1,721,014 research outputs found
A Smart Frequency Domain-Based Modeling Procedure of Rogowski Coil for Power Systems Applications
Full text linkRogowski coils are key measurement instruments in several applications due to their flexibility, large bandwidth, linearity, and so on. Like the majority of the instrument transformers (ITs), Rogowski coils are standardized and their use is regulated almost for every application. This article focuses on a smart way for the equivalent parameters' computation of the Rogowski coil. The parameter computation is performed by evaluating the Rogowski response when subjected to a single waveform generated to fulfill a specific requirement on its frequency content. Results demonstrate the equivalency of the presented method compared to the typical frequency sweep test. In addition, the results have been used to validate Rogowski's output estimate procedure presented by the authors in a previous work
Comparison of Algorithms for the AI-Based Fault Diagnostic of Cable Joints in MV Networks
Full text linkAbstract
Electrical utilities and system operators (SOs) are constantly looking for solutions to problems in the management and control of the power network. For this purpose, SOs are exploring new research fields, which might bring contributions to the power system environment. A clear example is the field of computer science, within which artificial intelligence (AI) has been developed and is being applied to many fields. In power systems, AI could support the fault prediction of cable joints. Despite the availability of many legacy methods described in the literature, fault prediction is still critical, and it needs new solutions. For this purpose, in this paper, the authors made a further step in the evaluation of machine learning methods (ML) for cable joint health assessment. Six ML algorithms have been compared and assessed on a consolidated test scenario. It simulates a distributed measurement system which collects measurements from medium-voltage (MV) cable joints. Typical metrics have been applied to compare the performance of the algorithms. The analysis is then completed considering the actual in-field conditions and the SOs’ requirements. The results demonstrate: (i) the pros and cons of each algorithm; (ii) the best-performing algorithm; (iii) the possible benefits from the implementation of ML algorithms
Uncertainty and Lack of Information Affecting the Training of Machine Learning Algorithms for Fault Prediction of Cable-Joints
No full textArtificial intelligence (AI) and machine learning algorithms are becoming more and more popular in power system applications. Their benefits have been proven in many different fields, and power systems will benefit from them too. For example, AI allows the analysis of huge amounts of data which could not be treated otherwise. Furthermore, well-trained algorithms may automatize operations which typically need a dedicated operator. A limit to the implementation of AI in power systems is the uncertainty. It can be divided into three main contributions: the uncertainty of the algorithms results, the uncertainty of the input data, and the lack of experience, information, or unique solutions associated to a specific application. Therefore, this paper aims to include the latter two uncertainty contributions into machine learning algorithms. Their output is used for the fault prediction of medium voltage cable joints, providing information about their health status. As a matter of fact, it is still not possible to exactly predict the cable joint fault. Therefore, such an application is a significant case study to address the uncertainty contributions affecting the training of machine learning algorithms. The obtained results clearly demonstrate the effect of the sources of uncertainty on the algorithm performance
Closed-form expressions to estimate the mean and variance of the total vector error
Full text linkThe need for accurate measurements and for estimating the uncertainties associated with measures are two pillars for researchers and metrologists. This is particularly true in distribution networks due to a mass deployment of new intelligent electronic devices. Among such devices, phasor measurement units are key enablers for obtaining the full observability of the grid. The phasor measurement unit performance is mostly evaluated by means of the total vector error, which combines the error on amplitude, phase, and time. However, the total vector error is typically pro-vided merely as a number, that could vary within an unknown interval. This may result into the phasor measurement unit incompliance with the final user expectancies. To this purpose, and with the aim of answering practical needs from the industrial world, this paper presents a closed-form expression that allows us to quantify, in a simple way, the confidence interval associated with the total vector error. The input required by the expression is the set of errors that typically affects the analog to digital converter of a phasor measurement unit. The obtained expression has been vali-dated by means of the Monte Carlo method in a variety of realistic conditions. The results confirm the applicability and effectiveness of the proposed expression. It can be then easily implemented in all monitoring device algorithms, or directly by the manufacturer to characterize their devices, to solve the lack of knowledge that affects the total vector error computation
On the long-period accuracy behavior of inductive and low-power instrument transformers
Full text linkThe accuracy evaluation of instrument transformers is always a key task when proper control and management of the power network is required. In particular, accuracy becomes a critical aspect when the grid or the instrumentation itself is operating at conditions different from the rated ones. However, before focusing on the above non-rated conditions, it is important to fully understand the instrument transformer behavior at rated conditions. To this end, this work analyzed the accuracy behavior of legacy, inductive, and low-power voltage transformers over long periods of time. The aim was to find patterns and correlations that may be of help during the modelling or the output prediction of voltage transformers. From the results, the main differences between low-power and inductive voltage transformers were pointed out and described in detail
Fast calibration procedure for low power voltage transformers up to 2.5 khz using sinc response
No full textA The progressive deployment of Low Power Instrument Transformers in power network for measurement purposes requires studies on their characterization and on the effect of different quantities on their accuracy. In this paper, a fast calibration procedure is introduced. By using a sinc signal, a Low Power Voltage Transformer has been characterized in the power quality frequency range (50 Hz to 2.5 kHz) under the rated voltage conditions
Accurate Damping Factor and Frequency Estimation for Damped Real-Valued Sinusoidal Signals
Full text linkThe interpolated discrete Fourier transform (IpDFT) is one of the most popular techniques to estimate the parameters of a damped real-valued sinusoidal signal (DRSS). However, its accuracy is affected by strong noise presence and short observation windows. To this end, this letter proposes a novel two-point IpDFT method, called I2pZDFT, for the parameter estimation of a DRSS. The proposed I2pZDFT uses the zero-padding technique to increase the sampling rate in the frequency domain. The conjugate symmetry and the parity of the zero-padded signal are utilized to eliminate the influence of the spectral leakage. Simulation results highlight that the proposed I2pZDFT outperforms the existing IpDFT-based methods in terms of noise immunity, especially in the case of observation windows as short as 0.5-1 cycles
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