Fault detection methods for voltage source converters based on state characterisation and gap-metric

Integration of Fault Detection and Diagnosis Methods

The day-by-day increase in digital loads draws attention towards the need for an efficient and compatible distribution network. An LVDC distribution network has the capability to fulfill such digital load demands. However, the major challenge of an LVDC distribution network is its vulnerability during a fault. The need for a high-speed fault detection method is inevitable before it can be widely adopted. This paper proposes a new fault detection method which extracts the features of the current during a fault. The proposed fault detection method uses the merits of overcurrent, the first and second derivative of current, and signal processing techniques. Three different features are extracted from a time domain current signal through a sliding window. The extracted features are based upon the root squared zero, second, and fourth order moments. The features are then set with individual thresholds to discriminate low-, high-, and very high-resistance faults. Furthermore, a fault is located through the superimposed power flow. Moreover, this study proposes a new method based on the vector sum of positive and negative pole currents to identify the faulty pole. The proposed scheme is verified by using a modified IEEE 13 node distribution network, which is implemented in Matlab/Simulink. The simulation results confirm the effectiveness of the proposed fault detection and identification method. The simulation results also confirm that a fault having a resistance of 1 m Ω is detected and interrupted within 250 μ s for the test system used in this study.

This paper conducts a study on the faults of common sensors involved in chemical static equipment. Firstly, the types and characteristics of commonly used sensors of chemical static equipment are analyzed, and the characteristics of sensor output signal changes are summarized with the working characteristics of chemical equipment. Then the faults of static equipment sensors are classified and a fault model is established. Through the study of sensor fault detection and isolation methods at home and abroad, the overall scheme of sensor system fault detection and isolation combining single sensor fault detection and isolation method and multi-sensor fault detection and isolation method is proposed. According to the characteristics that chemical processes are generally in a dynamic and stable state and there is a certain correlation between the signals of each detection point in the equipment, a sensor system model is established by using the correlation of multiple sensors on the equipment, and when a sensor in the sensor system fails, the system model changes beyond the threshold value, and a different form of residual generation is used to determine which sensor is faulty and achieve the detection and isolation of faulty sensors. The fault detection method is simulated and studied by using relevant software, combined with a support vector machine and neural network toolbox. The results show that the method proposed in this paper can effectively complete the fault detection and isolation of sensors commonly used in chemical static equipment. The accuracy and reliability of the prediction model are high.

The paper proposes a fast fault detection method for radial DC microgrids established on mathematical morphology (MM) denoising filters and detection principles utilizing only local measurements. The proposed fault detection algorithm utilizes the DC fault current signal from a Voltage Source Converter (VSC) and detects a fault. Problems related to communication delays are thus avoided, while preserving the low cost and computational burden. The proposed fault detection framework includes five different MM-based denoising filters, employed as pre-signal processing methods whose features are assessed and compared. The influence of selecting an appropriate type and length of a structuring element (SE) on the proposed method is analyzed. The developed fault detection method based on MM is used for extracting transient features and detecting pole-to-pole (PP) and pole-to-ground (PG) faults within milliseconds of their occurrence. The proposed method is demonstrated using simulation tests in MATLAB/Simulink. The results indicate that the proposed method enables reliable, accurate and fast detection of both the PP and the PG faults. Computer simulations are carried out to verify that the proposed method distinguishes faults from overload.

In modern chemical processes, varieties of fault detection and diagnosis methods have been used for ensuring process safety and product quality widely. As an important branch, fault detection and diagnosis methods based on data-driven are effective in large-scale chemical processes. However, they do not often show superior performance owing to the self-limitations and the characteristics of process data, such as nonlinearity, non-Gaussian, and multi-operating mode. To cope with these issues, k-NN (k-Nearest Neighbor) fault detection method and its extension have been developed in recent years. Nevertheless, these methods are used for fault detection mainly, few papers can be found about fault diagnosis. In this paper, a novel abnormal variables identification method is proposed, this method uses k-NN distance contribution analysis theory to evaluate which variables are most likely to be abnormal, meanwhile, the feasibility of this method is verified by contribution decomposition theory. The proposed search strategy can guarantee that all abnormal variables are found in each sample. The reliability and validity of the proposed method are verified by a numerical example and the Continuous Stirred Tank Reactor system.

Multi-terminal DC grids based on voltage source converters (VSCs) are widely recognized as a feasible solution to cope with increasing electricity demand and high penetration of new renewable energy sources. The VSCs are vulnerable to DC line faults due to the high discharge current of DC-link capacitance. The high current would damage the insulation of DC cable, and subsequently destroy the anti-parallel diodes during freewheeling stage. In this paper, a current limiting protection scheme, which includes a current limiting circuit and a fault detection method based transient boundary voltage, for multi-terminal DC grids. The current limiting circuit is proposed to suppress the fault current within the interrupting capability of hybrid DC circuit breakers (HCB). The fault detection method is proposed to achieve fast and reliable fault detection of DC line. Comparisons with some conventional protection methods reveal the promising performance of proposed fault detection method. Simulation results of several cases have been verified the effectiveness of the proposed protection scheme, and indicate that the faulted DC line can be identified correctly and quickly, and the non-fault system is able to recover to normal operation immediately after fault clearance without power interruption.

The method for ensuring availability in an existing cloud environment is primarily a metric-based fault detection method. However, the existing fault detection method makes it difficult to configure the environment as the cloud size increases and becomes more complex, and it is necessary to accurately understand the metric in order to use the metric accurately. Furthermore, additional changes are required whenever the monitoring environment changes. In order to solve these problems, various fault detection and prediction methods based on machine learning have recently been proposed. The machine learning-based fault detection and recovery model most commonly proposed in the cloud environment is a supervised machine learning method that learns data relating to fault situations and, based on this data, detects faults. However, there is a limit to fault learning because it is difficult to obtain all of the fault situation data necessary to learn all of the fault situations that occur in a large-scale cloud environment. In addition, it is difficult to detect a fault when a fault that differs from the learned fault pattern occurs. Furthermore, it is necessary to discuss the automatic recovery architecture leading to the fault recovery procedure based on the fault detection results. Therefore, in this paper, we designed and implemented a whole system that predicts faults by detecting fault situations using the anomaly detection method.

This paper reviews the state of the art of DC fault discrimination and detection methods of HVDC grids, and summarises the underlying principles and the characteristics of each method. To minimize HVDC grid disturbance and power transfer interruption due to DC faults, it is critically important to have protection schemes that can detect, discriminate and isolate DC faults at high speeds with full selectivity. On this basis, this paper lists the advantages and disadvantages of the most promising fault detection methods, with the aim of articulating the future directions of HVDC protection systems. From the qualitative comparison of relative merits, the initial recommendations on HVDC grid protection are presented. Moreover, a comprehensive quantitative assessments of different fault detection methods discussed above are carried out on a generic 4-terminal meshed HVDC grid, which is modelled in PSCAD environment. The presented simulation results identify that the voltage derivative and wavelet transform are the most promising methods for DC fault detection and discrimination.

Due to the rapid discharging of the dc-link capacitors, the short-circuit fault normally results in a fast-growing transient current in the voltage source converter (VSC) based dc power system. Therefore, a fast and sensitive fault detection method is required. In this article, the feasibility of Hilbert-Huang transform (HHT) for fault detection in VSC-based high-voltage direct current systems is analyzed. The instantaneous energy density level is used as the fault detection criterion, which emphasizes the fault characteristics in a predefined frequency range and suppresses the effect of steady-state ripple components. The theory of the proposed HHT-based fault detection method is presented in detail. Its effectiveness is tested on an OPAL-RT based multiterminal dc system and a point-to-point experimental dc system. A response delay within 2 ms after the fault inception is achieved. The comparison with other popular frequency-domain based fault detection methods including the wavelet transform, the short-time Fourier transform, the S transform, and the existing HHT-based analysis, using amplitude frequency coefficient as detection criterion, underlines the performance of the proposed method.

Data-driven design of fault detection and isolation method for distributed homogeneous systems

A novel fault detection method is proposed for detection process with nonlinearity and multimodal batches. Calculating the Mahalanobis distance of samples, the data with the similar characteristics are replaced by the mean of them; thus, the number of training data is reduced easily. Moreover, the super ball regions of mean and variance of training data are presented, which not only retains the statistical properties of original training data but also avoids the reduction of data unlimitedly. To accurately identify faults, two control limits are determined during investigating the distributions of distances and angles between training samples to their nearest neighboring samples in the reduced database; thus, the traditional k‐nearest neighbors (only considering distances) fault detection (FD‐kNN) method is developed. Another feature of the proposed detection method is that the control limits vary with updating database such that an adaptive fault detection technique is obtained. Finally, numerical examples and case study are given to illustrate the effectiveness and advantages of the proposed method.

Enhanced data validation strategy of air quality monitoring network

A method for fault detection and isolation of sensors in an inertial navigation system is presented, with capability of detecting several simultaneous faults of different magnitude. The method is based on projecting the measurement vector of sensor signals onto the orthogonal complement of the range space of the system matrix. The system matrix is given by the geometrical properties of sensor placements. A theorem is presented, proving that the method will detect and isolate several simultaneous faults, given that the axes of any n sensors of the full sensor set are linearly independent, where n is the navigation dimension. The theorem is given under the assumption of exact arithmetic and noise-free measurement. Practical algorithms are given based on the theorem and the QR-factorisation of the system matrix. The algorithms are tested on simulated inertial navigation system data. I. INTRODUCTION In an inertial measurement unit (IMU) a number of sensors are used to measure the two vector quantities acceleration and angular rate, of the IMU relative to an inertial reference frame. In a redundant inertial measurement unit (RIMU) the measure- ments are done by more sensors than are actually necessary, see Fig. 1. The use of extra sensors introduces additional complexity, but offers improved accuracy and robustness. Accuracy because the estimation of a navigation solution can be based on more, independent, measurements. Robustness because additional measurements makes fault detection and sensor isolation possible. RIMU:s are therefore used in in- ertial navigation systems and other applications where high availability, robustness and accuracy is required. In this report a method for fault detection and sensor isolation (FDI) is studied, which can isolate several sensor faults simultaneously.

The increasing performance demands and the growing complexity of heating, ventilation and air conditioning (HVAC) systems have created a need for automated fault detection and diagnosis (FDD) tools. Cost-effective fault detection and diagnosis method is critical to develop FDD tools. To this end, this paper presents a model-based online fault detection method for air handling units (AHU) of real office buildings. The model parameters are periodically adjusted by a genetic algorithm-based optimization method to reduce the residual between measured and predicted data, so high modeling accuracy is assured. If the residual between measured and estimated performance data exceeds preset thresholds, it means the occurrence of faults or abnormalities in the air handling unit system. In addition, an online adaptive scheme is developed to estimate and update the thresholds, which vary with system operating conditions. The model-based fault detection method needs no additional instrumentation in implementation and can be easily integrated with existing energy management and control systems (EMCS). The fault detection method was tested and validated using in real time data collected from a real office building.

The present paper proposes a genuine, simple to implement, robust and reliable method for current sensors fault detection and compensation used for 3-phase inverters. Its functionality is based on an algorithm programed into a Field Programmable Gate Array (FPGA) as general controller. Usually, 3-phase inverters are controlled using field oriented control (FOC) which is generally triggered to sample the measured currents on the established switching frequency. As the latter is much smaller than the base clock of the FPGA, this allows hundreds of free time samples to perform other calculations in-between two FOC samplings. In this paper, a method that uses these free and unused time samples is presented. This method performs calculations for fault detection and compensation ensuring that at each new FOC sampling, this will receive the correct current data to reach continuous operation despite faults. The interleaving principle of the FOC with the fault detection method, as it will be proven in the paper, ensures high reliability of the complete controller diminishing the possibility of undesired or faulted readings to disturb the FOC’s calculations. The fault detection philosophy is based on continuous comparison of the instantaneous measured currents (sensor’s response) against reference values. The experimental results presented in the paper prove reliable operational performances of the method in both steady state and transient conditions. The added value of the paper consists in its genuine approach to handle the fault detection and compensation in-between two PWM ticks, ensuring that no faulted measurements can reach the control unit. This added value is based on functionality divided on several functions triggered by an internal generated clock synchronizing and handshaking their operations towards one goal: fault detection and compensation.