Measurement, control and protection of microgrids at low frame rates supporting security of supply

Increasing penetrations of distributed generation at low power levels within electricity networks leads to the requirement for cheap, integrated, protection and control systems. To minimise unit cost, algorithms for the measurement of AC voltage and current waveforms should be implemented on a single microcontroller, which also carries out all other protection and control tasks, including communication and data logging. This limits the frame rate of the major algorithms, although ADCs can be over-sampled using peripheral control processors on suitable microcontrollers. Measurement algorithms also have to be tolerant of poor power quality which may arise, even transiently, within a microgrid, battlefield, or disaster-relief scenario. This thesis analyses the potential magnitude of these interfering signals, and presents suitably tolerant architectures and algorithms for measurements of AC waveforms (amplitude, phase and frequency). These algorithms are shown to be robust and accurate, with harmonic content up to the level of 53% THD, and with the major algorithms executing at only 500 samples per second. This is achieved by the careful optimisation and cascaded use of exact-time averaging techniques, which prove to be useful at all stages of the measurements: from DC bias removal to low-sample-rate Fourier analysis to sub-harmonic ripple removal. Algorithms for three-phase nodal power flow analysis are benchmarked on the Infineon TC1796 microcontroller and require less than 8% of the 2000μs frame time, leaving the remainder free for other algorithms. Furthermore, to optimise security of supply in a microgrid scenario, loss-of-mains must be detected quickly even when there is an accidental or deliberate balance between local active power generation and demand. The measurement techniques are extended to the detection of loss-of-mains using a new Phase Offset relay, in combination with a novel reactive power control technique to avoid the non-detection-zone. These techniques are tested using simulation, captured network transient events, and a real hardware microgrid including a synchronous generator and inverter

Digital Signal Processing Research Program

Contains table of contents for Section 2, an introduction and reports on seventeen research projects.U.S. Navy - Office of Naval Research Grant N00014-91-J-1628Vertical Arrays for the Heard Island Experiment Award No. SC 48548Charles S. Draper Laboratories, Inc. Contract DL-H-418472Defense Advanced Research Projects Agency/U.S. Navy - Office of Naval Research Grant N00014-89-J-1489Rockwell Corporation Doctoral FellowshipMIT - Woods Hole Oceanographic Institution Joint ProgramDefense Advanced Research Projects Agency/U.S. Navy - Office of Naval Research Grant N00014-90-J-1109Lockheed Sanders, Inc./U.S. Navy - Office of Naval Research Contract N00014-91-C-0125U.S. Air Force - Office of Scientific Research Grant AFOSR-91-0034AT&T Laboratories Doctoral ProgramU.S. Navy - Office of Naval Research Grant N00014-91-J-1628General Electric Foundation Graduate Fellowship in Electrical EngineeringNational Science Foundation Grant MIP 87-14969National Science Foundation Graduate FellowshipCanada Natural Sciences and Engineering Research CouncilLockheed Sanders, Inc

An Indirect Adaptive Control Scheme in the Presence of Actuator and Sensor Failures

The problem of controlling a system in the presence of unknown actuator and sensor faults is addressed. The system is assumed to have groups of actuators, and groups of sensors, with each group consisting of multiple redundant similar actuators or sensors. The types of actuator faults considered consist of unknown actuators stuck in unknown positions, as well as reduced actuator effectiveness. The sensor faults considered include unknown biases and outages. The approach employed for fault detection and estimation consists of a bank of Kalman filters based on multiple models, and subsequent control reconfiguration to mitigate the effect of biases caused by failed components as well as to obtain stability and satisfactory performance using the remaining actuators and sensors. Conditions for fault identifiability are presented, and the adaptive scheme is applied to an aircraft flight control example in the presence of actuator failures. Simulation results demonstrate that the method can rapidly and accurately detect faults and estimate the fault values, thus enabling safe operation and acceptable performance in spite of failures

Development of new fault detection methods for rotating machines (roller bearings)

Abstract Early fault diagnosis of roller bearings is extremely important for rotating machines, especially for high speed, automatic and precise machines. Many research efforts have been focused on fault diagnosis and detection of roller bearings, since they constitute one the most important elements of rotating machinery. In this study a combination method is proposed for early damage detection of roller bearing. Wavelet packet transform (WPT) is applied to the collected data for denoising and the resulting clean data are break-down into some elementary components called Intrinsic mode functions (IMFs) using Ensemble empirical mode decomposition (EEMD) method. The normalized energy of three first IMFs are used as input for Support vector machine (SVM) to recognize whether signals are sorting out from healthy or faulty bearings. Then, since there is no robust guide to determine amplitude of added noise in EEMD technique, a new Performance improved EEMD (PIEEMD) is proposed to determine the appropriate value of added noise. A novel feature extraction method is also proposed for detecting small size defect using Teager-Kaiser energy operator (TKEO). TKEO is applied to IMFs obtained to create new feature vectors as input data for one-class SVM. The results of applying the method to acceleration signals collected from an experimental bearing test rig demonstrated that the method can be successfully used for early damage detection of roller bearings. Most of the diagnostic methods that have been developed up to now can be applied for the case stationary working conditions only (constant speed and load). However, bearings often work at time-varying conditions such as wind turbine supporting bearings, mining excavator bearings, vehicles, robots and all processes with run-up and run-down transients. Damage identification for bearings working under non-stationary operating conditions, especially for early/small defects, requires the use of appropriate techniques, which are generally different from those used for the case of stationary conditions, in order to extract fault-sensitive features which are at the same time insensitive to operational condition variations. Some methods have been proposed for damage detection of bearings working under time-varying speed conditions. However, their application might increase the instrumentation cost because of providing a phase reference signal. Furthermore, some methods such as order tracking methods still can be applied when the speed variation is limited. In this study, a novel combined method based on cointegration is proposed for the development of fault features which are sensitive to the presence of defects while in the same time they are insensitive to changes in the operational conditions. It does not require any additional measurements and can identify defects even for considerable speed variations. The signals acquired during run-up condition are decomposed into IMFs using the performance improved EEMD method. Then, the cointegration method is applied to the intrinsic mode functions to extract stationary residuals. The feature vectors are created by applying the Teager-Kaiser energy operator to the obtained stationary residuals. Finally, the feature vectors of the healthy bearing signals are utilized to construct a separating hyperplane using one-class support vector machine. Eventually the proposed method was applied to vibration signals measured on an experimental bearing test rig. The results verified that the method can successfully distinguish between healthy and faulty bearings even if the shaft speed changes dramatically

Predictive condition monitoring of industrial systems for improved maintenance and operation

Maintenance strategies based on condition monitoring of the different machines and devices in an industrial process can minimize downtime, increase the safety of plant operations and help in the process of decision-taking for control and maintenance actions in order to reduce maintenance and operating costs. Multivariate statistical methods are widely used for process condition monitoring in modern industrial sites due to the quantity of data available and the difficulties of building analytical models in complex facilities. Nevertheless, the performance of these methodologies is still far away from being ideal, due to different issues such as process nonlinearities or varying operational conditions. In addition application of the latest approaches developed for process monitoring is not widely extended in real industry. The aim of this investigation is to develop new and improve existing methodologies for predictive condition monitoring through the use of multivariate statistical methods. The research focuses on demonstrating the applicability of multivariate algorithms in real complex cases, the improvement of these methods in terms of fault detection and diagnosis by means of data fusion and the estimation of process performance degradation caused by faults.Marie Curi

Digital Signal Processing Research Program

Contains table of contents for Section 2, an introduction, reports on sixteen research projects and a list of publications.Bose CorporationMIT-Woods Hole Oceanographic Institution Joint Graduate Program in Oceanographic EngineeringAdvanced Research Projects Agency/U.S. Navy - Office of Naval Research Grant N00014-93-1-0686Lockheed Sanders, Inc./U.S. Navy - Office of Naval Research Contract N00014-91-C-0125U.S. Air Force - Office of Scientific Research Grant AFOSR-91-0034AT&T Laboratories Doctoral Support ProgramAdvanced Research Projects Agency/U.S. Navy - Office of Naval Research Grant N00014-89-J-1489U.S. Navy - Office of Naval Research Grant N00014-93-1-0686National Science Foundation FellowshipMaryland Procurement Office Contract MDA904-93-C-4180U.S. Navy - Office of Naval Research Grant N00014-91-J-162