| 1. |
- Bagheri, Azam
(författare)
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Artificial Intelligence-Based Characterization and Classification Methods for Power Quality Data Analytics
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- One of the important developments in the electric power system is the fast increasing amount of data. An example of such data is formed by the voltages and currents coming from power-quality measurements. Power quality disturbances like voltage dips, harmonics and voltage transient can have a serious negative impact on the performance of equipment exposed to such disturbances. Voltage dips, short duration reductions in voltage magnitude, are especially considered as important disturbances because they regularly lead to stoppages in industrial process installations and subsequently to high costs.The overall aim of this dissertation is the development of automatic analysis methods and other methods for extracting information from large amounts of power-quality data. This includes, methods to detect and extract event characteristics from recorded data and classify the events, for instance, based on their origins or their impact on equipment. The classification facilitates further analysis steps including reasoning and interpretation. Once the data corresponding to each class is available, a proper characterization method can be used to create more semantic data useful for information extraction. The resulting information can be used to improve the performance of the whole system, e.g., updating grid-codes, or immunity requirements of sensitive installations or processes.This dissertation proposes different methods to fulfil each one of the above-mentioned steps. It proposes particularly a space-phasor model (SPM) of the three phase-to-neutral voltages as basis for analytic methods. The SPM is especially suitable as it is a time-domain transform without loss of any information. Another important contribution of the work is that most of the developed methods have been applied to a large dataset of about 6000 real-world voltage dips measured in existing HV and MV power networks.The main contributions of this dissertation are as follows:A complete framework has been proposed for automatic voltage quality analysis based on the SPM. The SPM has been used before, but this is the first time it has been used in a framework covering a range of voltage quality disturbances. A Gaussian-based anomaly detection method is used to detect and extract voltage quality disturbances. A principal component analysis (PCA) algorithm is used for event characterization. The obtained single-event characteristics are used to extract additional information like origin, fault type and location. Two deep learning-based voltage dip classifier has been developed. In both classifier a 2D convolutional neural network (2D-CNN) architecture has been employed to perform automatic feature extraction task. The soft-max activation function fulfills supervised classification method in first classifier. The second classifier uses a semi-supervised classification method based on generative-discriminative model pairs in active learning context.The same SPM was shown to enable the effective extraction of dip characteristics for multi-stage voltage dips. Applying the k-means clustering algorithm, the event is clustered into its individual stages. For each stage of the dip, a logistic regression algorithm is used to characterize that stage. The proposed method offers a new solution to the problem with transition segments that is one of the main challenges of existing methods for characterization of multi-stage dips. It is also shown in the dissertation that the SPM is an effective method for voltage transient analysis. It is possible to extract corresponding sample data and get appropriate single-event characteristics.A systematic way has been developed and applied for comparing different sets of voltage dip characteristics. With this method, both measured and synthetic voltage dips are applied to generic models of sensitive loads. The best set of characteristics is the one most accurately reproducing the behavior of equipment when exposed to measured voltage dips.The dissertation further contains a number of practical applications of the before-mentioned theoretical contributions: a proposal to an international standard-setting group; energy storage for voltage-dip ride-through of microgrids; impact of different voltage dips on wind-power installations.
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| 2. |
- Busatto, Tatiano
(författare)
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On Waveform Distortion in Modern Low-Voltage Installations with Multiple Nonlinear Devices
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The continuing society quest for more comfort combined with the need to minimize global environmental impacts is constantly introducing new technologies into our daily lives. Among the recent developments, the advances in energy-efficient lighting and renewable energy technologies have enabled a maturity level in cleaner electricity production and efficient use of energy. Aligned with these trends, more recently we are experiencing faster progress towards the electrification of the transport system. All these developments have been largely driven by advancements in power electronic technologies which ultimately introduces a significant number of nonlinear loads in the form of power converters into the low-voltage (LV) installations and networks for electricity distribution.The overall aim of this thesis is to investigate how these nonlinear loads (individually and together) impact the current waveform distortion in modern LV installations. The work addresses several issues related to the electrical interactions between the distribution grid and different nonlinear loads, such as LED lamps, power factor correction (PFC) converters, PV inverters, and electric vehicle chargers.As a first part, the influence of the network impedance is examined. A method combining analytical impedance network modelling with a probabilistic approach for the customer side equipment was developed to address the uncertainties associated with harmonic resonances in public LV networks. It was found that the main resonance is mainly due to the transformer inductance and the total customer capacitance, while cable capacitances and customer inductances have a small impact. Additionally, it was found that increasing PV penetration shifts the harmonic resonances to lower frequencies, but also decreases the impedance magnitude.The second part includes the examination of the so-called nonlinear interaction phenomenon. A methodology has been developed and applied to quantify the extent of nonlinear interaction between devices in the same LV installation. It was observed that the interaction of different power electronic devices creates nonlinearity deviation, changing the current harmonics emission mainly for low order harmonics. The harmonic phase angle is the most affected harmonic characteristic. Additionally, linked to the first part, it was observed that changes in the network impedance and voltage source waveform have a significant impact on the nonlinear interaction.As a third part, the current zero-crossing waveform distortion has been analysed with a focus on control instabilities. Prior measurements of multiple devices fitted with power-factor controller were compared with a simulation model and instabilities were evaluated. Results from this work have confirmed that zero-crossing distortion increases proportionally with the number of devices. In addition, it was found that the network impedance plays an important role in defining the stability-criteria of these devices.Results shown in this thesis have revealed the harmonic interdependency and its consequences in different frequency ranges: harmonics and supraharmonics. Understanding the details of these new scenarios becomes of fundamental importance to mitigate future power quality issues and ensure the functioning of equipment in modern LV installations. This work presents several findings and a comprehensive discussion serving as a guideline for future work on interaction analysis and its consequences for devices in the LV network.
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| 3. |
- de Oliveira, Roger Alves
(författare)
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Applications of Unsupervised Deep Learning for Analysing Time-Varying Power Quality Big Data
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Continuous power quality monitoring allows grid stakeholders to obtain information about the performance of the network and costumer facilities. Moreover, the analysis of continuous monitoring allows researchers to obtain knowledge on power quality phenomena. Power quality measurements result in a large amount of data. Power quality data can be classified as big data, not only for its volume, but also for the other complexities: velocity, variety, and veracity. Manual analysis of power quality data is possible but time-consuming. Moreover, data reports based on standardized indexes and classical statistical techniques might hide important information of the time-varying behaviour in power quality measurements. Artificial intelligence plays a role in providing automatic tools for proper analytics of big data. A subset of artificial intelligence called machine learning has enabled computers to learn without explicit programming. Driven by the huge improvements in computer processing, a subset of machine learning based on multiple layers of artificial neural networks has been developed to tackle increasingly complex problems. The so-called deep learning applications teach themselves to perform a specific task by automatically extracting essential features from the raw data. Despite the possibilities of automatic feature extraction, most applications of deep learning to power quality are still the same as expert systems or earliest machine learning tools. Moreover, most of the applications are based on synthetic generated data and supervised techniques. In this context, the main motivation of this thesis is providing a new tool based on unsupervised deep learning to handle analytics of time-varying power quality big data. The unsupervised deep learning method proposed in this thesis combines a deep autoencoder with clustering for extracting patterns and anomalies in power quality big data. The deep autoencoder maps the original data to a compressed format that contains the principal features of the data. Automatic results are provided by the deep learning, and inferences can be obtained without requiring prior knowledge of deep learning. The outputs from unsupervised deep learning can serve as a guide for further data analysis, highlighting important time steps within large power quality datasets. By following these indications from the deep learning results, experts gain valuable insights into power quality phenomena, which can be referred to as "learning from deep learning". The interpretation of the deep learning results in this thesis allowed to making proper inferences for patterns and anomalies. For power quality measurements synchronised with 24-h, the results allowed making inferences concerning daily variations, seasonality, and the origins of power quality disturbances. For power quality measurements non-synchronised with 24-h, the results could be interpreted visually through the distribution of the patterns in a physical variable, such as the dynamic operating conditions of an electrical railway power system.An important contribution of this thesis concerns the physical interpretation of the phenomena is related to the anomalies in harmonics caused by geomagnetically induced currents. An interesting finding by applying the deep anomaly detection to measurements in the Swedish transmission grid is the damping of the anomalies caused by geomagnetically induced currents in the winter due to the heating load. This thesis also demonstrated that the signatures for anomalies in harmonic measurements in a Swedish transmission location are similar to the ones found in a low-latitude transmission location at the South Atlantic Anomaly due to geomagnetically induced currents. Moreover, by cross-checking the anomalies at the South Atlantic Anomaly with protection trips with undetermined causes, this thesis demonstrated that anomaly harmonics due to geomagnetically induced currents can cause protection mal trips.This thesis demonstrates that unsupervised deep learning can serve as an additional tool for compressing time-varying power quality big data into a more interpretable form. Despite the application of an unsupervised method, power quality experts remain significant in power quality studies. The main conclusion of this thesis is that unsupervised deep learning enhances the understanding of power quality experts and provides a complementary approach for analysing and extracting insights from time-varying power quality big data.
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| 4. |
- Espin Delgado, Angela
(författare)
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Propagation of Supraharmonics in Low-Voltage Networks
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The need for measures towards a sustainable use of energy has incited the proliferation of devices and systems for the efficient use of electricity. Energy-efficient appliances, equipment for the electrification of transportation, electricity generators from renewable energy sources, and communication protocols, e.g., for smart metering are sources of supraharmonic distortion in electrical networks. Supraharmonics are voltage and current waveform distortion in the frequency range from 2 up to 150 kHz.The increase in sources of supraharmonics in the last decades and the propagation of this type of distortion have triggered a variety of unwanted consequences (interference) in the electrical networks. Interference associated to supraharmonics such as audible noise, degradation or failure in the operation of electrical equipment, and breakdown of insulation materials, have been reported around the world. A standardized framework for supraharmonics as a power quality phenomenon that involves both grid operators and equipment manufacturers is needed to limit these interferences. The limits to be set shall not hinder the modernization of the electrical system and the consequential energy transition.There are gaps in the standardization framework for supraharmonics as a power quality phenomenon. The study of supraharmonics as a power quality parameter should consider variables that affect emission levels and propagation of supraharmonics. At the same time, an assessment of the severity of given supraharmonics levels regarding their consequences is needed to settle realistic reference levels. Deterministic methods have been generally used to study supraharmonic propagation but they might not be suitable when considering many possible scenarios.This research introduces forefront methods and results on the study of supraharmonics emission, propagation, and consequences. The study has two focal points: 1) to study the impact of the impedance of the grid and low-voltage devices on the emission and propagation of supraharmonics; 2) to assess the severity of propagated supraharmonics in terms of the characteristics of the distortion and the probability of interference. Experimental and theoretical case studies are built to carry out the research. Measured and synthetic signals representative of supraharmonic distortion present in low-voltage networks are used.The main results of this research are summarized as:The levels of emitted and propagated supraharmonics depend on the impedance of the grid, the emitting device and the neighboring devices. Resonance can lead to significant levels of supraharmonics anywhere in the grid. The variability and diversity of low-voltage devices lead to high uncertainty in the estimation of their impedance. Stochastic methods are recommended to assess the probability of interference.Different attributes of supraharmonics are responsible for different interference phenomena. Indications of the severity of supraharmonics attributes are given for three phenomena: audible noise, negative impacton residual current devices, and light flicker of LED lamps.This research contributes to the establishment of supraharmonics as a power quality phenomenon with standardized solutions. It introduces methods for the assessment of: 1) supraharmonic emission from installations needed to recommend planning levels; 2) supraharmonic propagation in low-voltage networks, and 3) the probability of interference needed to define reference levels.
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| 5. |
- Hajeforosh, Seyede Fatemeh, 1988-
(författare)
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Multiple Aspects of Dynamic Rating in the Power System
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Increased consumption and increased use of renewable energy make overhead lines and transformers more often congested. Dynamic rating (DR) uses a time-depending maximum permissible current instead of a long-term-fixed rating; it is an effective solution to upgrade the capacity of existing grid assets to minimize this congestion. Dynamic line rating (DLR) considers how the weather affects the thermal behavior of the conductor and therewith its rating; environmental parameters as well as conductor characteristics have to be considered. Likewise, dynamic rating of transformers (DTR) relies on a thermal assessment of the hottest spot in the transformer windings. The hot-spot temperature is dependent on the ambient temperature, loading, and transformer’s cooling system.In this thesis, a number of lesser-studied aspects of DR are studied: stochastic modeling of the rating; relations between protection, reliability and rating; risk assessment for line selection when using DLR; and increased transformer hosting capacity (HC) in the presence of solar photovoltaic (PV).In the first part of the work, the focus is on the stochastic modeling of DLR and its relation to the protection operation. Actual line rating is considered a random variable in the protection system for a more flexible decision-making. Different sources of uncertainties are modeled using suitable probability density functions. The method allows for a transparent trade-off between the risk of failure to take measures and the risk of unnecessary measures against overload. The results depicts that deterministic DLR could result in high probabilities of overloading or would require large safety margins. While, a stochastic approach will allow for both small margins and appropriate risks. A generic model is introduced to consider DLR reliability from two different viewpoints: errors and failures of components that would affect the calculation of the rating; and the impact of a DLR failure on the power system. The qualitative reliability study highlights that it is important to update the protection settings on a real-time basis. In the second part of the work, a risk assessment framework is proposed to select the minimum set of overhead lines for DLR implementation. Results show a possible increase of permissible hosting capacity (HC) for electric-vehicle charging by up to 80% (depending on the test system and initial data) with low interruption costs and reduced risk of congestion. Furthermore, the improvement in the transformer HC by using DTR has been quantified. The results indicate that depending on the HC performance indices, the transformer can be loaded beyond the normal operational limits up to 35% to host PV and up to 100% for increased consumption.
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| 6. |
- Laury, John, 1984-
(författare)
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Stability of Low-Frequency AC Railways : Models and Transient Stability
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Low-frequency AC railway grids are unique in the sense that a only few countries around the world uses them, still however, they are an important parts of their countries infrastructures. Due to the usage of a dierent frequency than the public grid of the country, conversion of frequency is needed for the interconnection. The frequency conversion is done by machine based rotary frequency converters or power electronic based static frequency converters.When reinforcing with new power conversion capacity, mostly static frequency converters are installed since rotary frequency converters for railways have not been manufactured for some time. As more static frequency converterare introduced, the share of rotary frequency converters is reduced. It is not well explored how the stability of low-frequency AC railways is affected with a large share of static frequency converters.In this thesis, the main goal has been to obtain knowledge of the stability of low-frequency AC railway grids, with focus on synchronous ones. The electromechanical stability of a synchronous low-frequency AC railway is explored through numerical simulations, where the transient stability is the main focus.The main contributions of this thesis is proposing a model of a rotary frequency converter, proposing a model of a static frequency converter, and transient stability simulations. The model of the rotary frequency converter uses established machine models, whereas the static frequency converter model has been developed with help of measurements. It can be concluded that the proposed static frequency converter model captures the main behaviour of the measurements of a static frequency converter.The transient stability of synchronous AC railway grids is studied, through numerical simulations. The studied cases are for instance dierent railway grid congurations with dierent types rotary frequency converters and railway grids with mixes of static frequency converters and static frequency converter.The main conclusion is that the rotary frequency converter fed synchronous railway grids studied are transiently stable, and the studied railway grids where rotary frequency converters are gradually replaced with static frequency converter are also transiently stable. However, it was found that the studied railway grids obtain a heavier oscillatory behaviour when there is a mix of rotary frequency converters and static frequency converters.
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| 7. |
- Lennerhag, Oscar, 1989-
(författare)
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Managing uncertainties through efficient calculation of transients and harmonic propagation in power systems
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The electric power system is undergoing changes including large-scale introduction of renewable energy sources together with HVDC/FACTS, changes in the network such as increased amount of high voltage cables, industrial electrification, and changes in the load composition. These changes will impact the system in different ways and lead to challenges that must be addressed to facilitate planning, dimensioning, and operation of the system in a secure and economical way. The aforementioned changes introduce uncertainties in terms of operational state and modelling of both system and components. One example is the modelling of downstream networks and loads in harmonic propagation studies; the customer impedance may have a significant impact on both the resonance frequency and the damping, but its inclusion remains a challenge due to a lack of knowledge about its behaviour at harmonic frequencies. Another example is the calculation of overvoltages caused by line switching or transformer saturation in different operational states and for varying amounts of underground cable in the network. Methods for calculating overvoltages or harmonic propagation are often based on an assumed complete knowledge of the system under study; uncertainties in the system or components are typically addressed by performing Monte Carlo simulations. However, the use of Monte Carlo methods may be impractical or even unsuitable due to the number of calculations required. Deterministic methods, on the other hand, may provide overly pessimistic results leading to large design margins and high costs. This work investigates the application of different methods for managing uncertainties related to the calculation of overvoltages and harmonic propagation. The methods are described, and their advantages and limitations are discussed and illustrated through case studies considering typical uncertainties. Regarding harmonic propagation, two methods are considered: the first method uses copulas to aggregate the harmonic impedance of the downstream network and its loads while retaining its stochastic properties. The method is applied to several medium-voltage and low-voltage networks, and the results show that it is feasible to accurately represent the stochastic behaviour without modelling the downstream network in detail. The second method utilizes the unscented transform together with Cornish-Fisher expansion to calculate the harmonic distortion at the point of connection of a wind farm under different uncertainties. The method is able to estimate the 95% value of individual harmonics accurately when considering variations in emission and impedance, while using a limited number of calculations. Regarding overvoltages, three methods are considered: the first method can be used to determine representative fast front overvoltage levels for HVDC cable systems connected to HVDC overhead lines, from a limited number of calculations. The method, applicable to backflashover and shielding failure, accounts for the statistical distribution of lightning current magnitudes, as well as attenuation due to corona discharges on the line. To illustrate the proposed method, it is applied to a case study for a ±525 kV DC system. The second method considers the use of the unscented transform together with Cornish-Fisher expansion to estimate the 2%- value of switching overvoltages from a limited number of calculations. The method is evaluated by considering three-phase energization or reclosing of a line taking into account several aspects such as line length, type of feeding network, impact of trapped charge on the line, and attenuation of the overvoltage level by corona discharges. The method is shown to provide a good approximation of the 2%-value using only about one fifth to one tenth of the number of simulations typically used in traditional methods. The third method makes it possible to estimate a minimum VI-characteristic of surge arresters. This allows for accurate calculation of the absorbed energy when arresters are subjected to resonant overvoltages. While many uncertainties may be managed by carrying out a sufficient number of calculations, this may not always be the case. To this end, a method has been proposed to manage uncertainties during system operation, specifically considering the risk of resonant overvoltages due to transformer saturation following the clearing of a nearby line fault. The method utilizes partial disconnection of parallel cables according to a predetermined scheme to shift the system resonance frequency. The method is shown to reduce the duration of the temporary overvoltage and the stress on surge arresters and other equipment.
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| 8. |
- Mulenga, Enock
(författare)
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On the hosting capacity of distribution networks for solar power
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The future will bring changes in energy production and consumption that will affect the performance of electricity distribution networks. Electric vehicle charging will increase consumption; the installation of solar photovoltaic (PV) units will increase production. Both will change the energy flow and affect the power quality. The installation of solar PV units or electric vehicle (EV) charging has a limit above, which they unacceptably deteriorate the distribution networks' performance. This limit is referred to as the hosting capacity of the distribution network. This work is about developing, applying and studying methods for estimating the hosting capacity, especially solar PV. Three fundamentally different methods to estimate solar PV hosting capacity for single-phase and three-phase units have been identified by a detailed review of the literature: deterministic, stochastic and time-series. The methods were shown to differ in the required input data, accuracy, computation time, consideration of uncertainties, and time-correlation between different phenomena. The methods have also been compared in relation to their application for the assessment of connection requests (screening) or detailed analysis. Solar power production, energy consumption and distribution networks’ all have uncertainties associated with them. It is helpful to distinguish between two types of uncertainties when estimating the hosting capacity: aleatory (“certain”) and epistemic (“uncertain”) uncertainties. A stochastic approach, ‘mixed aleatory-epistemic’, was applied to about 1500 low-voltage distribution networks. A similar stochastic approach and models were applied to estimate low-voltage networks' hosting capacity for electric vehicle charging. A deterministic method was applied to determine the hosting capacity considering the thermal overload phenomenon for both PV and EV charging. A planning risk has been introduced to quantify the risk of the distribution network not being able to cope with a future penetration of solar PV or EV charging. The planning level entails that a distribution network operator accepts a certain risk of exceeding the overvoltage limit. The concept has been applied as part of the stochastic approach. The hosting capacity for a distribution network is quantified considering a performance index and a limit to what is an acceptable deterioration of that index. The 90th percentile of the annual peak demand (overvoltage or overload) has been used as a performance index in most of the hosting capacity studies in this work. The time-of-day (ToD) and time-of-year (ToY) concepts were introduced to model the aleatory uncertainties. The time-of-day exemplifies the relevant part of the day, and the time-of-year shows the parts of the year applicable for hosting capacity studies when high solar power production can be expected. The time-of-day of 10 am to 2 pm has been applied. The period from 21st March to 21st September was the applied time-of-year. The latter two, ToD and ToY, need to be defined for the application of the concept to other areas than those covered in this work. It was shown that the hosting capacity would be underestimated by about 10% if an incorrect ToD were used. Voltage magnitude and solar power production measurements, over one year with a 10-minute resolution, were obtained for thirty-three 10/0.4 kV distribution transformers in Northern Sweden. A method of obtaining the ‘background voltage’ from the measurements was formulated. The background voltage (including its uncertainties) was one of the factors with the greatest influence on the hosting capacity. Stochastic models for distribution networks were built, and the hosting capacity for low voltage distribution networks has been studied. The outcome shows that three-phase solar PV units have a higher hosting capacity than single-phase units. The model and method developed can be used as a planning tool by distribution network operators (DSOs). The inclusion of the uncertainties and correct handling of planning risks is paramount for decision making by DSOs. The results show that background voltage variations should be considered from measurements, and appropriate ToD/ToY should be used. The quantification of the hosting capacity requires both consumption and voltage measurements in the distribution networks. The work has also shown that the time of the day and year (ToD and ToY) need to be considered for the many hosting capacity methods. The impact is expected to be highest in the ToD and ToY. Also, the two types of uncertainties have been clarified in this work. They need to be considered as the decisions DSOs make will depend on them. This work has generally found that hosting capacity estimation methods are many and different. They are all applicable and useful tools for identifying the factors in distribution networks that can hold up solar PV and EV charging penetration. It has also been found that there is a strong link between distribution network planning and hosting capacity estimation methods. The hosting capacity methods in this work can undertake the risks connected to solar PV and EV charging.
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| 9. |
- Nakhodchi, Naser, 1981-
(författare)
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On Harmonics in Low-voltage Networks
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- On the road to reducing global warming, the use of renewable energy sources and efficient use of electricity are among the key aspects. The increasing number of energy efficient appliances such as LED lamps, booming demand for electric vehicles (EVs), and growing penetration of distributed energy resources such as photovoltaic (PV) systems in low-voltage (LV) networks are expected to affect the power quality in the entire electric power system and specifically in the LV network, where such new devices are connected. Waveform distortion, mainly expressed by the harmonic components, is one of the topics within power quality that is highly affected by the introduction of such new devices. However, there is a lack of publications discussing the existing level of voltage harmonics in LV networks or addressing the origin and transfer of harmonics in LV and MV distribution networks. This highlights the need for more research in this field.To evaluate the ability of the network to host new sources of harmonics, the existing harmonic voltage and current levels as well as the impact of these new sources on those levels should be investigated. Harmonic levels are determined by emission from harmonic sources, the propagation from other harmonic sources, and the aggregation between the contributions from different sources. Studies on harmonic emission from a variety of different individual devices under different conditions have already been carried out. However, limited knowledge is available about the harmonic aggregation and propagation in LV networks. This study aims to improve the understanding about the behaviour of harmonics in LV networks covering both aggregation and propagation.In the first part of this work, the impact of the MV network and remote LV loads on the harmonic voltage in the LV network are examined. Simulation results have revealed that for frequencies below the resonant frequency of the local LV network the harmonic voltage levels mainly are determined by aggregated emission of the whole distribution network (both LV and MV) rather than by the emission from local LV loads. Furthermore, a graphical method is introduced for harmonic propagation studies, using measurements but without the need for accurate synchronized measurements.In the second part of this work, the aggregated emission from a group of EV fast chargers is examined. A stochastic method, based on Bayesian statistics and harmonic correlation, was used to include uncertainties in harmonic hosting capacity calculation for an EV charging station equipped with fast chargers. The impact of MV network and remote LV loads on harmonic hosting capacity is investigated. It is also shown that harmonic hosting capacity studies are needed; and details of the distribution network must be included to get an accurate estimation of the harmonic hosting capacity.Finally, an alternative method for time aggregation of harmonic phase angle is proposed in this work.In general, this work contributes to reducing the research gaps recognized in harmonic analysis in the LV networks considering propagation and aggregation utilizing both simulation and measurement.
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| 10. |
- Nömm, Jakob
(författare)
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Power quality analysis and techno-economic modeling for microgrids
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The work done in this thesis considers microgrids from two different aspects. Power quality and techno-economics of microgrids. Detailed power quality measurements have been made at a single house hydrogen-solar microgrid that consists of state-of-the-art energy efficiency technology, energy production and energy storage. The microgrid can both connect to the grid and operate in islanded operation. The power quality is quantified from these measurements where several power quality parameters during islanded operation go beyond the limits set by standards such as EN 50160 and IEEE 519-2014. The effect on connected equipment from both frequency variations and voltage quality is also discussed. Four new performance indexes are presented in the thesis that are based on apparent impedances. The first with the name PHIPI quantifies how much the harmonic voltage magnitude changes with an increase in harmonic current magnitude on the same phase. The second with the name SHIPI quantifies how much the harmonic voltage magnitude changes with an increase in harmonic current magnitude on another phase. The third with the name AHSI uses the harmonic voltage and current magnitudes of all phases to create a single performance parameter expressed as an apparent impedance for the system. The fourth with the name ARMSSI quantifies the phase RMS voltage drop for a certain phase RMS current rise in terms of an apparent impedance. The thesis also shows techno-economic modeling with times series energy flow to study the investment risks related to consumption changes in a standalone microgrid. The results show that consumption changes are an important parameter when designing a standalone microgrid and that the risk can be mitigated with changes to the system design, but at a larger system cost. The projected cost reduction until the year 2050 for standalone hydrogen based microgrids and some risk aspects with hydrogen based microgrids are also discussed in the thesis.
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