Hasil untuk "Distribution or transmission of electric power"

Jacob Sweeten, Amr Elshazly, Abdulrahman Takiddin et al.

Recent research has shown promise in using machine learning for cyberattack detection in power systems. However, current studies face limitations: a) dependence on either physical or cyber features, overlooking multi-modal cyber-physical (CP) correlations; b) unrealistic full observability assumptions; c) focus on detecting basic attacks instead of advanced threats such as ransomware (RW); and d) use of deep learning (DL) models built for 2D data, despite the graph-structured nature of power systems. To address these gaps, we develop a CP testbed using OPAL-RT and a cyber range to simulate both physical and cyber layers under full and partial observability. The testbed produces a realistic multi-modal dataset covering normal operations and various cyberattacks, including RW, brute force, false data injection, reverse shell, and backdoor. Using this dataset, we design graph neural network (GNN)-based multi-modal intrusion detection systems (IDSs) that fuse CP features and capture spatio-temporal dependencies. Results show that CP fusion improves detection rates (DRs) by up to 16% compared to single-modal inputs. The proposed GNN-based IDSs outperform benchmarks by up to 26% in DR, remain effective under partial observability, and demonstrate up to 6% improvement in scalability when applied to larger system topologies.

Ran Hu, Haisong Xu, Xu Lu et al.

Abstract Due to the limitations imposed by urban power grid outages for maintenance, on‐line harmonic current detection technology for distribution network cables is expected to become an effective supplement to traditional offline diagnostic methods, enhancing the real‐time diagnosis of distribution network cable insulation conditions. This study established a 10 kV distribution network cable test platform and prepared typical defective cables subjected to moisture and long‐term thermal aging. Using COMSOL finite element electromagnetic simulation, the magnetic flux evolution laws of the cable insulation under typical defects were obtained. Experimental tests provided the harmonic current characteristics and statistical features of cables with typical defects. Based on these data, a method for analysing the degradation degree of distribution network cables was constructed using least absolute shrinkage and selection operator (LASSO) regression analysis. Furthermore, a defect‐type identification method based on cluster analysis was proposed. Results indicate that the odd harmonics and the 4th harmonic of the distribution network cable's harmonic current are closely related to the cable's degradation state. A model integrating principal component analysis (PCA) data dimensionality reduction and expectation‐maximization clustering analysis achieved a recognition accuracy of up to 75.64% in distinguishing between moisture‐affected and normal cable states. The proposed on‐line detection and evaluation methods can effectively identify high‐risk cables with latent defects.

Faraz Akbar, Zia ur Rahman

The management of power outages caused by severe weather disasters such as earthquakes, tornadoes, and weather disturbances is critical in power transmission and distribution systems. The transmission system is the key to any power system and can trigger severe or significant effects if it fails. Different causes of fires, extreme weather conditions, ageing components, poor maintenance and malfunctions, human error, mal-operative procedures, and high operating network variables may cause transmission components to fail. System reliability is the probability that, despite component failures, a system remains available or functional. Risk management of the failures describes it. There are several methods to predict the failure rate, including exponential and Bayesian distributions. In this study, Poisson distribution forecasted the outage patterns for transmission and distribution networks for one and five years. A probability distribution function generated this pattern. Moreover, previous data were obtained from the National Electric Power Regulatory Authority (NEPRA). On comparing the acquired results with previous outage data, it was reported that the average outages in the transmission system were 567. The average outages in the distribution system stood at 37 for five years with a 100% confidence level. However, the probability of getting 620 transmission outages and 50 distribution outages was the highest in the probability distribution function for five years. Poisson distribution proved to be a useful tool to assess the transmission and distribution system reliability by estimating the failure rate over the years. It would allow the risk professionals to schedule, reduce, and track the systems’ risks. It would also help to improve the system’s reliability.

Yunjie Bai, Kaigui Xie, Changzheng Shao et al.

Abstract Power system unreliability tracing model allocates the system's reliability index to individual components, identifying potential weaknesses. This study expands its scope by considering the impact of storage resources. Unreliable factors leading to load shedding are categorized into two groups: objective factors inherent to the component and insufficient storage resources. The latter requires a retrospective analysis of other components that caused unreliability previously. When allocating responsibility for load shedding at a certain time, it begins by allocating it among components based on differences between fixed expected output and actual supply. Expected output insufficiency is considered as the unreliable factor. This insufficiency due to insufficient storage resources is then decomposed into segments, each caused by excessive output in earlier instances of the same component. The expected output excess is attributed to the expected output insufficiency of other components in previous times, for which responsibility has been allocated to each component. Consequently, the expected output insufficiency at a particular time can be traced back based on a temporal recursive model, with the load shedding further allocated to components before that time. Case studies based on several systems demonstrate that the proposed model's allocation results are reasonable and more accurate than the traditional model.

Ehsan Erfani Haghani Kerman, Mohammad Amin Abavisani, Mohammad Eydi et al.

Abstract Proper current sharing, DC bus voltage deviation reduction, and SOCs balancing, along with ensuring stability are the vital challenges of DC microgrids control algorithms. Addressing these challenges without communication links and a central controller is one of the priorities of control methods. Motivated by the above mentions, this paper presents a novel communication‐free control method. In this regard, a new parameter called “virtual current” is defined according to the unit current and its SOC. Then using a piecewise droop curve and the droop curve shift technique, the virtual current for each unit is determined. The units control coefficients and the relationship of the virtual current are allocated based on the location and power of the loads and RESs such that in the worst case; 1) SOCs are converged; 2) the DC bus voltage deviation is reduced; and 3) the current is appropriately distributed. The simulation and experimental results confirm that the proposed method can balance SOCs like SOC‐based methods and share power properly like piecewise droop methods while reducing DC bus voltage deviation.

Andrew J. Hutchinson, Daniel T. Gladwin

With National Grid ESO introducing a suite of new Frequency Response Services for the GB electricity market, there is an opportunity to investigate the ability of low-energy capacity storage systems to participate in the frequency response market. In this study, the effects of varying the response envelope of the frequency response service on the performance of a standalone Flywheel Energy Storage System is assessed. In doing so, a new Frequency Response Service that would allow flywheels and other high-power, low-energy storage devices to participate in the frequency response market as standalone systems is designed. This results in a 20C FESS achieving a 95% availability over the course of a year of operation, representing an excellent level of performance under existing market conditions. This work shows that a far wider range of energy storage mediums have the capability to provide meaningful contributions to grid frequency control than was previously assumed. It is also shown for the first time that through tailoring a service to the advantages of a flywheel, significant economic benefits can be achieved, culminating in showing that a 20C FESS could provide a positive economic performance up to a total capital cost of £3,364/kW under current market conditions.

Xiangtian Zheng, Nan Xu, Loc Trinh et al.

The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change. With deepening penetration of renewable resources, the reliable operation of the electric grid becomes increasingly challenging. In this paper, we present PSML, a first-of-its-kind open-access multi-scale time-series dataset, to aid in the development of data-driven machine learning (ML)-based approaches towards reliable operation of future electric grids. The dataset is synthesized from a joint transmission and distribution electric grid to capture the increasingly important interactions and uncertainties of the grid dynamics, containing power, voltage and current measurements over multiple spatio-temporal scales. Using PSML, we provide state-of-the-art ML benchmarks on three challenging use cases of critical importance to achieve: (i) early detection, accurate classification and localization of dynamic disturbances; (ii) robust hierarchical forecasting of load and renewable energy; and (iii) realistic synthetic generation of physical-law-constrained measurements. We envision that this dataset will provide use-inspired ML research in safety-critical systems, while simultaneously enabling ML researchers to contribute towards decarbonization of energy sectors. Measurement(s) temperature • wind speed • solar zeinth angle • dew point • irradiance • voltage • current Technology Type(s) weather station • power grid model-based simulation Factor Type(s) load power • renewable generation power • disturbance location, type, and duration Measurement(s) temperature • wind speed • solar zeinth angle • dew point • irradiance • voltage • current Technology Type(s) weather station • power grid model-based simulation Factor Type(s) load power • renewable generation power • disturbance location, type, and duration

Morteza Asadi, Seyyed Mostafa Abedi, Hassan Siahkali

Abstract Creating highly reliable distribution networks is becoming increasingly vital in today's society. In a similar vein, utilities have issues in properly planning and developing distribution networks, both to reduce their imposed costs and to meet the demands of their consumers. In the presence of Demand Response Program (DRP), this research provides a coordinated architecture that considers automated switches and Energy Storage Units (ESUs) placement with the uncertainty of repair time to enhance the reliability of power distribution network. The suggested objective for optimal placement of automatic switches, ESUs and DRPs is to minimize a combination of reliability index (SAIDI) and Total Cost of system (TC). Customer interruption cost, energy not supplied cost, automated switch investment, ESU investment, ESU participation cost, and DRP cost are all considered as parts of the total cost. The proposed method is used in four different cases on a common reliability test system, followed by numerous sensitivity studies, in order to illustrate applicability and efficiency of suggested method.

Osama Bassam J. Rabie, Shitharth Selvarajan, Daniyal Alghazzawi et al.

Abstract Detection of cyber‐threats in the smart grid Supervisory Control and Data Acquisition (SCADA) is still remains one of the complex and essential processes need to be highly concentrated in present times. Typically, the SCADA is more prone to the security issues due to their environmental problems and vulnerabilities. Therefore, the proposed work intends to design a new detection approach by integrating the optimization and classification models for smart grid SCADA security. In this framework, the min‐max normalization is performed at first for noise removal and attributes arrangement. Here, the correlation estimation mechanism is mainly deployed to reduce the dimensionality of features by choosing the relevant features used for attack prediction. Moreover, the optimal features are selected by using the optimal solution provided by the Holistic Harris Hawks Optimization (H3O). Finally, the Perceptron Stochastic Neural Network (PSNN) is utilized to categorize the normal and attacking data flow in the network with minimal processing time and complexity. By using the combination of proposed H3O‐PSNN technique, the detection accuracy is improved up to 99% for all datasets used in this study, and also other measures such as precision to 99.2%, recall to 99%, f1‐score to 99.2% increased, when compared to the standard techniques.

Mahyar Tofighi‐Milani, Sajjad Fattaheian‐Dehkordi, Mahmud Fotuhi‐Firuzabad et al.

Abstract In recent years, distributed structures have been developed in distribution networks as a result of privatization as well as integration of independently operated distributed energy resources (DERs) into the grid. In this context, effective management techniques seem to be necessary to enable decentralized operation of systems that have been consisted of multiple agents. Accordingly, novel management schemes should be applied in such systems to enable the distributed reactive power management, which would finally improve voltage profile in the network. As a result, this paper develops a distributed algorithm for reactive power management in multi‐agent distribution systems with the aim of improving the voltage profile of the grid. Correspondingly, in the proposed framework, several cost functions are developed to model the effects of reactive power management on the system from the distribution system operator (DSO) as well as agents’ perspectives. Consequently, the proposed reactive power management algorithm would result in voltage profile improvement, while each agent of the system merely strives to maximize its own profits. Accordingly, the proposed scheme ensures the privacy of independent agents. Finally, the proposed scheme is applied on the modified IEEE‐37‐bus test system to investigate its effectiveness on Peer‐to‐Peer (P2P) reactive power management in multi‐agent systems.

Lingyun Wang, Xiang Zhou, Honglei Xu et al.

Abstract This paper proposes a method to enhance the accuracy of power load forecasting by considering the variability in the impact of multi‐dimensional meteorological information on power load in diverse regions. The proposed method employs spatio‐temporal fusion (SF) of multi‐dimensional meteorological information and applies the Copula theory to analyze the non‐linear coupling of meteorological information from multiple stations with power load to achieve SF in the spatial dimension. To enhance the accuracy of load forecasting in the time dimension, this paper improves the core parameters of the variational mode decomposition (VMD) using the marine predators algorithm (MPA) and utilizes the weighted permutation entropy (WPE) to construct the MPA‐VMD fitness function for the adaptive decomposition of the load sequence. Moreover, this paper constructs input sets for the long short‐term memory model and the MPA‐LSSVM model by combining each component of the time dimension and each meteorological information of the spatial dimension to obtain the prediction results of each component. The prediction model corresponding to each component is selected according to the evaluation index and reconstructed to obtain the overall prediction results. The analysis results demonstrate that the proposed forecasting method outperforms the traditional forecasting method and effectively enhances the accuracy of power load forecasting.

Huangbin Liang, N. Blagojević, Q. Xie et al.

Electrical substations are critical elements of a power grid, enabling the transmission and distribution of electric power from power generators to the end‐users. Experiences from previous earthquakes have shown that electrical substations can be damaged due to ground shaking, reducing their functionality and potentially preventing the generated electric power from reaching end‐users. To assess the seismic vulnerability of a substation, a modular quantitative assessment method is proposed. In this method, the relation between the functionality state of a substation and the damage state of its components was established through the connection matrix technique. A substation is viewed as a network system, whose topology is defined by the connections among various pieces of electrical equipment (i.e., the components), represented in the connection matrix. The maximum allowable power transmission capacity of the substation after an earthquake is adopted as the system functionality metric, which is jointly determined by the power input, transform, and output capacity of the substation. The seismic vulnerability of an electrical substation is quantified by probabilistically calculating its postearthquake functionality when exposed to various earthquake intensities using Monte Carlo sampling. Finally, the risk of substation functionality loss is quantified by integrating the seismic hazard curve with the seismic vulnerability model of the substation. Two realistic case studies on a distribution substation and a transmission substation, with the same equipment configuration but different power delivery paths, were performed using the proposed method. Furthermore, a sensitivity analysis regarding the equipment fragility parameters is conducted, providing a risk‐informed basis for improving the seismic performance of the substation system.

Mohammad Ekramul Kabir, Mohsen Ghafouri, Bassam Moussa et al.

P. William, Akanksha Gupta, N. K. Darwante et al.

Smart meters, smart appliances, renewable energy sources, and energy-efficient resources are only some of the operational and energy-efficiency elements of a “smart grid.” Smart grids rely heavily on electronic power conditioning and control of energy production and delivery. An intelligent generation, transmission, distribution, and consumption are the main components. Human-to-human contact is now the major way of communication on the Internet. The Internet of Things will replace the Internet. This technology has the potential to be used to the construction of smart grids. Smart grid research and development result in new technologies that makes life simpler for humans. This article provides an in-depth examination of the different technologies and standards for smart grids. This article explains the smart grid and innovative concepts such as electric vehicles and automated electric car charging on public roadways

Xiaodong Yang, Chongbo Xu, Youbing Zhang et al.

This paper proposes a real-time coordinated scheduling method for active distribution networks (ADNs) with soft open points (SOPs) and plug-in electric vehicles (PEVs) via multi-timescale framework under uncertainties. Specifically, this method is achieved with day-ahead pre-scheduling and intra-day corrective control stages by coordinating various flexible resources at different timescales. The day-ahead stage is designed to reduce operational cost, regulate voltage profile and avoid risk exposure through joint scheduling of traditional devices, SOPs and PEVs on hourly basis. In intra-day corrective control stage, an hour is further divided into two timescales. The slow-timescale scheduling (STS) aims to corrective coordination of SOPs and across-time-and-space energy transmission of PEVs, and nested within the STS, the fast-timescale scheduling optimally coordinates the active & reactive power of SOPs and PV inverters to handle fast voltage fluctuations as well as against real-time uncertainties. The formulated three models are all transformed into second-order cone programming problems via sample weighted average approximation (SWAA), linearization and conic relaxation, which can be thus efficiently solved. Case studies based on three modified distribution systems (including two IEEE test systems and one actual distribution network) are performed to verify the effectiveness of the proposed method.

Milad Qorbani, Turaj Amraee

Gang Lin, Jiayan Liu, Yang Zhou et al.

Abstract This paper proposes an inertia‐emulation‐based cooperative control strategy for the multi‐parallel energy storage system (ESS) to meet the requirements of state‐of‐charge (SoC) balance, inertia enhancement and zero‐steady‐state voltage deviation. The inertia emulation loop (IEL) is constructed by analogy with DC motors to dampen voltage oscillation, while the secondary voltage recovery loop is derived from the circuit equivalence of an inductor to indicate the system stiffness. Moreover, to equalize SoCs of energy storage units (ESUs) dynamically, a SoC self‐balance algorithm is developed. The redefined SoC mismatch degree and balance speed adjustment factor k are introduced into the droop resistance, adjusting the SoC self‐balance rate and eliminating the SoC deviation among ESUs. The dynamic performance of the SoC self‐balance algorithm is analyzed and the small signal model of the DC microgrid (DC‐MG) with proposed strategy is established. Based on eigenvalue analysis and step response, the system stability is assessed, and the influence of control parameters on transient characteristics and stability margin is investigated. Considering power constraint, voltage deviation constraint and dynamic stability constraint, the optimal design method of k is given. Finally, simulation and experiment verify that the proposed control, without modifying hardware, performs better dynamic and static characteristics and can equalize SoC among ESUs in charge and discharge mode.

Seyed‐Alireza Ahmadi, Vahid Vahidinasab, Mohammad Sadegh Ghazizadeh et al.

Abstract Automatic reconfiguration is one of the key actions in self‐healing distribution networks. In these networks, after detecting and isolating the faulted portion, an automatic reconfiguration procedure is performed to restore the maximum possible affected loads without further interruptions during repair operations. This procedure becomes more complicated in the networks with integrated distributed generation units as they can bring security challenges for the reconfigured network after a fault event. To overcome these challenges, a stochastic framework is proposed here. In this framework, the reconfiguration procedure is conducted with a fast and reliable method which is based on the graph theory. Besides, the security challenges of utilizing distributed generations after an event are highlighted. Then, since a faulted network is more prone to subsequent faults, different actions of changing the distribution generations output power, preventing the insecure increment of short circuit capacity, and also considering the loadability improvement are proposed in the reconfiguration framework. Then in the final stage, the vulnerability of the distribution system to the uncertainties of load demand is resolved through a chance‐constrained programming‐based approach. To see the performance of the proposed stochastic framework, it is tested on a standard test system and the results prove its goodness and applicability for real distribution networks.

Liuyang Shen, Yong Zhu, Chenghai Liu et al.

To understand the complex continuous microwave drying of germinated brown rice (GBR) and clarify the drying characteristics, the heat and mass transfer of GBR under continuous microwave drying was investigated numerically and experimentally. A three-dimensional model of coupled multi-physics fields involving the transmission of microwave field, heat transfer and mass (moisture) transfer was developed to characterise the drying process of GBR in a continuous microwave dryer. The implementation strategy based on discrete-combined approach was proposed to achieve the simulation of continuous microwave drying of moving materials with the mutual cooperation of the computer simulation software and independently developed program code. For the continuous microwave drying of GBR, the relatively uniform distribution of electric field strength applied to the grain layer depended on the reasonable arrangement pattern of magnetrons and suitable microwave power output. The movement of materials can effectively reduce excessive absorption of microwave energy by the grain layer, and achieve uniform distribution of temperature and moisture content and high drying uniformity assisted by the synergistic effects of microwave heating, moisture evaporation and ventilation convection. The developed model and simulation strategy may provide guidance for understanding and analysis of continuous microwave drying process of granular materials such as GBR.