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

Wavelet packet analysis for rotor bar breakage in an inverter induction motor

Introduction. In various industrial processes, squirrel cage induction motors are widely employed. These motors can be used in harsh situations, such as non-ventilated spaces, due to their high strength and longevity. These machines are subject to malfunctions such as short circuits and broken bars. Indeed, for the diagnosis several techniques are offered and used. Novelty of the proposed work provides the use of wavelet analysis technology in a continuous and discrete system to detect faults affecting the rotating part of an induction motor fed by a three-phase inverter. Purpose. This paper aims to present a novel technique for diagnosing broken rotor bars in the low-load, stationary induction machine proposed. The technique is used to address the problem of using the traditional Techniques like Fourier Transforms signal processing algorithm by analyzing the stator current envelope. The suggested method is based on the use of discrete wavelet transform and continuous wavelet transform. Methods. A waveform can be monitored at any frequency of interest using the suggested discrete wavelet transform and continuous wavelet transform. To identify the rotor broken bar fault, stator current frequency spectrum is analyzed and then examined. Based on a suitable index, the algorithm separates the healthy motor from the defective one, with 1, 2 and 3 broken bars at no-load. Results. In comparison to the healthy conditions, the recommended index significantly raises under the broken bars conditions. It can identify the problematic conditions with clarity. The possibility of detecting potential faults has been demonstrated (broken bars), using discrete wavelet transform and continuous wavelet transform. The diagnostic method is adaptable to temporary situations brought on by alterations in load and speed. Performance and efficacy of the suggested diagnostic method are demonstrated through simulation in Simulink® MATLAB environment.Вступ. У різних промислових процесах широко використовуються асинхронні двигуни із короткозамкненим ротором. Ці двигуни можуть використовуватися в суворих умовах, наприклад, в приміщеннях, що не вентилюються, завдяки їх високій міцності і довговічності. Ці машини схильні до несправностей, таких як коротке замикання і зламані стрижні. Зрозуміло, що для діагностики пропонується та використовується кілька методик. Новизна запропонованої роботи полягає у використанні технології вейвлет-аналізу в безперервній і дискретній системі для виявлення несправностей, що впливають на частину асинхронного двигуна, що обертається, що живиться від трифазного інвертора. Мета. У цій статті представлена нова методика діагностики зламаних стрижнів ротора в малонавантаженій стаціонарній асинхронній машині. Цей метод використовується для вирішення проблеми використання традиційних методів, таких як алгоритм обробки сигналів перетворення Фур’є, шляхом аналізу огинаючої струму статора. Пропонований метод заснований на використанні дискретного вейвлет-перетворення та безперервного вейвлет-перетворення. Методи. Форма сигналу може відстежуватися на будь-якій частоті, що цікавить, з використанням запропонованого дискретного вейвлет-перетворення і безперервного вейвлет-перетворення. Для виявлення несправності обриву стрижня ротора частотний спектр статора аналізується, а потім досліджується. На основі відповідного індексу алгоритм відокремлює справний двигун від несправного з 1, 2 і 3 зламаними стрижнями на холостому ході. Результати. Порівняно із нормальними умовами рекомендований показник значно підвищується за умов зламаних стрижнів. Він може чітко визначити проблемні умови. Було продемонстровано можливість виявлення потенційних несправностей (зламані стрижні) з використанням дискретного вейвлет-перетворення та безперервного вейвлет-перетворення. Метод діагностики адаптується до тимчасових ситуацій, викликаних змінами навантаження та швидкості. Працездатність та ефективність запропонованого методу діагностики продемонстровано за допомогою моделювання у середовищі Simulink® MATLAB

Fault Location in Grid Connected Ungrounded PV Systems Using Wavelets

Solar photovoltaic (PV) power has become one of the major sources of renewable energy worldwide. This thesis develops a wavelet-based fault location method for ungrounded PV farms based on pattern recognition of the high frequency transients due to switching frequencies in the system and which does not need any separate devices for fault location. The solar PV farm used for the simulation studies consists of a large number of PV modules connected to grid-connected inverters through ungrounded DC cables. Manufacturers report that about 1% of installed PV panels fail annually. Detecting phase to ground faults in ungrounded underground DC cables is also difficult and time consuming. Therefore, identifying ground faults is a significant problem in ungrounded PV systems because such earth faults do not provide sufficient fault currents for their detection and location during system operation. If such ground faults are not cleared quickly, a subsequent ground fault on the healthy phase will create a complete short-circuit in the system, which will cause a fire hazard and arc-flashing. Locating such faults with commonly used fault locators requires costly external high frequency signal generators, transducers, relays, and communication devices as well as generally longer lead times to find the fault. This thesis work proposes a novel fault location scheme that overcomes the shortcomings of the currently available methods. In this research, high frequency noise patterns are used to identify the fault location in an ungrounded PV farm. This high frequency noise is generated due to the switching transients of converters combined with parasitic capacitance of PV panels and cables. The pattern recognition approach, using discrete wavelet transform (DWT) multi-resolution analysis (MRA) and artificial neural networks (ANN), is utilized to investigate the proposed method for ungrounded grid integrated PV systems. Detailed time domain electromagnetic simulations of PV systems are done in a real-time environment and the results are analyzed to verify the performance of the fault locator. The fault locator uses a wavelet transform-based digital signal processing technique, which uses the high frequency patterns of the mid-point voltage signal of the converters to analyze the ground fault location. The Daubechies 10 (db10) wavelet and scale 11 are chosen as the appropriate mother wavelet function and decomposition level according to the characteristics of the noise waveform to give the proposed method better performance. In this study, norm values of the measured waveform at different frequency bands give unique features at different fault locations and are used as the feature vectors for pattern recognition. Then, the three-layer feed-forward ANN classifier, which can automatically classify the fault locations according to the extracted features, is investigated. The neural network is trained with the Levenberg-Marquardt back-propagation learning algorithm. The proposed fault locating scheme is tested and verified for different types of faults, such as ground and line-line faults at PV modules and cables of the ungrounded PV system. These faults are simulated in a real-time environment with a digital simulator and the data is then analyzed with wavelets in MATLAB. The test results show that the proposed method achieves 99.177% and 97.851% of fault location accuracy for different faults in DC cables and PV modules, respectively. Finally, the effectiveness and feasibility of the designed fault locator in real field applications is tested under varying fault impedance, power outputs, temperature, PV parasitic elements, and switching frequencies of the converters. The results demonstrate the proposed approach has very accurate and robust performance even with noisy measurements and changes in operating conditions

Fault diagnosis of power converters in a grid connected photovoltaic system using artificial neural networks

Introduction. The widespread use of photovoltaic systems in various applications has spotlighted the pressing requirement for reliability, efficiency and continuity of service. The main impediment to a more effective implementation has been the reliability of the power converters. Indeed, the presence of faults in power converters that can cause malfunctions in the photovoltaic system, which can reduce its performance. Novelty. This paper presents a technique for diagnosing open circuit failures in the switches (IGBTs) of power converters (DC-DC converters and three-phase inverters) in a grid-connected photovoltaic system. Purpose. To ensure supply continuity, a fault-diagnosis process is required throughout all phases of energy production, transfer, and conversion. Methods. The diagnostic approach is based on artificial neural networks and the extraction of features corresponding to the open circuit fault of the IGBT switch. This approach is based on the Clarke transformation of the three-phase currents of the inverter output as well as the calculation of the average value of these currents to determine the exact angle of the open circuit fault. Results. This method is able to effectively identify and localize single or multiple open circuit faults of the DC-DC converter IGBT switch or the three-phase inverter IGBT switches.Вступ. Широке використання фотоелектричних систем у різних застосуваннях висунуло на перший план нагальні вимоги до надійності, ефективності та безперервності обслуговування. Основною перешкодою для ефективнішого застосування була надійність силових перетворювачів. Справді, наявність несправностей у силових перетворювачах може спричинити збої в роботі фотоелектричної системи, що може знизити її продуктивність. Новизна. У цій статті представлена методика діагностики обриву кола в перемикачах (IGBT) силових перетворювачів (перетворювачів постійного струму та трифазних інверторів) у фотоелектричній системі, підключеній до мережі. Мета. Для забезпечення безперервності постачання потрібен процес діагностики несправностей на всіх етапах виробництва, передачі та перетворення енергії. Методи. Діагностичний підхід заснований на штучних нейронних мережах та вилучення ознак, що відповідають обриву кола IGBT-перемикача. Цей підхід ґрунтується на перетворенні Кларка трифазних струмів на виході інвертора, а т акож розрахунку середнього значення цих струмів для визначення точного кута обриву кола. Результати. Цей метод дозволяє ефективно ідентифікувати та локалізувати одиночні або множинні несправності розімкнутого кола IGBT-перемикача DC-DC перетворювача або IGBT-перемикача трифазного інвертора

A Robust Technique for Detection, Diagnosis, and Localization of Switching Faults in Electric Drives Using Discrete Wavelet Transform

Detection, diagnosis, and localization of switching faults in electric drives are extremely important for operating a large number of induction motors in parallel. This study aims to present the design and development of switching fault detection, diagnosis, and localization strategy for the induction motor drive system (IMDS) by using a novel diagnostic variable that is derived from discrete wavelet transform (DWT) coefficients. The distinctiveness of the proposed algorithm is that it can identify single/multiple switch open and short faults and locate the defective switches using a single mathematical computation. The proposed algorithm is tested by simulation in MATLAB/Simulink and experimentally validated using the LabVIEW hardware-in-the-loop platform. The results demonstrate the robustness and effectiveness of the proposed technique in identifying and locating faults

Fault detection in a three-phase inverter fed circuit: Enhancing the Tripping capability of a UPS circuit breaker using wave shape recognition algorithm

Uninterruptible power supplies (UPS) are electrical devices that protect sensitive loads from power line disturbances such as source side overcurrents caused by overvoltage and power surges. The critical load in a double conversion UPS system is supplied from an invert-er. When overcurrents occur on the load side of double conversion UPS systems, both the UPS system’s inverter and the critical load connected to it stand a high risk of damage. Load side overcurrents due to short circuits, ground faults and motor/transformer start-up are very damaging to power electronic components, electrical equipment and cable connections. There exists circuit breakers on the load side designed to trip when a huge overcurrent occurs, thereby clearing the fault. A circuit breaker is normally sized and installed based on the maxi-mum capacity of the host system and trips when a predetermined overcurrent is recorded within a specific period of time. The UPS system’s inverter has a pre-set current limit value to protect insulated-gate bipolar transistors (IGBTs) from damage. During an overcurrent, invert-ers can supply a fault current whose peak value is limited to the IGBT current limit value. This inverter supplied fault current is not high enough to trip the circuit breaker. After an extended period of overcurrent, UPS internal tripping will be activated and all loads lose power. Opera-tion of the UPS in bypass mode supplies the required fault current but exposes the sensitive load to power line distortions. Therefore, it is desired to always supply the critical load via the inverter. This study targets to design a detection algorithm for short circuits and ground faults with a detection time faster than the UPS system’s internal tripping in order to isolate the faulted ar-ea, when the inverter is supplying the critical load. To achieve this, first, a MATLAB model was designed to aid in preliminary studies of fault detection through analysing the system behaviour. Secondly, literature review was conducted and a fault detection method selected with the help of the MATLAB model. Next, laboratory tests on a real UPS system were carried out and compared to the MATLAB results. Lastly, the detection algorithm was designed, im-plemented and tested on a real double conversion UPS system. The test results indicate that the implemented detection algorithm successfully detects short circuits and ground faults well within the desired time. It also successfully distinguishes short circuits and ground faults from other sources of overcurrents such as overloading and transformer inrush current. Future development of this study includes additional features such as a fault classification method proposed for implementation to improve the UPS debugging process during maintenance. Moreover, the detection algorithm will also be refined and devel-oped further to activate a circuit that discharges a current pulse to increase the fault current fed to the circuit breaker

Recent Developments and Challenges on AC Microgrids Fault Detection and Protection Systems–A Review

The protection of AC microgrids (MGs) is an issue of paramount importance to ensure their reliable and safe operation. Designing reliable protection mechanism, however, is not a trivial task, as many practical issues need to be considered. The operation mode of MGs, which can be grid-connected or islanded, employed control strategy and practical limitations of the power electronic converters that are utilized to interface renewable energy sources and the grid, are some of the practical constraints that make fault detection, classification, and coordination in MGs different from legacy grid protection. This article aims to present the state-of-the-art of the latest research and developments, including the challenges and issues in the field of AC MG protection. A broad overview of the available fault detection, fault classification, and fault location techniques for AC MG protection and coordination are presented. Moreover, the available methods are classified, and their advantages and disadvantages are discussed

A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

The use of artificial intelligence (AI) is increasing in various sectors of photovoltaic (PV) systems, due to the increasing computational power, tools and data generation. The currently employed methods for various functions of the solar PV industry related to design, forecasting, control, and maintenance have been found to deliver relatively inaccurate results. Further, the use of AI to perform these tasks achieved a higher degree of accuracy and precision and is now a highly interesting topic. In this context, this paper aims to investigate how AI techniques impact the PV value chain. The investigation consists of mapping the currently available AI technologies, identifying possible future uses of AI, and also quantifying their advantages and disadvantages in regard to the conventional mechanisms

Pattern recognition and diagnosis of short and open circuit faults inverter in induction motor drive using neural networks

Nowadays, feeding induction motors with voltage source inverters under faulty conditions is a major challenge. For this reason, electrical systems must be well thought out to provide good diagnostics for these elements. Consequently, the early detection of faults is very important to establish strategies that allow us to control the operation and take preventive measures to avoid frequent failures. Our aim in this paper is to train multilayer neural networks using features extracted from currents and voltages measurements to detect and classify open and short-circuit switch faults in source voltage inverters. Simulation results show that instead of using several types of features extracted from measurements of several signal cycles as in previous works, a two-component feature obtained from one cycle is sufficient to obtain an excellent accuracy. The normalized mean Clark currents and the power spectrum using the fast Fourier transform have been used as features for open switches and short-circuit faults respectively