Fault Diagnostic System for Cascaded H-bridge Multilevel Inverter Drives Based on Artificial Intelligent Approaches Incorporating a Reconfiguration Technique

A fault diagnostic and reconfiguration system in a multilevel inverter drive (MLID) using artificial intelligent based techniques is developed in this dissertation. Output phase voltages of a MLID can be used as valuable information to diagnose faults and their locations. It is difficult to diagnose a MLID system using a mathematical model because MLID systems consist of many switching devices and their system complexity has a nonlinear factor. Therefore, a neural network (NN) classification is applied to the fault diagnosis of a MLID system. Multilayer perceptron (MLP) networks are used to identify the type and location of occurring faults. The principal component analysis (PCA) is utilized in the feature extraction process to reduce the NN input size. A lower dimensional input space will also usually reduce the time necessary to train a NN, and the reduced noise may improve the mapping performance. The genetic algorithm is also applied to select the valuable principal components. The comparison among MLP neural network (NN), principal component neural network (PC-NN), and genetic algorithm based selective principal component neural network (PC-GA-NN) are performed. Proposed neural networks are evaluated with simulation test set and experimental test set. The PC-NN has improved overall classification performance from NN by about 5% points, whereas PC-GA-NN has better overall classification performance from NN by about 7.5% points. Therefore, the application of a genetic algorithm improves the classification from PC-NN by about 2.5% point. The overall classification performance of the proposed networks is more than 90%. A reconfiguration technique is also developed. The effects of using the developed reconfiguration technique at high modulation index are addressed. The developed fault diagnostic system is validated with experimental results. The developed fault diagnostic system requires about 6 cycles at 60 Hz to clear an open circuit and about 9 cycles at 60 Hz to clear a short circuit fault. The experimental results show that the developed system performs satisfactorily to detect the fault type, fault location, and reconfiguration

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

Cybersecurity Strategy against Cyber Attacks towards Smart Grids with PVs

Cyber attacks threaten the security of distribution power grids, such as smart grids. The emerging renewable energy sources such as photovoltaics (PVs) with power electronics controllers introduce new potential vulnerabilities. Based on the electric waveform data measured by waveform sensors in the smart grids, we propose a novel cyber attack detection and identification approach. Firstly, we analyze the cyber attack impacts (including cyber attacks on the solar inverter causing unusual harmonics) on electric waveforms in distribution power grids. Then, we propose a novel deep learning based mechanism including attack detection and attack diagnosis. By leveraging the electric waveform sensor data structure, our approach does not need the training stage for both detection and the root cause diagnosis, which is needed for machine learning/deep learning-based methods. For comparison, we have evaluated classic data-driven methods, including -nearest neighbor (KNN), decision tree (DT), support vector machine (SVM), artificial neural network (ANN), and convolutional neural network (CNN). Comparison results verify the performance of the proposed method for detection and diagnosis of various cyber attacks on PV systems

On the identifiability, parameter identification and fault diagnosis of induction machines

PhD ThesisDue to their reliability and low cost, induction machines have been widely utilized in a large variety of industrial applications. Although these machines are rugged and reliable, they are subjected to various stresses that might result in some unavoidable parameter changes and modes of failures. A common practice in induction machine parameter identification and fault diagnosis techniques is to employ a machine model and use the external measurements of voltage, current, speed, and/or torque in model solution. With this approach, it might be possible to get an infinite number of mathematical solutions representing the machine parameters, depending on the employed machine model. It is therefore crucial to investigate such possibility of obtaining incorrect parameter sets, i.e. to test the identifiability of the model before being used for parameter identification and fault diagnosis purposes. This project focuses on the identifiability of induction machine models and their use in parameter identification and fault diagnosis. Two commonly used steady-states induction machine models namely T-model and inverse Γ- model have been considered in this thesis. The classical transfer function and bond graph identifiability analysis approaches, which have been previously employed for the T-model, are applied in this thesis to investigate the identifiability of the inverse Γ-model. A novel algorithm, the Alternating Conditional Expectation, is employed here for the first time to study the identifiability of both the T- and inverse Γ-models of the induction machine. The results obtained from the proposed algorithm show that the parameters of the commonly utilised Tmodel are non-identifiable while those of the inverse Γ-model are uniquely identifiable when using external measurements. The identifiability analysis results are experimentally verified by the particle swarm optimization and Levenberg-Marquardt model-based parameter identification approaches developed in this thesis. To overcome the non-identifiability problem of the T-model, a new technique for induction machine parameter estimation from external measurements based on a combination of the induction machine’s T- and inverse Γ-models is proposed. Results for both supply-fed and inverter-fed operations show the success of the technique in identifying the parameters of the machine using only readily available measurements of steady-state machine current, voltage and speed, without the need for extra hardware. ii A diagnosis scheme to detect stator winding faults in induction machines is also proposed in this thesis. The scheme uses time domain features derived from 3-phase stator currents in conjunction with particle swarm optimization algorithm to check characteristic parameters of the machine and detect the fault accordingly. The validity and effectiveness of the proposed technique has been evaluated for different common faults including interturn short-circuit, stator winding asymmetry (increased resistance in one or more stator phases) and combined faults, i.e. a mixture of stator winding asymmetry and interturn short-circuit. Results show the accuracy of the proposed technique and it is ability to detect the presence of the fault and provide information about its type and location. Extensive simulations using Matlab/SIMULINK and experimental tests have been carried out to verify the identifiability analysis and show the effectiveness of the proposed parameter identification and fault diagnoses schemes. The constructed test rig includes a 1.1 kW threephase test induction machine coupled to a dynamometer loading unit and driven by a variable frequency inverter that allows operation at different speeds. All the experiment analyses provided in the thesis are based on terminal voltages, stator currents and rotor speed that are usually measured and used in machine control.Libya, through the Engineering Faculty of Misurata- Misurata Universit

Induction Motors

AC motors play a major role in modern industrial applications. Squirrel-cage induction motors (SCIMs) are probably the most frequently used when compared to other AC motors because of their low cost, ruggedness, and low maintenance. The material presented in this book is organized into four sections, covering the applications and structural properties of induction motors (IMs), fault detection and diagnostics, control strategies, and the more recently developed topology based on the multiphase (more than three phases) induction motors. This material should be of specific interest to engineers and researchers who are engaged in the modeling, design, and implementation of control algorithms applied to induction motors and, more generally, to readers broadly interested in nonlinear control, health condition monitoring, and fault diagnosis

Fault Diagnosis in Medium Voltage Drive Based on Combination of Wavelet transform and Support Vector Machine

Nowadays, Medium Voltage Drive (MVD) has been widely applied in the field of high-powered motor speed-regulation. These types of converter use a lot of insulated gate bipolar translators (IGBTs). So it is very important to find an effective way to diagnose IGBT open-circuit faults. This study describes a method of diagnosis for IGBT open-circuit faults in MVD whose topology is cell series of multi-level. This method combines wavelet transform (WT) and support vector machine (SVM). The wavelet transform is used to extract fault features and SVM is used to classify the fault states of a single power unit. Then, the trained SVM classifier is used to scan all power units of MVD sequentially. Results of simulation on the platform of MATLAB/Simulink show that this method has a good diagnosis capability. It can diagnose the IGBT open-circuit faults of the whole inverter system, and diagnosis accuracy is up to 96%. So, this method has a good application prospect

Modeling and Validation of a Fault Mitigation Method in Induction Motor-Drive Systems Using Magnetic Equivalent Circuits

In this thesis, a fault mitigation method for delta-connected induction motor-drive systems under a two-phase open-delta faulty operating condition is analyzed and verified. More specifically, this fault mitigation technique can provide a set of almost balanced motor line currents, and significantly reduce torque ripples, even when the machine runs under the aforementioned two-phase open-delta faulty operating condition. This condition is analyzed using a Magnetic Equivalent Circuit (MEC) model. This model is developed for a delta-connected induction motor which is coupled to its drive system, including the fault mitigation controller. That is, the MEC model is linked to its associated PWM inverter to include the electronic switching effects. This global motor-inverter model was simulated in a Matlab-Simulink environment, under both healthy and faulty operating conditions, while the inverter is operated in both the open-loop scalar control and closed-loop vector control modes. The results obtained from the global model are compared in this thesis to the results obtained from the corresponding Time-Stepping Finite Element (TSFE) simulation and experimental motor-drive test data. A comparative analysis of the motor performance obtained from these results, under the two-phase open-delta faulty operating case, is presented in this work. The simulation and experimental data show that the delta-connected MEC model can provide reasonably accurate results. Thus, the validity and applicability of the fault mitigation technique is thereby verified

Reduced-order observer analysis in MBPC techniques applied to the six-phase induction motor drives

Raúl Gregor, Jorge Rodas, Derlis Gregor and Federico Barrero (2015). Reduced-order Observer Analysis in MBPC Techniques Applied to the Six-phase Induction Motor Drives, Induction Motors - Applications, Control and Fault Diagnostics, Dr. Raul Gregor (Ed.), ISBN: 978-953-51-2207-4, InTech, DOI: 10.5772/60778. Available from: http://www.intechopen.com/books/induction-motors-applications-control-and-fault-diagnostics/reduced-order-observer-analysis-in-mbpc-techniques-applied-to-the-six-phase-induction-motor-drivesModel-based predictive control techniques have been recently applied with success in power electronics, particularly in the fields of current control applied to AC multiphase electrical drives. In AC electrical drives control, most of state variables (i.e., rotor currents, rotor fluxes, etc.) cannot be measured, so they must be estimated. As a result of this issue, this chapter proposes a comparative study of reduced-order observers used to estimate the rotor currents in an model-based predictive current control applied to the six-phase induction motor. The proposed control techniques are evaluated using the Luenberger observer and the optimal estimator based on Kalman filter. Different operation modes are analyzed and are further compared in terms of statistical parameters of performance (i.e., covariance, standard deviation, mean square error, etc.). The effectiveness of proposed methods is verified by a set of comparative experiments obtained by using a six-phase induction motor system experimental setup

Bearing/Incipient/Open Phase Fault Detection and Diagnosis of Multi-Phase Induction Motor Drives Equipped By GBDTI2HO Technique

In this paper, a hybrid system is performed with fault detection and diagnosis on multi-phase induction motor (IM). The proposed method is hybrid of integrated Harris Hawk optimization (IHHO) and gradient boosting decision trees (GBDT) thus called the GBDTI2HO method. Here, additional operators are included in this paper to improve HHO’s search behaviour namely crossover and mutation. Distorted waveforms are generated by different frequency patterns to indicate the time domain frequency as an assessment of failure. For this signal representation, the discrete wavelet transformation (DWT) is suggested. It extracts the characteristics and forwards them to IHHO technique to form the possible data sets. After the generation of the data set, GBDT classifies the ways of failure reached as winding of stator in multi-phase IM. The implementation of the proposed system is compared with existing systems, such as ANN, S-Transform and GBDT. The proposed method is executed on MATLAB/Simulink work platform to demonstrate the successfulness of proposed system, statistical measures are determined, as precision, sensitivity and specificity, mean median and standard deviation. For demonstrating the successfulness of proposed system, statistical measures are determined as precision, sensitivity, specificity, mean median as well as standard deviation. In 50 trails the proposed method, 0.98 for accuracy, 0.96 for specificity, 1.60 for recall as well as 0.97 for precision. In 100 trail the proposed method, 0.96 for accuracy, 0.93 for specificity, 0.87 for recall as well as 0.99 for precision