Miona Andrejević Stošović

ANALOG FILTER DIAGNOSIS USING THE OSCILLATION BASED METHOD

Oscillation Based Testing (OBT) is an effective and simple solution to the testing problem of continuous time analogue electronic filters. In this paper, diagnosis based on OBT is described for the first time. It will be referred to as OBD. A fault dictionary is created and used to perform diagnosis with artificial neural networks (ANNs) implemented as classifiers. The robustness of the ANN diagnostic concept is also demonstrated by the addition of white noise to the “measured” signals. The implementation of the new concept is demonstrated by testing and diagnosis of a second order notch cell realized with one operational amplifier. Single soft and catastrophic faults are considered in detail and an example of the diagnosis of double soft faults is also given.

Unified theory and state-variable implementation of critical-monotonic all-pole filters

The subject of synthesis of critical-monotonic low-pass amplitude characteristics will be revisited. Several new contributions will be given in order to: facilitate the choice of the proper transfer function, to allow cataloguing the transfer functions, to simplify the circuit synthesis procedure, and to perform synthesis in the form of a state-variable continuous time active filter. Four main criteria for transfer function synthesis will be implemented: maximally flat at the origin, maximum slope at the band-edge, maximal asymptotic attenuation, and minimal amplitude distortion in the pass-band. For every criterion, a class of filters will be generated and the coefficients of the transfer functions will be calculated and published for the first time with one exception. Properties of the classes so generated will be quantitatively compared for the first time. The state-variable structure will be advised as the one with the simplest synthesis procedure. The procedure will be explained and the design process will be exemplified. Statistical tolerance analysis will be performed for the example solutions in order to complete the information for comparison. Copyright

Frequency domain characterization of a solar cell

The generation of a small signal dynamic model of a solar cell was investigated. As a starting structure the usual one diode large signal dynamic model was used with known parameter values. A simple parallel linear RC circuit was used to represent the model while the element values were put to be functions of the illumination here represented by the photo-current. The element value versus photocurrent dependences were captured by artificial neural networks one per element. Verification of the model was performed by comparisons of the responses of the original nonlinear dynamic model and the linear RC model to a chirp signal of small amplitude.

HIERARCHICAL APPROACH TO DIAGNOSIS OF MIXED-MODE CIRCUITS USING ARTIFICIAL NEURAL NETWORKS

Feed-forward artiflcial neural networks (ANNs) have been applied to the diagnosis of mixed-mode electronic circuit. In order to tackle the circuit com- plexity and to reduce the number of test points, hierarchical approach to the diag- nosis generation was implemented with two levels of decision: the system level and the circuit level. For every level, using the simulation-before-test (SBT) approach, fault dictionary was created flrst, containing data relating to the fault code and the circuit response for a given input signal. ANNs were used to model the fault dictio- naries. During the learning phase, the ANNs were considered as an approximation algorithm to capture the mapping enclosed within the fault dictionary. Later on, in the diagnostic phase, the ANNs were used as an algorithm for mapping the measured data into fault code, which is equivalent to searching the fault dictio- nary performed by some other diagnostic procedures. At the topmost level, the fault dictionary was split into parts simplifying the implementation of the concept. A voting system was created at the topmost level in order to distinguish which ANNs output is to be accepted as the flnal diagnostic statement. The approach was tested on an example of an analog-to-digital converter, and only one test point was used, i.e. the digital output. Full diversity of faults was considered in both digital (stuck-at and delay faults) and analog (parametric and catastrophic faults) parts of the diagnosed system. Special attention was paid to the faults related to the A/D and D/A interfaces within the circuit.

A small-signal model of a solar cell

Starting with the experience that the output voltage and the output current of a photovoltaic panel are not pure direct current constants due to the inevitable connection to a converter (or inverter) that is working as a switching system, we came to the conclusion that interest exists for the behavior of the solar cell at the frequencies of the harmonics of the converter’s switching frequency, which is subject to change according to the maximum power-point tracking. In other words, a need exists for frequency domain characterization of the solar cell, for which a linear small-signal model is necessary. To enable simulation for small signals, development of a linear reactive model was considered. Since a one-diode large-signal model already exists, it was used as a basis for the extraction of the parameters of the small-signal model. The new model was represented in the form of a parallel RC two-terminal circuit, the R and C being functions of the photocurrent (acting as a map of the illumination) and the diode voltage. Since the R and C of the model are quiescent-point dependent, their values as a function of the illumination and the diode voltages were approximated by artificial neural networks (ANNs). Separate ANNs were created for modeling R and C. To verify the model, two small-signal simulations were performed. The first one was done with the existing nonlinear model, while the second was done with the new linear model (running the ANNs). Excellent agreement was obtained.

Computer Security Vulnerability as Concerns the Electricity Distribution Grid

The usual way of creating a cyber attack is through implementation of malware via the Internet. Among many types of malware, of special interest are those that enable eavesdropping on the activities within the computer, making it possible to define the software on which the computer is running. An adversary can benefit from this information in a way that is convenient for him or her. Here, we expose a new, entirely different way of eavesdropping and of monitoring the activities within the computer. It is based on measurement of the supply current taken from the electricity distribution grid. Because the computer, as many other electronic loads, is a nonlinear one, abundance of harmonics can be found in that current. Our discovery is the fact that the harmonic content is dependent on the type of activity within the computer, so, by proper analysis of the current waveform, one may recognize what is going on in it. We propose an artificial neural network-based method that unambiguously recognizes which software is running. We also propose a proper measurement procedure based on the technology we described in our earlier articles.

Hierarchical approach to diagnosis of electronic circuits using ANNs

Feed-forward artificial neural networks (ANNs) have been applied to the diagnosis of mixed-mode electronic circuit. In order to tackle the circuit complexity and to reduce the number of test points hierarchical approach to the diagnosis generation was implemented with two levels of decision: the system level and the circuit level. For every level, using the simulation-before-test (SBT) approach, fault dictionary was created first, containing data relating the fault code and the circuit response for a given input signal. ANNs were used to model the fault dictionaries. At the topmost level, the fault dictionary was split into parts simplifying the implementation of the concept. During the learning phase, the ANNs were considered as an approximation algorithm to capture the mapping enclosed within the fault dictionary. Later on, in the diagnostic phase, the ANNs were used as an algorithm for searching the fault dictionary. A voting system was created at the topmost level in order to distinguish which ANNs output is to be accepted as the final diagnostic statement. The approach was tested on an example of an analog-to-digital converter, and only one test point was used i.e. the digital output. Full diversity of faults was considered in both digital (stuck-at and delay faults) and analog (parametric and catastrophic faults) part of the diagnosed system. Special attention was paid to the faults related to the A/D and D/A interfaces within the circuit.

Electronic Circuits Diagnosis Using Artificial Neural Networks

Whenever we think about why something does not behave as it should, we are starting the process of diagnosis. Diagnosis is therefore a common activity in our everyday lives (Benjamins & Jansweijer, 1990). Every complex system is liable to faults or failures. In the most general terms, a fault is every change in a system that prevents it from operating in the proper manner. We define diagnosis as the task of identifying the cause and location of a fault manifested by some observed behaviour. This is often considered to be a two-stage process: first the fact that fault has occurred must be recognized – this is referred to as fault detection. That is, in general, achieved by testing. Secondly, the nature and location should be determined such that appropriate remedial action may be initiated. The explosion of integrated circuit technology has brought with it some difficult testing problems. The recent growth of mixed analogue and digital circuits complicates the testing problem even further. It becomes more complicated to determine a set of input test signals and output measurements that will provide a high degree of fault coverage. There is also a timing problem of testing the circuits even on the fastest automated equipment. The general structure of a diagnostic system is shown in Fig. 1. Signals u(t) and y(t) are input and output to the system, respectively. Faults and disturbances (here measurement errors) also influence the system under test, here denoted as the “Process”, but there is no information about the values of these errors. The task of the diagnostic system is to generate a diagnostic statement S, which contains information about fault modes that can explain the behaviour of the Process. Note that the diagnostic system is assumed to be passive i.e. it cannot affect the Process itself. The whole diagnostic system can be divided into smaller parts referred here to as tests. These tests are also diagnostic systems, DSi. It is assumed that each of them generates diagnostic statement Si. The purpose of the decision logic (voting system) is then to combine this information in order to form the final diagnostic statement S. The number of possible faults in an electronic system may be large and can be located everywhere in the system. To diagnose in such conditions one frequently uses hierarchical approach where successive diagnostic statements are generated as the level of description of the system is lowered going down towards the fault itself (Ho et al., 2001; Sheu & Chang, 1997). This allows for smaller sets of faults to be considered at a time for the given hierarchical level. Modern automatic test pattern generator may support such concepts (Soma et al., 2001).

Application of Artificial Neural Networks in Electronics

In this paper we will give short overview of different applications of artificial neural networks in electronics. Artificial neural networks are shown to be universal approximators, so they were successfully used in applications in modelling of electronic circuits, as well as in fault diagnosis and classification.

Compensation of harmonics in smart grid using photovoltaic energy source

In this proceeding we present basic steps for design a system for harmonic and reactive power compensation in smart grid, using photovoltaic (PV) system. First, methods for harmonic monitoring and measurement will be elaborated. Next, we will discuss harmonic reduction techniques that are usually used. The system for harmonic compensation based on photovoltaic energy generation that provides the functions of a power quality conditioner will be proposed. We will also address the issue of proper quantity for harmonic compensation contribution measurement at power grid.