Hasil untuk "Distribution or transmission of electric power"

S. Engel, M. Stieneker, N. Soltau et al.

Xingbo Han, Guanjie Qiao, Xingliang Jiang

TianLi Song, RuanMing Huang, Shengjin Huang et al.

Nestor Rodriguez-Perez, Javier Matanza, Lukas Sigrist et al.

The increasing penetration of Distributed Energy Resources (DER) expands the cyberattack surface of power systems. This paper analyses, using PowerFactory, the impact and success of MaDIoT 3.0 attacks in the PST-16 model, a simplified model of the European system. MaDIoT 3.0 attacks are a novel type of attack that manage to compromise both high-wattage IoT demand devices and DER devices at the same time. The results indicate that the inclusion of distributed solar PV generation in the PST-16 system reduces the success ratio and impact of load-altering MaDIoT attacks when compared to the same system without DER, mainly due to an increment of the initial voltages. For MaDIoT 3.0 attacks, the demand had a more significant influence on the attack’s success than DER in the PST-16 system. Distributing the attacked demand across more buses or targeting the demand from other areas would decrease the success ratio of the attack. Therefore, the local scalability and replicability of vulnerable high-wattage demand devices in the analysed system become more critical than their distributed deployment in larger areas.

Mohammadhossein Nazemi, Xiaodong Liang

This paper presents a non-invasive threshold-based method for early detection of stator inter-turn faults (SITFs) and single phasing (SP) faults in induction motors by measuring the three-phase voltages at the motor terminal. These voltage signals are processed to extract the sequence components. The Negative Voltage Factor (NVF) is defined as the ratio of the magnitudes of negative sequence voltage <inline-formula> <tex-math notation="LaTeX">$\vert $ </tex-math></inline-formula>V<inline-formula> <tex-math notation="LaTeX">${}_{\mathbf {2}} \vert $ </tex-math></inline-formula> to positive sequence voltage <inline-formula> <tex-math notation="LaTeX">$\vert $ </tex-math></inline-formula>V<inline-formula> <tex-math notation="LaTeX">${}_{\mathbf {1}} \vert $ </tex-math></inline-formula>, and is used as a fault indicator. The proposed method uses a dual-threshold strategy: a lower threshold for SITFs detection and a higher threshold for SP faults detection by comparing with the VNF values. Unlike traditional current-based approaches, this voltage-based technique proves to be more sensitive and load-independent. Simulation results using ANSYS Maxwell and experiments in the lab for a 2.2 kW induction motor demonstrate the method&#x2019;s effectiveness to detect incipient SITFs and SP faults accurately under various motor loadings and fault severities.

Denis Mende, Andrea Schoen, Nils Bornhorst et al.

The energy transition poses significant challenges for the electrical distribution grid. This paper examines curative system operation in the 110 kV distribution grid that aims to tackle some of these challenges. Unlike preventive system operation, which avoids potential violations of operating thresholds during normal operation before they occur, curative system operation aims to maintain the grid within permissible thresholds only after a critical event through prepared, targeted measures. This paper analyses the complex requirements and challenges in implementing curative measures, particularly in comparison to existing approaches and concerning the high node density and the multitude of possible measures involving renewable generation plants and other customer facilities in the high-voltage distribution grid. By defining relevant thresholds and considering influencing factors, a unified understanding of curative system operation in the distribution grid is established. The paper demonstrates how curative system operation approaches can contribute to ensuring system security and describes the advantages of their utilization. Finally, perspectives for future research in the area of curative system operation in the 110 kV distribution grid are outlined.

Angel Pan Du, Miguel Arana-Catania, Enric Grustan Gutiérrez

Artificial Intelligence algorithms are introduced in this work as a tool to predict the performance of new chemical compounds as alternative propellants for electric propulsion, focusing on predicting their ionisation characteristics and fragmentation patterns. The chemical properties and structure of the compounds are encoded using a chemical fingerprint, and the training datasets are extracted from the NIST WebBook. The AI-predicted ionisation energy and minimum appearance energy have a mean relative error of 6.87% and 7.99%, respectively, and a predicted ion mass with a 23.89% relative error. In the cases of full mass spectra due to electron ionisation, the predictions have a cosine similarity of 0.6395 and align with the top 10 most similar mass spectra in 78% of instances within a 30 Da range.

Ivan Stošić, Ivan Damnjanović

The transmission of a vertex in a connected graph is the sum of distances from that vertex to all the other vertices. A connected graph is transmission irregular if any two distinct vertices have different transmissions. We present an efficient algorithm that generates all the transmission irregular trees up to a given order, up to isomorphism.

Lorenzo Zapparoli, Blazhe Gjorgiev, Giovanni Sansavini

The growing penetration of renewable energy sources is expected to drive higher demand for power reserve ancillary services (AS). One solution is to increase the supply by integrating distributed energy resources (DERs) into the AS market through virtual power plants (VPPs). Several methods have been developed to assess the potential of VPPs to provide services. However, the existing approaches fail to account for AS products' requirements (reliability and technical specifications) and to provide accurate cost estimations. Here, we propose a new method to assess VPPs' potential to deliver power reserve capacity products under forecasting uncertainty. First, the maximum feasible reserve quantity is determined using a novel formulation of subset simulation for efficient uncertainty quantification. Second, the supply curve is characterized by considering explicit and opportunity costs. The method is applied to a VPP based on a representative Swiss low-voltage network with a diversified DER portfolio. We find that VPPs can reliably offer reserve products and that opportunity costs drive product pricing. Additionally, we show that the product's requirements strongly impact the reserve capacity provision capability. This approach aims to support VPP managers in developing market strategies and policymakers in designing DER-focused AS products.

Fatemeh Afsari, Mehdi Ahmadi Jirdehi

Abstract The increasing integration of microgrids into distribution networks has highlighted the significance of evaluating and managing intelligent microgrids from both technical and economic perspectives. In this paper, a decentralized approach using agents is employed to optimize the operation of an intelligent microgrid within the telecommunications platform. The decentralized control method comprises two layers. The first layer represents the main microgrid, which includes loads and their controllers, as well as renewable and conventional resources. In the secondary layer, there is a telecommunication platform in which agents can operate as a control processor along with the means of communication. It should be noted that agents interact with the primary layer and neighbouring agents and exchange information with each other. This exchange takes place until the best state of optimization for the power supply occurs. In this study, the operation cost is calculated for decentralized control rules and considering telecommunication links. Also, the effect of performance on cost reduction is examined and compared with normal conditions and centralized methods. It can be seen that the operation cost of the network has decreased to 9.034% after the implementation of the mentioned method in comparison with the normal condition and it has decreased to 6.957% in comparison with the centralized method. Then, using a demand side management program, the cost will be reduced by 2.5%. In the next step, the uncertainty of available resources is taken into account where the uncertainties increase the cost by 7.8%.

Ozgur Alaca, Ali Riza Ekti, Jhi-Young Joo et al.

Rapid and accurate identification of events in power grids is critical to ensuring system reliability and security. This study introduces a novel event-type identification method, utilizing a Spectral Correlation Function (SCF)-aided Convolutional Neural Network (CNN). The proposed method employs a six-stage cascaded structure consisting of: (1) data collection, (2) clipping, (3) augmentation, (4) feature extraction (FE), (5) training, and (6) testing. Real-world power grid signals sourced from the Grid Event Signature Library are used for both training and testing. To improve robustness, additive white Gaussian noise (AWGN) is introduced at various signal-to-noise ratio (SNR) levels to augment the dataset. The SCF-based FE method captures distinctive event-type characteristics by exploiting the spectral correlation of signals, allowing the CNN architecture to effectively learn and generalize event patterns. The proposed method is benchmarked against seven conventional techniques, using real-world power grid signals representing four distinct event types: blown fuse, line switching, low amplitude arcing, and transformer energization. Key performance metrics-prediction accuracy, mean absolute error (MAE), precision, recall, F1-score, and confusion matrix&#x2014;are employed to evaluate the performance. Results demonstrate that the SCF-CNN method outperforms traditional approaches across all metrics and SNR levels, achieving over 99% prediction accuracy and nearly zero error for SNR values above 6 dB. This signifies its efficacy in reliable event-type identification for power grid applications.

Rui Ma, Sara Eftekharnejad, Chen Zhong

Online transient stability assessment (TSA) is essential for the reliable operation of power systems. The increasing deployment of phasor measurement units (PMUs) across power systems provides a wealth of fast, accurate, and detailed transient data, offering significant opportunities to enhance online TSA. Unlike conventional data-driven methods that require large volumes of transient PMU data for accurate TSA, this paper develops a new TSA method that requires significantly less data. This data reduction is enabled by generative and adversarial networks (GAN), which predict voltage time-series data following a transient event, thereby minimizing the need for extensive data. A classifier embedded in the generative network deploys the predicted data to determine the stability of the system. The developed method preserves the temporal correlations in the multivariate time series data. Hence, compared to the state-of-the-art methods, it is more accurate using only one sample of the measured PMU data and has a shorter response time.

Leigh Tesfatsion

This study establishes that Locational Marginal Pricing (LMP) is conceptually problematic for grid-supported centrally-managed wholesale power markets transitioning to decarbonized grid operations with increasingly diverse participants, hence with increasingly uncertain and volatile net loads. LMP assigns a common per-unit price LMP(b,T) (<inline-formula> <tex-math notation="LaTeX">${\$}$</tex-math></inline-formula>/MWh) to each next unit (MWh) of grid-delivered energy, conditional on delivery location b and delivery period T. However, this entails a serious many-to-one benefit/cost measurement error: namely, the valuation of this next unit by a market participant or system operator will typically depend strongly on the dynamic attributes of the path of power injections and/or withdrawals (MW) used to implement its delivery at b during T. One option is to muddle through, forcing market participants and system operators to express benefit/cost valuations for next units of grid-delivered energy in per-unit form without regard for the true benefits and costs of flexible power delivery. Another option, advocated in this study, is to explore conceptually-coherent nodal multi-interval pricing mechanisms permitting grids to function efficiently as flexibility-support insurance mechanisms, i.e., as mechanisms enabling just-in-time nodal power deliveries to meet just-in-time nodal power demands as well as system reliability requirements.

Reza Afsharisefat, Mohsen Jannati, Mohammadreza Shams

Abstract Power transformers play a critical role in the performance of power systems. This equipment is costly due to significant copper and iron prices and manufacturing costs. Therefore, maintenance and protection of such equipment is essential. Despite its robust performance, maloperation of differential protection (DP) in transformers may cause operational challenges to power system operators. The differential relay may operate incorrectly after the transformer energization leads to an inrush current (IC) and the relay identifies the event as an internal fault, and consequently issues the trip command. The other case of maloperation includes, but not limited to, a moment when the current transformer saturates due to an external fault. In this paper, a novel approach for DP is proposed, that is based on signal processing methods. In this paper, variational mode decomposition (VMD) and the deep neural network are implemented by using the convolutional neural network (CNN) and bi‐directional long short‐term memory (BiLSTM) models. The VMD decomposes differential current signal (DCS) to intrinsic mode functions with corresponding narrow‐band property frequency spectrums, which provides more detailed information about signal characteristics in different frequency bands. At the next stage, an effective feature for the BiLSTM is extracted by the CNN with the convolutional layers to classify events and proper discrimination. Extensive simulations on a 500 MVA transformer in MATLAB demonstrate the effectiveness of the proposed protection approach to differentiate ICs from internal and external faults with 99.8% accuracy in less than 1/8th of a power cycle.

Prabaakaran Kandasamy, Chandrasekaran Kumar, Muthuramalingam Lakshmanan et al.

Abstract The accurate classification of power quality disturbances (PQDs) is crucial for advancing real‐time monitoring and classification systems within the modern power grid. The proposed system must ensure dependable, safeguarded, and stable operating conditions amidst diverse power quality issues. This paper presents an approach to classifying power quality disturbances using a deep learning model that synergizes deep convolutional neural networks (DCNN) and Bidirectional Long Short‐Term Memory (BiLSTM). This amalgamation effectively extracts and classifies disturbance signals in real time, grounded on noise levels. The initial feature extraction from the signal is accomplished through a time‐frequency matrix. Subsequently, secondary extraction employs the BiLSTM layer to intricately and significantly classify disturbances in the power signal. This aids in transforming high‐dimensional matrices into a reduced set for enhanced performance. The detailed classification is facilitated by the softmax layer. The simulation results support the power quality evaluations under varied constraints and underscore the substantial classification of power quality disturbances through the DCNN‐BiLSTM algorithm, in comparison to alternative classification algorithms in terms of computational speed and accuracy.

Sumit Kumar Jha, Deepak Kumar, Prabhat Ranjan Tripathi et al.

Abstract The droop mechanism is widely utilized in a stand‐alone microgrid (MG) to regulate power‐sharing among distributed generators (DG). However, over the years, the droop phenomena are continually modified to lessen the deviation in voltage and frequency parameters caused due to classical droop. This study suggests computing the droop coefficient for voltage–current (V–I) droop to take into account for proportional power distribution among DGs. In grid utility networks, the conservation voltage reduction (CVR) strategy is widely used to curtail the use of energy. Hence, this paper investigates the CVR's performance for stand‐alone MG by performing the adaptive vector control scheme in the two‐phase d–q reference frame. In addition to it, the paper is intended to develop a coordinated control strategy for stand‐alone MG involving classical P–f droop and self‐sustained V–I droop employed to perform the function of CVR during peak demand and overloading conditions. Further, a load‐shedding control approach is also considered to cut out some segments of load during overloading conditions to operate the MG in the stable zone. The validation of the proposed strategy is conducted on MATLAB/Simulink software.

Xiaoyan Bian, Yong Wu, Qibin Zhou et al.

Abstract The damage of wind turbines suffered from lightning strikes has been a key issue for the safe and reliable operation of wind farms. Accurate determination of the annual lightning flash number to a wind turbine is essential for designing proper lightning protection measures. The interaction of downward and multiple upward leaders (MULs) is studied in this paper, which also considers the stochastic nature and branched behaviour of the lightning attachment phenomenon. Firstly, with an improved stochastic lightning model, the relationship among the striking distance, the height of wind turbines and the return stroke current is established. Moreover, a modified method for predicting the annual lightning flash number strikes to a wind turbine is proposed. The simulation results show that the striking distance and the collection area not only depend on the return stroke current, but also on the height and blade angle. Besides, the comparations between the calculation results and field statistics indicate that the conventional electric geometry model is not satisfied with the need of lightning protection for wind turbines. Note that there is only a difference of 4% between the modified method and the observation value. These quantitative researches provide the guidance of calculation results for the optimization of the blade lightning protection system (LPS) design.

Shaotong Pei, Haichao Sun

Abstract Transmission lines may suffer from pin defects, necessitating regular patrols and maintenance. The traditional maintenance approach not only involves a substantial workload and low efficiency but also poses a threat to the safety of maintenance personnel. To address the issue of pin defects in high‐voltage transmission lines, this paper introduces the design of an intelligent pin‐eliminating robot. The research focuses on two main aspects: firstly, analyzing the transmission line environment and employing Solidworks software to design the mechanical structure of the robot. Subsequently, a static stress analysis of the robot's structure is conducted to ensure the rationality of the design. The second aspect addresses the impact of the robot on the electric field distribution of the transmission line in high electric field intensity areas and whether the robot itself experiences discharge phenomena. COMSOL simulation software is utilized to globally calculate and analyze the electric field of the robot. Research results indicate that the threshold for the curvature radius of the pin‐eliminating robot's tip is 2.5mm, and values below this threshold may lead to the occurrence of discharge phenomena. The optimized pin‐eliminating robot, under normal operation, does not exhibit discharge phenomena in a 500kV strong electric field.

Yonggang Li, Hui Lin, Yichen Zhou et al.

Abstract As electric vehicles (EVs) begin to participate in the peak‐shaving auxiliary service market, the question of how price aggregators can maximize the peak‐shaving capacity provided by EVs and maximize their own profit has become a major problem. This paper proposes a bargaining game pricing method based on the psychological cost of EVs and the risk assessment of aggregators. First, the comprehensive psychological cost of EV users is obtained based on the impact of users’ participation in peak shaving on the battery life of EVs and on users’ original travel plans and time, and the impact of aggregator pricing on users’ psychology. Then, based on users’ psychological cost and the law of gravitation, the evaluation scheme for the peak‐shaving capacity of EVs is obtained. On the basis of conditional value at risk (CVaR), the mixed CVaR is obtained by considering the risk‐chasing behaviour of users. Based on the mixed CVaR, the risk assessment of aggregators’ participation in the peak‐shaving auxiliary service market is carried out. According to the above information, the aggregator and EV groups are engaged in a bargaining game based on the peak‐shaving pricing problem, which is divided into complete information game and incomplete information game. Finally, the feasibility of the proposed method is verified by an example analysis which is run by the MATLAB R2019b. The method proposed in this paper provides aggregators with a complete peak‐shaving electricity pricing scheme, effectively improving the dispatching capacity of EVs. It is helpful to rationalize the pricing of aggregators and maximize their profits. EVs can also obtain satisfactory profits, attracting more EVs to participate in peak shaving, and reduce the pressure on power grid peak shaving.

Yunfeng Ma, Chao Zhang, Bangkun Ding et al.

Abstract As renewable power generation increases in distribution networks, the real‐time power balance is becoming a tough challenge. Unlike simple peak‐load shedding or demand turn‐down scenarios, generation following (GF) requires persistent and precise control due to the temporal response performance of controlled resources. This motivates a comprehensive control design considering the temporal response limits and execution performance of air conditioner clusters (ACCs) when providing GF. Accordingly, this paper proposes a self‐constraint model predictive control (SMPC) that properly allocates the generation following task among different ACCs, consisting of three main parts: response rehearsal, distributed consistency‐based power allocation, and real‐time task execution. Specifically, the rehearsal knowledge of ACCs is evaluated by introducing model predictive control (MPC) to track power signals with different values and thus obtain prior factors, including the upward/downward limits and control cost function. On this basis, the coherence of the incremental response costs of different clusters is achieved to allocate the GF signals. Once the optimised following signals (OFS) are obtained, a real‐time MPC for generation following task execution is employed, where the OFS are used as reference and the upward/downward limits are used as constraints. Simulations are conducted to verify the feasibility and effectiveness of the proposed method.