp & IT Secur Robert Gerwig Pl 1 D-78120 Furtwangen Germany

ISBN: (纸本)9789897586507

On the way to the smart factory, the manufacturing companies investigate the potential of Machine Learning approaches like visual quality inspection, process optimisation, maintenance prediction and more. In order to be able to assess the influence of Machine Learning based systems on business-relevant key figures, many companies go down the path of test before invest. This paper describes a novel and inexpensive distributed Data Acquisition system, ARTHUR (dAta collectoR system witH distributed sensoRs), to enable the collection of data for AI-based projects for research, education and the industry. ARTHUR is arbitrarily expandable and has so far been used in the field of data acquisition on machine tools. Typical measured values are Acoustic Emission values, force plate X-Y-Z force values, simple SPS signals, OPC-UA machine parameters, etc. which were recorded by a wide variety of sensors. The ARTHUR system consists of a master node, multiple measurement worker nodes, a local streaming system and a gateway that stores the data to the cloud. The authors describe the hardware and software of this system and discuss its advantages and disadvantages.

关键词： distributed monitoring system Machine Learning Cloud Data Acquisition IoT-Cloud Integration

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Local distributed Node for Power Quality Event Detection Based on Multi-Sine Fitting Algorithm

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SENSORS 2024年第8期24卷 2474页

作者： Carni, Domenico Luca Lamonaca, Francesco Univ Calabria Dept Comp Engn Modeling Elect & Syst Sci I-87046 Arcavacata Di Rende Italy

The new power generation systems, the increasing number of equipment connected to the power grid, and the introduction of technologies such as the smart grid, underline the importance and complexity of the Power Quality (PQ) evaluation. In this scenario, an Automatic PQ Events Classifier (APQEC) that detects, segments, and classifies the anomaly in the power signal is needed for the timely intervention and maintenance of the grid. Due to the extension and complexity of the network, the number of points to be monitored is large, making the cost of the infrastructure unreasonable. To reduce the cost, a new architecture for an APQEC is proposed. This architecture is composed of several Locally distributed Nodes (LDNs) and a Central Classification Unit (CCU). The LDNs are in charge of the acquisition, the detection of PQ events, and the segmentation of the power signal. Instead, the CCU receives the information from the nodes to classify the PQ events. A low-computational capability characterizes low-cost LDNs. For this reason, a suitable PQ event detection and segmentation method with low resource requirements is proposed. It is based on the use of a sliding observation window that establishes a reasonable time interval, which is also useful for signal classification and the multi-sine fitting algorithm to decompose the input signal in harmonic components. These components can be compared with established threshold values to detect if a PQ event occurs. Only in this case, the signal is sent to the CCU for the classification;otherwise, it is discarded. Numerical tests are performed to set the sliding window size and observe the behavior of the proposed method with the main PQ events presented in the literature, even when the SNR varies. Experimental results confirm the effectiveness of the proposal, highlighting the correspondence with numerical results and the reduced execution time when compared to FFT-based methods.

关键词： measurement monitoring system distributed monitoring system power signal event power quality sinusoidal signal alteration detection

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What Else Do the Deep Learning Techniques Tell Us about Voltage Dips Validity? Regional-Level Assessments with the New QuEEN system Based on Real Network Configurations

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ENERGIES 2023年第3期16卷 1189-1189页

作者： Zanoni, Michele Chiumeo, Riccardo Tenti, Liliana Volta, Massimo Ric Sistema Energet RSE SpA I-20134 Milan Italy

The paper presents the performance evaluation of the DELFI (Deep Learning for False voltage dip Identification) classifier for evaluating voltage dip validity, now available in the QuEEN monitoring system. In addition to the usual event characteristics, QuEEN now automatically classifies events in terms of validity based on criteria that make use of either a signal processing technique (current criterion) or an artificial intelligence algorithm (new criterion called DELFI). Some preliminary results obtained from the new criterion had suggested its full integration into the monitoring system. This paper deals with the comparison of the effectiveness of the DELFI criterion compared to the current one in evaluating the events validity, starting from a large set of events. To prove the enhancement achieved with the DELFI classifier, an in-depth analysis has been carried out by cross-comparing the results both with the neutral system configuration and with the events characteristics (duration/residual voltage). The results clearly show a better match of DELFI classifications with network and events characteristics. Moreover, the DELFI classifier has allowed us to highlight specific situations concerning power quality at regional level, resolving the uncertainties due to the current validity criterion. In details, three groups of regions can be highlighted with respect to the frequency of the occurrence of false events.

关键词： power quality voltage dips distributed monitoring system machine learning deep learning network neutral operation

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Design on distributed Fault monitoring system for Navigation Lights

Design on Distributed Fault Monitoring System for Navigation...

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International Conference on Information Technology and Management Innovation (ICITMI2012)

作者： Tan, Ying Xiao, Chun Zhang, Xingfei Li, Qiang He, Yi Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China

ISBN: (纸本)9783037855744

For the environment of using the navigation lights and its requirement, a distributed fault monitoring system on the navigation lights is designed. The three-layer structure with slave station and master station and Voyage Data Recorder is employed in the system, which can monitor broken wire fault, short circuit fault and power failure on navigation lights. The mature RS485 communication circuit is adopted in communication interface not only between the master and the slave, but also between the master and VDR (Voyage Data Recorder). The master station gets the information of all navigation lights from the slave stations, and sends these received data to VDR after being processed. The system has been successfully applied in practice, and can effectively monitor the state of the navigation lights and give the alarm information when in fault. In addition, the monitoring system can quickly and accurately trace the fault position and type, so the navigation lights can be maintain timely.

关键词： navigation lights RS485 distributed monitoring system

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