Hasil untuk "Distribution or transmission of electric power"

Hamza Bokhari, Ilja Krybus, Maik Scholz et al.

This paper presents the theory and implementation of techniques to predict the time available for the Control Center personnel of transmission and distribution system operators to respond to an alarming event related to a grid asset. The described techniques include trendline, linear regression, value-at-risk and k-means classification-based prediction and are implemented to support decision-making even with poor quality SCADA data. These techniques have already been deployed in a modular transmission control center alarming and logging system and can be applied for a variety of assets in power systems as well as in other utilities and process industries.

Aristide Giuliano, Domenico Brancaccio, Antonio Ricca et al.

First, Europe and then the rest of the world will undergo the transition to electric mobility in the coming decades. This shift will lead to a significant increase in power demand to charge all new electric vehicles. Therefore, power transmission and distribution management could become the most challenging problem to solve in the near future, worldwide. High costs and related emissions could nullify the benefits of electric mobility, making it less cost-effective. A potential solution to meet decarbonization goals is to employ generators fueled by biofuels, providing energy to charging stations in remote and/or extra-urban areas. The environmental advantages of biofuels and the existing fuel distribution infrastructures could offset the higher power generation costs using biofuels. In this work, an economic and environmental analysis is performed to compare three sustainable mobility scenarios: full electric, endothermic using biofuels, and the off-board hybrid. The results, obtained from the well-to-wheel analysis and cost assessment, show that the full electric scenario is the best from an environmental point of view, while the biofuel-based off-board hybrid can be a viable solution for a more sustainable energy transition. In particular, the CO2 equivalent emissions related to full electric mobility were found to be 6.5 kgCO2eq/100km, while the utilization of biofuels in internal combustion engines would guarantee a cost of 27.0 €/100km.

Aniruddha Venkata

We study the first power correction to the heavy electron form factor in QED and show that it factorizes as a derivative operator. We discuss the result in QED with no light fermions, where the first power correction can be written explicitly in terms of one-loop integrals and the anomalous magnetic moment. In the presence of light fermions, the heavy electron form factor admits a representation as a sum over matrix elements, each of which receives corrections from higher orders in perturbation theory. From this analysis, we are able to extract the next-to-leading power soft photon theorem in the limit of heavy fermion-initiated dijet events. This is a first step towards studying the heavy quark form factor in the non-abelian theory.

Chinmoy Biswas, Nafis Faisal, Vivek Chowdhury et al.

Sparsity, defined as the presence of missing or zero values in a dataset, often poses a major challenge while operating on real-life datasets. Sparsity in features or target data of the training dataset can be handled using various interpolation methods, such as linear or polynomial interpolation, spline, moving average, or can be simply imputed. Interpolation methods usually perform well with Strict Sense Stationary (SSS) data. In this study, we show that an approximately 62\% sparse dataset with hourly load data of a power plant can be utilized for load forecasting assuming the data is Wide Sense Stationary (WSS), if augmented with Gaussian interpolation. More specifically, we perform statistical analysis on the data, and train multiple machine learning and deep learning models on the dataset. By comparing the performance of these models, we empirically demonstrate that Gaussian interpolation is a suitable option for dealing with load forecasting problems. Additionally, we demonstrate that Long Short-term Memory (LSTM)-based neural network model offers the best performance among a diverse set of classical and neural network-based models.

Aleksandr Skamyin

Abstract The paper presents a new method for estimating the contribution of distortion sources based on the application of passive harmonic filters. The method does not require the measurement of harmonic network impedance and is based on the measurement of harmonic currents of the grid, consumers and passive harmonic filters. Evaluation of the consumer contribution using a passive harmonic filter is necessary to select the parameters and connection points for harmonic reduction devices. A feature of the method is the correct determination of share contributions, regardless of background harmonic distortions. Based on this method, a single consumer can evaluate both the harmonic contributions of its own loads, even in the presence of background distortions, and the harmonic voltage distortions in the case of installing harmonic reduction devices. The research results are confirmed in laboratory conditions with various combinations of electrical loads connected both at the grid side and at the consumer side. For such conditions, the proposed method was compared with existing methods, among which are methods based on the measurement of harmonic voltage vectors, harmonic current vectors and active harmonic power. The application of the developed method was demonstrated using the example of a gas production field.

Majid Mohtashami, Abbas Saberi Noghabi

Abstract Faults in power systems occur for various reasons, such as aging or natural disasters. Detecting, locating, and promptly clearing these faults is crucial for maintaining the safety and reliability of transmission lines (TLs). Distance relays (DRs), which protect TLs, detect faults, estimate their location, and send the required commands. However, these relays may experience mis‐detection due to manipulated impedance arising from both internal and external factors. These factors include measurement device errors, network topology changes, the presence of fault resistance (FR), and injected currents from remote line terminals. To address this challenge, an innovative adaptive protection scheme that considers FR, changes in network topology, and injected current from the opposite end of the line is proposed. By estimating the equivalent circuit impedances (ECIs) of the network connected to the terminal of the TL, this protection scheme utilizes impedance estimation techniques at the line terminals and offline network information. Simulation studies (tested on the IEEE 39‐bus standard network) show that the proposed scheme accurately estimates fault location (FL) and FR with high precision. The simulation results demonstrate its effectiveness in improving the performance of conventional distance protection relays in both the first and second protection zones (Zone1 and Zone2).

Zhongqi Cai, Xiuli Wang, Kun Yang et al.

Abstract The Fractional Frequency Transmission System (FFTS) operates with low frequency to decrease the electrical length of AC transmission lines, consequently enhancing transmission capacity. The integration of renewable energy sources via FFTS will significantly impact power system planning. This paper presents a Fractional Frequency Transmission Expansion Planning (FF‐TEP) model, which considers the interaction between FFTS and the industrial‐frequency power grid. This model addresses the challenges posed by the integration of offshore wind power through FFTS, and takes several key factors into account, including the active power coupling of Modular Multilevel Matrix Converter (M3C), inverter capacity, and converter transformer capacity. To address the non‐convexity caused by AC power flow, this paper introduces a Genetic‐Particle Swarm Optimization algorithm (G‐PSO) with dynamic penalties tailored to the M3C structure. This approach incorporates both crossover and mutation operators from Genetic Algorithms (GA) into Particle Swarm Optimization (PSO). The penalty function dynamically adapts according to the M3C structure, resulting in improved global optimization while maintaining computational efficiency. The results of two case studies demonstrate that FFTS can significantly enhance transmission capacity while reducing cable charging power. This reduces investment costs and decreases voltage offset issues.

Wan Feng, Mengdi Li, Wenjuan Zhang et al.

Abstract A novel parameter estimation method is proposed for the permanent magnet synchronous generator (PMSG), which is implemented by an enhanced self‐learning particle swarm optimization algorithm with Levy flight (SLPSO), and the problem of lower parameter estimation precision of standard PSO is obviated. This method injects currents of different intensities into the d‐axis in a time‐sharing manner to solve the problem of equation under‐ranking, and the mathematical model for full‐rank parameter estimation is developed. The speed term of PSO is simplified to expedite the convergence of PSO, and a strategy with chaotic decline for the inertia weight of PSO is adopted to strengthen its ability to jump out of the local optimum. Moreover, the self‐learning dense fleeing strategy (SLDF) is proposed where particles perform diffusion learning based on population density information and Levy flight, the evolutionary unitary problem and human intervention in the evolutionary process is averted. Furthermore, the memory tempering annealing algorithm (MTA) and greedy algorithm (GA) is integrated into the algorithm, MTA can facilitate the exploration of potentially better regions, and GA for local optimization enhances the convergence speed and accuracy in late stage of the algorithm. Comparing the proposed method with several existing PSO algorithms through simulation and experiments, the experimental data show that the proposed method can effectively track variable parameters under different working conditions and has better robustness.

Qiang Ou, Longfu Luo, Yong Li et al.

Abstract The extra‐high voltage (EHV) transformer winding disks are subjected to dynamic electromagnetic forces caused by short‐circuit currents. These periodically decaying impact forces may endanger the safety of the transformer. The winding constraint conditions around the circumference are different due to the influence of axial pressure structures and magnetic boundaries. Moreover, this difference becomes more evident as the voltage and capacity increase. To investigate the spatial differences and time effects of EHV transformer short‐circuit faults, this paper proposes a reliability evaluation method based on dynamic relative displacement (DRDEM) for the first time. Firstly, the suitable and efficient analysis method is summarized by comparing the deviation among different calculated impedances and the measured value. Then, the space‐time effect of windings is obtained by the static magnetic field and dynamic structural field based on equivalent compression supports. Furthermore, a 50 MVA/110 kV transformer is designed for the destructive short‐circuit test to simulate the short‐circuit ability feature of the EHV transformer. The disassembly phenomena after all the tests indicate that the DRDEM is more adaptable in characterizing the winding ability to withstand short circuit when investigating the spatial differences and time effects.

Daning Zhang, Xinyuan Feng, Zhi Yang et al.

Abstract Epoxy‐impregnated paper transformer bushings are prone to water absorption due to the presence of water‐attracting moieties in the cellulose chemical structure and epoxy resin, leading to moisture‐induced insulation defects. To understand the mechanism of moisture's impact on epoxy‐impregnated paper, this study developed a testing platform that integrated moisture diffusion, adsorption, and dielectric properties of insulating materials. Insulation specimens made of epoxy resin and epoxy‐impregnated paper were prepared and their water diffusion characteristics were investigated using the Fick diffusion model and the Langmuir model, respectively. The Guggenheim–Anderson–de Boer equation was used to examine the vacuum vapour adsorption characteristics of the materials, providing a theoretical foundation for examining the form of water present within them. The dielectric response of materials with varying moisture levels was then tested to study the effect of water on the dielectric spectrum curve. Using the Extended Derivative Method, the characteristic frequency reflecting the water content of the epoxy‐impregnated paper was extracted. The results demonstrate that 0.01‐Hz characteristic frequency provides superior accuracy for moisture assessment in epoxy resins compared to 50 Hz, and extracting and fitting the relaxation peak characteristic frequency in epoxy‐impregnated paper moisture evaluation yields higher accuracy than using tan δ at the power frequency.

Al‐Amin B. Bugaje, Jochen L. Cremer, Goran Strbac

Abstract The classical formulation of the transmission switching problem as a mixed‐integer problem is intractable for large systems in real‐time control settings. Several heuristics have been proposed in the past to speed up the computation time, which only limits the number of switchable lines. In this paper, a real‐time switching heuristic based on neural networks that provides almost instantaneous switching actions, are presented. The findings are shown on case studies of the IEEE 118‐bus test system, and the results show that the proposed heuristic is robust to out of distribution data. Additionally, the proposed heuristic has significant computational savings while all other performance metrics like accuracy are similar to state‐of‐the‐art machine learning methods proposed for transmission switching.

Qiwei Zhang, Fangxing Li, Linquan Bai et al.

Multienergy system (MES) operation has proven beneficial to the flexibility and efficiency of energy usage. Although efforts have been made toward the simulation and optimization of MESs, successful MES deployment is a grand challenge for market operations. The previous literature focuses on the modeling of the MES and the formulation of corresponding locational marginal prices (LMPs). A detailed analysis of LMP properties in an MES market-clearing model has not been fully developed. Although coupling analysis between different energy systems has been conducted from a system and power flow perspective, a coupling analysis with a specific indicator from a LMP perspective could provide operators with new insight into MES integration. Therefore, this paper first identifies six properties of the LMP in a unified MES market-clearing model. Then, based on the identified properties, two new types of critical load levels (CLL), Self-CLL and Cross-CLL, are defined and discussed to investigate the MES price pattern. Next, the Self-CLLs and Cross-CLLs for an MES are quantified by grey relation analysis (GRA) to deliver an MES coupling analysis from the perspective of LMP patterns. The identified properties and proposed analysis are demonstrated with a small MES case and a large one.

Danial Masihabadi, Mohsen Kalantar, Zahra Majd et al.

Abstract Demand response programs (DRPs) have paved a meaningful role in the power supply–demand balance in a smart grid. Also, a residential community with the presence of renewable energy sources (RESs) and electric vehicles (EVs) provides a new way to tackle growing concerns about energy efficiency and environmental pollution. The inherent uncertainty of RESs generation and EVs behaviour leads to difficulty in the economic scheduling of the demand side. Different types of uncertainty modelling have been investigated, such as Monte Carlo (MC) simulation, fuzzy method, and robust optimization. They are faced with many scenarios and computational complexity. This paper uses the information gap decision theory (IGDT) method to study variations of uncertainty radius on residential community electricity costs. Therefore, to achieve an optimal strategy for scheduling the appliances considering the deep uncertainties of RESs and EVs, a novel IGDT‐based demand response scheduling for a residential community is proposed. Impacts of different levels of uncertainties are studied. The simulation results depict the privileges of the proposed method when confronting deep uncertainties. By increasing the radius of the uncertainty of RES and the initial charge of EVs, energy consumption costs grew 20% and 2%, respectively, which indicates the system operator can manage the costs effectively.

Odin Foldvik Eikeland, Colin C. Kelsall, Kyle Buznitsky et al.

As variable renewable energy sources comprise a growing share of total electricity generation, energy storage technologies are becoming increasingly critical for balancing energy generation and demand. In this study, we modeled an existing thermal energy storage unit with estimated capital costs that are sufficiently low to enable large-scale deployment in the electric power system. Our analysis emphasizes the value of using such units to cost-effectively improve renewable energy dispatchability. This study modeled an existing real-world grid rather than simulating hypothetical future electric power systems. The storage unit coupled with a photovoltaic (PV) system was modeled with different storage capacities, whereas each storage unit size had various discharge capacities. The modeling was performed under a baseline case with no emission constraints and under hypothetical scenarios in which CO$_2$ emissions were reduced. The results show that power availability increases with increasing storage size and vastly increases in the hypothetical CO$_2$ reduction scenarios, as the storage unit is utilized differently. When CO$_2$ emissions are reduced, the power system must be less dependent on fossil fuel technologies that currently serve the grid, and thus rely more on the power that is served from the PV + storage unit. The proposed approach can provide increased knowledge to power system planners regarding how adding PV + storage systems to existing grids can contribute to the efficient stepwise decarbonization of electric power systems.

Ken Power, Shailendra Deva, Ting Wang et al.

Computing platforms in autonomous vehicles record large amounts of data from many sensors, process the data through machine learning models, and make decisions to ensure the vehicle's safe operation. Fast, accurate, and reliable decision-making is critical. Traditional computer processors lack the power and flexibility needed for the perception and machine vision demands of advanced autonomous driving tasks. Hardware accelerators are special-purpose coprocessors that help autonomous vehicles meet performance requirements for higher levels of autonomy. This paper provides an overview of ML accelerators with examples of their use for machine vision in autonomous vehicles. We offer recommendations for researchers and practitioners and highlight a trajectory for ongoing and future research in this emerging field.

Sairam Sampath Kumar, Alankrita Asthana, Avinash Katikireddy et al.

Jianfeng Sun, Yuan Zhao, Nianchu Zhang et al.

Abstract Maintaining frequency stability is a crucial but challenging task for the stable operation of a power system. The distributed energy storage (DES) can charge or discharge for both upward and downward frequency regulation, exploring and effectively using the regulation capabilities will provide a strong backup for frequency regulation. Here, a dynamic DES control strategy for providing primary frequency regulation is proposed. The different behaviours of storage owners are considered when they respond to the regulation requests from the aggregator. This kind of uncertainty would lead to a mismatch between the final aggregation result and the expected target. Hence, the multi‐armed bandit approach is applied to learn users’ response behaviour and select the optimal set of users to mitigate the mismatch. Case studies on the IEEE RTS 24‐bus system demonstrate that the proposed method's mismatch between the actual aggregation result and the regulation target is less than half as much as the conventional method, and it can restore the frequency 20 events earlier than the traditional method.

Mahmoud Hojjaty, Bahador Fani, Iman Sadeghkhani

Abstract Despite various advantages of integrating distributed generation (DG) units into distribution networks such as power loss reduction and voltage profile improvement, they may disrupt the performance of the conventional protection system due to bidirectional power flow and change of short‐circuit power. With the development of multi‐agent systems (MASs), intelligent protection schemes have been introduced that operate on the basis of data processing by a central controller. However, the possibility of communication failure and cyber attacks reduce reliability of these protection systems. To address this problem, this paper proposes an intelligent protection scheme that relies on peer‐to‐peer communication between agents in the first layer of the MAS. In the case of communication failure, the agents react to the fault condition by using the available data for similar conditions. The main advantages of the proposed scheme are reduction of number of employed agents by using only relay agents and no need for communicating with higher layers of MAS. Implementation of the proposed scheme in the ETAP simulation model of a 14 bus active distribution network shows that data exchange between relay agents properly modifies the operating time of the relays, resulting in protection coordination between main and backup protections.

Zhibin Qiu, Xuan Zhu, Caibo Liao et al.

Abstract Efficient bird damage prevention of transmission lines is a long‐term challenge for power grid operation and maintenance. An approach combined lightweight convolutional neural network (CNN), image processing and object detection is presented in this paper to detect typical bird species related to transmission line faults. An image dataset of 20 bird species that threaten transmission line security is constructed. The YOLOv4‐tiny algorithm model is constructed and trained combining stage‐wise training, mosaic data enhancement, cosine annealing, and label smoothing. The mean average precision (mAP) can reach 92.04% on the test set by adjusting the parameters of the training process. Then, the validity of the proposed method is verified according to the test results and performance indexes by comparing with other methods, including Faster RCNN, SSD, YOLOv4 etc. Some image pre‐processing methods such as motion blur, defocus blur, contrast and brightness adjustment are used to simulate the scenarios in practical engineering applications. The proposed method can detect bird species perched around transmission lines with high‐efficiency, which is useful for differential prevention of bird‐related outages of power grids.

Yi Zhang, Zaijun Wu, Jiajie Xu et al.

Abstract Cross‐linked polyethylene (XLPE) cables are a critical component of power transmission systems. As operating conditions sometimes exceed the thresholds set in discipline, some cables fail to reach their designed lifespan, and consequently evaluating the aging degree of cables is particularly necessary. Here ten cables with an operating duration between 1 and 11 years from adjacent lines were sampled. Investigations were performed through various aging evaluation methods. Then typical lifespan prediction indices were concluded from test results and filtrated through correlation calculation. Meanwhile, a lifespan prediction method based on GM (1, N) model was proposed to establish the equivalent relationship between operating time and these indices. The research results indicated that, lifespan prediction indices obtained were all significantly correlated with operating time, which can be used to characterize aging degree of XLPE cables. Besides, due to the high prediction accuracy of experimental verification, the GM (1, N) model was proved to be applicable to evaluate the aging degree quantitatively and determine the remaining lifespan (relative to the designed lifespan) of XLPE cables. In future research, this method can be used to formulate a reasonable and economic withdrawal strategy for XLPE cables operating in conditions that exceed the thresholds set in the discipline.