Power System Fault Research Articles

A Quantitative Study on the Requirement for Additional Inertia in the European Power System until 2050 and the Potential Role of Wind Power

A significant amount of conventional power plants in the European power system is anticipated to be replaced by solar and wind power in the future. This may require alternative sources for inertia support. The purpose of the paper is to learn about the consequences on the frequency deviation after a fault in the European power system when more wind and solar are introduced and when wind is considered as a possible provider of inertia. This study quantifies the expected maximum requirement for additional inertia in the future European power system up to 2050. Furthermore, we investigated the possibility of wind power to meet this additional need by providing emulated inertia. The European power system of the EU-28 countries has been clustered to the five synchronous grids, UCTE, Nordic, UK, Baltic and Irish. The future European energy mix is simulated considering twelve different scenarios. Production units are dispatched according to their expected environmental impacts, which closely follow the minimum natural contribution of inertia, in descending order. The available capacity for all the types of production is considered the same as the installed. For all the simulated scenarios the worst case is examined, which means that a sudden disconnection of the largest production unit of the dispatched types is considered. Case study results reveal that, in most cases, additional inertia will be required but wind power may fully cover this need for up to 84% of all simulated horizons among all the scenarios on the UCTE grid, and for up to 98%, 86%, 99% and 86% on the Nordic, UK, Baltic and Irish grids, respectively.

A weighted corrective fuzzy reasoning spiking neural P system for fault diagnosis in power systems with variable topologies

This paper focuses on power system fault diagnosis based on Weighted Corrective Fuzzy Reasoning Spiking Neural P Systems with real numbers (rWCFRSNPSs) to propose a graphic fault diagnosis method, called FD-WCFRSNPS. In the FD-WCFRSNPS, an rWCFRSNPS is proposed to model the logical relationships between faults and potential warning messages triggered by the corresponding protective devices. In addition, a matrix-based reasoning algorithm for the rWCFRSNPS is devised to reason about the fault alarm messages using parallel representations. Besides, a layered modeling method based on rWCFRSNPSs is developed to adapt to topological changes in power systems and a Temporal Order Information Processing Method based on Cause–Effect Networks is designed to correct fault alarm messages before the fault reasoning. Finally, in a case study considering a local subsystem of a 220kV power system, the diagnosis results of five test cases prove that the proposed FD-WCFRSNPS is viable and effective.

Improved S Transform-Based Fault Detection Method in Voltage Source Converter Interfaced DC System

The short circuit fault in the voltage source converter-based dc power system typically generates rapidly rising transient current which may have serious repercussions on dc grid operation and health of the integrated power electronic devices. Thus, the dc grid requires a high speed and robust fault detection for reliable system operation. With this regard, this article proposes a fault detection method based on the S transform (ST) with adaptive adjustment. This improved ST is based on frequency-domain and is able to detect the fault condition within 0.3 ms. It consists of high-frequency detection, which is responsible for fast response due to high time resolution, and low-frequency screening which is used to differentiate faults from other transient conditions. Introducing a correction factor into a Gaussian function when computing ST could extract the high-frequency spectrum, while the low frequency spectrum information is still retained. The proposed method is validated with the multiterminal dc system developed in the OPAL-RT-based real-time simulator. Additionally, its performance is tested with the point-to-point experimental dc test bed. Comparative analysis with other popular frequency-domain fault detection methods, namely, wavelet transform and short-time Fourier transform substantiates the effectiveness of this method.

Overhead Line Fault Detection and Control using Internet of Things (IoT)

In the modern era transmission line is the most important part of the power system. Its allegiance and requirement of power is grown up exponentially. The major role of transmission lines is to transmit electric power from the source area to distribution network. Transmission line protection is an important issue in power system because 85-87% of power system faults are occurring in transmission line. Energy leakage is one of the major problems that corporates faces in recent times. Only way to solve this problem is to come up with a mechanism that can detect the fault in transmission line automatically and intimate the authorities with a specific location. In this work the device uses the sensor to sense the voltage flow in the transmission line and detect if there is a variation in the voltage flow. If fault is detected, it can be automatically controlled by using relay and the system is also integrated with IoT mechanism, to intimate the responsible person with location information.

A novel discrete wavelet transform‐based graphical language classifier for identification of high‐impedance fault in distribution power system

A novel discrete wavelet transform‐based graphical language classifier for identification of high‐impedance fault in distribution power system

Electrical faults in high temperature superconducting power cables for MVDC power systems of all-electric ships

High temperature superconducting (HTS) power cables are expected to be used in the future in electric ships that will have integrated power and energy systems (IPES). There have been a few studies on understanding electrical faults in power systems consisting of HTS cables; however, there are no comprehensive studies on the response of HTS cables for various types of electrical faults. We have recently initiated a research project on understanding various electrical faults in shipboard medium voltage direct current (MVDC) power systems and the duration of the fault, maximum voltage, and maximum current that the HTS cables will encounter during the fault. This paper presents investigations on the potential cable architectures being developed for MVDC power systems, and the type of faults that HTS cables will encounter in such systems. The paper will assess the relative merits of these architectures in terms of their suitability for accommodating the limitations of cryogenically cooled HTS cables and offer design suggestions for resilient power systems. Electrical and cryogenic thermal models of HTS cables that are suitable for assessing the response of HTS cables for electrical faults, as well as HTS cable designs that can endure electrical faults without catastrophic damage will also be discussed.

Simulation and Research of Non-effectively Grounding Network of Single-phase Ground-fault

When the non-effectively grounding network of single-phase ground fault happens, the voltage, the current, the zero-sequence voltage and the zero-sequence current have a special change and this problem has been the focus of research attention. Subsequently, it has important effect about the analysis of power system fault characteristics and choice of protection scheme to modeling system of single-phase fault for little grounding current network and to use the zero sequence current amplitude and phase of identifying the fault line and fault phase based on MATLAB software platform in this paper.

High-Speed Arcing Fault Detection: Using the Light Spectrum

Unlike short circuit faults in power systems, arcing faults produce intense light during fault events. Light-sensing technology has been under development to detect arc faults since the 1980s. Currently, optical fiber and point sensors are two types of light sensors applied, along with the simultaneous overcurrent mechanism, for arc-flash relays. Due to the characteristics of light sensors, sensitivity and reliability of the relay may be affected by ambient light. This article proposes a new approach for arc-flash fault detection by using the spectrum of light. Electromagnetic radiation means different elements would emit spectra with unique wavelengths when their atoms are excited. Elements can then be identified if a specific emission spectrum is detected during the excitation period. In general, copper and aluminum are commonly used for conductors. By examining the light spectrum, arc flash can be accurately and quickly detected. In this study, copper and aluminum are applied as conductors. The light spectrum for both materials is measured and recorded by an optic spectrometer during the arcing incidents. The results show that accuracy and reliability of the lightbased arc-flash fault-detection operation can be improved by using the proposed method.

Enhanced Fault Diagnostic Technique Applied to IEEE 14-bus Smart Grid Standard

Due to the gigantic power quality (PQ) demands for modern systems, power systems fault detection and diagnosis have become a significant issue. For this aim, it’s very important to detect the fault at early time and determine its location through any signal. Several methods and techniques are applied to solve this problem such as discrete wavelet transform (DWT). Although DWT has the ability of fast time detection of the fault, it has a problem to discriminate between faulty and noisy signals. DWT succeeds to extract features from altered transient disturbances, but it fails to differentiate between transient disturbances due to healthy or noisy signals. Fusion between DWT for its speed and radial basis function for its accuracy is done. The fusion technique used has a major disadvantage of its delay time as the fault can be detected after the exact location with several samples. In this paper new technique will be proposed to overcome the DWT and data fusion method problems, to achieve the classification between noisy and faulty signals with high accuracy. The proposed method is executed to classify the signals premised on weights of them, complex tree classifier uses the energy of the signal as a feature. All simulations are achieved and done on IEEE standard 14 bus system to confirm the ability and capacity of new suggested technique. Simulation results show a better performance of the proposed system in comparison with other methods, and that it capable to differentiate between faulty and noisy signals and precisely locate the fault position.

Electric Power System Fault Analysis

Fault analysis is an important aspect in the successful operation of a power utility grid. The occurrence of faults in the system is not avoidable as causes of faults are not particularly one but of many with different causes and nature. It is one of the most important as well as it one of the most complex tasks in power engineering. The studies and discovery of faults in electric power systems is essential in ensuring the consistency and the stability if the power grid. Another importance of carrying out fault analysis gives a fair idea of the voltages and the current magnitudes under faulty conditions for different scenarios. This is helpful in the design of protection devices such as fuses, circuit breaker ratings, over current protection relays and other devices that are used for the protection of the system. Protection devices are important features of the power system grid mainly because their role in saving equipment and also human lives. In this paper, we investigate and analyze the behavior of electric power systems under fault conditions and then evaluate various practical scenarios.

A new intelligent scheme for power system faults detection and classification: A hybrid technique

AbstractThis paper proposes a hybrid technique to detect and classify the transmission line faults in the power system. The proposed approach is the joint execution of linear discriminant analysis (LDA) and cuttlefish optimizer (CFO) learning process‐based random forest algorithm (RFA), ie, named as LDA‐CFRFA technique. Here, two modules are utilized for fault analysis in power system: fault detection and fault classification. The first procedure of the proposed method is the power system transmission line parameters in normal and abnormal condition dataset preparation by using LDA. LDA‐based dataset preparation process consists of feature extraction of power flow parameters and defines the nature of signals occurred by the system. The extracted dataset is assessed by CFO‐based RFA technique for classifying the fault type occurred in the transmission system. By then, the proposed model is executed in MATLAB/Simulink working stage, and the execution is assessed with the existing techniques such as CFO, RFA, Feed forward neural network (FNN), and artificial neural network (ANN). In our research, the faults implemented on test system are phase A, phase B, phase C, phase A to ground (AG), phase B to ground (BG), phase C to ground (CG), phase AB, phase AC and phase BC, phase AB to ground (ABG), phase AC to ground (ACG), and phase BC to ground (BCG). From the results, the proposed technique guarantees the system with less complexity and less consumption time for the detection and classification of the fault, and hence, the accuracy of the system is increased.

Kung’s component extraction in power system fault location

This paper presents a method for the accurate estimation of phasors in fault location applications. One of the essential tasks of power system digital relays is to decrease the consequences that follow faults by fast detection and localization. A fault signal (current and voltage) is typically composed of the fundamental component in addition to a damped DC signal, together with damped harmonics and inter-harmonics. A significant challenge in impedance-based fault analysis is the accurate estimation of the fundamental frequency component. While different variations of the Fourier-based algorithm are the most commonly used in the estimation of the fundamental component, their accuracy tends to be sufficient for protection relaying schemes but not for fault location. This paper proposes a novel fault feature extraction method that is highly accurate; it combines Kung’s method for signal decomposition followed by a nonlinear least-squares estimation. The proposed technique has been successfully evaluated in the context of a fault location method; the numerical tests show that the proposed two-stage approach can accurately estimate phasor information in the presence of transients and noise.

A new hybrid multilevel converter for DFIG-based wind turbines fault ride-through and transient stability enhancement

The increasing participation of wind turbines based on doubly fed induction generator (DFIG) in the power system around the world is motivating the investigation of novel strategies to enhance the reliability of DFIG and power system connection. This paper proposes the use of a new hybrid multilevel back-to-back converter on DFIG wind turbine to improve its fault ride-through capability and power system transient stability. The proposed topology is built adding single-phase full-bridge inverters in series with each phase of traditional three-phase inverter. This converter is able to generate higher voltages in rotor circuit whenever it is required, which can effectively protect power converter and at the same time contribute with grid transient performance. Simulation results, using PSCAD®, are presented for a 2 MW DFIG machine submitted to symmetrical faults in single machine—infinite bus and three-bus system. Results show that the proposal is effective as a protection and works well as an alternative to traditional solutions, limiting currents and maintaining power control during voltage dips. It is also effective in contribution with the improvement of transient stability margin by the possibility of reactive power injection.

Detection of the natural frequency of transmission power lines applying S-transform on Transient Recovery Voltage

This paper presents a new approach for detecting the natural frequency of transmission power lines applying S-transform on Transient Recovery Voltage (TRV). TRV usually appears between terminals of a circuit breaker during an isolation of a fault. The TRV phenomenon contains wideband frequency information such as the natural frequency of the system that depends not only on the electrical transmission line characteristics but also on the location of the fault. S-transform was applied to the TRV signal and the energy content in the time-frequency contours was evaluated using Parseval's theorem. This analysis ensures the accurate identification of the natural frequency of the resulting system by locating the maximum relative of the energy content. The case studies studied involved taking actual systems and using frequency-dependent phase models. The experiments were performed to show that the proposed approach can robustly determine the frequency associated with the parameters of the line. Different cases were considered: distances, inception angles, fault resistances, circuit breakers and models of electric arc. The simulation results demonstrate that using S-transform to analyse TRV provides a simple and reliable method for determining the natural frequency and opens a new way for studying the behaviour of protections and faults in power systems.

Electrical Insulator Fault Forecasting Based on a Wavelet Neuro-Fuzzy System

The surface contamination of electrical insulators can increase the electrical conductivity of these components, which may lead to faults in the electrical power system. During inspections, ultrasound equipment is employed to detect defective insulators or those that may cause failures within a certain period. Assuming that the signal collected by the ultrasound device can be processed and used for both the detection of defective insulators and prediction of failures, this study starts by presenting an experimental procedure considering a contaminated insulator removed from the distribution line for data acquisition. Based on the obtained data set, an offline time series forecasting approach with an Adaptive Neuro-Fuzzy Inference System (ANFIS) was conducted. To improve the time series forecasting performance and to reduce the noise, Wavelet Packets Transform (WPT) was associated to the ANFIS model. Once the ANFIS model associated with WPT has distinct parameters to be adjusted, a complete evaluation concerning different model configurations was conducted. In this case, three inference system structures were evaluated: grid partition, fuzzy c-means clustering, and subtractive clustering. A performance analysis focusing on computational effort and the coefficient of determination provided additional parameter configurations for the model. Taking into account both parametrical and statistical analysis, the Wavelet Neuro-Fuzzy System with fuzzy c-means showed that it is possible to achieve impressive accuracy, even when compared to classical approaches, in the prediction of electrical insulators conditions.

Online trajectory optimization for power system fault of launch vehicles via convex programming

This paper presents an online trajectory optimization algorithm for launch vehicles based on convex programming to ensure flight safety in case of power system fault. Due to high complexity of the power system, the engine may break down during the flight, causing significant decrease of thrust or energy. In this case, the nominal trajectory will be infeasible as the dynamical model and energy state is different from the normal status, thus the online trajectory optimization and re-planning are considered. For different kinds of engine failures, different terminal orbital constraints are proposed. When the mass flow rate of fuel decreases, the energy loss is little but the dynamical model changes obviously, so the location of the injection point cannot be guaranteed. In this case, the terminal orbital elements are constrained except the true anomaly, so that the payload of launch vehicles can still settle into the nominal orbit, and the true anomaly is optimized for minimum fuel consumption. As for the energy-loss failure, the strategy to change the target orbit is proposed considering the requirement of launch mission and subsequent orbit transfer insertion. The terminal constraints are proposed analytically in this paper. In order to solve the nonconvex trajectory optimization problem accurately and rapidly, the optimization problem is transformed into convex optimization problems by various convexification techniques, including the lossless convexification and successive convexification. Finally, the high efficiency and accuracy of the proposed algorithm is verified by numerical experiments. The algorithm proposed in this paper has potential applications in onboard trajectory optimization and re-planning of launch vehicles in case of power system fault to ensure the accomplishment of the launch mission.

A Data-Driven Vulnerability Evaluation Method in Grid Edge Based on Random Matrix Theory Indicators

In order to solve the problem of vulnerability assessment of complex power systems facing complex structures and large sizes, a novel data driven method based on random matrix theory is proposed in this paper. Firstly, with the use of phasor measurement units (PMUs) big data, evaluation matrices are constructed to extract statistical characteristics of power systems operation. Then, with the combination of random matrix theory and entropy theory, vulnerability evaluation index are constructed considering the degree of influence of some faults in power systems. With full use of big data, the model-free method is more accurate and comprehensive. Simulation results in IEEE 39-bus test system and a real-world power grid in China verify the effectiveness of the method.

Dynamic grid fault analysis in wind power plant with DFIG by using supervisory control and data acquisition (SCADA) viewer

Faults in electrical power systems are among the key factors and sources to network disturbances, however control strategies are among key faults clearing techniques for the sake of safe operational mode of the system.Some researchers have shown various limitations of control strategies such as slow dynamic response,inability to switch Off and On network remotely and fault clearing time. For a system with wind energy technologies, if the power flow of a wind turbine is interrupted by a fault, the intermediate-circuit voltage between the machine-side converter and line-side converter will fall in unacceptably high values.To overcome the aforementioned issues, this paper used a Matlab simulations and experiments in order to analyze and validate the results.The results showed that fault ride through (FRT) with SCADA Viewer software are more adaptable to the variations of voltage and wind speed in order to avoid loss of synchronism. Therefore at the speed of 12.5m/s a wind produced a rated power of 750W and remained in synchronization before and after a fault created and cleared but worked as generator meanwhile at speed of 3.4m/s wind disconnected from grid and started working as a motor and consumed active power (P=-25watts) and voltage dip at 100% .For the protection purpose, the DC chopper and crowbar should be integrated towards management of excess energy during faults cases.

Relay Protection Simulation and Testing of Online Setting Value Modification Based on RTDS

Analyzing the feasibility of modifying setting values on the condition of the running line without exiting the protection function is of great importance for 110 kV substations. A system-level test of settings modification is proposed to verify that the values are correctly modified during the process and the protection function is still in effect. A cyber-physical automatic test bed using a real-time digital simulator (RTDS) is developed for relay protection to modify settings online, which distinctly improves work efficiency. Based on actual power grid parameters, a full-process closed-loop RTDS automatic control system is applied for performance testing when setting groups are changed. The experiments are focused on blocking time tests, setting groups switch and coverage tests, DC power supply intermittent tests, unwanted operations in setting value, changing load and protection starting tests, short-circuit fault tests and power system frequency oscillation tests. The results of the trial indicate that the automatic test bed is an effective technology for checking and verifying the reliability of modifying setting values online. In addition, 36 line protection devices from 12 relay protection manufacturers are tested. The reliability of each test item is analyzed and the relay performance is evaluated using a comprehensive method. The test bed provides reliable technical support for the design, engineering, commissioning and substation operation of online setting modification.

Research on Ship Electronic Power System Fault Diagnosis Based on Expert System

Aiming at the difficulty in locating the fault point quickly and accurately after the ship electronic power system fault. In this paper, fault diagnosis expert system is used to diagnose the fault occurred to ensure stability of power supply of ship power system. The possible faults are divided into four categories on the base of analysis of ship electrical system fault characteristics. Frame structure is used to describe the connections between the various parts of the ship power system in knowledge presentation. Expert system uses production rules to describe the specific fault and combine the ideal of fuzzy inference to dealing with the uncertainty in fault diagnosis at the same time. The expert system designed in this paper can find fault points and provide solutions quickly and accurately and improves the reliability of the ship power system by test.