Fault detection algorithm for grid-connected photovoltaic plants

The present paper proposes a novel data-driven Fault Detection and Diagnosis algorithm for induction motors based on Motor Current Signature Analysis. Principal Component Analysis is used to reduce the three-phase currents space in two dimensions. Then, Kernel Density Estimation is adopted to estimate the Probability Density Function of healthy and of each faulty motors, which will give typical patterns that can be used to identify each fault. Kullback-Leibler divergence is used as an index to identify the dissimilarity between two determined probability distributions, that allows the automatic identification of distinct fault types. Several simulations and experimental results are carried out using two benchmarks in order to verify the effectiveness of the proposed methodology: the first is used to prove appropriateness of the method for air gap eccentricity fault diagnosis and the second is used to prove suitability of the method for rotor broken bars and connectors fault diagnosis. Simulations and classification results prove that the proposed Fault Detection and Diagnosis procedure is able to detect and diagnose different induction motor fault types.

Current signature analysis of induction motor becomes very much popular for its Condition monitoring purpose. Most of these studies in recent years are focused on hardware. Though, to build an experimental set-up for fault emulating machines is not an easy task. It involves too much cost and risk of damage. Moreover, it is not possible to create different types of faults in the same machine. In this paper an accurate but simple model of 3-phase Induction motor is being proposed which describes the machine at low frequency as well as at high frequency for different types of fault situations. The model, based on DM, CM characteristics of the machine, is well suited for power frequency (50 Hz) and high frequency applications like adjustable speed drives (ASDs). A mechanical model of a three-phase induction machine is incorporated to this electrical model to make a universal model of dynamic squirrel cage induction motor for overall simulation. The proposed model has the feature to incorporate different types of electrical fault like, stator inter-turn fault, phase-to-phase and phase-to ground insulation fault etc. The premature conditions of the above faults can also be emulated.

Fault detection (FD) algorithms based on supervisory control and data acquisition (SCADA) data have been widely used in the operation and maintenance (O&M) of wind turbines (WTs). However, the performance of FD models will degrade due to the time-varying operating conditions (TVOC). Incremental learning (IL) methods can be employed to update the models online to adapt to TVOC, but the error accumulation caused by false negatives will lead to a continuous decrease in the FD rate (FDR). A novel IL method considering false negatives is proposed to improve the performance of WT FD models. First, a data buffer is built to cache some new normal data used in model updating. Second, a processing strategy for false negatives is proposed to block some high-risk data from being added to the buffer, thereby weakening the error accumulation. Third, when the buffer is full, update the FD model using the data in it. A real-world SCADA dataset with gearbox faults and four different FD algorithms with various model updating strategies are used in the experiments. The results demonstrate that the proposed method can lower the false alarm rate (FAR) of all four FD algorithms. After processing false negatives, for example, for the FD model based on Gaussian kernel regression, its FDR increased by about 11%, and for the FD model based on multivariate state estimation technique, its FDR increased by about 14%. The results of hyperparameter experiments and early stopping experiments show that the proposed method has good potential for practical applications.

The demand for the Internet of Things (IoT) has raised in today's era significantly. Wireless Sensor Networks (WSNs) is the main component of IoT. However, the sensor nodes are prone to failures due to their deployment in hostile environments. In WSNs, fault in sensor nodes extensively degrades the network performances. The existing fault detection approaches are unable to diagnosis different types of faults in the sensor. Also, the fault detection accuracy of the existing approaches is very less. In this paper, a deep Bidirectional LSTM model is used to detect the fault in nodes. The proposed methodology can detect various types of faults such as soft permanent, intermittent and transient faults. The results show that the proposed algorithm outperforms the other state-of-the- art fault detection approaches in terms of fault detection accuracy, false alarm rate, and false-positive rate.

Brushless DC (BLDC) motors are used extensively in the industry for a wide variety of applications. In the Aerospace industry, they are mainly used in the control actuation system in launch vehicles. Different types of faults can develop in a BLDC motor. It is essential to detect these faults in the early stage itself before they develop into system failures. In this paper, fault detection in BLDC motors using parameter estimation technique is presented. Detailed fault diagnosis is subsequently carried out by a fuzzy inference system. Discrete Square Root Filtering in Covariance form (DSFC) algorithm is used initially for estimating the physical parameters of the BLDC motor. These parameters are then passed on to the fuzzy inference system which carries out the detailed diagnosis. Simulation studies carried out indicates that the developed fault detection and isolation scheme is capable of isolating different types of faults like open circuit, short circuit faults, performance degradation of magnets etc. in BLDC motors.

Consumption of electrical energy has increased many folds in the recent time due to its use for industrial, commercial, agricultural, domestic and social purposes. HVDC technology is increasingly used for bulk power transmission due to advantages like low loss, ease of control, etc. However, the protection system for the HVDC system is not as mature as AC system. HVDC system requires fast and accurate fault detection. Recently, various time- and frequency-domain algorithms are proposed. In this paper, Stockwell (S-) transform is utilized for real-time fault detection. Different type of faults in HVDC transmission line are simulated using PSCAD. The generated fault signals are analyzed using MATLAB. S-transform can detect the transient signal effectively in time-frequency domain. It can be used to robustly differentiate the different type of faults and the load variation.

A combined image processing and Nearest Neighbor Algorithm tool for classification of incipient faults in induction motor drives

A general framework and a method for model based diagnosis are suggested. In the framework, faults and modeling of faults are formally described. Then the method is used to construct a diagnosis system based on a structure of hypothesis tests. Using these principles, it is possible to diagnose a large variety of different types of faults. The framework and the method are applied to the design of a diagnosis system for an automotive engine. This application clearly shows the strength of these principles because several different types of sensor faults and leakage need to be diagnosed. The resulting diagnosis system is demonstrated using experiments on a real automotive engine.

To ensure high reliability of the Grid-Connected Photovoltaic (GCPV) systems, promptly faults detection, diagnosis and automatic process monitoring are essential tools to keep the PV and the grid network under optimal functioning. Regardless of fault types, incipient faults are usually more difficult to detect and accurately isolate. As an alternative and effective method, the principal components analysis (PCA) is proposed to extract and select more relevant features and support vector machines (SVM) technique is applied to quickly detect the faults that occur in a GCPV system. The T2 and squared weighted errors (SWE) statistics, generally used as fault detection indices, are appropriately extracted and selected within the PCA framework. Both of these features are fed to a SVM classifier handling the incipient fault detection. This task is carried out on a simulated GCPV operating under maximum power point trackers (MPPT) and matching realistic outdoor to demonstrate the effectiveness and robustness of the proposed technique.

This paper presents a stator winding faults detection in induction generator based wind turbines by using artificial neural network (ANN). Stator winding faults of induction generators are the most common fault found in wind turbines. This fault may lead to wind turbine failure. Therefore, fault detection in induction generator based wind turbines is vital to increase the reliability of wind turbines. In this project, the mathematical model of induction generator based wind turbine was developed in MATLAB Simulink. The value of impedance in the induction generators was changed to simulate the inter-turn short circuit and open circuit faults. The simulated responses of the induction generators were used as inputs in the ANN model for fault detection procedures. A set of data was taken under different conditions, i.e. normal condition, inter-turn short circuit and open circuit faults as inputs for the ANN model. The target outputs of the ANN model were set as ‘0’ or ‘1’, based on the fault conditions. Results obtained showed that the ANN model can detect different types of faults based on the output values of the ANN model. In conclusion, the stator winding faults detection procedure for induction generator based wind turbines by using ANN was successfully developed.

Electric motor drives with a wide variety of applications are usually derived with inverters, where the inverter switches are always prone to different types of faults. Short circuit faults can rapidly shut down systems, and open-circuit ones can lead to secondary damage if they are not detected and tolerated in time. Due to this fact, in this paper, a novel data-driven fault detection and diagnosis (FDD) method has been proposed to detect and locate all types of inverter switch faults. Three deep learning algorithms, including fully connected neural networks (FCNs), convolutional neural networks (CNNs), and bidirectional long short-term memory (BiLSTM), have been implemented and compared. The BiLSTM network with 98.45% accuracy outperforms the others and can detect all types of faults in less than half a fundamental period under different and variable speeds with the existence of noise. The results show that the proposed method is highly effective and is a great candidate for real-time applications.

Fault detection systems are typically applied in the railway industry to examine the structural health status of the wheel/rail system. We herein propose a time-domain kurtosis beamforming technique using an array of microphones for the fault identification and localisation of the wheel/rail system under an environment with high background noise. As an acoustics-based noncontact diagnosis method, this technique overcomes the challenge of the contact between the sensors and examined structures, and it is more applicable for impulsive signals of broadband nature, such as impact noise generated from faults on the wheel surface. Moreover, the application of kurtosis enables the identification and localisation at low signal-to-noise ratio. Under such circumstance, the impulsive signals generated by faults were totally merged in rolling noise and background noise. Meanwhile, different types of faults on the wheels could be identified and localised by observing the kurtosis value on the beamforming sound map. The effectiveness of the proposed method to diagnose the type of wheel fault with low signal-to-noise ratio and moving source has been validated experimentally. This method may provide a useful tool for the routine maintenance of trains.

Occurrence of unwanted resistance at terminal of IGBTs is a type of Incipient faults which occurs in 3-phase voltage source Inverter. This type of faults does not trigger the protective circuit and in due course of time leads to severe faults. The present work had made an attempt to identify this type of faults related to two IGBTs in the VSI scheme. Rigorous simulation is done with different cases of faults in a PSIM software platform. The current profiles obtained from the simulation are processed by data processing tools to obtain Mean Current vector (MCV). A two phase reference frame shows the distribution of MCV for different types of faults. The distribution is found to follow a particular pattern. This has encouraged the authors to use SVM classifier for analyzing the distributed pattern and classify different types of faults.

Over the last decades, the deployment of large solar-based photovoltaic power plants has grown tremendously. The undesirable impact of high integration level of photovoltaic systems has led energy stakeholders to regulate such penetration to avoid this negative impact. One major concern with regard to photovoltaic penetration is the issue of power quality. Poor power quality can be a source of system disturbance and major economic losses. However, the power quality analysis is not widely discussed in the literature, with most of the studies focusing on the harmonic issues as potential power quality problem, but this study shows that there are a number of power quality issues, such as undervoltage, overvoltage, power fluctuation, and power factor. This study presents practical approaches to a grid-connected solar photovoltaic plant with associated control circuits developed in the time-domain. The power quality of a grid-connected solar photovoltaic plant is investigated by an analysis of the inverter output voltage and nominal current for different photovoltaic plant sizes. Also, the effect of different conditions of solar irradiance and ambient temperature on the power quality is analyzed. To identify power quality issues, a photovoltaic plant time-domain model is developed using Power Systems Computer Aided Design software. Various solar photovoltaic plant controls such as maximum power point tracking and modulation signals sinusoidal pulse width modulation and pulse width modulation for direct current-to-direct current boost converter are developed and integrated into the simulation environment. Several case studies are performed taking into account different photovoltaic plant ratings such as 250 kW–3 MW, where the point of common coupling is monitored.

Fault detection and diagnosis (FDD) is of utmost importance in ensuring the safety and reliability of electric vehicles (EVs). The EV’s power train and energy storage, namely the electric motor drive and battery system, are critical components that are susceptible to different types of faults. Failure to detect and address these faults in a timely manner can lead to EV malfunctions and potentially catastrophic accidents. In the realm of EV applications, Permanent Magnet Synchronous Motors (PMSMs) and lithium-ion battery packs have garnered significant attention. Consequently, fault detection methods for PMSMs and their drives, as well as for lithium-ion battery packs, have become a prominent area of research. An effective FDD approach must possess qualities such as accuracy, speed, sensitivity, and cost-effectiveness. Traditional FDD techniques include model-based and signal-based methods. However, data-driven approaches, including machine learning-based methods, have recently gained traction due to their promising capabilities in fault detection. This paper aims to provide a comprehensive overview of potential faults in EV motor drives and battery systems, while also reviewing the latest state-of-the-art research in EV fault detection. The information presented herein can serve as a valuable reference for future endeavors in this field.