Distribution Transformers Research Articles

Pengaruh Panjang Jaringan Terhadap Drop Tegangan Pada Penyulang Duku dan Kurma PT. PLN (Persero) ULP Rumbia

Voltage drops in distribution systems can occur in medium voltage networks (JTM), distribution transformers, low voltage networks (JTR) and house lines. Voltage drops in the network cause suboptimal distribution and increase power losses. PT. PLN (Persero) ULP Rumbia has two feeders with pure radial network characteristics sourced from the Seputih Banyak Main Substation. The length of the Duku Feeder network has a voltage of 96.8 KMS with a load of 150 A while the length of the Kurma Feeder network is 207.21 kV with a load of 155 A. The network reconfiguration of the Dente Teladas Substation was carried out to shorten the network length. This research aims to calculate the voltage drop that occurs at the Duku and Kurma feeders before and after network configuration. This research uses the quantity method to compare the calculation results with the voltage drop standards set by PT. PLN (Persero). After reconfiguring the Duku and Kurma feeders, there was an increase in tip voltage which indicated improvement. From the calculation results before adding the network to the Duku feeder, the voltage at the receiving end was 19.5 kV with a voltage drop of 7.1%, and at the Kurma feeder the voltage at the end of the line was 17 kV with a voltage drop of 19%. This is clearly no longer in accordance with the local TMP, and requires further study to overcome this problem. The voltage on the two feeders goes through a step up process using a Medium Voltage OLTC (On Load Tap Changer) so that the voltage on each feeder becomes between 19.86 kV to 20.16 kV.

Aggregated demand-side response in residential distribution areas based on tiered incentive prices

The residential area refers to the power supply area from distribution transformers to the end users that contains multiple types of flexible resources, such as photovoltaics, energy storage, and power users. Focusing on the challenge of insufficient demand response incentives to multiple types of users in residential distribution areas, a tiered incentive price-based demand-side aggregated response method is proposed in this paper. Users in residential distribution areas are classified with an improved k-means clustering method for obtaining typical types of users. Thereafter, initial scores of users are calculated, and their grades are assigned based on their scores. Corresponding tiered incentive prices are designed for different grades. On this basis, a leader–follower game is proposed to obtain the demand response base price, and tiered incentives are provided to users of different grades to increase their enthusiasm for participating in demand response. In the case study, an actual urban residential distribution area is studied. The results show that the proposed user clustering method has an accuracy of 99.8% in classifying users in a residential distribution area. In addition, the proposed method has better performance in terms of improving the benefit of the load aggregator and users in the residential distribution area compared with methods such as potential game, hidden Markov, and Monte Carlo. Specifically, from the results, the benefit of load aggregators is increased by 101.96%, 76.07%, and 112.37%, and the income of the users is increased by 54.51%, 36.94%, and 64.91%.

Improving Dynamic Thermal Characteristics of Power Transformers Using an Earth-Oil Heat Exchanger

A significant aspect affecting the power transformer’s performance and lifetime is the maximum temperature within the machine. This research estimates the top-oil temperature (TOT), bottom-oil temperature (BOT), and hot-spot temperature (HST) for a 2500 kVA oil natural air natural (ONAN) distribution transformer with or without an earth-oil heat exchanger (EOHE). The differential equations of the thermal models are solved using the Runge-Kutta fourth-order method. MATLAB code is used to simulate the created models. The present model takes into account how oil properties such as dynamic viscosity, thermal conductivity, and density change with temperature. The IEEE model is used for model validation. The estimated findings are of high accuracy compared with the others. Results show that the maximum reductions in the HST, TOT, and BOT due to EOHE are 48.82%, 41.93%, and 29.59%, respectively. The use of EOHE decreases the temperature of the transformer oil as well as increases the transformer’s lifetime.

Multi-Mode Control of a Hybrid Transformer for the Coordinated Regulation of Voltage and Reverse Power in Active Distribution Network

The unprecedented growth of distributed renewable generation is changing the distribution network from passive to active, resulting in issues like reverse power flow, voltage violations, malfunction of protection relays, etc. To ensure the reliable and flawless operation of active distribution networks, an electrical device enabling active network management is necessary, and a hybrid distribution transformer offers a promising solution. This study introduces a novel hybrid transformer topology and multi-mode control strategy to achieve coordinated voltage and reverse power regulation in active distribution networks. The proposed hybrid transformer combines conventional transformer windings with a partially rated SiC-MOSFET-based back-to-back converter, reducing additional investment costs and enhancing system reliability. A multi-mode control strategy is proposed to facilitate the concurrent reverse power control and voltage violation mitigation of the presented hybrid transformer, allowing a smooth transition between the P–Q control mode and the V–f control mode. The control mode switching can be activated manually or autonomously in response to voltage violations or reverse power overloading. The effectiveness of the proposed hybrid transformer configuration and its control mode transition mechanism are examined through comprehensive case studies conducted in the PSCAD/EMTDC environment. The proposed HT design has been confirmed to achieve a voltage regulation range of ±20% of the nominal voltage and effectively regulate bidirectional active power flow within a range of −25% to 25% of the rated power.

Water Renewal Time in Lakes with Transformed Water Distribution in the Catchment Areas

Water exchange in lake basins is a very important process in regulating the health of the aquatic environment, e.g., by shaping algal blooms. Thus, knowledge of the process is also required to develop management strategies. The paper presents a dynamic of water renewal time in the Uściwierz chain of lakes, in which the natural hydrological connectivity of the catchment areas has been altered due to human impact. Calculations of water renewal were limited to the part of the lake basin corresponding to the active (dynamic) retention layer. A comparative analysis of the rate of potential water renewal, based on the structure difference index, was used as an indicator of the degree of anthropogenic transformation of water distribution in the lake catchments. The smallest differences in the structure of the water renewal rate between the neighboring lakes in the chain system were observed in the cold period, and the largest differences were observed in the warm period. The results showed that the shorter the timescale (5-day period), the higher the similarity in the structure of the water renewal rate between the lakes. Very large differences between the structure of the potential water renewal rate in Lakes Uściwierz and Bikcze indicated a significant transformation of the water cycle down the lake chain. The water renewal rate proved to be a good indicator of the degree of anthropogenic transformation in the catchments located in close proximity.

A Multimodal Fusion System for Object Identification in Point Clouds with Density and Coverage Differences

Data fusion, which involves integrating information from multiple sources to achieve a specific objective, is an essential area of contemporary scientific research. This article presents a multimodal fusion system for object identification in point clouds in a controlled environment. Several stages were implemented, including downsampling and denoising techniques, to prepare the data before fusion. Two denoising approaches were tested and compared: one based on neighborhood technique and the other using a median filter for each “x”, “y”, and “z” coordinate of each point. The downsampling techniques included Random, Grid Average, and Nonuniform Grid Sample. To achieve precise alignment of sensor data in a common coordinate system, registration techniques such as Iterative Closest Point (ICP), Coherent Point Drift (CPD), and Normal Distribution Transform (NDT) were employed. Despite facing limitations, variations in density, and differences in coverage among the point clouds generated by the sensors, the system successfully achieved an integrated and coherent representation of objects in the controlled environment. This accomplishment establishes a robust foundation for future research in the field of point cloud data fusion.

Localized management of distributed flexible energy resources

The integration of distributed energy resources continues to be a challenge for the traditional electricity grid. Aside from additional variability in the net power demand, there is often a temporal discrepancy between generation and consumption schedules which can exacerbate demand peaks and troughs, leading to system overloading, voltage violations, and a low system load factor. This study proposes a control system approach to unify the management of the different types of flexible energy resources through Localized Demand Control. In this system, all flexible energy resources are coordinated using a power line carrier frequency signal to follow a preferred demand curve at the distribution transformer level. The system’s functionality has been confirmed in an actual 5-house microgrid without adverse effects on end-user comfort. Meanwhile, scaled-up simulation studies have shown improvements in the system load factor by as much as 189%, and increased hosting capacity by 40% for electric vehicles and 16% for solar photovoltaic panels.

Comparative analysis of the characteristics of outlet short circuit and winding insulation fault of distribution transformer and its preventive measures

AbstractOutlet short circuit on the low‐voltage (LV) side and winding inter‐turn short circuit faults are hazardous to transformer operation. To investigate the formation mechanism of winding insulation faults of distribution transformer, ANSYS Maxwell was used to build a coupled magnetic field‐circuit model with the same structural dimension as the actual distribution transformer. An outlet short circuit and winding inter‐turn insulation faults were set by using the voltage‐controlled switch in the external circuit of the model. Subsequently, the differences in the electromagnetic characteristics and the electrodynamic force distributions of the windings under three operating conditions, namely, nominal load, three‐phase outlet short circuit on the LV side and inter‐turn insulation failure were studied, respectively. The results show that compared with the rated load, in the cases of outlet short circuit and inter‐turn insulation faults, the amplitude of winding current increases by 20 and 50 times, the magnetic field strength grows by 20 and 17 times, and the electrodynamic force rises by 400 and 230 times, respectively. Outlet short circuit fault is more likely to cause the winding instability and deformation, and inter‐turn short circuit fault can easily burn out winding insulation. Therefore, corresponding preventive measures were proposed.

Comparative the effect of distribution transformer coil shape on electromagnetic forces and their distribution using the FEM

Abstract During the normal life of the transformers, they are subjected to different electromagnetic stresses. One of these stresses is the electromagnetic forces resulting from the passage of short-circuit current in the transformer coils due to internal or external faults and may lead to the failure of the transformer when this electromagnetic force exceeds the threshold level. This work deals with the computation and analysis of leakage magnetic flux and electromagnetic forces when the worst-case fault (symmetrical short-circuit current) occurs in distribution transformer (DT) coils using the finite element method (FEM). The three types of DTs that were adopted in this work are similar in capacity and voltage transformation ratio (250 kVA and 11,000/416 V), but they are different in the shape of coils (oval, cylindrical, and rectangular coils). ANSYS software was used to build two-dimensional models of the three transformers, which were different in coil shapes and in the type of iron core. The objective of this work is to compare the effect of coil shape on the distribution of electromagnetic forces and their value, in order to find out which coil shape is the best to withstand electromagnetic forces when short-circuit current is passed in the coils. The results of the simulation of the finite element models were approximately equivalent to the results of the design calculations that depend on the classical method (the analytical method), but the FEM is more accurate, due to the accuracy of calculating the magnetic flux and its distribution, which cannot be calculated using the classical method. One of the most important contributions of this research is the analysis and calculation of electromagnetic forces for three types of DTs with different coils by applying the same design parameters to all the transformers. The research also contributed to determining the best coil shape by comparing simulation results, and it was found that transformers with cylindrical coils are the best to withstand electromagnetic forces due to the homogeneity of the cylindrical coil structure from all sides.

Service scheduling strategy for microservice and heterogeneous multi-cores-based edge computing apparatus in smart girds with high renewable energy penetration

The microservice-based smart grid service (SGS) organization and the heterogeneous multi-cores-based computing resource supply are the development direction of edge computing in smart grid with high penetration of renewable energy sources and high market-oriented. However, their application also challenges the service schedule for edge computing apparatus (ECA), the physical carrier of edge computing. In the traditional scheduling strategy of SGS, an SGS usually corresponds to an independent application or component, and the heterogeneous multi-core computing environment is also not considered, making it difficult to cope with the above challenges. In this paper, we propose an SGS scheduling strategy for the ECA. Specifically, we first present an SGS scheduling framework of ECA and give the essential element of meeting SGS scheduling. Then, considering the deadline and importance attributes of the SGS, a microservice scheduling prioritizing module is proposed. On this basis, the inset-based method is used to allocate the microservice task to the heterogeneous multi-cores to utilize computing resources and reduce the service response time efficiently. Furthermore, we design the scheduling unit dividing module to balance the delay requirement between the service with early arrival time and the service with high importance in high concurrency scenarios. An emergency mechanism (EM) is also presented for the timely completion of urgent SGSs. Finally, the effectiveness of the proposed service scheduling strategy is verified in a typical SGS scenario in the smart distribution transformer area.

LeGO-LOAM-FN: An Improved Simultaneous Localization and Mapping Method Fusing LeGO-LOAM, Faster_GICP and NDT in Complex Orchard Environments.

To solve the problem of cumulative errors when robots build maps in complex orchard environments due to their large scene size, similar features, and unstable motion, this study proposes a loopback registration algorithm based on the fusion of Faster Generalized Iterative Closest Point (Faster_GICP) and Normal Distributions Transform (NDT). First, the algorithm creates a K-Dimensional tree (KD-Tree) structure to eliminate the dynamic obstacle point clouds. Then, the method uses a two-step point filter to reduce the number of feature points of the current frame used for matching and the number of data used for optimization. It also calculates the matching degree of normal distribution probability by meshing the point cloud, and optimizes the precision registration using the Hessian matrix method. In the complex orchard environment with multiple loopback events, the root mean square error and standard deviation of the trajectory of the LeGO-LOAM-FN algorithm are 0.45 m and 0.26 m which are 67% and 73% higher than those of the loopback registration algorithm in the Lightweight and Ground-Optimized LiDAR Odometry and Mapping on Variable Terrain (LeGO-LOAM), respectively. The study proves that this method effectively reduces the influence of the cumulative error, and provides technical support for intelligent operation in the orchard environment.

Parametric and Nonparametric Machine Learning Techniques for Increasing Power System Reliability: A Review

Due to aging infrastructure, technical issues, increased demand, and environmental developments, the reliability of power systems is of paramount importance. Utility companies aim to provide uninterrupted and efficient power supply to their customers. To achieve this, they focus on implementing techniques and methods to minimize downtime in power networks and reduce maintenance costs. In addition to traditional statistical methods, modern technologies such as machine learning have become increasingly common for enhancing system reliability and customer satisfaction. The primary objective of this study is to review parametric and nonparametric machine learning techniques and their applications in relation to maintenance-related aspects of power distribution system assets, including (1) distribution lines, (2) transformers, and (3) insulators. Compared to other reviews, this study offers a unique perspective on machine learning algorithms and their predictive capabilities in relation to the critical components of power distribution systems.

Development of the sleep-wake switch in rats during the P2-P21 early infancy period.

In early infancy, rats randomly alternate between the sleeping and waking states-from postnatal day 2-10 (P2-P10), sleep and wake bouts are both exponentially distributed with increasing means, while from P10-P21 sleep and wake bout means continue to increase, though there is a striking qualitative shift in the distribution of wake bouts from exponential to power law. The behavioral states of sleep and wakefulness correspond to the activity of sleep-active and wake-active neuronal brainstem populations, with reciprocal inhibition between the two ensuring that only one population is active at a time. The locus coeruleus (LC) forms a third component of this circuit that rises in prominence during the P10-P21 period, as experimental evidence shows that an as-of-yet undeciphered interaction of the LC with sleep-active and wake-active populations is responsible for the transformation of the wake bout distribution from exponential to power law. Interestingly, the LC undergoes remarkable physiological changes during the P10-P21 period-gap junctions within the LC are pruned and network-wide oscillatory synchrony declines and vanishes. In this work, we discuss a series of models of sleep-active, wake-active, and the LC populations, and we use these models to postulate the nature of the interaction between these three populations and how these interactions explain empirical observations of sleep and wake bout dynamics. We hypothesize a circuit in which there is reciprocal excitation between the LC and wake-active population with inhibition from the sleep-active population to the LC that suppresses the LC during sleep bouts. During the P2-P10 period, we argue that a noise-based switching mechanism between the sleep-active and wake-active populations provides a simple and natural way to account for exponential bout distributions, and that the locked oscillatory state of the LC prevents it from impacting bout distributions. From P10-P21, we use our models to postulate that, as the LC gradually shifts from a state of synchronized oscillations to a state of continuous firing, reciprocal excitation between the LC and the wake-active population is able to gradually transform the wake bout distribution from exponential to power law.

A diffusion probabilistic model for traditional Chinese landscape painting super-resolution

AbstractTraditional Chinese landscape painting is prone to low-resolution image issues during the digital protection process. To reconstruct high-quality images from low-resolution landscape paintings, we propose a novel Chinese landscape painting generation diffusion probabilistic model (CLDiff), which is similar to the Langevin dynamic process, and realizes the transformation of the Gaussian distribution into the empirical data distribution through multiple iterative refinement steps. The proposed CLDiff can provide ink texture clear super-resolution predictions by gradually transforming the pure Gaussian noise into a super-resolution landscape painting condition on a low-resolution input through a parameterized Markov Chain. Moreover, by introducing an attention module with an energy function into the U-Net architecture, we turn the denoising diffusion probabilistic model into a powerful generator. Experimental results show that CLDiff achieves better visual results and highly competitive performance in traditional Chinese Landscape painting super-resolution tasks.

Heavy-Tailed Distribution for Combining Dependent P-Values With Asymptotic Robustness

The issue of combining individual $p$-values to aggregate multiple small effects is prevalent in many scientific investigations and is a long-standing statistical topic. Many classical methods are designed for combining independent and frequent signals in a traditional meta-analysis sense using the sum of transformed $p$-values with the transformation of light-tailed distributions, in which Fisher's method and Stouffer's method are the most well-known. Since the early 2000, advances in big data promoted methods to aggregate independent, sparse and weak signals, such as the renowned higher criticism and Berk-Jones tests. Recently, Liu and Xie(2020) and Wilson(2019) independently proposed Cauchy and harmonic mean combination tests to robustly combine $p$-values under "arbitrary" dependency structure, where a notable application is to combine $p$-values from a set of often correlated SNPs in genome-wide association studies. The proposed tests are the transformation of heavy-tailed distributions for improved power with the sparse signal. It calls for a natural question to investigate heavy-tailed distribution transformation, to understand the connection among existing methods, and to explore the conditions for a method to possess robustness to dependency. In this paper, we investigate the regularly varying distribution, which is a rich family of heavy-tailed distribution and includes Pareto distribution as a special case. We show that only an equivalent class of Cauchy and harmonic mean tests have sufficient robustness to dependency in a practical sense. We also show an issue caused by large negative penalty in the Cauchy method and propose a simple, yet practical modification. Finally, we present simulations and apply to a neuroticism GWAS application to verify the discovered theoretical insights and provide practical guidance.

МЕТОДИКА ОПРЕДЕЛЕНИЯ ЭФФЕКТИВНЫХ КАНАЛОВ СБЫТА В ОПТОВОЙ ТОРГОВЛЕ МАЛОГО ПРЕДПРИНИМАТЕЛЬСТВА В СОВРЕМЕННЫХ УСЛОВИЯХ

With the growing availability of digital tools in the wholesale trade of small businesses, it became possible to automate internal business processes and integrate with the business processes of external counterparties, which inevitably led to the transformation of product distribution patterns, raising the question of the effectiveness of using traditional distribution channels in the new realities. The author proposed possible models for the transformation of product distribution and developed a methodology for determining effective sales channels in the conditions of the digital economy and the development of logistics services. In the course of the study, the author identified the main vectors for identifying effective sales channels, which require mandatory analysis of their impact on the calculation results. Based on the results of the study, the author developed practical recommendations for managers of wholesale small business companies, taking into account the level of their entrepreneurial competence and access to qualified personnel, in the form of designating possible models of transformation of product distribution and methods for calculating effective distribution channels, taking into account new opportunities.

Distributional Transform Based Information Reconciliation

In this paper, we present an information reconciliation protocol designed for Continuous-Variable QKD using the Distributional Transform. By combining tools from copula and information theory, we present a method for extracting independent symmetric Bernoulli bits for Gaussian-modulated CVQKD protocols, which we called the Distributional Transform Expansion (DTE). We derived the expressions for the maximum reconciliation efficiency for both homodyne and heterodyne measurements, which, for the last, is achievable with an efficiency greater than 0.9 at a signal-to-noise ratio lower than -3.6 dB.

Investigation of non-uniformity of temperature distribution and phase transformation in spiral bevel gears during carburizing and quenching

Investigation of non-uniformity of temperature distribution and phase transformation in spiral bevel gears during carburizing and quenching

NDT RC: Normal Distribution Transform Occupancy 3D Mapping With Recentering

The Normal Distribution Transform Occupancy Map (NDT OM) is a mapping algorithm able to represent a dynamic 3D environment. The resulting map has fixed boundaries, thus a robot with unbounded displacement might fall outside of the map due to memory limitation. In this paper, a recentering algorithm called NDT RC is proposed to avoid this issue. NDT RC extends the use of NDT OM for vehicles with unbounded displacements. NDT RC provides a seamless translation of the map as the robot gets far from the center of the previous map. The influence of NDT RC on the precision of the estimated trajectory of the robot, or odometry, is examined on two publicly available datasets, the KITTI and Ford datasets. An analysis of the sensitivity of the NDT RC to its tuning parameters is carried out using the Ford dataset, while the KITTI dataset is used to measure the influence of the density of the input point cloud. The results show that the proposed recentering strategy improves the accuracy of the odometry calculated by registering the latest lidar scan on the generated map compared to other NDT based approaches (NDT OM, NDT OM Fusion, SE-NDT). In particular, the proposed method, which does not perform loop closure, reduces the mean absolute translation error by 16% and the runtime by 88% compared to the NDT OM Fusion on the Ford dataset.

LiDAR-Based Optimized Normal Distribution Transform Localization on 3-D Map for Autonomous Navigation

LiDAR-Based Optimized Normal Distribution Transform Localization on 3-D Map for Autonomous Navigation