Sensors

Research

17 pages, 2147 KiB

Open AccessArticle

Group Method of Data Handling Using Christiano–Fitzgerald Random Walk Filter for Insulator Fault Prediction

by Stefano Frizzo Stefenon, Laio Oriel Seman, Nemesio Fava Sopelsa Neto, Luiz Henrique Meyer, Viviana Cocco Mariani and Leandro dos Santos Coelho

Sensors 2023, 23(13), 6118; https://doi.org/10.3390/s23136118 - 3 Jul 2023

Cited by 14 | Viewed by 1649

Abstract

Disruptive failures threaten the reliability of electric supply in power branches, often indicated by the rise of leakage current in distribution insulators. This paper presents a novel, hybrid method for fault prediction based on the time series of the leakage current of contaminated [...] Read more.

Disruptive failures threaten the reliability of electric supply in power branches, often indicated by the rise of leakage current in distribution insulators. This paper presents a novel, hybrid method for fault prediction based on the time series of the leakage current of contaminated insulators. In a controlled high-voltage laboratory simulation, 15 kV-class insulators from an electrical power distribution network were exposed to increasing contamination in a salt chamber. The leakage current was recorded over 28 h of effective exposure, culminating in a flashover in all considered insulators. This flashover event served as the prediction mark that this paper proposes to evaluate. The proposed method applies the Christiano–Fitzgerald random walk (CFRW) filter for trend decomposition and the group data-handling (GMDH) method for time series prediction. The CFRW filter, with its versatility, proved to be more effective than the seasonal decomposition using moving averages in reducing non-linearities. The CFRW-GMDH method, with a root-mean-squared error of

3.44 \times 10^{- 12}

, outperformed both the standard GMDH and long short-term memory models in fault prediction. This superior performance suggested that the CFRW-GMDH method is a promising tool for predicting faults in power grid insulators based on leakage current data. This approach can provide power utilities with a reliable tool for monitoring insulator health and predicting failures, thereby enhancing the reliability of the power supply. Full article

(This article belongs to the Special Issue Sensors for Measurements and Diagnostic in Electrical Power Systems)

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18 pages, 1434 KiB

Open AccessArticle

Optimized EWT-Seq2Seq-LSTM with Attention Mechanism to Insulators Fault Prediction

by Anne Carolina Rodrigues Klaar, Stefano Frizzo Stefenon, Laio Oriel Seman, Viviana Cocco Mariani and Leandro dos Santos Coelho

Sensors 2023, 23(6), 3202; https://doi.org/10.3390/s23063202 - 17 Mar 2023

Cited by 37 | Viewed by 3113

Abstract

Insulators installed outdoors are vulnerable to the accumulation of contaminants on their surface, which raise their conductivity and increase leakage current until a flashover occurs. To improve the reliability of the electrical power system, it is possible to evaluate the development of the [...] Read more.

Insulators installed outdoors are vulnerable to the accumulation of contaminants on their surface, which raise their conductivity and increase leakage current until a flashover occurs. To improve the reliability of the electrical power system, it is possible to evaluate the development of the fault in relation to the increase in leakage current and thus predict whether a shutdown may occur. This paper proposes the use of empirical wavelet transform (EWT) to reduce the influence of non-representative variations and combines the attention mechanism with a long short-term memory (LSTM) recurrent network for prediction. The Optuna framework has been applied for hyperparameter optimization, resulting in a method called optimized EWT-Seq2Seq-LSTM with attention. The proposed model had a 10.17% lower mean square error (MSE) than the standard LSTM and a 5.36% lower MSE than the model without optimization, showing that the attention mechanism and hyperparameter optimization is a promising strategy. Full article

(This article belongs to the Special Issue Sensors for Measurements and Diagnostic in Electrical Power Systems)

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23 pages, 17440 KiB

Open AccessArticle

PV Defects Identification through a Synergistic Set of Non-Destructive Testing (NDT) Techniques

by Socrates Kaplanis, Eleni Kaplani and Paul Nicolae Borza

Sensors 2023, 23(6), 3016; https://doi.org/10.3390/s23063016 - 10 Mar 2023

Cited by 3 | Viewed by 1996

Abstract

A synergistic set of NDT techniques, including I–V analysis, UVF imaging, IR thermography, and EL imaging, supports a diagnostics methodology developed in this work to qualitatively and quantitatively identify a wide range of PV defects. The methodology is based on (a) the deviation [...] Read more.

A synergistic set of NDT techniques, including I–V analysis, UVF imaging, IR thermography, and EL imaging, supports a diagnostics methodology developed in this work to qualitatively and quantitatively identify a wide range of PV defects. The methodology is based on (a) the deviation of the module electrical parameters at STC from their nominal values, for which a set of mathematical expressions was developed that provide an insight into potential defects and their quantitative impact on the module electrical parameters, and (b) the variation analysis of EL images captured at a sequence of bias voltages for a qualitative investigation on the spatial distribution and strength of the defects. The synergy of these two pillars, supported by UVF imaging, IR thermography, and I–V analysis cross-correlating their findings, makes the diagnostics methodology effective and reliable. It was applied on c-Si and pc-Si modules operating from 0–24 years, exhibiting a diversity of defects of varying severity, either pre-existing or formed by natural ageing or externally induced degradation. Defects such as EVA degradation, browning, corrosion in the busbar/interconnect ribbons, EVA/cell-interface delamination, pn-junction damage, e⁻+hole recombination regions, breaks, microcracks, finger interruptions, and passivation issues are detected. Degradation factors triggering a cascade of internal degradation processes through cause and effect are analysed and additional models are proposed for the temperature pattern under current mismatch and corrosion along the busbar, further empowering the cross-correlation of NDT results. Power degradation was determined from 1.2% in 2 years of operation to more than 50% in modules with film deposition. Full article

(This article belongs to the Special Issue Sensors for Measurements and Diagnostic in Electrical Power Systems)

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33 pages, 5720 KiB

Open AccessArticle

Configuration of the Geometric State of Railway Tracks in the Sustainability Development of Electrified Traction Systems

by Arkadiusz Kampczyk and Katarzyna Rombalska

Sensors 2023, 23(5), 2817; https://doi.org/10.3390/s23052817 - 4 Mar 2023

Cited by 5 | Viewed by 2556

Abstract

The state-space interface of the railway track (track) geometry system with an electrified traction system (ETS) constitutes the geometric configuration that is utilised in this study. Importantly, driving comfort, smooth operation (smooth running), and compliance with the ETS are the desired aims. Direct [...] Read more.

The state-space interface of the railway track (track) geometry system with an electrified traction system (ETS) constitutes the geometric configuration that is utilised in this study. Importantly, driving comfort, smooth operation (smooth running), and compliance with the ETS are the desired aims. Direct measurement methods were used in the interaction with the system, especially in regard to the fixed-point, visual, and expert methods. In particular, track-recording trolleys were used. The subjects belonging to the insulated instruments also included the integration of certain methods, such as in the brainstorming, mind mapping, system approach, heuristic, failure mode and effect analysis, and system failure mode effects analysis methods. These were based on a case study and are representative of three real objects, i.e., electrified railway lines, direct current (DC), and scientific research objects (which specifically cover five research objects). The aim of the scientific research work is to increase the interoperability of the railway track geometric state configurations in regard to the sustainability development of the ETS. The results of this work confirmed their validity. By ensuring that the six-parameter defectiveness D₆ was defined and implemented, the D₆ parameter of the railway track condition was first estimated. The new approach reinforces the improvement in preventive maintenance and reductions in corrective maintenance; moreover, it is an innovative supplement to the existing direct measurement method in the configuration of the geometric condition of railway tracks and in the sustainability development of the ETS via interacting with the indirect measurement method. Full article

(This article belongs to the Special Issue Sensors for Measurements and Diagnostic in Electrical Power Systems)

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17 pages, 6543 KiB

Open AccessArticle

Pearson Correlation in Determination of Quality of Current Transformers

by Davorin Burgund, Srete Nikolovski, Dario Galić and Nedeljko Maravić

Sensors 2023, 23(5), 2704; https://doi.org/10.3390/s23052704 - 1 Mar 2023

Cited by 8 | Viewed by 2596

Abstract

The article elaborates on the accuracy of current transformers (CT) in interaction with temperature and frequency using Pearson’s correlation. The first part of the analysis compares the accuracy of the mathematical model of the current transformer and the result of the measurement on [...] Read more.

The article elaborates on the accuracy of current transformers (CT) in interaction with temperature and frequency using Pearson’s correlation. The first part of the analysis compares the accuracy of the mathematical model of the current transformer and the result of the measurement on the real CT using the Pearson correlation calculation. The mathematical model of CT is determined by deriving the formula of the functional error with the display of the accuracy of the measured value. The accuracy of the mathematical model is affected by the accuracy of current transformer model parameters and the calibration characteristic of the ammeter used to measure the CT current. Variables that cause deviation in the accuracy of CT are temperature and frequency. The calculation shows the effects on accuracy in both cases. The second part of the analysis refers to the calculation of the partial correlation of three quantities: (1) CT accuracy, (2) temperature, and (3) frequency on a set of 160 measurements. First, the influence of temperature on the correlation of CT accuracy and frequency is proven, following the proof of the influence of frequency on the correlation of CT accuracy and temperature. In the end, the analysis is combined by comparing the measured results of the first and second part of the analysis. Full article

(This article belongs to the Special Issue Sensors for Measurements and Diagnostic in Electrical Power Systems)

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20 pages, 8273 KiB

Open AccessArticle

CNN-LSTM vs. LSTM-CNN to Predict Power Flow Direction: A Case Study of the High-Voltage Subnet of Northeast Germany

by Fachrizal Aksan, Yang Li, Vishnu Suresh and Przemysław Janik

Sensors 2023, 23(2), 901; https://doi.org/10.3390/s23020901 - 12 Jan 2023

Cited by 36 | Viewed by 16096

Abstract

The massive installation of renewable energy sources together with energy storage in the power grid can lead to fluctuating energy consumption when there is a bi-directional power flow due to the surplus of electricity generation. To ensure the security and reliability of the [...] Read more.

The massive installation of renewable energy sources together with energy storage in the power grid can lead to fluctuating energy consumption when there is a bi-directional power flow due to the surplus of electricity generation. To ensure the security and reliability of the power grid, high-quality bi-directional power flow prediction is required. However, predicting bi-directional power flow remains a challenge due to the ever-changing characteristics of power flow and the influence of weather on renewable power generation. To overcome these challenges, we present two of the most popular hybrid deep learning (HDL) models based on a combination of a convolutional neural network (CNN) and long-term memory (LSTM) to predict the power flow in the investigated network cluster. In our approach, the models CNN-LSTM and LSTM-CNN were trained with two different datasets in terms of size and included parameters. The aim was to see whether the size of the dataset and the additional weather data can affect the performance of the proposed model to predict power flow. The result shows that both proposed models can achieve a small error under certain conditions. While the size and parameters of the dataset can affect the training time and accuracy of the HDL model. Full article

(This article belongs to the Special Issue Sensors for Measurements and Diagnostic in Electrical Power Systems)

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20 pages, 6293 KiB

Open AccessArticle

Anomaly Detection Based on Time Series Data of Hydraulic Accumulator

by Min-Ho Park, Sabyasachi Chakraborty, Quang Dao Vuong, Dong-Hyeon Noh, Ji-Woong Lee, Jae-Ung Lee, Jae-Hyuk Choi and Won-Ju Lee

Sensors 2022, 22(23), 9428; https://doi.org/10.3390/s22239428 - 2 Dec 2022

Cited by 9 | Viewed by 2760

Abstract

Although hydraulic accumulators play a vital role in the hydraulic system, they face the challenges of being broken by continuous abnormal pulsating pressure which occurs due to the malfunction of hydraulic systems. Hence, this study develops anomaly detection algorithms to detect abnormalities of [...] Read more.

Although hydraulic accumulators play a vital role in the hydraulic system, they face the challenges of being broken by continuous abnormal pulsating pressure which occurs due to the malfunction of hydraulic systems. Hence, this study develops anomaly detection algorithms to detect abnormalities of pulsating pressure for hydraulic accumulators. A digital pressure sensor was installed in a hydraulic accumulator to acquire the pulsating pressure data. Six anomaly detection algorithms were developed based on the acquired data. A threshold averaging algorithm over a period based on the averaged maximum/minimum thresholds detected anomalies 2.5 h before the hydraulic accumulator failure. In the support vector machine (SVM) and XGBoost model that distinguish normal and abnormal pulsating pressure data, the SVM model had an accuracy of 0.8571 on the test set and the XGBoost model had an accuracy of 0.8857. In a convolutional neural network (CNN) and CNN autoencoder model trained with normal and abnormal pulsating pressure images, the CNN model had an accuracy of 0.9714, and the CNN autoencoder model correctly detected the 8 abnormal images out of 11 abnormal images. The long short-term memory (LSTM) autoencoder model detected 36 abnormal data points in the test set. Full article

(This article belongs to the Special Issue Sensors for Measurements and Diagnostic in Electrical Power Systems)

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15 pages, 4535 KiB

Open AccessArticle

Power Maximisation of Wind Energy Using Wind Speed Sensors on Stewart Island

by Navid Majdi Nasab, Jeff Kilby and Leila Bakhtiaryfard

Sensors 2022, 22(21), 8428; https://doi.org/10.3390/s22218428 - 2 Nov 2022

Cited by 1 | Viewed by 2241

Abstract

This paper evaluates the feasibility of using wind power for power supply to coastal communities isolated from the main supply grid. The case study is Stewart Island, where the cost of electricity provided by a central diesel power station is higher than the [...] Read more.

This paper evaluates the feasibility of using wind power for power supply to coastal communities isolated from the main supply grid. The case study is Stewart Island, where the cost of electricity provided by a central diesel power station is higher than the grid network in New Zealand. The Princeton Ocean Model (POM) conducted by MetOcean Solutions Limited (MSL) is used to find Foveaux as an optimized site for generating wind power. Global Wind Atlas is used to plot the wind rose of current wind patterns in New Zealand. In the next step, wind speed data from each site are imported from the NASA database to WRPLOT view software and Homer Pro to find wind frequency distribution and output power in the area. The maximum annual power can be seen in WSW (32,299 kW hours/year), SW (20,111 kW hours/year) and W (15,622 kW hour/year) directions, respectively. Full article

(This article belongs to the Special Issue Sensors for Measurements and Diagnostic in Electrical Power Systems)

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18 pages, 9705 KiB

Open AccessArticle

New Smart Sensor for Voltage Unbalance Measurements in Electrical Power Systems

by Santiago Bogarra, Jaime Saura and Alejandro Rolán

Sensors 2022, 22(21), 8236; https://doi.org/10.3390/s22218236 - 27 Oct 2022

Cited by 2 | Viewed by 2162

Abstract

This paper deals with voltage unbalances and how they can be quantified according to the standards. Firstly, a comparison between the different unbalance voltage factors is conducted in order to remark on their divergences. Secondly, according to the standard that better represents the [...] Read more.

This paper deals with voltage unbalances and how they can be quantified according to the standards. Firstly, a comparison between the different unbalance voltage factors is conducted in order to remark on their divergences. Secondly, according to the standard that better represents the phenomenon, i.e., EN 50160, a new methodology is proposed to quantify the voltage unbalance factor (VUF). In order to do so, it is recommended to measure the voltage unbalance in three-phase installations by means of a new smart sensor based on a single voltage sensor, which measures the direct-current (DC) voltage at the output of a three-phase diode bridge rectifier, while current methods make use of three voltage sensors (which can measure either phase-to-neutral voltages or phase-to-phase voltages). Furthermore, both simulation and experimental results have been carried out to validate the proposed methodology. Finally, a new voltage unbalance factor (and the corresponding methodology to obtain it from the measured DC voltage) is proposed. Full article

(This article belongs to the Special Issue Sensors for Measurements and Diagnostic in Electrical Power Systems)

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30 pages, 7112 KiB

Open AccessArticle

Sensor Effects in LCL-Type Grid-Connected Shunt Active Filters Control Using Higher-Order Sliding Mode Control Techniques

by Mohamad Alaa Eddin Alali, Yuri B Shtessel, Jean-Pierre Barbot and Stefano Di Gennaro

Sensors 2022, 22(19), 7516; https://doi.org/10.3390/s22197516 - 3 Oct 2022

Cited by 13 | Viewed by 2362

Abstract

The effects of measuring devices/sensors on improving the power quality (PQ) of electric networks are studied in this paper. In this context, improving the performance of an LCL-type grid connected to a three-phase three-wire shunt active filter (SAF) in the presence of voltage [...] Read more.

The effects of measuring devices/sensors on improving the power quality (PQ) of electric networks are studied in this paper. In this context, improving the performance of an LCL-type grid connected to a three-phase three-wire shunt active filter (SAF) in the presence of voltage perturbations is studied. In order to ensure the high-quality performance of LCL-SAF in the presence of voltage perturbations, the robust continuous second-order sliding mode controller (2-SMC), including twisting and super-twisting controllers, and continuous higher-order sliding mode controller (C-HOSMC)-based approaches are employed. These controllers, whose outputs are processed by pulse-width modulation (PWM), allow minimization of the phase shift and prevent the generation of discontinuous chattering commands, which can severely damage the VSI components. Moreover, an integration of a generalized instantaneous power identification algorithm with an advanced phase locked loop (PLL) was proposed and experimentally tested to validate the effective performances of SAF under severe perturbations. Additionally, the studied approaches were tested via simulations taking into account a conventional nonlinear industrial load in a real textile factory environment, using measurements provided by power quality analyzers. Finally, the effects of the measuring devices, including the current and voltage sensors, on the accuracy and reliability of the SAF and, consequently, on the PQ of the electric power grid were studied via simulations and experimentally. The results of this study support the validity of the recently published patent. Full article

(This article belongs to the Special Issue Sensors for Measurements and Diagnostic in Electrical Power Systems)

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14 pages, 2880 KiB

Open AccessArticle

Novel and Simplified Procedure to Test Immunity of Low-Power Voltage Transformers

by Alessandro Mingotti, Lorenzo Peretto and Roberto Tinarelli

Sensors 2022, 22(15), 5804; https://doi.org/10.3390/s22155804 - 3 Aug 2022

Cited by 2 | Viewed by 1734

Abstract

International technical committees put considerable efforts into the writing process of standards. They always try to find a tradeoff between the rigorous scientific requirements and the practical needs of manufacturers and final users. In addition, researchers keep investigating to improve the existing standards [...] Read more.

International technical committees put considerable efforts into the writing process of standards. They always try to find a tradeoff between the rigorous scientific requirements and the practical needs of manufacturers and final users. In addition, researchers keep investigating to improve the existing standards with new procedures, achievements, and findings. The purpose of this work is to contribute to that direction. It introduces a simplified and low-cost procedure to test low-power voltage transformers (LPVTs). The procedure is designed to assess the immunity of LPVTs when subjected to external electric fields. The need for this procedure comes from the existing immunity test, which is efficient but sometimes difficult to implement. The proposed one, instead, is simpler, cheaper, does not require the application of the rated voltage, and can be replicated at all voltage levels. In the paper, the procedure is described and demonstrated with experimental tests. From the results, it is possible to appreciate the validity of the proposed solution and the different ways it could be developed, implemented, and improved. Full article

(This article belongs to the Special Issue Sensors for Measurements and Diagnostic in Electrical Power Systems)

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28 pages, 7307 KiB

Open AccessArticle

A Configurable Monitoring, Testing, and Diagnosis System for Electric Power Plants

by Anca Albița and Dan Selișteanu

Sensors 2022, 22(15), 5618; https://doi.org/10.3390/s22155618 - 27 Jul 2022

Cited by 2 | Viewed by 2030

Abstract

The specific equipment, installation and machinery infrastructure of an electric power system have always required specially designed data acquisition systems and devices to ensure their safe operation and monitoring. Besides maintenance, periodical upgrade must be ensured for these systems, to meet the current [...] Read more.

The specific equipment, installation and machinery infrastructure of an electric power system have always required specially designed data acquisition systems and devices to ensure their safe operation and monitoring. Besides maintenance, periodical upgrade must be ensured for these systems, to meet the current practical requirements. Monitoring, testing, and diagnosis altogether represent key activities in the development process of electric power elements. This work presents the detailed structure and implementation of a complex, configurable system which can assure efficient monitoring, testing, and diagnosis for various electric power infrastructures, with proven efficiency through a comprehensive set of experimental results obtained in real running conditions. The developed hardware and software implementation is a robust structure, optimized for acquiring a large variety of electrical signals, also providing easy and fast connection within the monitored environment. Its high level of configurability and very good price–performance ratio makes it an original and handy solution for electric power infrastructures. Full article

(This article belongs to the Special Issue Sensors for Measurements and Diagnostic in Electrical Power Systems)

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13 pages, 9078 KiB

Open AccessArticle

Design and Development of a Battery State of Health Estimation Model for Efficient Battery Monitoring Systems

by Hyoung Sun Choi, Jin Woo Choi and Taeg Keun Whangbo

Sensors 2022, 22(12), 4444; https://doi.org/10.3390/s22124444 - 12 Jun 2022

Cited by 5 | Viewed by 2532

Abstract

An uninterruptible power supply (UPS) is a device that can continuously supply power for a certain period when a power outage occurs. UPS devices are used by national institutions, hospitals, and servers, and are located in numerous public places that require continuous power. [...] Read more.

An uninterruptible power supply (UPS) is a device that can continuously supply power for a certain period when a power outage occurs. UPS devices are used by national institutions, hospitals, and servers, and are located in numerous public places that require continuous power. However, maintaining such devices in good condition requires periodic maintenance at specific time points. Efficient monitoring can currently be achieved using a battery management system (BMS). However, most BMSs are administrator-centered. If the administrator is not careful, it becomes difficult to accurately grasp the data trend of each battery cell, which in turn can lead to a leakage or heat explosion of the cell. In this study, a deep-learning-based intelligent model that can predict battery life, known as the state of health (SoH), is investigated for the efficient operation of a BMS applied to a lithium-based UPS device. Full article

(This article belongs to the Special Issue Sensors for Measurements and Diagnostic in Electrical Power Systems)

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11 pages, 1240 KiB

Open AccessCommunication

Influence of Oil Status on Membrane-Based Gas–Oil Separation in DGA

by Tunan Chen, Kang Li, Zhenghai Liao, Xiongjie Xie and Guoqiang Zhang

Sensors 2022, 22(10), 3629; https://doi.org/10.3390/s22103629 - 10 May 2022

Cited by 2 | Viewed by 1978

Abstract

Gas–oil separation by membrane stands for a promising technique in dissolved gas analysis (DGA). Since the accuracy of DGA relies on the results of gas–oil separation to a great extent, it is necessary to study the influence factor of membrane for better performance. [...] Read more.

Gas–oil separation by membrane stands for a promising technique in dissolved gas analysis (DGA). Since the accuracy of DGA relies on the results of gas–oil separation to a great extent, it is necessary to study the influence factor of membrane for better performance. Although plentiful studies have been conducted aiming at membrane modification to obtain better separation performance, it cannot be ignored that the conditions of oil also affect the performance of membrane much. In this work, a photoacoustic spectroscopy-based sensor for DGA, which employed membrane for gas–oil separation, was established first. By detecting the photoacoustic signal, the performance of membrane could be evaluated. Furthermore, the influences of feed velocity and pressure have on the performance of membrane were analyzed. Both simulation and experiment were employed in this work to evaluate the influences by collecting the equilibrium time of membrane under different conditions. As a result, the simulation and experiment agreed with each other well. Moreover, it was reasonable to draw the conclusion that the equilibrium time was evidently reduced with the raise of feed velocity but remained with a minimum change when pressure changed. The conclusion may serve as a reference for the application of membrane in optical sensor and DGA. Full article

(This article belongs to the Special Issue Sensors for Measurements and Diagnostic in Electrical Power Systems)

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11 pages, 2462 KiB

Open AccessCommunication

Diagnosis of Partial Discharge Based on the Air Components for the 10 kV Air-Insulated Switchgear

by Qipeng Tan, Tiandong Zhang, Shaocheng Wu, Jiachen Gao and Bin Song

Sensors 2022, 22(6), 2395; https://doi.org/10.3390/s22062395 - 20 Mar 2022

Cited by 7 | Viewed by 2873

Abstract

Partial discharge (PD) is a common phenomenon of insulation aging in air-insulated switchgear and will change the gas composition in the equipment. However, it is still a challenge to diagnose and identify the defect types of PD. This paper conducts enclosed experiments based [...] Read more.

Partial discharge (PD) is a common phenomenon of insulation aging in air-insulated switchgear and will change the gas composition in the equipment. However, it is still a challenge to diagnose and identify the defect types of PD. This paper conducts enclosed experiments based on gas sensors to obtain the concentration data of the characteristic gases CO, NO₂, and O₃ under four typical defects. The random forest algorithm with grid search optimization is used for fault identification to explore a method of identifying defect types through gas concentration. The results show that the gases concentration variations do have statistical characteristics, and the RF algorithm can achieve high accuracy in prediction. The combination of a sensor and a machine learning algorithm provides the gas component analysis method a way to diagnose PD in an air-insulated switchgear. Full article

(This article belongs to the Special Issue Sensors for Measurements and Diagnostic in Electrical Power Systems)

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18 pages, 3941 KiB

Open AccessArticle

Simplified and Low-Cost Characterization of Medium-Voltage Low-Power Voltage Transformers in the Power Quality Frequency Range

by Alessandro Mingotti, Christian Betti, Lorenzo Peretto and Roberto Tinarelli

Sensors 2022, 22(6), 2274; https://doi.org/10.3390/s22062274 - 15 Mar 2022

Cited by 6 | Viewed by 2574

Abstract

The distribution network is experiencing a massive deployment of intelligent electronic devices (IEDs) such as energy meters, protective devices, and phasor measurement units (PMUs). This phenomenon resulted, on the one hand, in (i) the availability of distributed measurement systems capable of monitoring and [...] Read more.

The distribution network is experiencing a massive deployment of intelligent electronic devices (IEDs) such as energy meters, protective devices, and phasor measurement units (PMUs). This phenomenon resulted, on the one hand, in (i) the availability of distributed measurement systems capable of monitoring and collecting measurements from the distribution network, and (ii) increasing awareness of the system operator about the status of the network. On the other hand, such a significant number of devices require to be characterized, over the years, and assessed in both sinusoidal and distorted conditions. However, the characterization process may require a huge investment of money and time considering the low availability of reference instruments and accredited laboratories. To this purpose, this paper presents a simple and fast test procedure, performed with cheap and low-voltage instrumentation, to characterize two off-the-shelf low-power medium-voltage sensors in the power quality frequency range. In detail, the paper describes the measurement setup developed for the characterization and the performed tests. In addition, the method was also reproduced with reference equipment for validation purposes. Lastly, for both tests, an uncertainty evaluation was performed to quantify the goodness of the proposed method. From the results, it is possible to appreciate that the designed cheap and simple test can achieve as accurate results as those of a sophisticated and expensive equipment. Full article

(This article belongs to the Special Issue Sensors for Measurements and Diagnostic in Electrical Power Systems)

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20 pages, 7059 KiB

Open AccessArticle

Accuracy Type Test for Rogowski Coils Subjected to Distorted Signals, Temperature, Humidity, and Position Variations

by Alessandro Mingotti, Federica Costa, Lorenzo Peretto and Roberto Tinarelli

Sensors 2022, 22(4), 1397; https://doi.org/10.3390/s22041397 - 11 Feb 2022

Cited by 11 | Viewed by 3463

Abstract

Low-Power Instrument Transformers (LPITs) are becoming the first choice for distributed measurement systems for medium voltage networks. However, there are still a lot of challenges related to their operation. Such challenges include their accuracy variation when several influence quantities are acting on them. [...] Read more.

Low-Power Instrument Transformers (LPITs) are becoming the first choice for distributed measurement systems for medium voltage networks. However, there are still a lot of challenges related to their operation. Such challenges include their accuracy variation when several influence quantities are acting on them. Among the most significant influence quantities are temperature, electromagnetic field, humidity, etc. Another aspect that increases the importance of studying the LPITs’ accuracy behavior is that, once installed, they cannot be calibrated for several years; hence, one cannot compensate for in-field conditions. Hence, this work aims at introducing a simple type test for a specific LPIT, the Rogowski coil. First, an experimental setup to assess the effect of temperature, humidity, and positioning on the power quality accuracy performance of the Rogowski coil is described. Second, from the results and the experience of the authors it has been possible to design a specific type test. The test has the aim of finding the limits of the accuracy variations of a single Rogowski coil. Afterwards, such limits can be used to compensate for the in-field measurements, obtaining an overall higher accuracy. The results of this work may contribute to the always-evolving standardization work on LPITs. Full article

(This article belongs to the Special Issue Sensors for Measurements and Diagnostic in Electrical Power Systems)

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19 pages, 6992 KiB

Open AccessArticle

Effect of Proximity, Burden, and Position on the Power Quality Accuracy Performance of Rogowski Coils

by Alessandro Mingotti, Federica Costa, Lorenzo Peretto and Roberto Tinarelli

Sensors 2022, 22(1), 397; https://doi.org/10.3390/s22010397 - 5 Jan 2022

Cited by 9 | Viewed by 2988

Abstract

Power quality evaluation is the process of assessing the actual power network parameters with respect to the ideal conditions. However, several new assets and devices among the grid include mining the voltage and current quality. For example, the power converters needed for renewable [...] Read more.

Power quality evaluation is the process of assessing the actual power network parameters with respect to the ideal conditions. However, several new assets and devices among the grid include mining the voltage and current quality. For example, the power converters needed for renewable energy sources’ connection to the grid, electric vehicles, etc., are some of the main sources of disturbances that inject high-frequency components into the grid. Consequently, instrument transformers (ITs) should be capable of measuring distorted currents and voltages with the same level of accuracy guaranteed for the ideal frequency (50–60 Hz). This is not a simple task if one considers that several other influence quantities endlessly act on the ITs. To this purpose, considering the lack of a standard, this work presents a measurement setup and specific tests for testing a commonly used type of low-power current transformer, the Rogowski coil (RC). In particular, the accuracy performance (ratio error and phase displacement) of the RCs was evaluated when measuring distorted signals while other influence quantities affected the RCs. Such quantities included positioning, burden, and magnetic field. The results indicate which quantities (or combination of them) have the greatest effect on the RC’s accuracy performance. Full article

(This article belongs to the Special Issue Sensors for Measurements and Diagnostic in Electrical Power Systems)

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