1,948 research outputs found
Metrological characterisation of Low Power Voltage Transformers by using impulse response analysis
this thesis presents a new approach in dealing with characterize LPVT and proposes determining the impulse response of LPVT, purposing to find transfer function (h(t)) which contains most electrical characteristics of LPVTs as a dynamic system
Distributed Online Load Sensitivity Identification by Smart Transformer and Industrial Metering
Load power sensitivity to voltage changes continuously in the distribution grid due to the increased variability of the load demand (e.g., electric vehicles charging) and generation production (e.g., photovoltaic). Classical sensitivity identification methods do not respect the fast dynamics of such changes: they require long data history and/or high computational power to update the load sensitivity. The proposed online load sensitivity identification (OLLI) approach is able to identify the load sensitivity in real time (e.g., every minute). This paper demonstrates that the OLLI can be achieved not only with the advanced smart transformer metering system but also with commercial industrial metering products. It is shown that OLLI is able to identify correctly the load sensitivity also in the presence of noise or fast stochastic variation of power consumption. The industrial metering-based OLLI application has been proven by means of a power-hardware-in-loop evaluation applied on an experimental microgrid
A Novel Power-Band based Data Segmentation Method for Enhancing Meter Phase and Transformer-Meter Pairing Identification
This paper presents a novel power-band-based data segmentation (PBDS) method
to enhance the identification of meter phase and meter-transformer pairing.
Meters that share the same transformer or are on the same phase typically
exhibit strongly correlated voltage profiles. However, under high power
consumption, there can be significant voltage drops along the line connecting a
customer to the distribution transformer. These voltage drops significantly
decrease the correlations among meters on the same phase or supplied by the
same transformer, resulting in high misidentification rates. To address this
issue, we propose using power bands to select highly correlated voltage
segments for computing correlations, rather than relying solely on correlations
computed from the entire voltage waveforms. The algorithm's performance is
assessed by conducting tests using data gathered from 13 utility feeders. To
ensure the credibility of the identification results, utility engineers conduct
field verification for all 13 feeders. The verification results unequivocally
demonstrate that the proposed algorithm surpasses existing methods in both
accuracy and robustness.Comment: Submitted to the IEEE Transactions on Power Delivery. arXiv admin
note: text overlap with arXiv:2111.1050
Phase topology identification in low-voltage distribution networks: a Bayesian approach
Knowledge of customer phase connection in low-voltage distribution networks is important for Distribution
System Operators (DSOs). This paper presents a novel data-driven phase identification method based on Bayesian
inference, which uses load consumption profiles as inputs. This method uses a non-linear function to establish the
probability of a customer being connected to a given phase, based on variations in the customer’s consumption
and those in the phase feeders. Owing to the Bayesian inference, the proposed method can provide up-to-date
certainty about the phase connection of each customer. To improve the detection of those customers that are
more difficult to identify, after obtaining the up-to-date certainty for all users, the consumption of those who
have an up-to-date certainty above a certain percentile compared with the rest of the substation (those that are
more likely to be correctly classified) is subtracted from the phase in which they are classified. The performance
of the proposed method was evaluated using a real (non-synthetic) low-voltage distribution network. Favourable
results (with accuracies higher than 97 %) were obtained in almost all cases, regardless of the percentage of
Smart Meter penetration and the size of the substation. A comparison with other state-of-the-art methods showed
that the proposed method outperforms (or equals) them. The proposed method does not necessarily require
previously labelled data; however, it can handle them even if they contain errors. Having previous information
(partial or complete) increases the performance of phase identification, making it possible to correct erroneous
previous labelling
Monitoring and Fault Location Sensor Network for Underground Distribution Lines
One of the fundamental tasks of electric distribution utilities is guaranteeing a continuous
supply of electricity to their customers. The primary distribution network is a critical part of these
facilities because a fault in it could affect thousands of customers. However, the complexity of
this network has been increased with the irruption of distributed generation, typical in a Smart
Grid and which has significantly complicated some of the analyses, making it impossible to apply
traditional techniques. This problem is intensified in underground lines where access is limited. As a
possible solution, this paper proposes to make a deployment of a distributed sensor network along
the power lines. This network proposes taking advantage of its distributed character to support new
approaches of these analyses. In this sense, this paper describes the aquiculture of the proposed
network (adapted to the power grid) based on nodes that use power line communication and energy
harvesting techniques. In this sense, it also describes the implementation of a real prototype that
has been used in some experiments to validate this technological adaptation. Additionally, beyond
a simple use for monitoring, this paper also proposes the use of this approach to solve two typical
distribution system operator problems, such as: fault location and failure forecasting in power cables.Ministerio de Economía y Competitividad, Government of Spain project Sistema Inteligente Inalámbrico para Análisis y Monitorización de Líneas de Tensión Subterráneas en Smart Grids (SIIAM) TEC2013-40767-RMinisterio de Educación, Cultura y Deporte, Government of Spain, for the funding of the scholarship Formación de Profesorado Universitario 2016 (FPU 2016
Performance Evaluation of Communication Technologies and Network Structure for Smart Grid Applications
The design of an effective and reliable communication network supporting smart grid applications requires the selection of appropriate communication technologies and protocols. The objective of this study is to study and quantify the capabilities of an advanced metring infrastructure (AMI) to support the simultaneous operation of major smart grid functions. These include smart metring, price-induced controls, distribution automation, demand response, and electric vehicle charging/discharging applications in terms of throughput and latency. OPNET is used to simulate the performance of selected communication technologies and protocols. Research findings indicate that smart grid applications can operate simultaneously by piggybacking on an existing AMI infrastructure and still achieve their latency requirements
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