Aurinkosähkövaihtosuuntaajan tilastollinen lämpötilan estimointi

The purpose of this work is to understand whether a broken temperature sensor can be identified from time series data, if a probabilistic temperature model can be formulated for a single measurement for an outdoor inverter, and whether the inverter can continue converting power under the probabilistic model if the sensor is broken. Data given for this study were acquired from different experiments during the design and verification of a 2-MW outdoor central inverter for large utility-scale PV power plants. Based on these objectives, probabilistic methodology was constructed to identify outliers in the data, simulate very short-term temperature time series, and evaluate whether a certain temperature threshold is exceeded as a safety measure for continuing inverter operation. The proposed model is constructed of two blocks: an outlier detection block and an estimation block. The first block is based on principal component analysis, K-means and elliptical density estimation. The second block is based on Markov chain. The proposed methodology uses temperature time series data only without knowing the internals of the system. The proposed model was validated by inputting time-series data containing data from faulty temperature sensors under different failure scenarios, and by comparing simulated temperature time series data to historical temperature data under different cases. Moreover, the simulated time series data were used to verify whether the model can anticipate exceeding a certain temperature threshold. The model always detected the failed sensors. The error metrics of the simulated temperature time series were low. Furthermore, the model anticipated exceeding the given temperature threshold ahead of time.Tämän diplomityön tarkoituksena on tutkia mahdollisuutta tunnistaa vaihtosuuntaajan vioittunut lämpötila-anturi lämpötila-aikasarjoista, muodostaa tilastollinen malli yhdelle lämpötilamittaukselle sekä arvioida, voidaanko vaihtosuuntaajan toimintaa jatkaa tilastollisen mallin avulla lämpötila-anturin vioittuessa. Materiaalina käytettiin suuriin aurinkovoimaloihin suunnitellun 2 MW:n keskusinvertterin erilaisista kokeista kerättyjä lämpötilamittauksia. Työn tavoitteiden pohjalta muodostettiin tilastollinen menetelmä, joka tunnistaa vioittuneen lämpötila-anturin, simuloi lyhytaikaisia lämpötila-aikasarjoja sekä ennustaa vaihtosuuntaajan toiminnan jatkamisen kannalta, ylittyykö ennalta-asetettu lämpötilaraja. Esitetty malli on rakennettu vioittuneen lämpötila-anturin tunnistavasta lohkosta ja lämpötilaa estimoivasta lohkosta. Ensimmäinen lohko perustuu pääkomponenttianalyysiin, K:n keskiarvon klusterointimenetelmään ja virhe-ellipsiin. Toinen lohko perustuu Markovin ketjuun. Esitetty malli käyttää lähtötietona vain aikaisempia lämpötila-aikasarjoja. Menetelmän toimivuutta tutkittiin ensin tunnistamalla viallinen lämpötila-anturi sekä vertaamalla estimoitujen lämpötila-aikasarjojen jakaumia historiallisiin lämpötilatietoihin erilaisissa vioittumistapauksissa. Lisäksi menetelmän kykyä ennakoida ennalta-asetetun lämpötilarajan ylittämistä tutkittiin eri esimerkkien avulla. Esitetty menetelmä havaitsi vioittuneet lämpötila-anturit poikkeuksetta. Ennustettujen ja havaittujen lämpötila-aikasarjojen väliset erot olivat hyvin pieniä. Malli pystyi myös ennakoimaan tietyn lämpötilarajan ylittymisen

Scalable Data-driven Modeling and Analytics for Smart Buildings

Buildings account for over 40% of the energy and 75% of the electricity usage. Thus, by reducing our energy footprint in buildings, we can improve our overall energysustainability. Further, the proliferation of networked sensors and IoT devices in recent years have enabled monitoring of buildings to provide data at various granularity. For example, smart plugs monitor appliance level usage inside the house, while solar meters monitor residential rooftop solar installations. Furthermore, smart meters record energy usage at a grid-scale. In this thesis, I argue that data-driven modeling applied to the IoT data from a smart building, at varying granularity, in association with third party data can help to understand and reduce human energy consumption. I present four data-driven modeling approaches — that use sophisticated techniques from Machine Learning, Optimization, and Time Series Analysis — applied at different granularities. First, I study IoT devices inside the house and discuss an approach called NIMD that au- tomatically models individual electrical loads found in a household. The analytical model resulting from this approach can be used in several applications. For example, these models can improve the performance of NILM algorithms to disaggregate loads in a given household. Further, faulty or energy-inefficient appliances can be identified by observing deviations in model parameters over its lifetime. Second, I examine data from solar meters and present a machine learning framework called SolarCast to forecast energy generation from residential rooftop installations. The predictions enable exploiting the benefits of locally-generated solar energy. Third, I employ a sensorless approach utilizing a graphical model representation to re- port city-scale photovoltaic panel health and identify anomalies in solar energy production. Immediate identification of faults maximizes the solar investment by aiding in optimal operational performance. Finally, I focus on grid-level smart meter data and use correlations between energy usage and external weather to derive probabilistic estimates of energy, which is leveraged to identify the least efficient buildings from a large population along with the underlying cause of energy inefficiency. The identified homes can be targeted for custom energy efficiency programs

Detection of Clouds in Multiple Wind Velocity Fields using Ground-based Infrared Sky Images

Horizontal atmospheric wind shear causes wind velocity fields to have different directions and speeds. In images of clouds acquired using ground-based sky imagers, clouds may be moving in different wind layers. To increase the performance of an intra-hour global solar irradiance forecasting algorithm, it is important to detect multiple layers of clouds. The information provided by a solar forecasting algorithm is necessary to optimize and schedule the solar generation resources and storage devices in a smart grid. This investigation studies the performance of unsupervised learning techniques when detecting the number of cloud layers in infrared sky images. The images are acquired using an innovative infrared sky imager mounted on a solar tracker. Different mixture models are used to infer the distribution of the cloud features. The optimal decision criterion to find the number of clusters in the mixture models is analyzed and compared between different Bayesian metrics and a sequential hidden Markov model. The motion vectors are computed using a weighted implementation of the Lucas-Kanade algorithm. The correlations between the cloud velocity vectors and temperatures are analyzed to find the method that leads to the most accurate results. We have found that the sequential hidden Markov model outperformed the detection accuracy of the Bayesian metrics

Situation Awareness for Smart Distribution Systems

In recent years, the global climate has become variable due to intensification of the greenhouse effect, and natural disasters are frequently occurring, which poses challenges to the situation awareness of intelligent distribution networks. Aside from the continuous grid connection of distributed generation, energy storage and new energy generation not only reduces the power supply pressure of distribution network to a certain extent but also brings new consumption pressure and load impact. Situation awareness is a technology based on the overall dynamic insight of environment and covering perception, understanding, and prediction. Such means have been widely used in security, intelligence, justice, intelligent transportation, and other fields and gradually become the research direction of digitization and informatization in the future. We hope this Special Issue represents a useful contribution. We present 10 interesting papers that cover a wide range of topics all focused on problems and solutions related to situation awareness for smart distribution systems. We sincerely hope the papers included in this Special Issue will inspire more researchers to further develop situation awareness for smart distribution systems. We strongly believe that there is a need for more work to be carried out, and we hope this issue provides a useful open-access platform for the dissemination of new ideas

Sensitive parameter analysis for solar irradiance short-term forecasting: application to LoRa-based monitoring technology

Due to the relevant penetration of solar PV power plants, an accurate power generation forecasting of these installations is crucial to provide both reliability and stability of current grids. At the same time, PV monitoring requirements are more and more demanded by different agents to provide reliable information regarding performances, efficiencies, and possible predictive maintenance tasks. Under this framework, this paper proposes a methodology to evaluate different LoRa-based PV monitoring architectures and node layouts in terms of short-term solar power generation forecasting. A random forest model is proposed as forecasting method, simplifying the forecasting problem especially when the time series exhibits heteroscedasticity, nonstationarity, and multiple seasonal cycles. This approach provides a sensitive analysis of LoRa parameters in terms of node layout, loss of data, spreading factor and short time intervals to evaluate their influence on PV forecasting accuracy. A case example located in the southeast of Spain is included in the paper to evaluate the proposed analysis. This methodology is applicable to other locations, as well as different LoRa configurations, parameters, and networks structures; providing detailed analysis regarding PV monitoring performances and short-term PV generation forecasting discrepancies.This research was funded by the Fondo Europeo de Desarrollo Regional/Ministerio de Ciencia e Innovación–Agencia Estatal de Investigación (FEDER/MICINN-AEI), project RTI2018–099139–B–C21

Holistic modelling techniques for the operational optimisation of multi-vector energy systems

Modern district energy systems are highly complex with several controllable and uncontrollable variables. To effectively manage a multi-vector district requires a holistic perspective in terms of both modelling and optimisation. Current district optimisation strategies found in the literature often consider very simple models for energy generation and conversion technologies. To improve upon the state of the art, more realistic and accurate models must be produced whilst remaining computationally and mathematically simple enough to complete within short periods. Therefore, this paper provides a comprehensive review of modelling techniques for common district energy conversion technologies including Power-to-Gas. In addition, dynamic building modelling techniques are reviewed as buildings must be considered active and flexible participants in a district energy system. In both cases, a specific focus is placed on artificial intelligence-based models suitable for implementation in the real-time operational optimisation of multi-vector systems. Future research directions identified from this review include the need to integrate simplified models of energy conversion units, energy distribution networks, dynamic building models and energy storage into a holistic district optimisation. Finally, a future district energy management solution is proposed. It leverages semantic modelling to allow interoperability of heterogeneous data sources to provide added value inferencing from contextually enriched informatio

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Performance estimation of photovoltaic energy production

This article deals with the production of energy through photovoltaic (PV) panels. The efficiency and quantity of energy produced by a PV panel depend on both deterministic factors, mainly related to the technical characteristics of the panels, and stochastic factors, essentially the amount of incident solar radiation and some climatic variables that modify the efficiency of solar panels such as temperature and wind speed. The main objective of this work is to estimate the energy production of a PV system with fixed technical characteristics through the modeling of the stochastic factors listed above. Besides, we estimate the economic profitability of the plant, net of taxation or subsidiary payment policies, considered taking into account the hourly spot price curve of electricity and its correlation with solar radiation, via vector autoregressive models. Our investigation ends with a Monte Carlo simulation of the models introduced. We also propose the pricing of some quanto options that allow hedging both the price risk and the volumetric risk

Cuban energy system development – Technological challenges and possibilities

This eBook is a unique scientific journey to the changing frontiers of energy transition in Cuba focusing on technological challenges of the Cuban energy transition. The focus of this milestone publication is on technological aspects of energy transition in Cuba. Green energy transition with renewable energy sources requires the ability to identify opportunities across industries and services and apply the right technologies and tools to achieve more sustainable energy production systems. The eBook is covering a large diversity of Caribbean country´s experiences of new green technological solutions and applications. It includes various technology assessments of energy systems and technological foresight analyses with a special focus on Cuba