FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of Induction Motor Drives

In modern industrial manufacturing processes, induction motors are broadly utilized as industrial drives. Online condition monitoring and diagnosis of faults that occur inside and/or outside of the Induction Motor Drive (IMD) system makes the motor highly reliable, helping to avoid unsched-uled downtimes, which cause more revenue loss and disruption of production, thus making it as the extensively used industrial drive. This can be achieved only when the irregularities produced out of the fault circumstance are sensed at that instant itself and diagnosed as to what and where happened for suitable action by the protective equipment employed. This requires intelligent control with high performance scheme. Hence, Field Programmable Gate Array (FPGA) based Neuro-Genetic implementation with Back Propagation Neural Network (BPN) is suggested in this article to diagnose the fault more efficiently and almost instantly. It is reported that the classifica-tion of neural network will provide the output within 2 µs although the clone procedure with mi-crocontroller requires 7 ms. This intelligent control with high performance technique is applied to the IMD fed by Voltage Source Inverter (VSI) to diagnose the fault external to the induction motor occurring in the VSI supply system. The proposed approach was simulated and experimentally validated.publishedVersio

A Small Acoustic Goniometer for General Purpose Research

Understanding acoustic events and monitoring their occurrence is a useful aspect of many research projects. In particular, acoustic goniometry allows researchers to determine the source of an event based solely on the sound it produces. The vast majority of acoustic goniometry research projects used custom hardware targeted to the specific application under test. Unfortunately, due to the wide range of sensing applications, a flexible general purpose hardware/firmware system does not exist for this purpose. This article focuses on the development of such a system which encourages the continued exploration of general purpose hardware/firmware and lowers barriers to research in projects requiring the use of acoustic goniometry. Simulations have been employed to verify system feasibility, and a complete hardware implementation of the acoustic goniometer has been designed and field tested. The results are reported, and suggested areas for improvement and further exploration are discussed

Assessment of the usability and accuracy of two-diode models for photovoltaic modules

Many diode-based equivalent circuits for simulating the electrical behaviour of photovoltaic (PV) cells and panels are reported in the scientific literature. Two-diode equivalent circuits, which require more complex procedures to calculate the seven model parameters, are less numerous. The model parameters are generally calculated using the data extracted from the datasheets issued by the PV panel manufactures and adopting simplifying hypotheses and numerical solving techniques. A criterion for rating both the usability and accuracy of two-diode models is proposed in this paper with the aim of supporting researchers and designers, working in the area of PV systems, to select and use a model that may be fit for purpose. The criterion adopts a three-level rating scale that considers the ease of finding the data used by the analytical procedure, the simplicity of the mathematical tools needed to perform calculations and the accuracy achieved in calculating the current and power. The analytical procedures, the simplifying hypotheses and the operative steps to calculate the parameters of the most famous two-diode equivalent circuits are exhaustively described in this paper. The accuracy of the models is tested by comparing the characteristics issued by the PV panel manufacturers with the current-voltage (I-V) curves, at constant solar irradiance and/or cell temperature, calculated with the analysed models with. The results of the study show that the two-diode models recently proposed reach accuracies that are comparable with the values derived from the one-diode models

Compressive Sensing with Low-Power Transfer and Accurate Reconstruction of EEG Signals

Tele-monitoring of EEG in WBAN is essential as EEG is the most powerful physiological parameters to diagnose any neurological disorder. Generally, EEG signal needs to record for longer periods which results in a large volume of data leading to huge storage and communication bandwidth requirements in WBAN. Moreover, WBAN sensor nodes are battery operated which consumes lots of energy. The aim of this research is, therefore, low power transmission of EEG signal over WBAN and its accurate reconstruction at the receiver to enable continuous online-monitoring of EEG and real time feedback to the patients from the medical experts. To reduce data rate and consequently reduce power consumption, compressive sensing (CS) may be employed prior to transmission. Nonetheless, for EEG signals, the accuracy of reconstruction of the signal with CS depends on a suitable dictionary in which the signal is sparse. As the EEG signal is not sparse in either time or frequency domain, identifying an appropriate dictionary is paramount. There are a plethora of choices for the dictionary to be used. Wavelet bases are of interest due to the availability of associated systems and methods. However, the attributes of wavelet bases that can lead to good quality of reconstruction are not well understood. For the first time in this study, it is demonstrated that in selecting wavelet dictionaries, the incoherence with the sensing matrix and the number of vanishing moments of the dictionary should be considered at the same time. In this research, a framework is proposed for the selection of an appropriate wavelet dictionary for EEG signal which is used in tandem with sparse binary matrix (SBM) as the sensing matrix and ST-SBL method as the reconstruction algorithm. Beylkin (highly incoherent with SBM and relatively high number of vanishing moments) is identified as the best dictionary to be used amongst the dictionaries are evaluated in this thesis. The power requirements for the proposed framework are also quantified using a power model. The outcomes will assist to realize the computational complexity and online implementation requirements of CS for transmitting EEG in WBAN. The proposed approach facilitates the energy savings budget well into the microwatts range, ensuring a significant savings of battery life and overall system’s power. The study is intended to create a strong base for the use of EEG in the high-accuracy and low-power based biomedical applications in WBAN

Assessment of the Usability and Accuracy of the Simplified One-Diode Models for Photovoltaic Modules

Models for photovoltaic (PV) cells and panels, based on the diode equivalent circuit, have been widely used because they are effective tools for system design. Many authors have presented simplified one-diode models whose three or four parameters are calculated using the data extracted from the datasheets issued by PV panel manufactures and adopting some simplifying hypotheses and numerical solving techniques. Sometimes it may be difficult to make a choice among so many models. To help researchers and designers working in the area of photovoltaic systems in selecting the model that is fit for purpose, a criterion for rating both the usability and accuracy of simplified one-diode models is proposed in this paper. The paper minutely describes the adopted hypotheses, analytical procedures and operative steps to calculate the parameters of the most famous simplified one-diode equivalent circuits. To test the achievable accuracy of the models, a comparison between the characteristics of some commercial PV modules issued by PV panel manufacturers and the calculated current-voltage (I-V) curves, at constant solar irradiance and/or cell temperature, is carried out. The study shows that, even if different usability ratings and accuracies are observed, the simplified one-diode models can be considered very effective tools

A Criterion for Rating the Usability and Accuracy of the One-Diode Models for Photovoltaic Modules

In selecting a mathematical model for simulating physical behaviours, it is important to reach an acceptable compromise between analytical complexity and achievable precision. With the aim of helping researchers and designers working in the area of photovoltaic systems to make a choice among the numerous diode-based models, a criterion for rating both the usability and accuracy of one-diode models is proposed in this paper. A three-level rating scale, which considers the ease of finding the data used by the analytical procedure, the simplicity of the mathematical tools needed to perform calculations and the accuracy achieved in calculating the current and power, is used. The proposed criterion is tested on some one-diode equivalent circuits whose analytical procedures, hypotheses and equations are minutely reviewed along with the operative steps to calculate the model parameters. To assess the achievable accuracy, the current-voltage (I-V) curves at constant solar irradiance and/or cell temperature obtained from the analysed models are compared to the characteristics issued by photovoltaic (PV) panel manufacturers and the differences of current and power are calculated. The results of the study highlight that, even if the five parameter equivalent circuits are suitable tools, different usability atings and accuracies can be observed

Reciclado de carga y circuitos para mejora de la eficiencia en conversores DC/DC integrados de ultra baja potencia

El presente trabajo profundiza en el estudio de conversores DC/DC de capacitores conmutados totalmente integrados para aplicaciones de ultra bajo consumo como ser: dispositivos implantables, redes de sensores inalámbricos, dispositivos portátiles, etc. El objetivo de este tipo de sistemas es el de suministrar energía al circuito correspondiente pero con una tensión diferente a la de la batería utilizada. Básicamente hay dos razones para suministrar una tensión diferente a la de la fuente principal. Por un lado, en los circuitos digitales existe un compromiso entre performance (velocidad de procesamiento) y consumo, que en general se puede manejar mediante la técnica de escalado dinámico de voltaje y frecuencia ("Dynamic Voltage and Frequency Scaling"), esta técnica básicamente disminuye la tensión de alimentación y la frecuencia del sistema cuando la exigencia de procesamiento es baja y los sube cuando es alta. De esta forma, en muchas aplicaciones se puede ahorrar una cantidad significativa de energía. Por otro lado, el escalado de las nuevas tecnologías ha alcanzado un punto donde los transistores básicos no soportan la tensión de las baterías que se consiguen en el mercado. Para ambos casos, tener un conversor DC/DC que sea capaz de manejar todo el rango (o al menos una buena parte) entre tierra y la tensión de alimentación es esencial. En esta tesis, se contribuye a la mejora de la e ciencia de este tipo de conversores con varias técnicas que permiten reciclar parte de la carga asociada a capacidades parásitas, y por técnicas de diseño de circuitos de bloques auxiliares. La idea de reciclar la carga de las capacidades parásitas ha sido explorada en la literatura, sin embargo todos los antecedentes están limitados a arquitecturas particulares del conversor DC/DC. En este trabajo se proponen técnicas generales para reciclar la carga de capacidades parásitas asociadas a las placas de los capacitores principales (capacidades parásitas de "top/bottom plate") y capacidad de gate independientemente de la arquitectura del conversor. Dichas técnicas son independientes de la arquitectura del conversor

Low Power Memory/Memristor Devices and Systems

This reprint focusses on achieving low-power computation using memristive devices. The topic was designed as a convenient reference point: it contains a mix of techniques starting from the fundamental manufacturing of memristive devices all the way to applications such as physically unclonable functions, and also covers perspectives on, e.g., in-memory computing, which is inextricably linked with emerging memory devices such as memristors. Finally, the reprint contains a few articles representing how other communities (from typical CMOS design to photonics) are fighting on their own fronts in the quest towards low-power computation, as a comparison with the memristor literature. We hope that readers will enjoy discovering the articles within

Applications in Electronics Pervading Industry, Environment and Society

This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs

Nonlinear Distortion in Wideband Radio Receivers and Analog-to-Digital Converters: Modeling and Digital Suppression

Emerging wireless communications systems aim to flexible and efficient usage of radio spectrum in order to increase data rates. The ultimate goal in this field is a cognitive radio. It employs spectrum sensing in order to locate spatially and temporally vacant spectrum chunks that can be used for communications. In order to achieve that, flexible and reconfigurable transceivers are needed. A software-defined radio can provide these features by having a highly-integrated wideband transceiver with minimum analog components and mostly relying on digital signal processing. This is also desired from size, cost, and power consumption point of view. However, several challenges arise, from which dynamic range is one of the most important. This is especially true on receiver side where several signals can be received simultaneously through a single receiver chain. In extreme cases the weakest signal can be almost 100 dB weaker than the strongest one. Due to the limited dynamic range of the receiver, the strongest signals may cause nonlinear distortion which deteriorates spectrum sensing capabilities and also reception of the weakest signals. The nonlinearities are stemming from the analog receiver components and also from analog-to-digital converters (ADCs). This is a performance bottleneck in many wideband communications and also radar receivers. The dynamic range challenges are already encountered in current devices, such as in wideband multi-operator receiver scenarios in mobile networks, and the challenges will have even more essential role in the future.This thesis focuses on aforementioned receiver scenarios and contributes to modeling and digital suppression of nonlinear distortion. A behavioral model for direct-conversion receiver nonlinearities is derived and it jointly takes into account RF, mixer, and baseband nonlinearities together with I/Q imbalance. The model is then exploited in suppression of receiver nonlinearities. The considered method is based on adaptive digital post-processing and does not require any analog hardware modification. It is able to extract all the necessary information directly from the received waveform in order to suppress the nonlinear distortion caused by the strongest blocker signals inside the reception band.In addition, the nonlinearities of ADCs are considered. Even if the dynamic range of the analog receiver components is not limiting the performance, ADCs may cause considerable amount of nonlinear distortion. It can originate, e.g., from undeliberate variations of quantization levels. Furthermore, the received waveform may exceed the nominal voltage range of the ADC due to signal power variations. This causes unintentional signal clipping which creates severe nonlinear distortion. In this thesis, a Fourier series based model is derived for the signal clipping caused by ADCs. Furthermore, four different methods are considered for suppressing ADC nonlinearities, especially unintentional signal clipping. The methods exploit polynomial modeling, interpolation, or symbol decisions for suppressing the distortion. The common factor is that all the methods are based on digital post-processing and are able to continuously adapt to variations in the received waveform and in the receiver itself. This is a very important aspect in wideband receivers, especially in cognitive radios, when the flexibility and state-of-the-art performance is required