Simulation and calculation of internal faults in long-stator linear synchronous motor based on winding function theory

To guarantee the safety of the high speed maglev train system, a novel model based on the winding function theory is proposed for the long-stator linear synchronous motor（LSM）, which is suitable for the real-time calculation of the running state. The accurate coupled mathematical models under different internal fault conditions of the LSM are derived based on the normal model. Then the fault currents and electromagnetic forces are simulated and calculated for the major potential internal faults of the LSM, such as the single-phase short circuit, the phase-phase short circuit and the single-phase open circuit. The characteristic curve between the electromagnetic force and the armature current of the LSM, which is compared with the results from the finite element method, proves the validation of the proposed method. The fault rule is determined and the proposed analytical model also shows its feasibility in the fast fault diagnosis through the comparison of the simulation results of currents and electromagnetic forces under different internal fault types and short circuit ratios.

State-Model Based Time-Domain Diagnosis of Internal Faults for Permanent Magnet Synchronous Machine Wind Application

Upon occurrence of an internal fault on the PMSM （permanent magnet synchronous machine）, the topology of the stator is amended causing structural imbalances due to the change of the connection within the windings. In this work, a state model of internal faults of the PMSM is developed. This model is in the （abc） reference frame. The modeling approach is based on the assumption that each stator phase is replaced by two major and minor sub-windings. This model is used subsequently in the residual generation for diagnosis. The fault indicators are obtained by the projection in parity space and estimated using the Luenberger observer. A scenario of fault inter-turn by the short-circuit occurring between phase （a and b） is validated by simulation.

An Effective Detection of Inrush and Internal Faults in Power Transformers Using Bacterial Foraging Optimization Technique

Power transformers in transmission network are utilized for increasing or decreasing the voltage level. Power Transformers fail to connect directly to the consumers that result in the less load fluctuations. Power transformer operation under any abnormal condition decreases the lifetime of the transformer. Power Transformer protection from inrush and internal fault is critical issue in power system because the obstacle lies in the precise and swift distinction between them. Due to the limitation of heterogeneous resources, occurrence of fault poses severe problem. Providing an efficient mechanism to differentiate between faults (i.e. inrush and internal) is the key for efficient information flow. In this paper, the task of detecting inrush and internal fault in power transformers is formulated as an optimization problem which is solved by using Hyperbolic S-Transform Bacterial Foraging Optimization (HS-TBFO) technique. The Gaussian Frequency- based Hyperbolic S-Transform detects the faults at much earlier stage and therefore minimizes the computation cost by applying Cosine Hyperbolic S-Transform. Next, the Bacterial Foraging Optimization (BFO) technique has been proposed and has demonstrated the capability of identifying the maximum number of faults covered with minimum test cases and therefore improving the fault detection efficiency in a wise manner. The HS-TBFO technique is evaluated and validated in various simulation test cases to detect inrush and internal fault in a significant manner. This HS-TBFO technique is investigated based on three phase power transformer embedded in a power system fed from both ends. Results have confirmed that the HS-TBFO technique is capable of categorizing the inrush and internal faults by identifying maximum number of faults with minimum computation cost as compared to the state-of-the-art works.

Damage and geological assessment of the 18 September 2011 M_w 6.9 earthquake in Sikkim,India using very high resolution satellite data

Post-disaster very high resolution（VHR） satellite data are potential sources to provide detailed information on damage and geological changes for a large area in a short time.In this paper,we studied landslides triggered by the M_w 6.9 earthquake in Sikkim,India which occurred on 18 September 2011 using VHR data from Cartosat-1,GeoEye-1,QuickBird-2 and WorldView-2 satellites.Since the earthquake-affected area is located in mostly inaccessible Himalayan terrain,VHR data from these satellites provided a unique opportunity for quick and synoptic assessment of the damage.Using visual change analysis technique through comparison of pre- and post-earthquake images,we assessed the damage caused by the event.A total of 123 images acquired from eight satellites,covering an area of4105 km2 were analysed and 1196 new landslides triggered by the earthquake were mapped.Road blockages and severely affected villages were also identified.Geological assessment of the terrain highlighted linear disposition of landslides along existing fault scarps,suggesting a reactivation of fault.The landslide inventory map prepared from VHR images also showed a good correlation with the earthquake shake map.Results showed that several parts of north Sikkim,particularly Mangan and Chungthang,which are close to the epicentre,were severely affected by the earthquake,and that the event-based landslide inventory map can be used in future earthquake-triggered landslide susceptibility assessment studies.

An International Answer to Capturing Corporate Criminality

In the era of increased corporate globalization, the lingering principles of directing mind and will have plagued neighboring territories from manufacturing a realist model of corporate fault. It is of fundamental importance to combat transnational corporate activity from a world order perspective by establishing a legal regime which supports the philosophy that corruption becomes entrenched within the corporate culture. A culture that is indicative of corporate guilt. This paper will suggest that the corporate culture doctrine within the Australian Criminal Code Act 1995 is the first step in the courses of action required against the evolution of structurally complex corporate arrangements.

Internal electrical fault detection techniques in DFIG-based wind turbines: a review

The keys factor in making wind power one of the main power sources to meet the world’s growing energy demands is the reliability improvement of wind turbines(WTs).However,the eventuality of fault occurrence on WT com-ponents cannot be avoided,especially for doubly-fed induction generator(DFIG)based WTs,which are operating in severe environments.The maintenance need increases due to unexpected faults,which in turn leads to higher operating cost and poor reliability.Extensive investigation into DFIG internal fault detection techniques has been carried out in the last decade.This paper presents a detailed review of these techniques.It discusses the methods that can be used to detect internal electrical faults in a DFIG stator,rotor,or both.A novel sorting technique is presented which takes into consideration different parameters such as fault location,detection technique,and DFIG modelling.The main mathematical representation used to detect these faults is presented to allow an easier and faster under-standing of each method.In addition,a comparison is carried out in every section to illustrate the main differences,advantages,and disadvantages of every method and/or model.Some real monitoring systems available in the market are presented.Finally,recommendations for the challenges,future work,and main gaps in the field of internal faults in a DFIG are presented.This review is organized in a tutorial manner,to be an effective guide for future research for enhancing the reliability of DFIG-based WTs.

Sampled Value Attack Detection for Busbar Differential Protection Based on a Negative Selection Immune System

Considering a variety of sampled value(SV)attacks on busbar differential protection(BDP)which poses challenges to conventional learning algorithms,an algorithm to detect SV attacks based on the immune system of negative selection is developed in this paper.The healthy SV data of BDP are defined as self-data composed of spheres of the same size,whereas the SV attack data,i.e.,the nonself data,are preserved in the nonself space covered by spherical detectors of different sizes.To avoid the confusion between busbar faults and SV attacks,a self-shape optimization algorithm is introduced,and the improved self-data are verified through a power-frequency fault-component-based differential protection criterion to avoid false negatives.Based on the difficulty of boundary coverage in traditional negative selection algorithms,a self-data-driven detector generation algorithm is proposed to enhance the detector coverage.A testbed of differential protection for a 110 kV double busbar system is then established.Typical SV attacks of BDP such as amplitude and current phase tampering,fault replays,and the disconnection of the secondary circuits of current transformers are considered,and the delays of differential relay operation caused by detection algorithms are investigated.

Hydraulic directional valve fault diagnosis using a weighted adaptive fusion of multi-dimensional features of a multi-sensor

Because the hydraulic directional valve usually works in a bad working environment and is disturbed by multi-factor noise,the traditional single sensor monitoring technology is difficult to use for an accurate diagnosis of it.Therefore,a fault diagnosis method based on multi-sensor information fusion is proposed in this paper to reduce the inaccuracy and uncertainty of traditional single sensor information diagnosis technology and to realize accurate monitoring for the location or diagnosis of early faults in such valves in noisy environments.Firstly,the statistical features of signals collected by the multi-sensor are extracted and the depth features are obtained by a convolutional neural network(CNN)to form a complete and stable multi-dimensional feature set.Secondly,to obtain a weighted multi-dimensional feature set,the multi-dimensional feature sets of similar sensors are combined,and the entropy weight method is used to weight these features to reduce the interference of insensitive features.Finally,the attention mechanism is introduced to improve the dual-channel CNN,which is used to adaptively fuse the weighted multi-dimensional feature sets of heterogeneous sensors,to flexibly select heterogeneous sensor information so as to achieve an accurate diagnosis.Experimental results show that the weighted multi-dimensional feature set obtained by the proposed method has a high fault-representation ability and low information redundancy.It can diagnose simultaneously internal wear faults of the hydraulic directional valve and electromagnetic faults of actuators that are difficult to diagnose by traditional methods.This proposed method can achieve high fault-diagnosis accuracy under severe working conditions.