The effect of the integration interval on the measurement accuracy of RMS values and powers in systems with nonsinusoidal waveforms

In this paper the possibility of errors in the measurement of average values (in particular rms values or active powers) in power systems under nonsinusoidal conditions are discussed. The errors considered are either due to the fact that the measurement time interval is not an exact multiple of the fundamental period of the voltage and current signals, or due to the presence of interharmonics or subharmonics. The errors are calculated and the results are illustrated by means of simple examples

Smart characterization of rogowski coils by using a synthetized signal

With the spread of new Low-Power Instrument Transformers (LPITs), it is fundamental to provide models and characterization procedures to estimate and even predict the LPITs\u2019 behavior. In fact, distribution system operators and designers of network models are looking for all forms of information which may help the management and the control of power networks. For this purpose, the paper wants to contribute to the scientific community presenting a smart characterization procedure which easily provides sufficient information to predict the output signal of a Low-Power Current Transformer (LPCT), the Rogowski coil. The presented procedure is based on a synthetized signal applied to the Rogowski coil. Afterwards, the validity of the procedure is assessed within the Matlab environment and then by applying it on three off-the-shelf Rogowski coils. Simulations and experimental tests and results involving a variety of distorted signals in the power quality frequency range and by adopting a quite simple measurement setup demonstrated the effectiveness and the capability of the procedure to correctly estimate the output of the tested device

On the long-period accuracy behavior of inductive and low-power instrument transformers

The 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

Uncertainty analysis of a test bed for calibrating voltage transformers vs.Temperature

The paper addresses the evaluation of the uncertainty sources of a test bed system for calibrating voltage transformers vs. temperature. In particular, the Monte Carlo method has been applied in order to evaluate the effects of the uncertainty sources in two different conditions: by using the nominal accuracy specifications of the elements which compose the setup, or by exploiting the results of their metrological characterization. In addition, the influence of random effects on the system accuracy has been quantified and evaluated. From the results, it emerges that the choice of the uncertainty evaluation method affects the overall study. As a matter of fact, the use of a metrological characterization or of accuracy specifications provided by the manufacturers provides respectively an accuracy of 0.1 and 0.5 for the overall measurement setup

Are inductive current transformers performance really affected by actual distorted network conditions? An experimental case study

The aim of this work is to assess whether actual distorted conditions of the network are really affecting the accuracy of inductive current transformers. The study started from the need to evaluate the accuracy performance of inductive current transformers in off-nominal conditions, and to improve the related standards. In fact, standards do not provide a uniform set of distorted waveforms to be applied on inductive or low-power instrument transformers. Moreover, there is no agreement yet, among the experts, about how to evaluate the uncertainty of the instrument transformer when the operating conditions are different from the rated ones. To this purpose, the authors collected currents from the power network and injected them into two off-the-shelf current transformers. Then, their accuracy performances have been evaluated by means of the well-known composite error index and an approximated version of it. The obtained results show that under realistic non-rated conditions of the network, the tested transformers show a very good behavior considering their nonlinear nature, arising the question in the title. A secondary result is that the use of the composite error should be more and more supported by the standards, considering its effectiveness in the accuracy evaluation of instrument transformers for measuring purposes

Low-Power Instrument Transformers and Energy Meters: Opportunities and Obstacles

Low-Power Instrument Transformers (LPITs) are becoming the preferred measurement device in the medium voltage (MV) distribution network (DN). They have several benefits compared to legacy solutions. However, the adoption of LPITs results in the need for adapting the grid and its assets to accept them. One practical example is using LPITs as the current and voltage source for energy meters (EMs), which are also used for billing purposes. The resulting measurement chain introduces several metrological challenges that must be studied and investigated. Therefore, in this work, the scenarios of LPITs and energy meters are introduced along with the latest relevant international standards. Afterwards, the opportunities and obstacles due to the implementation of the LPIT plus energy meter measurement chain are discussed. The discussion focuses on metrological requirements, accuracy evaluation, target uncertainty, and influence quantities affecting the performance of the devices

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

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 alwaysevolving standardization work on LPITs

Adaptively Determination of Model Order of SVD-based Harmonics and Interharmonics Estimation

The singular value decomposition (SVD) is one of the most popular methods in harmonics and interharmonics estimation. However, its accuracy strongly depends on the correctness of the selected model order. To this purpose, this work aims at contributing to the correct estimation of the model order. This is achieved by exploiting the energy of the singular values (SVs). Firstly, the relationship between one frequency component and its corresponding SVs is theoretically investigated. Secondly, a new indicator is proposed for determining the model order, which denotes the energy of the k-th pair of consecutive SVs. Thirdly, an adaptive threshold is defined for separating signal components from noise. This way, the number of components can be obtained for unknown noise levels. Finally, the effectiveness and robustness of the proposed method has been validated by simulations. They have been run implementing typical signals designed according to the harmonics and interharmonics measurements standard, the IEC standard 61000-4-7

Comparison of Algorithms for the AI-Based Fault Diagnostic of Cable Joints in MV Networks

Abstract 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

From chemical neuroanatomy to an understanding of the olfactory system

The olfactory system of mammals is the appropriate model for studying several aspects of neuronal physiology spanning from the developmental stage to neural network remodelling in the adult brain. Both the morphological and physiological understanding of this system were strongly supported by classical histochemistry. It is emblematic the case of the Olfactory Marker Protein (OMP) staining, the first, powerful marker for fully differentiated olfactory receptor neurons and a key tool to investigate the dynamic relations between peripheral sensory epithelia and central relay regions given its presence within olfactory fibers reaching the olfactory bulb (OB). Similarly, the use of thymidine analogues was able to show neurogenesis in an adult mammalian brain far before modern virus labelling and lipophilic tracers based methods. Nowadays, a wealth of new histochemical techniques combining cell and molecular biology approaches is available, giving stance to move from the analysis of the chemically identified circuitries to functional research. The study of adult neurogenesis is indeed one of the best explanatory examples of this statement. After defining the cell types involved and the basic physiology of this phenomenon in the OB plasticity, we can now analyze the role of neurogenesis in well testable behaviours related to socio-chemical communication in rodents