Power Measurement Methodology for FPGA Devices

The efficiency of power optimization tools depends on information on design power provided by the power estimation models. Power models targeting different power groups can enable fast identification of the most power consuming parts of design and their properties. The accuracy of these estimation models is highly dependent on the accuracy of the method used for their characterization. The highest precision is achieved by using physical onboard measurements. In this paper, we present a measurement methodology that is primarily aimed at calibrating and validating high-level dynamic power estimation models. The measurements have been carefully designed to enable the separation of the interconnect power from the logic power and the power of the clock circuitry, so that each of these power groups can be used for the corresponding model validation. The standard measurement uncertainty is lower than 2% of the measured value even with a very small number of repeated measurements. Additionally, the accuracy of a commercial low-level power estimation tool has been also assessed for comparison purposes. The results indicate that the tool is not suitable for power estimation of data path-oriented designs.

Monitoring maximum peak power density values as a more reliable marker for measurement procedures to elucidate RF EMF health effects: Leganes project

Considering the measurement procedures recommended by the ICNIRP, this communication is a proposal for a measurement procedure based in the maximum peak values of equivalent plane wave power density. This procedure has been included in a project being developed in Leganés, Spain. The project plans to deploy a real time monitoring system for RF to provide the city with a useful tool to adapt the environmental EM conditions to the new regulations approved. A first stage consisting of 105 measurement points has been finished and all the values are under the threshold of the regulation.

Smoothing of PV power fluctuations by geographical dispersion

The quality and the reliability of the power generated by large grid-connected photovoltaic (PV) plants are negatively affected by the source characteristic variability. This paper deals with the smoothing of power fluctuations because of geographical dispersion of PV systems. The fluctuation frequency and the maximum fluctuation registered at a PV plant ensemble are analyzed to study these effects. We propose an empirical expression to compare the fluctuation attenuation because of both the size and the number of PV plants grouped. The convolution of single PV plants frequency distribution functions has turned out to be a successful tool to statistically describe the behavior of an ensemble of PV plants and determine their maximum output fluctuation. Our work is based on experimental 1-s data collected throughout 2009 from seven PV plants, 20 MWp in total, separated between 6 and 360 km.

Statistical models for short-term wind power ramp forecasting

La predicción de energía eólica ha desempeñado en la última década un papel fundamental en el aprovechamiento de este recurso renovable, ya que permite reducir el impacto que tiene la naturaleza fluctuante del viento en la actividad de diversos agentes implicados en su integración, tales como el operador del sistema o los agentes del mercado eléctrico. Los altos niveles de penetración eólica alcanzados recientemente por algunos países han puesto de manifiesto la necesidad de mejorar las predicciones durante eventos en los que se experimenta una variación importante de la potencia generada por un parque o un conjunto de ellos en un tiempo relativamente corto (del orden de unas pocas horas). Estos eventos, conocidos como rampas, no tienen una única causa, ya que pueden estar motivados por procesos meteorológicos que se dan en muy diferentes escalas espacio-temporales, desde el paso de grandes frentes en la macroescala a procesos convectivos locales como tormentas. Además, el propio proceso de conversión del viento en energía eléctrica juega un papel relevante en la ocurrencia de rampas debido, entre otros factores, a la relación no lineal que impone la curva de potencia del aerogenerador, la desalineación de la máquina con respecto al viento y la interacción aerodinámica entre aerogeneradores. En este trabajo se aborda la aplicación de modelos estadísticos a la predicción de rampas a muy corto plazo. Además, se investiga la relación de este tipo de eventos con procesos atmosféricos en la macroescala. Los modelos se emplean para generar predicciones de punto a partir del modelado estocástico de una serie temporal de potencia generada por un parque eólico. Los horizontes de predicción considerados van de una a seis horas. Como primer paso, se ha elaborado una metodología para caracterizar rampas en series temporales. La denominada función-rampa está basada en la transformada wavelet y proporciona un índice en cada paso temporal. Este índice caracteriza la intensidad de rampa en base a los gradientes de potencia experimentados en un rango determinado de escalas temporales. Se han implementado tres tipos de modelos predictivos de cara a evaluar el papel que juega la complejidad de un modelo en su desempeño: modelos lineales autorregresivos (AR), modelos de coeficientes variables (VCMs) y modelos basado en redes neuronales (ANNs). Los modelos se han entrenado en base a la minimización del error cuadrático medio y la configuración de cada uno de ellos se ha determinado mediante validación cruzada. De cara a analizar la contribución del estado macroescalar de la atmósfera en la predicción de rampas, se ha propuesto una metodología que permite extraer, a partir de las salidas de modelos meteorológicos, información relevante para explicar la ocurrencia de estos eventos. La metodología se basa en el análisis de componentes principales (PCA) para la síntesis de la datos de la atmósfera y en el uso de la información mutua (MI) para estimar la dependencia no lineal entre dos señales. Esta metodología se ha aplicado a datos de reanálisis generados con un modelo de circulación general (GCM) de cara a generar variables exógenas que posteriormente se han introducido en los modelos predictivos. Los casos de estudio considerados corresponden a dos parques eólicos ubicados en España. Los resultados muestran que el modelado de la serie de potencias permitió una mejora notable con respecto al modelo predictivo de referencia (la persistencia) y que al añadir información de la macroescala se obtuvieron mejoras adicionales del mismo orden. Estas mejoras resultaron mayores para el caso de rampas de bajada. Los resultados también indican distintos grados de conexión entre la macroescala y la ocurrencia de rampas en los dos parques considerados. Abstract One of the main drawbacks of wind energy is that it exhibits intermittent generation greatly depending on environmental conditions. Wind power forecasting has proven to be an effective tool for facilitating wind power integration from both the technical and the economical perspective. Indeed, system operators and energy traders benefit from the use of forecasting techniques, because the reduction of the inherent uncertainty of wind power allows them the adoption of optimal decisions. Wind power integration imposes new challenges as higher wind penetration levels are attained. Wind power ramp forecasting is an example of such a recent topic of interest. The term ramp makes reference to a large and rapid variation (1-4 hours) observed in the wind power output of a wind farm or portfolio. Ramp events can be motivated by a broad number of meteorological processes that occur at different time/spatial scales, from the passage of large-scale frontal systems to local processes such as thunderstorms and thermally-driven flows. Ramp events may also be conditioned by features related to the wind-to-power conversion process, such as yaw misalignment, the wind turbine shut-down and the aerodynamic interaction between wind turbines of a wind farm (wake effect). This work is devoted to wind power ramp forecasting, with special focus on the connection between the global scale and ramp events observed at the wind farm level. The framework of this study is the point-forecasting approach. Time series based models were implemented for very short-term prediction, this being characterised by prediction horizons up to six hours ahead. As a first step, a methodology to characterise ramps within a wind power time series was proposed. The so-called ramp function is based on the wavelet transform and it provides a continuous index related to the ramp intensity at each time step. The underlying idea is that ramps are characterised by high power output gradients evaluated under different time scales. A number of state-of-the-art time series based models were considered, namely linear autoregressive (AR) models, varying-coefficient models (VCMs) and artificial neural networks (ANNs). This allowed us to gain insights into how the complexity of the model contributes to the accuracy of the wind power time series modelling. The models were trained in base of a mean squared error criterion and the final set-up of each model was determined through cross-validation techniques. In order to investigate the contribution of the global scale into wind power ramp forecasting, a methodological proposal to identify features in atmospheric raw data that are relevant for explaining wind power ramp events was presented. The proposed methodology is based on two techniques: principal component analysis (PCA) for atmospheric data compression and mutual information (MI) for assessing non-linear dependence between variables. The methodology was applied to reanalysis data generated with a general circulation model (GCM). This allowed for the elaboration of explanatory variables meaningful for ramp forecasting that were utilized as exogenous variables by the forecasting models. The study covered two wind farms located in Spain. All the models outperformed the reference model (the persistence) during both ramp and non-ramp situations. Adding atmospheric information had a noticeable impact on the forecasting performance, specially during ramp-down events. Results also suggested different levels of connection between the ramp occurrence at the wind farm level and the global scale.

Testing a GIS-based methodology for optimal location of Pumped Storage power plants in Norway

Europe needs to restructure its energy system. The aim to decrease the reliance on fossil fuels to a higher dependence on renewable energy has now been imposed by The European Commission. In order to achieve this goal there is a great interest in Norway to become "The Green Battery of Europe". In the pursuit of this goal a GIS-tool was created to investigate the pump storage potential in Norway. The tool searches for possible connections between existing reservoirs and dams with the criteria selected by the user. The aim of this thesis was to test the tool and see if the results suggested were plausible, develop a cost calculation method for the PSH lines, and make suggestions for further development of the tool. During the process the tool presented many non-feasible pumped storage hydropower (PSH) connections. The area of Telemark was chosen for the more detailed study. The results were discussed and some improvements were suggested for further development of the tool. Also a sensitivity test was done to see which of the parameters set by the user are the most relevant for the PSH connection suggestion. From a range of the most promising PSH plants suggested by the tool, the one between Songavatn and Totak was chosen for a case study, where there already exists a power plant between both reservoirs. A new Pumped Storage Plant was designed with a power production of 1200 MW. There are still many topics open to discussion, such as how to deal with environmental restrictions, or how to deal with inflows and outflows of the reservoirs from the existing power plants. Consequently the GIS-tool can be a very useful tool to establish the best possible connections between existing reservoirs and dams, but it still needs a deep study and the creation of new parameters for the user.

Comparison between SAM and Thermoflow for Linear Fresnel Solar Power Plants design

System Advisor Model is a software tool develped by National Renewable Laboratory (NREL), Department Of Energy, USA to design Solar Power Plants.

Real time power plant simulation platform for training on Electrical Protections and Automatic Voltage Regulators

Electrical Protection systems and Automatic Voltage Regulators (AVR) are essential components of actual power plants. Its installation and setting is performed during the commissioning, and it needs extensive experience since any failure in this process or in the setting, may entails some risk not only for the generator of the power plant, but also for the reliability of the power grid. In this paper, a real time power plant simulation platform is presented as a tool for improving the training and learning process on electrical protections and automatic voltage regulators. The activities of the commissioning procedure which can be practiced are described, and the applicability of this tool for improving the comprehension of this important part of the power plants is discussed. A commercial AVR and a multifunction protective relay have been tested with satisfactory results.

On the simulation of the UPMSAT-2 microsatellite power

Simulation of satellite subsystems behaviour is extramely important in the design at early stages. The subsystems are normally simulated in the both ways : isolated and as part of more complex simulation that takes into account imputs from other subsystems (concurrent design). In the present work, a simple concurrent simulation of the power subsystem of a microsatellite, UPMSat-2, is described. The aim of the work is to obtain the performance profile of the system (battery charging level, power consumption by the payloads, power supply from solar panels....). Different situations such as battery critical low or high level, effects of high current charging due to the low temperature of solar panels after eclipse,DoD margins..., were analysed, and different safety strategies studied using the developed tool (simulator) to fulfil the mission requirements. Also, failure cases were analysed in order to study the robustness of the system. The mentioned simulator has been programed taking into account the power consumption performances (average and maximum consumptions per orbit/day) of small part of the subsystem (SELEX GALILEO SPVS modular generators built with Azur Space solar cells, SAFT VES16 6P4S Li-ion battery, SSBV magnetometers, TECNOBIT and DATSI/UPM On Board Data Handling -OBDH-...). The developed tool is then intended to be a modular simulator, with the chance of use any other components implementing some standard data.

PV power forecast using a nonparametric PV model

Forecasting the AC power output of a PV plant accurately is important both for plant owners and electric system operators. Two main categories of PV modeling are available: the parametric and the nonparametric. In this paper, a methodology using a nonparametric PV model is proposed, using as inputs several forecasts of meteorological variables from a Numerical Weather Forecast model, and actual AC power measurements of PV plants. The methodology was built upon the R environment and uses Quantile Regression Forests as machine learning tool to forecast AC power with a confidence interval. Real data from five PV plants was used to validate the methodology, and results show that daily production is predicted with an absolute cvMBE lower than 1.3%.

Detecting and characterising ramp events in wind power time series

In order to implement accurate models for wind power ramp forecasting, ramps need to be previously characterised. This issue has been typically addressed by performing binary ramp/non-ramp classifications based on ad-hoc assessed thresholds. However, recent works question this approach. This paper presents the ramp function, an innovative wavelet- based tool which detects and characterises ramp events in wind power time series. The underlying idea is to assess a continuous index related to the ramp intensity at each time step, which is obtained by considering large power output gradients evaluated under different time scales (up to typical ramp durations). The ramp function overcomes some of the drawbacks shown by the aforementioned binary classification and permits forecasters to easily reveal specific features of the ramp behaviour observed at a wind farm. As an example, the daily profile of the ramp-up and ramp-down intensities are obtained for the case of a wind farm located in Spain

Design Parameter Analysis of Point Absorber WEC via an Evolutionary-Algorithm-Based Dimensioning Tool

Wave energy conversion has an essential difference from other renewable energies since the dependence between the devices design and the energy resource is stronger. Dimensioning is therefore considered a key stage when a design project of Wave Energy Converters (WEC) is undertaken. Location, WEC concept, Power Take-Off (PTO) type, control strategy and hydrodynamic resonance considerations are some of the critical aspects to take into account to achieve a good performance. The paper proposes an automatic dimensioning methodology to be accomplished at the initial design project stages and the following elements are described to carry out the study: an optimization design algorithm, its objective functions and restrictions, a PTO model, as well as a procedure to evaluate the WEC energy production. After that, a parametric analysis is included considering different combinations of the key parameters previously introduced. A variety of study cases are analysed from the point of view of energy production for different design-parameters and all of them are compared with a reference case. Finally, a discussion is presented based on the results obtained, and some recommendations to face the WEC design stage are given.

A clustering technique for partial discharge and noise sources identification in power cables by means of waveform parameters

On-line partial discharge (PD) measurements have become a common technique for assessing the insulation condition of installed high voltage (HV) insulated cables. When on-line tests are performed in noisy environments, or when more than one source of pulse-shaped signals are present in a cable system, it is difficult to perform accurate diagnoses. In these cases, an adequate selection of the non-conventional measuring technique and the implementation of effective signal processing tools are essential for a correct evaluation of the insulation degradation. Once a specific noise rejection filter is applied, many signals can be identified as potential PD pulses, therefore, a classification tool to discriminate the PD sources involved is required. This paper proposes an efficient method for the classification of PD signals and pulse-type noise interferences measured in power cables with HFCT sensors. By using a signal feature generation algorithm, representative parameters associated to the waveform of each pulse acquired are calculated so that they can be separated in different clusters. The efficiency of the clustering technique proposed is demonstrated through an example with three different PD sources and several pulse-shaped interferences measured simultaneously in a cable system with a high frequency current transformer (HFCT).