Electrical Transmission Research Articles

Optimizing 2D path planning for unmanned aerial vehicle inspection of electric transmission lines

The inspection of electric power transmission lines using unmanned aerial vehicles represents an innovative maintenance approach that several countries, including Morocco, are considering adopting. The primary aim is to enhance existing procedures, capitalize on their advantages, and address challenges associated with traditional methods. Given the precision, efficiency, and safety required for this application to ensure reliable results, we propose a functional architecture for unmanned aerial vehicle-based electric power transmission line inspection. This architecture encompasses key phases such as data collection, planning, and inspection, while identifying electromagnetic interferences and minimum approach distances as major constraints. To streamline mission planning, we introduce a trajectory optimization algorithm integrated into a mission planning assistance application for transmission line inspection. This algorithm presents a novel approach by hybridizing three algorithms: genetic algorithm, ant colony optimization and particle swarm optimization. Consequently, our work represents a significant contribution to the integration of unmanned aerial vehicles in electric power transmission lines inspection operations for electrical energy transport, facilitating potential advancements and discoveries in the field.

Integration assessment of turquoise hydrogen in the European energy sector

Turquoise hydrogen from natural gas pyrolysis has recently emerged as a promising alternative for low-carbon hydrogen production with a high-value pure carbon by-product. However, the implications of this technology on the broader energy system are not well understood at present. To close this literature gap, this study presents an assessment of the integration of natural gas pyrolysis into a simplified European energy system. The energy system model minimizes the cost by optimizing investment and hourly dispatch of a broad range of electricity and fuel production, transmission, and storage technologies, as well as imports/exports on the global market. Norway is included as a major natural gas producer, and Germany as a major energy importer. Results reveal that pyrolysis is economically attractive at modest market shares where the carbon by-product can be sold into high-value markets for 400 €/ton. However, pyrolysis-dominated scenarios that employ methane as a hydrogen carrier also hold promise as they facilitate deep decarbonization without the need for vast expansions of international electricity, hydrogen, and CO2 transmission networks. The simplicity and security benefits of such pyrolysis-led decarbonization pathways justify the modest 11 % cost premium involved for an energy system where natural gas is the only energy trade vector. In conclusion, there is a strong case for turquoise hydrogen in future energy systems and further efforts for commercialization of natural gas pyrolysis are recommended.

Effect of welding speed on mechanical properties of dissimilar friction stir lap-joint welding between AA6061 and C1100 copper

Nowadays, renewable energy such as wind and solar energy can be employed for marine application to enhance clean and green technology. So, using battery is necessary to apply power for ship activities. To contribute sustainable development, optimizing structure in electric transmission such as bimetal connector Cu-Al is necessary to improve joint quality. This study focused the influence of welding parameters on the quality of the lap-joint between AA6061 aluminum alloy and C1100 copper. Experimental results indicate that the welding interface was sensitive to welding speed. Some welding defect was found in the joint such as tunnel and hook defects. The highest tensile strength produced by the welding speed of 200 mm/min was approximately 1100 N. In all experimental regimes, the weld zone structure, hardness distribution, tensile strength, fracture location, and local deformation were analyzed and discussed in detail. Keywords: Friction stir welding, microstructure, mechanical properties, hardness

Load Balance Forecasting Based on Hybrid Deep Neural Network

Load forecasting is the foundation of utility design, and it is a fundamental business problem in the utility industry. Load forecasting, mainly referring to forecasting electricity demand and energy, is being used throughout all segments of the electric power industry, including generation, transmission, distribution, and retail. In this paper, a long short-term memory network with a hybrid approach is improved with a dense algorithm and proposed for electricity load forecasting. A long short-term memory network is designed to effectively exhibit the dynamic behavior of load time series. The proposed model is tested for Panama study including historical data and weather variables. The prediction accuracy is validated by performance metrics, and the best of the metrics are attained when mean absolute error is 5.262, mean absolute percentage error 0.0000376, and root mean square error 18.243. The experimental results show a high prediction rate for load balance forecasting of electric power consumption.

ANALYSIS REDUCTION OF ENERGY LOSSES IN DISTRIBUTION NETWORKS

The article analyzes the reduction of energy losses in distribution networks, which is a serious problem requiring an integrated approach, including technological, regulatory, and behavioral interventions. Some measures that can be developed and implemented to solve this problem are given, such as infrastructure modernization, taking into account investments in the modernization of distribution infrastructure, affecting a significant reduction in energy loss, and the introduction of load management strategies that optimize energy distribution and reduce losses. This includes measures such as load balancing, voltage regulation, and demand management programs to shift peak loads to off-peak hours. The integration of smart grid technologies allows monitoring and management of the distribution network in real-time. This includes the deployment of advanced measurement infrastructure, sensors, and automation systems to more effectively detect and reduce energy losses. Technological losses are associated with the technology of the process of transmission of electricity through networks. Commercial losses are measured by the difference between actual estimated losses and technological losses, taking into account commercial losses, electric energy entering the electric grid, electric energy supplied to consumers, and electricity costs for own needs of substations. Circuit engineering methods for accounting for energy losses in electrical networks are used in various combinations. At the same time, it should be borne in mind that the sequence of reporting operations using the results of specified specific parameters should give an accurate result. To determine the loss of electricity, data on the minimum and maximum load of the network and the amount of electricity consumed over the same period are required. The calculation is given by the method of average load. Information about the minimum and maximum network load is a key element for effective design, management and maintenance of electrical networks. This information allows you to optimize the use of resources and ensure reliable network operation in various conditions. Minimum network load - the minimum load is the minimum amount of energy that is consumed by the network during a certain period of time, for example, during night hours or periods of low activity. Information about the minimum load allows you to optimize network operation, for example, by turning off part of the etquipment or managing energy consumption during off-peak periods. Maximum network load - is the maximum amount of energy consumed by the network at a given time. This may be during periods of peak demand, for example, during a heat wave when a lot of air conditioners are running, or during an increase in production in industrial areas. The maximum load information allows you to determine the required power and capacity of the network equipment to ensure reliable operation under maximum load conditions. Keywords: distribution networks, network load, average load, power, period, power factor, electrical energy, duty cycle, consumers, practical research, voltage.

Wind Energy Conversion System with Integrated Power Smoothing Capability based on an EVT-coupled Flywheel

This paper presents a wind energy conversion system that integrates, in a compact form, both generation and power smoothing functions. The system is based on a vertical axis wind turbine (VAWT) and a low-speed flywheel that are coupled by an electric variable transmission (EVT). The EVT performs a dual function by simultaneously injecting power to the grid and regulating the power flow to the flywheel to smooth the power fluctuation caused by wind turbulences. The main advantages of the presented generation/smoothing topology are integrating two machines (generator and motor) in the same physical structure and the reduced power demanded by the converter that feeds the flywheel’s rotor. A theoretical analysis is carried out to determine an appropriate control law and flywheel sizing to satisfy a desired attenuation of the power fluctuation. Simulation results show an effective smoothing action, as the power fluctuation is reduced to an adequate level. In addition, the validity of the flywheel sizing procedure is verified.

Assessing the Nationwide Benefits of Vehicle–Grid Integration during Distribution Network Planning and Power System Dispatching

The diffusion of electric vehicles is fundamental for transport sector decarbonization. However, a major concern about electric vehicles is their compatibility with power grids. Adopting a whole-power-system approach, this work presents a comprehensive analysis of the impacts and benefits of electric vehicles’ diffusion on a national power system, i.e., Italy. Demand and flexibility profiles are estimated with a detailed review of the literature on the topic, allowing us to put forward reliable charging profiles and the resulting flexibility, compatible with the Italian regulatory framework. Distribution network planning and power system dispatching are handled with dedicated models, while the uncertainty associated with EV charging behavior is managed with a Monte Carlo approach. The novelty of this study is considering a nationwide context, considering both transmission and distribution systems, and proposing a set of policies suitable for enabling flexibility provision. The results show that the power and energy demand created by the spread of EVs will have localized impacts on power and voltage limits of the distribution network, while the consequences for transmission grids and dispatching will be negligible. In 2030 scenarios, smart charging reduces grid elements’ violations (−23%, −100%), dispatching costs (−43%), and RES curtailment (−50%).

Transmission Line Fault Classification Based on the Combination of Scaled Wavelet Scalograms and CNNs Using a One-Side Sensor for Data Collection.

This research focuses on leveraging wavelet transform for fault classification within electrical power transmission networks. This study meticulously examines the influence of various parameters, such as fault resistance, fault inception angle, fault location, and other essential components, on the accuracy of fault classification. We endeavor to explore the interplay between classification accuracy and the input data while assessing the efficacy of combining wavelet analysis with deep learning methodologies. The data, sourced from network recorders, including phase currents and voltages, undergo a scaled continuous wavelet transform (S-CWT) to generate scalogram images. These images are subsequently utilized as inputs for pretrained deep learning models. The experiments encompass various fault scenarios, spanning distinct fault types, locations, times, and resistance values. A remarkable feature of the proposed work is the attainment of 100% classification accuracy, obviating the need for additional algorithmic enhancements. The foundation of this achievement is the deliberate selection of the right input. The decision to employ an identical number of samples as the number of scales for the CWT emerges as a pivotal factor. This approach underpins the high accuracy and renders supplementary algorithms superfluous. Furthermore, this research underscores the versatility of this approach, showcasing its effectiveness across diverse networks and scenarios. Wavelet transform, after rigorous experimentation, emerges as a reliable tool for capturing transient fault characteristics with an optimal balance between time and frequency resolutions.

A survey on lattice‐based security and authentication schemes for smart‐grid networks in the post‐quantum era

SummaryThe present scenario witnesses “the second quantum revolution,” which has enabled the development of revolutionary novel quantum tools. Quantum computing endeavors to establish higher computing standards that can potentially solve complex structures. Post‐quantum cryptography (PQC) has emerged as one of the new domains of cryptography, which is resilient to quantum attacks owing to the revolution in quantum computing. To ensure quantum security, the lattice‐based cryptosystem (LB‐cryptosystem) is one of the promising tools of PQC to address quantum‐based threats. The traditional security algorithms, such as RSA and Diffie–Hellman (DH), are strong enough to resist present security threats. However, it has been predicted that quantum technologies have the capability to break the security of most traditional algorithms whose security is based on prime factorization and DH‐type hard problems. Therefore, research is currently focused on addressing the security and privacy threats by using LB‐cryptosystems to secure various applications, organizations' data, and information infrastructure in the quantum era. The purpose of this article is to investigate recent advances in LB‐cryptosystems that may allow the design of secure models for smart‐grid networks (SGNs) against existing and future quantum attacks. SGNs have been explored as the bi‐directional assimilation of communication in terms of electricity generation, transmission, allocation, and utilization. This survey provides a comprehensive overview of LB‐cryptosystems, as well as their potential applications in securing SGNs. Lastly, the article summarizes the various PQC primitives, NIST selected algorithms, open‐source tools along with their packages, and various PQC industrial initiatives, and also compares traditional cryptographic schemes with other PQC.

Effects of high-low frequency electromagnetic radiation on vibrational resonance in Hodgkin–Huxley neuronal system

In this paper, based on the Hodgkin–Huxley (H–H) neuron model, the effects of high-low frequency (HLF) electromagnetic radiation on vibrational resonance (VR) in a single neuron is investigated. It is found that VR can be observed in a single H–H neuron model with or without considering HLF electromagnetic radiation. However, HLF electromagnetic radiation can cause changes in the structure of the resonance peaks and make the maximum region and intensity of the response amplitude smaller, which weakens the VR in the neuronal system. This is because the HLF electromagnetic radiation makes the neuronal electrical activities less regular, resulting in weaker synchronization between input low-frequency signal and output firing. In addition, the effects of the system parameters such as the HF electromagnetic radiation amplitude on the Fourier coefficients are investigated, and it is found that these parameters can also cause changes in the number of resonance peaks, resulting in vibrational mono-resonance and vibrational bi-resonances. When the frequency of the HLF electromagnetic radiation is between 0.014 and 0.041, local anti-resonance can be also observed. This has practical significance in reducing some of the unexpected vibration in aerospace engineering and mechanical fields. The HLF electromagnetic radiation plays an important role in weakening the VR of neuronal systems. Therefore, this study could provide new insights to further control the abnormal electrical activities and information transmission of neurons, which can lead to better design and development of therapeutic programs for neurological disorders and neuroplasticity therapies, and advance neuroscience.

Research on the Reliability of Advanced Packaging under Multi-Field Coupling: A Review.

With the advancement of Moore's Law reaching its limits, advanced packaging technologies represented by Flip Chip (FC), Wafer-Level Packaging (WLP), System in Package (SiP), and 3D packaging have received significant attention. While advanced packaging has made breakthroughs in achieving high performance, miniaturization, and low cost, the smaller thermal space and higher power density have created complex physical fields such as electricity, heat, and stress. The packaging interconnects responsible for electrical transmission are prone to serious reliability issues, leading to the device's failure. Therefore, conducting multi-field coupling research on the reliability of advanced packaging interconnects is necessary. The development of packaging and the characteristics of advanced packaging are reviewed. The reliability issues of advanced packaging under thermal, electrical, and electromagnetic fields are discussed, as well as the methods and current research of multi-field coupling in advanced packaging. Finally, the prospect of the multi-field coupling reliability of advanced packaging is summarized to provide references for the reliability research of advanced packaging.

Assessing and enhancing the regional sustainability of electricity generation technologies in an energy-importing megaregion in China

Decision-makers are increasingly concerned about the sustainability of power generation technologies to achieve a secure and sustainable electricity supply in the future. This study aims to assess the sustainability of the eight key electricity generation technologies in the energy-importing Yangtze River Delta region of China and further enhance the regional sustainability of the electricity generation mix. We employed the multi-criteria decision-making process to rank the sustainability of the eight electricity generation technologiesfrom the perspective of various decision-makers. First, the results revealed that no technology is absolutely most sustainable. Second, the subjective findings show that hydropower is the most sustainable technology among the assessed ones, followed by nuclear and onshore wind power. Third, policymakers regard fossil fuel energy as more sustainable than investors and experts. Furthermore, local renewable diffusion and substantial electricity imports from renewable-rich areas can enhance the sustainability of the localelectricity system. In addition,an interregional electricity transmission grid with the neighbor Anhui province as the primary backup power supplier can sufficiently enhance the sustainability of the electricity system in the Yangtze River Delta region.

Abnormal line loss identification and category classification of distribution networks based on semi-supervised learning and hierarchical classification

Line loss refers to the electrical energy that is dissipated as heat during the transmission and distribution of electricity through power lines. However, unusual causes, such as grid topology mismatch and communication failure, can cause abnormal line loss. Efficient abnormal line loss detection contributes not only to minimizing energy wastage and reducing carbon emissions but also to maintaining the stability and reliability of the entire distribution network. In actual situations, the cause of abnormal line loss is not labeled due to the expensive labor cost. This paper proposes a hierarchical abnormal line loss identification and category classification model, considering the unlabeled and unbalanced sample problem. First, an abnormal line loss identification model-based random forest is established to detect whether the line loss is abnormal. Then, an abnormal line loss category classification model is developed with semi-supervised learning for line loss abnormal category classification, considering the unlabeled samples. The real dataset in China is utilized to validate the performance of the proposed model. Its reliability implies the potential to be applied to real-world scenarios to improve the management level and safety of the power grid.

Assessing the impact of renewable energy penetration and geographical allocation on transmission expansion cost: A comparative analysis of two large-scale systems

Ambitious renewable energy development plans require an efficient electricity grid connection of massive generation capacity. Significant transmission network investments are considered necessary to support the energy transition. This paper analyses the transmission grid reinforcement requirements of two large-scale electricity systems with notably different geographical distributions of renewable resource availability and load density. The results indicate that optimal transmission expansion accounts for less than 10% of total annualised system investment (generation capacity + transmission grid). This is true even in transmission systems with significant existing bottlenecks. The study includes a sensitivity analysis of the location of renewable energy source (RES) capacity. The sensitivities examine the impact of varying the concentration of RES in resource-rich areas, as well as the proportion of RES capacity connected at the distribution level. Both sensitivities are evaluated over a common base case scenario. The results show that a higher concentration of RES capacity in resource-rich network zones can more than double the optimal transmission expansion requirements. In contrast, the implementation of distributed generation (DG) leads to the allocation of generation closer to demand centres, resulting in transmission grid savings of up to 30%. These effects are more pronounced in networks where RES capacity is located further away from major demand centres. This is because existing bottlenecks are exacerbated by higher RES concentration and relieved as the share of DG increases.

The application of the internet of things technology in the smart grid

The development of electric power and the energy industry is an important guarantee for national economic development. With the explosion of population and the development of science and technology, the power system needs to be more intelligent and networked to facilitate management and optimization. In recent years, the Internet of Things (IoT) technology has continued to develop through the combination of Internet technology and sensor technology, and the fast network transmission speed as well as the miniaturization and refinement of sensing can help the construction of the smart grid. This paper analyzes the application of IoT technology in the smart grid, including different types of smart grid architecture and the practical application of the IoT technology in the whole process of power transmission. Besides, the paper also discusses different application links and network levels. At present, the IoT technology has been applied to the smart grid information collection, transmission, and processing process, participating in information management in the power station, power transmission, substation, and other links. However, due to the complexity of processing the massive data of the power grid and the multiple network connections during the transmission process, ensuring the security of information and networks has become a challenge. While realizing high-speed communication, it is necessary to ensure the security of users as well as the information communication and networks.

Surface Structural Changes in Silicone Rubber Due to Electrical Tracking.

There is a growing interest in the use of silicone composite insulators for electrical power transmission and distribution applications. However, such materials are susceptible to degradation as they are exposed to electrical and environmental stresses during operating conditions. Therefore, it is crucial to gain a thorough understanding of the degradation mechanism through changes in the material structure that may provide insight into potential failures in the electrical grid. Attenuated total reflection Fourier transform infrared spectroscopy and two-dimensional correlation spectroscopy (2D-COS) were used along with contact angle measurements to characterize changes in silicone rubber samples from actual insulators subjected to tracking wheel testing. The results showed a decrease in absorbance of different infrared bands representing different functional groups, such as Si-O-Si, methyl functional groups, and both Al-O and hydroxyl groups of alumina trihydrate as a function of the number of tracking cycles. The sequence of changes in the functional groups was determined by 2D-COS as Al-O and OH followed by Si-O-Si polymer backbone modes, followed by polymer methyl side chains. An enhancement in the average contact angle with the number of tracking cycles revealed a concomitant increase in surface roughness with electrical tracking.

Artificial multisensory system with optical feedback for multimodal perceptual imaging

Artificial sensory systems require appropriate input–output modalities to enhance interaction efficiency and accuracy. It is crucial to mimic an organism to perceive and integrate multiple external stimuli and to construct systems with multiple sensory inputs. In addition, the application of optical output to the sensory system not only solves the problem of electrical signal transmission, but also enables direct imaging through optical output. Here, we propose an artificial multisensory system with optical feedback (AMSOF), which can simultaneously receive pressure and thermal stimuli and then generate neuromorphic optical responses along with feedback. Among them, the triboelectric nanogenerator (TENG) is responsible for receiving pressure stimuli, while the artificial light-emitting synaptic device utilizes the quantum dot thermal quenching mechanism to processes temperature signals and generate optical feedback. Additionally, if the AMOFS is applied to the fingerprint recognition system, it can enable fingerprint imaging through dual recognition of these two signals. In other words, it can enhance the security of biometric authentication by adding the capability to distinguish the live nature of objects on top of traditional fingerprint recognition. Therefore, this system aims to achieve more intelligent and efficient human–computer interaction and environmental perception capabilities, bringing forth a multitude of applications and innovations for smart systems.

Evaluating the environmental and economic performance of biological and advanced biological wastewater treatment plants by life cycle assessment and life cycle costing.

The primary objective of this study is to assess and establish benchmarks for environmental and economic sustainability of biological and advanced biological wastewater treatment plants (WWTPs) with different treatment technologies and characteristics. Furthermore, the study aims to determine the beneficial role of WWTPs to reduction of eutrophication potential. Environmental and economic sustainability of ten municipal WWTPs was assessed using life cycle assessment (LCA) and life cycle costing (LCC). In the first section of the study, LCA was performed to determine the environmental performance of the WWTPs. Furthermore, net environmental benefit (NEB) approach was implemented to reveal the beneficial role of WWTPs to eutrophication potential. In the subsequent section, LCA-based LCC was conducted by integrating the results of LCA. The most significant environmental impact was determined as marine aquatic ecotoxicity, which is highly affected from the generation and transmission of electricity consumed in the WWTPs. Wastewater recovery and co-incineration of sewage sludge in cement kiln ensure significant environmental savings on ozone layer depletion, human toxicity, acidification, photochemical oxidation, and abiotic depletion (fossil fuel) potential. Considering NEB approach, the highest NEB values were found for the WWTPs with the higher organic load and nutrient concentration in the influent. The results of LCC in WWTPs varied between 0.21 and 0.53 €/m3. External (environmental) costs were evaluated higher than internal (operational) costs for all selected WWTPs. While eutrophication was the highest among environmental costs, electricity cost was the highest among operational costs for almost all WWTPs.

Multi‐Task Residential Short‐Term Load Prediction Based on Contrastive Learning

AbstractLoad forecasting is crucial for the operation and planning of electricity generation, transmission, and distribution. In the context of short‐term electricity load prediction for residential users, single‐task learning methods fail to consider the relationship among multiple residential users and have limited feature extraction capabilities for residential load data. It is challenging to obtain sufficient information from individual residential user load predictions, resulting in poor prediction performance. To address these issues, we propose a framework for multi‐task residential short‐term load prediction based on contrastive learning. Firstly, clustering techniques are used to select residential users with similar electricity consumption patterns. Secondly, contrastive learning is employed for pre‐training, extracting trend and seasonal feature representations of load sequences, thereby enhancing the feature extraction capability for residential load Feature. Lastly, a multi‐task learning prediction framework is utilized to learn shared information among multiple residential users' loads, enabling short‐term load prediction for multiple residences. The proposed load prediction framework has been implemented on two real‐world load data sets, and the experimental results demonstrate that it effectively reduces the prediction errors for residential load prediction. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Influence of Using ACO Algorithm in STATCOM Performance

Flexible alternating current transmission systems (FACTS) are technologies that rely on the use of high-power electronic equipment and advanced control methods to increase the ability of electrical transmission lines to transmit the largest amount of electrical power.Static_ synchronous _compensator (STATCOM) is one of FACTS_ family It has a rapid ability to supply or absorb reactive power from the system, traditional controller in STATCOM is (PID) or (PI). The controller is used to control the currents and voltages. In (STATCOM) there are two control loop, the inner loop which is represented by controlling the source current, and the outer loop which is used to control the voltage of the DC-link energy –storage- capacitor (vdc). Many algorithms are used in optimization to predict a specific behavior and to find the best solution for it. Two optimization methods were used Ant Colony Algorithm (ACO) and Genetic Algorithm (GA) to adjust the parameters of the outer PI controller. This unit is responsible for adjusting (vdc). The capacitor voltage is converted by the STATCOM inverter into a three-phase voltage, which is used to supply the load with required reactive power. Through the simulation and control process, the results demonstrated the effectiveness of the compensator in supplying the reactive power to the linear or non-linear loads, The results also showed that using the ACO technique that the integrated time absolute error ( ITAE) was reduced which gives more precise in adjusting the PI control parameters Which is achieved best Vd.c response ,compared with the GA and ziklur-nichlos method , by measuring the response of the lowest settling time (TS) and minimal peak overshoot (P-ov) .