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This paper presents an extensive review of the most effective and modern monitoring methods for electrical power lines, with particular attention to high-voltage (HV) and medium-voltage (MV) systems. From a general point of view, the main objective of these techniques is to prevent catastrophic failures by detecting the partial damage or deterioration of components and allowing maintenance operations to be organized. In fact, the protection devices commonly used in transmission and distribution networks guarantee the location of faults, such as short-circuits, putting the non-functioning branch of the network out of service. Nowadays, alongside these devices, it is possible to introduce new intelligent algorithms capable of avoiding the total loss of functionality, thus improving the reliability of the entire network. This is one of the main challenges in modern smart grids, which are characterized by the massive integration of renewable energy sources and a high level of complexity. Therefore, in the first part of this paper, a general overview of the most common protection devices is proposed, followed by an analysis of the most modern prevention algorithms. In the first case, the coordination of the relays plays a fundamental role in obtaining the fault location with a high level of selectivity, while in the field of preventive analysis, it is necessary to address the implementation of artificial intelligence methods. The techniques presented in this paper provide a comprehensive description of the different monitoring approaches currently used in distribution and transmission lines, highlighting the coordination of protection relays, the computational algorithms capable of preventing failures, and the influence of the distributed generation in their management. Therefore, this paper offers an overview of the main diagnostic techniques and protection devices, highlights the critical issues that can be overcome through the introduction of artificial intelligence, and describes the main prognostic methods, focusing on their invasive level and the possibility of operating directly online. This work also highlights the main guidelines for the classification and choice between the different approaches. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to propose two innovative monitoring methods for electrical disturbances in low-voltage networks. The two approaches present a focus on the classification of voltage signals in the frequency domain using machine learning techniques. The first technique proposed here uses the Fourier transform (FT) of the voltage waveform and classifies the corresponding complex coefficients through a multilayered neural network with multivalued neurons (MLMVN). In this case, the classifier structure has three layers and a small number of neurons in the hidden layer. This allows complex-valued inputs to be processed without the need for pre-coding, thus reducing computational cost and keeping training time short. The second technique involves the use of the short-time Fourier transform (STFT) and a convolutional neural network (CNN) with 2D convolutions in each layer for feature extraction and dimensionality reduction. The voltage waveform perturbations taken into consideration are: voltage sag, voltage swell, harmonic pollution, voltage notch, and interruption. The comparison between the two proposed techniques is developed in two phases: initially, the simulated data used during the training phase are considered and, subsequently, various experimental measurements are processed, obtained both through an artificial disturbance generator and through a variable load. The two techniques represent an innovative approach to this problem and guarantee excellent classification results. [ABSTRACT FROM AUTHOR]

In this article, we propose a monitoring procedure based on a multilayer neural network with multivalued neurons (MLMVN) capable of preventing catastrophic failures of dc–dc converters. The neural classifier allows both the detection of any malfunction and its localization. Thanks to the low computational complexity, the proposed method operates online, estimating the deviations of the passive components from their nominal values: this allows control strategies to be promptly adopted and operation of the dc–dc converter to be kept in high efficiency and reliability conditions. Since measuring the voltage and current on each component increases the complexity of the system, a testability analysis is proposed with the aim of identifying the minimum number of measurements needed to distinguish the classes of failure. To make the testability phase easier and more intuitive, a graphical representation is proposed. As a case study, prognostic analysis has been applied to prevent catastrophic failures in a synchronous Zeta converter. Several fault conditions have been analyzed through simulations and experimental tests. The obtained results confirm the ability of the proposed method to prevent failures and, also, show that the application of MLMVN results in a better performance than classic solutions available in the literature, such as support vector machine. [ABSTRACT FROM AUTHOR]

DC–DC converter fault diagnosis, executed via neural networks built by exploiting the information deriving from testability analysis, is the subject of this paper. The networks under consideration are complex valued neural networks (CVNNs), whose fundamental feature is the proper treatment of the phase and the information contained in it. In particular, a multilayer neural network based on multi-valued neurons (MLMVN) is considered. In order to effectively design the network, testability analysis is exploited. Two possible ways for executing this analysis on DC–DC converters are proposed, taking into account the single-fault hypothesis. The theoretical foundations and some applicative examples are presented. Computer programs, based on symbolic analysis techniques, are used for both the testability analysis and the neural network training phase. The obtained results are very satisfactory and demonstrate the optimal performances of the method. [ABSTRACT FROM AUTHOR]

The use of electronic loads has improved many aspects of everyday life, permitting more efficient, precise and automated process. As a drawback, the nonlinear behavior of these systems entails the injection of electrical disturbances on the power grid that can cause distortion of voltage and current. In order to adopt countermeasures, it is important to detect and classify these disturbances. To do this, several Machine Learning Algorithms are currently exploited. Among them, for the present work, the Long Short Term Memory (LSTM), the Convolutional Neural Networks (CNN), the Convolutional Neural Networks Long Short Term Memory (CNN-LSTM) and the CNN-LSTM with adjusted hyperparameters are compared. As a preliminary stage of the research, the voltage and current time signals are simulated using MATLAB Simulink. Thanks to the simulation results, it is possible to acquire a current and voltage dataset with which the identification algorithms are trained, validated and tested. These datasets include simulations of several disturbances such as Sag, Swell, Harmonics, Transient, Notch and Interruption. Data Augmentation techniques are used in order to increase the variability of the training and validation dataset in order to obtain a generalized result. After that, the networks are fed with an experimental dataset of voltage and current field measurements containing the disturbances mentioned above. The networks have been compared, resulting in a 79.14% correct classification rate with the LSTM network versus a 84.58% for the CNN, 84.76% for the CNN-LSTM and a 83.66% for the CNN-LSTM with adjusted hyperparameters. All of these networks are tested using real measurements. [ABSTRACT FROM AUTHOR]

This paper presents an effective approach to the fault diagnosis of pulsewidth modulated (PWM) DC–DC power converters. It is based on a multilayer multivalued neuron neural network with a complex QR decomposition. This network is used to identify the parameter values running out of tolerance range in two topologies of PWM DC–DC converters, namely, the buck and boost circuits. The proposed technique is based on measurements of steady-state voltages and currents waveforms. [ABSTRACT FROM AUTHOR]

In this paper, a new efficient model of neural network is proposed, which is realized by the combination of two recent and successful neurocomputing paradigms. The idea behind the work is to realize a neural network constituted by multi-valued complex neurons, which are trained with the principles of extreme learning machine (ELM). The specific kind of used neuron allows a very straightforward derivation of the ELM, with no substantial modification in the weight adjustment procedure. The main advantages that clearly emerge by this model are represented by the further increasing of generalization performances in presence of noise, together with a generalized reduction of needed nodes (neurons) in the hidden layer. The effectiveness of the proposed model will be shown by some benchmark results, also compared with the original techniques. [ABSTRACT FROM AUTHOR]

The second paper [[4]] proposes instead a parametric fault diagnosis of analog circuits operating at a very high frequency and consisting of a Distributed Parameter Transmission Line (DPTL), terminated at both ends by lumped one-ports. Diagnosis, in its most general meaning, is the process aimed at identifying the causes that have produced a behavior, normally anomalous, in a system of either biological or artificial nature. 10.3390/electronics10050550 5 Aizenberg I., Belardi R., Bindi M., Grasso F., Manetti S., Luchetta A., Piccirilli M.C. A Neural Network Classifier with Multi-Valued Neurons for Analog Circuit Fault Diagnosis. [Extracted from the article]

Testability analysis is a fundamental prerequisite to any fault location or parameter identification technique (FLPIT) for analog circuits. However, differently from the linear time-invariant field, this topic has been poorly investigated with regard to nonlinear circuits. In this paper, for the first time, to the best of our knowledge, the question is addressed with reference to periodically switched networks with dc stimuli (PSNDSs), a class of inherently nonlinear time-varying circuits that subsumes dc-dc converters as noteworthy members. A rigorous testability measure at both a circuit and a component level is thereby obtained, which provides information as to how many and which components can be unambiguously diagnosed or estimated from time-domain measurements. Consequently, it yields an upper bound, independent of the parameter values and the test instants, to the performance of whatever method actually employed as an FLPIT. This theoretical testability metric has been converted into an efficient computer program for the fully automated testability analysis of PSNDSs presented herein. Such a computer program represents the first practical testability gauge for PSNDSs: it can be employed by both test engineers and circuit designers as a guide to properly selecting injection and measurement points as well as estimating the number of necessary measurement instants, so as to shape their own FLPITs. Several examples are provided for which an in-depth pencil-and-paper testability analysis is contrasted with its automated counterpart provided by the aforementioned software: in particular, popular dc-dc converter topologies are thoroughly analyzed. Comparisons with a previously proposed frequency-domain testability measure are also considered. [ABSTRACT FROM PUBLISHER]

The rational use and management of energy is considered a key societal and technological challenge. Home energy management systems (HEMS) have been introduced especially in private home domains to support users in managing and controlling energy consuming devices. Recent studies have shown that informing users about their habits with appliances as well as their usage pattern can help to achieve energy reduction in private households. This requires instruments able to monitor energy consumption at fine grain level and provide this information to consumers. While the most existing approaches for load disaggregation and classification require high-frequency monitoring data, in this paper we propose an approach that exploits low-frequency monitoring data gathered by meters (i.e., Smart Plugs) displaced in the home. Moreover, while the most existing works dealing with appliance classification delegate the classification task to a remote central server, we propose a distributed approach where data processing and appliance recognition are performed locally in the Home Gateway. Our approach is based on a distributed load monitoring system made of Smart Plugs attached to devices and connected to a Home Gateway via the ZigBee protocol. The Home Gateway is based on the OSGi platform, collects data from home devices, and hosts both data processing and user interaction logic. [ABSTRACT FROM AUTHOR]

In this paper, a monitoring method for DC-DC converters in photovoltaic applications is presented. The primary goal is to prevent catastrophic failures by detecting malfunctioning conditions during the operation of the electrical system. The proposed prognostic procedure is based on machine learning techniques and focuses on the variations of passive components with respect to their nominal range. A theoretical study is proposed to choose the best measurements for the prognostic analysis and adapt the monitoring method to a photovoltaic system. In order to facilitate this study, a graphical assessment of testability is presented, and the effects of the variable solar irradiance on the selected measurements are also considered from a graphical point of view. The main technique presented in this paper to identify the malfunction conditions is based on a Multilayer neural network with Multi-Valued Neurons. The performances of this classifier applied on a Zeta converter are compared to those of a Support Vector Machine algorithm. The simulations carried out in the Simulink environment show a classification rate higher than 90%, and this means that the monitoring method allows the identification of problems in the initial phases, thus guaranteeing the possibility to change the work set-up and organize maintenance operations for DC-DC converters. [ABSTRACT FROM AUTHOR]

In this paper, a modified learning algorithm for the multilayer neural network with the multi-valued neurons (MLMVN) is presented. The MLMVN, which is a member of complex-valued neural networks family, has already demonstrated a number of important advantages over other techniques. A modified learning algorithm for this network is based on the introduction of an acceleration step, performing by means of the complex QR decomposition and on the new approach to calculation of the output neurons errors: they are calculated as the differences between the corresponding desired outputs and actual values of the weighted sums. These modifications significantly improve the existing derivative-free backpropagation learning algorithm for the MLMVN in terms of learning speed. A modified learning algorithm requires two orders of magnitude lower number of training epochs and less time for its convergence when compared with the existing learning algorithm. Good performance is confirmed not only by the much quicker convergence of the learning algorithm, but also by the compatible or even higher classification/prediction accuracy, which is obtained by testing over some benchmarks (Mackey-Glass and Jenkins-Box time series) and over some satellite spectral data examined in a comparison test. [ABSTRACT FROM AUTHOR]

In this work, a strategy for scheduling a battery energy storage system (BESS) in a renewable energy community (REC) is proposed. RECs have been defined at EU level by the 2018/2001 Directive; some Member States transposition into national legislation defined RECs as virtual microgrids since they still use the existing low voltage local feeder and share the same low-medium voltage transformer. This work analyzes a REC which assets include PV generators, BESS and non-controllable loads, operating under the Italian legislative framework. A methodology is defined to optimize REC economic revenues and minimize the operation costs during the year. The proposed BESS control strategy is composed by three different modules: (i) a machine learning-based forecast algorithm that provides a 1-day-ahead projection for microgrid loads and PV generation, using historical dataset and weather forecasts; (ii) a mixed integer linear programming (MILP) algorithm that optimizes the BESS scheduling for minimal REC operating costs, taking into account electricity price, variable feed-in tariffs for PV generators, BESS costs and maximization of the self-consumption; (iii) a decision tree algorithm that works at the intra-hour level, with 1 min timestep and with real load and PV generation measurements adjusting the BESS scheduling in real time. Validation of the proposed strategy is performed on data acquired from a real small-scale REC set up with an Italian energy provider. A 10% average revenue increase could be obtained for the prosumer alone when compared to the non-optimized BESS usage scenario; such revenue increase is obtained by reducing the BESS usage by around 30% when compared to the unmanaged baseline scenario. [ABSTRACT FROM AUTHOR]

A new procedure for the selection of test frequencies in the parametric fault diagnosis of analog circuits is presented. It is based on the evaluation of algebraic indices, as the condition number and the norm of the inverse, of a sensitivity matrix of the circuit under test. This matrix is obtained starting from the testability analysis of the circuit. A Test Index (T.I.) that permits the selection of the set of frequencies that better leads to locating parametric faults in analog circuits is defined. By exploiting symbolic analysis techniques, a program that implements the proposed procedure has been developed. It yields the requested set of frequencies by means of an optimization procedure based on a genetic algorithm that minimizes the T.I. Examples of the application of the proposed procedure are also included. [ABSTRACT FROM AUTHOR]

Cylindrical post-based waveguide filters are a relevant component of antenna feeding networks. Their synthesis performed via automatic optimization based on full-wave analyses can be very time consuming. In this article a novel fast-design approach based on Levy's and Moore's algorithms and an artificial neural network (ANN) architecture is presented. © 2006 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2006. [ABSTRACT FROM AUTHOR]

Presents information on a study which focused on a symbolic procedure used for testability evaluation of analog linear circuits. Testability evaluation; Approach for testability evaluation; Examples; Conclusions.

Focuses on the applicability of Forecast, a neural network, in monitoring road surface temperature in Italy. Prediction of subzero temperatures and ice formation; Functionality; Efficacy in providing temperature data.

In this paper, we present a new method designed to recognize single parametric faults in analog circuits. The technique follows a rigorous approach constituted by three sequential steps: calculating the testability and extracting the ambiguity groups of the circuit under test (CUT); localizing the failure and putting it in the correct fault class (FC) via multi-frequency measurements or simulations; and (optional) estimating the value of the faulty component. The fabrication tolerances of the healthy components are taken into account in every step of the procedure. The work combines machine learning techniques, used for classification and approximation, with testability analysis procedures for analog circuits. [ABSTRACT FROM AUTHOR]

A smart monitoring system capable of detecting and classifying the health conditions of MV (Medium Voltage) underground cables is presented in this work. Using the analysis technique proposed here, it is possible to prevent the occurrence of catastrophic failures in medium voltage underground lines, for which it is generally difficult to realize maintenance operations and carry out punctual inspections. This prognostic method is based on Frequency Response Analysis (FRA) and can be used online during normal network operation, resulting in a minimally invasive tool. In order to obtain the good results shown in the simulation section, it is necessary to develop a lamped equivalent circuit of the network branch under consideration. The standard π-model is used in this paper to analyse sections of a medium voltage cable and the parameter variations with temperature are used to classify the state of health of the line. In fact, the variation of the electrical parameters produces a corresponding variation in the frequency response. The proposed system is based on the use of a complex neural network with feedforward architecture. It processes the frequency response, allowing the classification of the cable conditions with an accuracy higher than 90%. [ABSTRACT FROM AUTHOR]

Accurate parameter identification is a patently crucial aspect of system modeling as far as analysis, design, control, maintenance, and fault diagnosis are concerned. In this respect, an essential prerequisite is to establish a priori the solvability degree of the parameter identification problem, which is accomplished by means of testability analysis. The aim of this work is to present a program suitable for fully automated testability analysis of any physical system that can be modeled by an equivalent analog linear time‐invariant electrical circuit. Such a program has been obtained by combining the potentialities of symbolic techniques with the disclosures of a recently proposed theoretical approach to the testability measure concept, able to circumvent the main shortcomings of early methods. An efficient program, characterized by better performances with respect to previous software packages, has been thereby developed, which is herein described from both an algorithmic and a functional viewpoint. It can be advantageously used by the workers interested in system modeling as a guide to devising or refining their own parameter identification strategies. Thoroughly discussed examples of application are also given, which include a complete testability analysis and a novel parameter identification algorithm for electromagnetic harvesters. [ABSTRACT FROM AUTHOR]

Features the positive outcomes generated after using SAPWIN, a computer-based tool used in electrical engineering courses. Features of SAPWIN; Approaches in providing automatic analysis tools.