Crowbarless fault ride-through of the brushless doubly fed induction generator in a wind turbine under symmetrical voltage dips

The brushless doubly fed induction generator (BDFIG) shows commercial promise for wind power generation due to its lower cost and higher reliability when compared with the conventional DFIG. In the most recent grid codes, wind generators are required to be able to ride through a low-voltage fault and meet the reactive current demand from the grid. A low-voltage ride-through (LVRT) capability is therefore important for wind generators which are integrated into the grid. In this paper, the authors propose a control strategy enabling the BDFIG to successfully ride through a symmetrical voltage dip. The control strategy has been implemented on a 250-kW BDFIG, and the experimental results indicate that the LVRT is possible without a crowbar. © 1982-2012 IEEE.

Screening of salt tolerant watercress variants on natural seawater contained medium

The responses of stem segments of watercress (Nasturtium officinale R. Br.) to 6-BA,NAA and 2,4-D were studied. MS medium supplemented with 2.0 mg/L 6-BA, 0.2 mg/L 2,4-D was used for callus initiation and maintenance. MS medium supplemented with 4.0 mg/L 6-BA was suitable for plant regeneration and MS medium without plant hormone supplement was used for rooting and plant propagation. For screening of salt tolerant calli, stem segments of watercress were plated onto callus initiation medium containing 1/3 natural seawater. Seventeen out of the 325 plated explants produced calli. The growth curves demonstrated that the growth rate of salt-tolerant calli on saline medium almost matched that of the control calli on normal medium. Some of the salt-tolerant calli were transferred to the normal regeneration medium or saline regeneration medium to induce plant regeneration. In the first case, buds and shoots were regenerated in the same way as those of control calli on normal regeneration medium. More than 1 000 regenerated shoots were obtained of which 83 regenerated shoots were cut and transferred to saline MS base medium. At first, all shoot growth was inhibited, but 40 days after the transfer, rapid-growing axillary shoots were observed on 16 of the original shoots but none on the control shoots on saline MS base medium. Moreover, green spots appeared on most calli 10 days after they were transferred to saline medium, however buds appeared only on 5 calli from the 30 transferred calli and at the end only 2 rapid-growing shoots were obtained from two calli. In total, 18 variant lines were obtained through. propagation of the salt-tolerant shoots on saline MS base medium. RAPD analysis was performed in 10 of the 18 salt-tolerant variant lines and DNA variation was detected in all the tested variant lines.

Robust Agent Control of an Autonomous Robot with Many Sensors and Actuators

This thesis presents methods for implementing robust hexpod locomotion on an autonomous robot with many sensors and actuators. The controller is based on the Subsumption Architecture and is fully distributed over approximately 1500 simple, concurrent processes. The robot, Hannibal, weighs approximately 6 pounds and is equipped with over 100 physical sensors, 19 degrees of freedom, and 8 on board computers. We investigate the following topics in depth: distributed control of a complex robot, insect-inspired locomotion control for gait generation and rough terrain mobility, and fault tolerance. The controller was implemented, debugged, and tested on Hannibal. Through a series of experiments, we examined Hannibal's gait generation, rough terrain locomotion, and fault tolerance performance. These results demonstrate that Hannibal exhibits robust, flexible, real-time locomotion over a variety of terrain and tolerates a multitude of hardware failures.

TCP Boston: A Fragmentation-tolerant TCP Protocol for ATM Networks

The popularity of TCP/IP coupled with the premise of high speed communication using Asynchronous Transfer Mode (ATM) technology have prompted the network research community to propose a number of techniques to adapt TCP/IP to ATM network environments. ATM offers Available Bit Rate (ABR) and Unspecified Bit Rate (UBR) services for best-effort traffic, such as conventional file transfer. However, recent studies have shown that TCP/IP, when implemented using ABR or UBR, leads to serious performance degradations, especially when the utilization of network resources (such as switch buffers) is high. Proposed techniques-switch-level enhancements, for example-that attempt to patch up TCP/IP over ATMs have had limited success in alleviating this problem. The major reason for TCP/IP's poor performance over ATMs has been consistently attributed to packet fragmentation, which is the result of ATM's 53-byte cell-oriented switching architecture. In this paper, we present a new transport protocol, TCP Boston, that turns ATM's 53-byte cell-oriented switching architecture into an advantage for TCP/IP. At the core of TCP Boston is the Adaptive Information Dispersal Algorithm (AIDA), an efficient encoding technique that allows for dynamic redundancy control. AIDA makes TCP/IP's performance less sensitive to cell losses, thus ensuring a graceful degradation of TCP/IP's performance when faced with congested resources. In this paper, we introduce AIDA and overview the main features of TCP Boston. We present detailed simulation results that show the superiority of our protocol when compared to other adaptations of TCP/IP over ATMs. In particular, we show that TCP Boston improves TCP/IP's performance over ATMs for both network-centric metrics (e.g., effective throughput) and application-centric metrics (e.g., response time).

Fault Diagnosis in Internal Combustion Engines Using Nonlinear Multivariate Statistics

This paper presents a statistical-based fault diagnosis scheme for application to internal combustion engines. The scheme relies on an identified model that describes the relationships between a set of recorded engine variables using principal component analysis (PCA). Since combustion cycles are complex in nature and produce nonlinear relationships between the recorded engine variables, the paper proposes the use of nonlinear PCA (NLPCA). The paper further justifies the use of NLPCA by comparing the model accuracy of the NLPCA model with that of a linear PCA model. A new nonlinear variable reconstruction algorithm and bivariate scatter plots are proposed for fault isolation, following the application of NLPCA. The proposed technique allows the diagnosis of different fault types under steady-state operating conditions. More precisely, nonlinear variable reconstruction can remove the fault signature from the recorded engine data, which allows the identification and isolation of the root cause of abnormal engine behaviour. The paper shows that this can lead to (i) an enhanced identification of potential root causes of abnormal events and (ii) the masking of faulty sensor readings. The effectiveness of the enhanced NLPCA based monitoring scheme is illustrated by its application to a sensor fault and a process fault. The sensor fault relates to a drift in the fuel flow reading, whilst the process fault relates to a partial blockage of the intercooler. These faults are introduced to a Volkswagen TDI 1.9 Litre diesel engine mounted on an experimental engine test bench facility.

Feasibility of online collaborative voltage stability control of power systems

This paper presents the results of feasibility study of a novel concept of power system on-line collaborative voltage stability control. The proposal of the on-line collaboration between power system controllers is to enhance their overall performance and efficiency to cope with the increasing operational uncertainty of modern power systems. In the paper, the framework of proposed on-line collaborative voltage stability control is firstly presented, which is based on the deployment of multi-agent systems and real-time communication for on-line collaborative control. Then two of the most important issues in implementing the proposed on-line collaborative voltage stability control are addressed: (1) Error-tolerant communication protocol for fast information exchange among multiple intelligent agents; (2) Deployment of multi-agent systems by using graph theory to implement power system post-emergency control. In the paper, the proposed on-line collaborative voltage stability control is tested in the example 10-machine 39-node New England power system. Results of feasibility study from simulation are given considering the low-probability power system cascading faults.

Nonlinear PCA with Local Approach for Diesel Engine Fault Detection and Diagnosis

This brief examines the application of nonlinear statistical process control to the detection and diagnosis of faults in automotive engines. In this statistical framework, the computed score variables may have a complicated nonparametric distri- bution function, which hampers statistical inference, notably for fault detection and diagnosis. This brief shows that introducing the statistical local approach into nonlinear statistical process control produces statistics that follow a normal distribution, thereby enabling a simple statistical inference for fault detection. Further, for fault diagnosis, this brief introduces a compensation scheme that approximates the fault condition signature. Experimental results from a Volkswagen 1.9-L turbo-charged diesel engine are included.

Fault reconstruction in linear dynamic systems using multivariate statistics

Treasure et al. (2004) recently proposed a new sub space-monitoring technique, based on the N4SID algorithm, within the multivariate statistical process control framework. This dynamic-monitoring method requires considerably fewer variables to be analysed when compared with dynamic principal component analysis (PCA). The contribution charts and variable reconstruction, traditionally employed for static PCA, are analysed in a dynamic context. The contribution charts and variable reconstruction may be affected by the ratio of the number of retained components to the total number of analysed variables. Particular problems arise if this ratio is large and a new reconstruction chart is introduced to overcome these. The utility of such a dynamic contribution chart and variable reconstruction is shown in a simulation and by application to industrial data from a distillation unit.

Improved fault diagnosis in multivariate systems using regression-based reconstruction

Subspace monitoring has recently been proposed as a condition monitoring tool that requires considerably fewer variables to be analysed compared to dynamic principal component analysis (PCA). This paper analyses subspace monitoring in identifying and isolating fault conditions, which reveals that the existing work suffers from inherent limitations if complex fault senarios arise. Based on the assumption that the fault signature is deterministic while the monitored variables are stochastic, the paper introduces a regression-based reconstruction technique to overcome these limitations. The utility of the proposed fault identification and isolation method is shown using a simulation example and the analysis of experimental data from an industrial reactive distillation unit.

Application of auto-associative neural networks to transient fault detection in an IC engine

The tailpipe emissions from automotive engines have been subject to steadily reducing legislative limits. This reduction has been achieved through the addition of sub-systems to the basic four-stroke engine which thereby increases its complexity. To ensure the entire system functions correctly, each system and / or sub-systems needs to be continuously monitored for the presence of any faults or malfunctions. This is a requirement detailed within the On-Board Diagnostic (OBD) legislation. To date, a physical model approach has been adopted by me automotive industry for the monitoring requirement of OBD legislation. However, this approach has restrictions from the available knowledge base and computational load required. A neural network technique incorporating Multivariant Statistical Process Control (MSPC) has been proposed as an alternative method of building interrelationships between the measured variables and monitoring the correct operation of the engine. Building upon earlier work for steady state fault detection, this paper details the use of non-linear models based on an Auto-associate Neural Network (ANN) for fault detection under transient engine operation. The theory and use of the technique is shown in this paper with the application to the detection of air leaks within the inlet manifold system of a modern gasoline engine whilst operated on a pseudo-drive cycle. Copyright © 2007 by ASME.

PLS modelling and fault detection on the Tennessee Eastman benchmark

This paper describes the application of multivariate regression techniques to the Tennessee Eastman benchmark process for modelling and fault detection. Two methods are applied : linear partial least squares, and a nonlinear variant of this procedure using a radial basis function inner relation. The performance of the RBF networks is enhanced through the use of a recently developed training algorithm which uses quasi-Newton optimization to ensure an efficient and parsimonious network; details of this algorithm can be found in this paper. The PLS and PLS/RBF methods are then used to create on-line inferential models of delayed process measurements. As these measurements relate to the final product composition, these models suggest that on-line statistical quality control analysis should be possible for this plant. The generation of `soft sensors' for these measurements has the further effect of introducing a redundant element into the system, redundancy which can then be used to generate a fault detection and isolation scheme for these sensors. This is achieved by arranging the sensors and models in a manner comparable to the dedicated estimator scheme of Clarke et al. 1975, IEEE Trans. Pero. Elect. Sys., AES-14R, 465-473. The effectiveness of this scheme is demonstrated on a series of simulated sensor and process faults, with full detection and isolation shown to be possible for sensor malfunctions, and detection feasible in the case of process faults. Suggestions for enhancing the diagnostic capacity in the latter case are covered towards the end of the paper.

Decoupled-DFIG fault ride-through strategy for enhanced stability performance during grid faults

This paper proposes a decoupled fault ride-through strategy for a doubly fed induction generator (DFIG) to enhance network stability during grid disturbances. The decoupled operation proposes that a DFIG operates as an induction generator (IG) with the converter unit acting as a reactive power source during a fault condition. The transition power characteristics of the DFIG have been analyzed to derive the capability of the proposed strategy under various system conditions. The optimal crowbar resistance is obtained to exploit the maximum power capability from the DFIG during decoupled operation. The methods have been established to ensure proper coordination between the IG mode and reactive power compensation from the grid-side converter during decoupled operation. The viability and benefits of the proposed strategy are demonstrated using different test network structures and different wind penetration levels. Control performance has been benchmarked against existing grid code standards and commercial wind generator systems, based on the optimal network support required (i.e., voltage or frequency) by the system operator from a wind farm installed at a particular location.

Fault Diagnostics for Internal Combustion Engines – Current and Future Techniques

The adoption of each new level of automotive emissions legislation often requires the introduction of additional emissions reduction techniques or the development of existing emissions control systems. This, in turn, usually requires the implementation of new sensors and hardware which must subsequently be monitored by the on-board fault detection systems. The reliable detection and diagnosis of faults in these systems or sensors, which result in the tailpipe emissions rising above the progressively lower failure thresholds, provides enormous challenges for OBD engineers. This paper gives a review of the field of fault detection and diagnostics as used in the automotive industry. Previous work is discussed and particular emphasis is placed on the various strategies and techniques employed. Methodologies such as state estimation, parity equations and parameter estimation are explained with their application within a physical model diagnostic structure. The utilization of symptoms and residuals in the diagnostic process is also discussed. These traditional physical model based diagnostics are investigated in terms of their limitations. The requirements from the OBD legislation are also addressed. Additionally, novel diagnostic techniques, such as principal component analysis (PCA) are also presented as a potential method of achieving the monitoring requirements of current and future OBD legislation.