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Abstract This paper presents a methodology for the generation of a static reluctance network, with magnetomotive sources only in the rotor, applied in the analysis and design of surface mounted permanent magnet synchronous generators. In the analysis stage, this network can provide the magnetic flux in several points of the generator, enabling an estimate of the generator performance. In the design process, on the other hand, it provides the armature linkage flux and hence the induced EMF can be calculated. It is worth mentioning that the proposed methodology makes use of a good discretization level (high number of elements) on the teeth tips and in the air gap of the generator, in order to get better precision on the flux modeling of these regions. The main contribution of this paper is to present the development process of a static reluctance network applied to surface mounted permanent magnets synchronous generators with different dimensions. The proposed model has proved to be robust and flexible as it can be seen in the results. The reluctance network (RN) developed with the proposed methodology presented in this paper shows low variations of the magnetic flux values, when compared with finite element simulations.

The increased use of intermittent renewable energy sources makes the use of machine learning methods combined with demand-side management more and more frequent. Machine learning algorithms rely on data to identify patterns and learn insights. Hence, data availability is of utmost importance, and the more, the merrier. Therefore, this data report aims to present a dataset concerning the electricity consumption of a tertiary building located in the French Alps region (Grenoble) in 2017 and 2018. It is a massively monitored and controlled building with about 330 electricity meters, whose measurement data constitute the dataset. The data were collected directly from the building management system and correspond to raw data, without any pre-treatment. The dataset also includes Python notebooks that allow for understanding the system design, navigating the data, and performing some simple analyses. This is a publicly available dataset that tries to fill the gap of the availability of electricity consumption data, especially regarding tertiary buildings.

Condition monitoring of synchronous generators through non-invasive methods is widely requested by maintenance teams for not interfering the machine operation. Among the techniques used, external magnetic field monitoring is a recent strategy with great potential for detecting incipient faults. In this context, this paper proposes the application of a simple strategy with low computational cost to process data of external magnetic field time derivative signals for the purposes of condition monitoring and fault detection in synchronous machines. The information of interest is extracted from changes in the magnetic signature of the synchronous generator, obtained from frequency spectra of monitored signals using induction magnetic field sensors. The process forms a set of time series that reflects constructive and operational characteristics of the machine. The Shewhart control chart method is applied for anomaly detection in these time series, allowing the detection of changes in the machine magnetic signature. This method is employed in an algorithm for continuous condition monitoring of synchronous generators, presenting as output a global change indicator for the multivariable problem associated with magnetic signature monitoring. Correlation matrices are used to improve the algorithm response, filtering series with similar variation patterns associated with detected events. The proposed method is validated through tests on an experimental bench that allows the controlled imposition of faults in a synchronous generator. The proposed global change indicator allows the automatic detection of stator and rotor faults with the machine synchronized with the commercial power grid. The proposed methodology is also applied on data obtained from an equipment installed in a 305 MVA synchronous generator of a hydroelectric power plant where the evolution of an incipient fault, i.e., a mechanical vibration fault, has been detected.

Following the recent European directives highlighting the need to increase energy efficiency in the European Union, this work aims to show the possibility of using Non-Intrusive Load Monitoring (NILM) techniques to improve energy audits by estimating the compressed air leakage from a dataset of a tertiary building. The first step towards the reduction of energy consumption is performing an energy audit, in which a detailed analysis of the energy performance is executed. This analysis usually uses on-site measured data by the auditors. However, the time available for these measurements is limited and may not include some modes of operation. One example of that is the quantification of compressed air leaks. This task can be performed by estimating the flow rate during a no compressed air consumption period. However, these periods may not coincide with the auditors’ original schedule. This problem could be addressed by using historical data. Nevertheless, historical data from energy management systems usually are only available for global consumption, and rarely for individual appliances. In this context, a NILM approach would be helpful to enhance energy audits carrying analysis of modes of operation not included in the on-site measurements. In this paper, the leaks are firstly quantified using measurements mostly for benchmarking purposes. The results suggested 62% of leaks in the study case. In a second step, the Factorial Hidden Markov Model (FHMM) was applied to the data. Five typical working days, simulating the context of an energy audit, were used as training data, while one week during vacation time, with no compressed air consumption, was used to quantify the leaks. The results show that it was possible, in the context of an energy audit, to estimate the compressed air leakage using NILM techniques in this dataset with less than a 1% difference when compared to the estimation made with actual measurement. Finally, savings estimations considering the elimination of the leaks were performed, varying between 10% and 100% of the leakage repair. Considering the ideal scenario of complete leaks elimination, the savings would represent around 44% in the compressed air system and 4.75% of the current annual global consumption.

Buildings play a central role in energy transition, as they were responsible for 67.8% of the total consumption of electricity in France in 2017. Because of that, detecting anomalies (outliers) is crucial in order to identify both potential opportunities to reduce energy consumption and malfunctioning of the metering system. This work aims to compare the performance of several outlier detection methods, such as classical statistical methods (as boxplots) applied to the actual measurements and to the difference between the measurements and their predictions, in the task of detecting outliers in the power consumption data of a tertiary building located in France. The results show that the combination of a regression method, such as random forest, and the adjusted boxplot outlier detection method have promising potential in detecting this type of data quality problem in electricity consumption.

Abstract The single phase equivalent circuit is largely used to model the three-phase induction motors in steady-state operation and under sinusoidal balanced voltages. Depending on the desired application, the circuit may or may not represent core losses, a double cage or even the variation of parameters due to skin effect and saturation. However, the determination of the circuit parameters through standard methods, such as those described in IEEE Standard 112, may not be possible in many situations given the lack of the necessary resources. This paper presents initially a survey on the determination of circuit parameters from alternative methods, i.e., non-standard tests. Special focus is given to methods which employ only data usually provided by manufacturers on catalogs and nameplates. Six analytical methodologies used in the context of efficiency estimation at steady-state operation are assessed, compared and then combined in order to improve results. The assessment is based on the closeness of the resulting parameter values to reference values and on the inexistence of absurd results, such as negative electrical resistances. The combination of methods has improved the accuracy of calculations for the studied motors.

This paper presents a steady state modeling of the non-reversible series-converter operating in Zero Voltage Switching (ZVS) mode. This modeling is based on the representation in the state plane of the converter steady state operation. This method enables a full characterization of the converter. The studied structure is composed of a full bridge inverter and a full bridge rectifier. It can be equipped with snubbers. The modeling method presented can also be applied to the variants of the basic one. Moreover, the analytical work is based on normalized quantities which lead to the derivation of dimensionless equations that characterize a structure instead of a particular circuit. The results of these analytical studies are integrated into a software which constitutes a powerful tool to design the converter.