Synchronous machines parameters identification using load rejection test data

This work shows a computational methodology for the determination of synchronous machines parameters using load rejection test data. The quadrature axis parameters are obtained with a rejection under an arbitrary reference, reducing the present difficulties.

Identification of synchronous machine parameters using load rejection test data

This work shows a computational methodology for the determination of synchronous machines parameters using load rejection test data. By machine modeling one can obtain the quadrature parameters through a load rejection under an arbitrary reference, reducing the present difficulties. The proposed method is applied to a real machine.

Electric power systems transient stability analysis by neural networks

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Transient stability analysis of electrical power systems using a neural network based on fuzzy ARTMAP

This work presents a methodology to analyze transient stability for electric energy systems using artificial neural networks based on fuzzy ARTMAP architecture. This architecture seeks exploring similarity with computational concepts on fuzzy set theory and ART (Adaptive Resonance Theory) neural network. The ART architectures show plasticity and stability characteristics, which are essential qualities to provide the training and to execute the analysis. Therefore, it is used a very fast training, when compared to the conventional backpropagation algorithm formulation. Consequently, the analysis becomes more competitive, compared to the principal methods found in the specialized literature. Results considering a system composed of 45 buses, 72 transmission lines and 10 synchronous machines are presented. © 2003 IEEE.

Comparison of FACTS STATCOM models for damping oscillations in power system due to small perturbations

This paper presents small-signal stability studies of a multimachine power system, considering Static Synchronous Compensators (STATCOM)and discussed control modes of the STATCOM. The Power Sensitivity Model(PSM)is used to represent the electric power system. The study is based on modal analysis and time domain simulations. The results obtained allow concluding that the STATCOM improves the stabilization in the electric power system. © 2011 IEEE.

The velocity synchronous discrete Fourier transform for order tracking in the field of rotating machinery

Diagnostics of rotating machinery has developed significantly in the last decades, and industrial applications are spreading in different sectors. Most applications are characterized by varying velocities of the shaft and in many cases transients are the most critical to monitor. In these variable speed conditions, fault symptoms are clearer in the angular/order domains than in the common time/frequency ones. In the past, this issue was often solved by synchronously sampling data by means of phase locked circuits governing the acquisition; however, thanks to the spread of cheap and powerful microprocessors, this procedure is nowadays rarer; sampling is usually performed at constant time intervals, and the conversion to the order domain is made by means of digital signal processing techniques. In the last decades different algorithms have been proposed for the extraction of an order spectrum from a signal sampled asynchronously with respect to the shaft rotational velocity; many of them (the so called computed order tracking family) use interpolation techniques to resample the signal at constant angular increments, followed by a common discrete Fourier transform to shift from the angular domain to the order domain. A less exploited family of techniques shifts directly from the time domain to the order spectrum, by means of modified Fourier transforms. This paper proposes a new transform, named velocity synchronous discrete Fourier transform, which takes advantage of the instantaneous velocity to improve the quality of its result, reaching performances that can challenge the computed order tracking.

A visual tool for building synchronous generator capability curves

Synchronous generators are essential components of electric power systems. They are present both in hydro and thermal power plants, performing the function of converting mechanical into electrical energy. This paper presents a visual approach to manipulate parameters that affect operation limits of synchronous generators, using a specifically designed software. The operating characteristics of synchronous generators, for all possible modes of operation, are revised in order to link the concepts to the graphic objects. The approach matches the distance learning tool requirements and also enriches the learning process by developing student trust and understanding of the concepts involved in building synchronous machine capability curves. © 2012 IEEE.

Synchronous learner support for music sequencing software

In the field of music technology there is a distinct focus on networking between spatially disparate locales to improve teaching and learning through real-time communication. This article proposes a new delivery model for learner support based on a review of technical and learning services, pilot research using remote desktops to teach music-sequencing software, and recent education research regarding professional development. A 24/7 delivery model using remote desktops, mobile devices and shared calendars offers a flexible real-time addition to the learner support services already on offer. Treating every user of the service as a potential expert, the model aims to deliver universal support situated in a personalized context, which will serve the technical and education requirements of teachers and learners.

Synchronous fluorescence and UV–vis spectroscopic studies of interactions between the tetracycline antibiotic, aluminium ions and DNA with the aid of the Methylene Blue dye probe

Synchronous fluorescence spectroscopy (SFS) was applied for the investigation of interactions of the antibiotic, tetracycline (TC), with DNA in the presence of aluminium ions (Al3+). The study was facilitated by the use of the Methylene Blue (MB) dye probe, and the interpretation of the spectral data with the aid of the chemometrics method, parallel factor analysis (PARAFAC). Three-way synchronous fluorescence analysis extracted the important optimum constant wavelength differences, Δλ, and showed that for the TC–Al3+–DNA, TC–Al3+ and MB dye systems, the associated Δλ values were different (Δλ = 80, 75 and 30 nm, respectively). Subsequent PARAFAC analysis demonstrated the extraction of the equilibrium concentration profiles for the TC–Al3+, TC–Al3+–DNA and MB probe systems. This information is unobtainable by conventional means of data interpretation. The results indicated that the MB dye interacted with the TC–Al3+–DNA surface complex, presumably via a reaction intermediate, TC–Al3+–DNA–MB, leading to the displacement of the TC–Al3+ by the incoming MB dye probe.

Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

Development of an online condition monitoring system for slow speed machinery

One of the main challenges of slow speed machinery condition monitoring is that the energy generated from an incipient defect is too weak to be detected by traditional vibration measurements due to its low impact energy. Acoustic emission (AE) measurement is an alternative for this as it has the ability to detect crack initiations or rubbing between moving surfaces. However, AE measurement requires high sampling frequency and consequently huge amount of data are obtained to be processed. It also requires expensive hardware to capture those data, storage and involves signal processing techniques to retrieve valuable information on the state of the machine. AE signal has been utilised for early detection of defects in bearings and gears. This paper presents an online condition monitoring (CM) system for slow speed machinery, which attempts to overcome those challenges. The system incorporates relevant signal processing techniques for slow speed CM which include noise removal techniques to enhance the signal-to-noise and peak-holding down sampling to reduce the burden of massive data handling. The analysis software works under Labview environment, which enables online remote control of data acquisition, real-time analysis, offline analysis and diagnostic trending. The system has been fully implemented on a site machine and contributing significantly to improve the maintenance efficiency and provide a safer and reliable operation.