Electrical Transmission Research Articles

Integrating Artificial Intelligence for Enhanced Fault Detection in Power Transmission Systems: A Smart Grid Approach

Modern electrical systems rely heavily on sensors and relays for fault detection in three-phase transmission lines and distribution transformers. However, these devices often need more time complexity and are prone to false alarms (erroneous signals). The study was guided by the PRISMA guidelines and methodology. The study's objective was to compare the levels of accuracy of fault detection presented by different models studied between 2023 and 2024. The general objective was further divided into sub-objectives, which included determining types of faults in power transmission systems, examining fault detection machine-learning models in power transmission systems, and assessing levels of accuracy of fault detection techniques in power transmission systems. The study used a qualitative research design, which entailed a systematic literature review that involved identifying scholarly articles from respectable international journal websites that aligned with examining how the deep-learning model enhanced fault detection capabilities in a three-phase transmission line simulated dataset. The inclusion criteria were that the study used journals published between 2023 and 2024. The study also included journals that tested Artificial Intelligence systems' accuracy in fault detection in three-phase transmission lines and distribution transformers. The study excluded sources that were older than 2023. The sources of information were journals written by professionals and scholars in the field of Artificial intelligence and engineering. The sourced journals used simulated and real databases to test fault detection accuracy in three-phase transmission lines and distribution transformers. The source of data for review was obtained from the internet via Google search with various credible academic journal websites being searched by the researcher. A total of 12 sources were searched. To assess the risk of bias in the included studies, the researcher used the Robvis methods that visualized risk-of-bias during the inclusion criteria for sources used in the systematic review. The techniques used to present the findings were narratives, graphs, and tables. The method used to synthesize results was comparative data analysis. The study included two studies, the sample size from the three possible journals the researcher identified. The characteristics of the studies were that they both informed the study's general and specific sub-objectives. The two studies also used the same methods to arrive at their findings, which made them ideal for comparative analysis. Finally, both studies compared older systems to new systems regarding Artificial Intelligence accuracy in fault detection. The study's findings were as follows: The study ranked the Novel glass box-based model as the best artificial intelligence machine-learning approach for accurately detecting electric power transmission lines and systems faults. The Novel glass box-based approach's accuracy was 99%, followed by the Convolution Neural Network model's accuracy of 98%, the Gated Recurrent Unit model's accuracy of 92%, the Random Forest model's accuracy of 90%, the Logistic Regression model's accuracy of 74%, and the Support Vector Classifier model's accuracy of 63%. The review was adequate to conclude that the Novel glass-box-based proposed approach was the most advanced and suitable model for detecting faults in electric power transmission lines. The study had one limitation: it reported the results based on two sources, which made the exclusion criteria a point of bias. The survey results implied that builders of new Artificial intelligence systems for fault detection in electric line transmitters should aim to produce 99% accurate systems to meet the recommendations and fill the gap that this study has reported. This study was funded by the researcher using family contributions. The study's author is Nijat Taghizad (ORCID ID: 0009-0005-0505-8767).

Lubricity performance of Jatropha oil incorporated PTFE and h-BN as additives for electric vehicles transmission lubrication

Purpose Concerns over the pollution caused by internal combustion vehicles have increased owing to population and industrialization increment. Addressing the confrontations, the demand for electric vehicles (EVs) as a combustion engine substitute became necessary in responding to environmental worries from internal combustion. The development of bio lubricant in lubricating the sliding parts of EVs is required to maintain the sustainability idea and to improve the system performance, which this research tends to explore. Design/methodology/approach In this research, the enhancement of base Jatropha oil was done using polytetrafluoroethylene (PTFE) and hexagonal boron nitrate (h-BN) as additives. Different characterization was conducted on the new formulation to ascertain its anticorrosion tendency. The wear and friction behavior of the formulations on the tribo-pairs surfaces in contact were investigated using ball on flat tribometer to determine their tribological responsiveness as mineral lubricant alternative. To explore the surface topography, surface profilometer, scanning electron microscope and energy dispersive X-ray investigations were PTFE, lubrication and EV carried out. Findings The test’s input parameters were EVs’ usual load and sliding speed, and the addition concentrations for PTFE were 0.3 Wt.%, 0.4 Wt.%, 0.5 Wt.% and 0.6 Wt.%, whereas h-BN were 0.4 Wt.%, 0.8 Wt.% and 1.2 Wt.%, respectively. The study on corrosion demonstrated resistance when applied PTFE and h-BN additives in Jatropha oil. The analysis revealed that 0.5 Wt.% PTFE + 0.8 Wt.% h-BN concentrations significantly improved the tribological characteristics when compared to the base Jatropha oil. The application of formulations yielded percentage reduction of 8.67%, 10.98%, 7.34% and 7.35%, respectively, for 0.5% poly + 0.5% h-BN, 0.5% poly + 0.6% h-BN, 0.5% poly + 0.7% h-BN, 0.5% poly + 0.8% h-BN against base Jatropha oil under 20 N. Originality/value The formulation of PTFE and h-BN for electric transmission with wear and friction effects was accomplished in this paper. The mechanism of particle diffusing at the sliding contact on tribological behavior could be examined based on the created model of operation. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2023-0235/

Deep learning-based electric vehicle transmission system speed ratio prediction research

Deep learning-based electric vehicle transmission system speed ratio prediction research

Research on Pantograph Defect Classification Based on Vibration Signals

Pantograph-based electrical current transmission systems are used in electric traction vehicles. The contact surface between the pantograph and the catenary wire experiences mechanical and thermal effects during the train’s movement. Typically, this contact surface on the pantograph is covered by a segmented carbon or copper rod, attached to an aluminum base. Railways implement organizational measures for pantograph condition monitoring, based on scheduled inspections. Constitutionally, the option to replace contact elements or individual segments of the pantograph exists if wear is detected. Many scientific publications describe ideas for pantograph visualization and automated condition monitoring. These ideas are based on analyzing mechanical vibrations generated by the pantograph, acoustic vibration signal analysis, 3D geometric data of the pantograph surface captured by laser scanning, and combinations of several methods. However, in these publications, mechanical vibration analysis is limited to signal shape and spectral analysis. The possibility of treating the vibration signal as a random process using statistical methods has not been utilized. This study describes the possibility of evaluating classified mechanical pantograph vibrations using the signal’s autocorrelation transformation. A laboratory experiment confirmed the proposed method for evaluating informative signal classification features. The proposed method can distinguish between signals generated by a defective pantograph surface and identify different types of defects.

Research on the Influence of Genetic Algorithm Parameters on XGBoost in Load Forecasting

Electric load forecasting is crucial in a power system comprising electricity generation, transmission, distribution, and retail. Due to its high accuracy, the ensemble learning method XGBoost has been widely applied in load forecasting. XGBoost's performance depends on its hyperparameters and the Genetic Algorithm (GA) is a commonly used algorithm in determining the optimal hyperparameters for this model. In this study, we propose a flowchart algorithm to investigate the impact of GA parameters on the accuracy of XGBoost models over the hyperparameter grid for load forecasting. The maximum load data of Queensland, Australia, are used for the research. The analysis of the results indicates that the accuracy of the XGBoost model significantly depends on the values of its hyperparameters. Using default hyperparameter values may lead to substantial errors in load forecasts, while selecting appropriate values for the GA to determine the optimal hyperparameters for the XGBoost model can significantly improve its accuracy.

Cost–Benefit Analysis of Cross-Regional Transmission of Renewable Electricity: A Chinese Case Study

While the establishment of a unified regional power market that promotes the optimal use of renewable energy is considered to be a pathway to meeting the policy targets of “carbon peaking” and “carbon neutrality”, the economic feasibility of the power transmission project is not well understood. To fill this gap, this study conducted a cost–benefit analysis of a proposed project that transmits hydropower, photovoltaic power, and/or wind power whose ratios are 100:0:0, 79:13:8, and 65:22:13 in three scenarios from Dian to Yu in China. It was found that the project has economic feasibilities for each scenario; however, this highly depends on its external benefits, discount rates, and transmission quantity. As the ratio of hydropower becomes lower, the net present value decreases from 117.32 billion to 112.99 billion for an annual transmission of 7.498 billion kWh of electricity. Since the substitution of coal-fired power with renewable power contributes the highest benefit to the project, the cross-regional transmission of renewable electricity should be promoted jointly with the internalization mechanism of externalities.

Electricity storage or transmission? Comparing social welfare between electricity arbitrages

Electricity storage and inter-regional transmission are expected to play a greater role in mitigating the power surplus caused by the large-scale introduction of solar power generation. In this study, we evaluate the impacts of these two power arbitrages and provide their welfare implications. We develop a simple analytical framework based on the demand and supply in the power market, and apply the framework to Kyushu area in Japan to quantify the social benefits of current storage and transmission practice. We estimate electricity demand curves and define supply curves from the data to describe the social impacts of the two arbitrages. Our main findings can be summarized as follows. First, the estimation results indicate that the price elasticity of electricity demand is −0.228 and −0.252 in high and low solar hours, respectively. Second, the results show that the current storage and transmission operations provide social benefits of 73,000 and 59,000 USD per day, respectively. Third, both arbitrages lead to external benefits by reducing CO2 emissions from thermal power generation. These results suggest that the current storage and transmission operations provide positive social benefits without detrimental effects on consumer, producer or environment. Therefore, both storage and transmission are preferable measures for mitigating the impact of variable solar power generation on society and the environment.

Optimal design of spatial structures by a novel meta-heuristic algorithm: Sound energy optimizer

Sound signals are used by various species of living creaters to commonicate with each other. Scientific experiments have demonstrated that sound energy can travel through media and cause the receiver objects to vibrate or move. The present work simulates such a phenomenon in development of a new meta-heuristic algorithm called: Sound Energy Optimizer (SEO). Concerning physical rules, SEO leverages natural or ambient sounds for optimization. SEO utilizes a tunable virtual machine to control the decaying pattern of the sound energy in a medium representing the search space. Performance of the proposed algorithm is first evaluated on unconstrained optimization of some mathematical test functions. Then, a variety of pin-jointed structural problems are optimized including space tower, double-layer grid, barrel vault, helipad structure and power transmission towers. In this set, performance evaluation is done not only against the previous works but also by running SEO versus eight recent meta-heuristics under fair comparison conditions. New quantitative indices are introduced and traced as well as the common statistical measures and convergence curves. The results demonstrate competitive performance of SEO versus recent meta-heuristics in the discrete and continuous design of spatial structures.

Zooming in or zooming out: Energy strategy, developmental parity and regional entrepreneurial dynamism

In this paper, we analyze the economic impacts of the West-East Electricity Transmission Project (WEETP) project using the multi-period difference-in-difference (DID) method based on county-level data from 2000 to 2020. Our findings indicate that the WEETP project inhibits firm entry in electricity-exporting regions while encouraging firm entry in electricity-importing regions, thus hindering regional development equalization. Specifically, the initiation of WEETP discouraged firm activity in the southern and central corridors' exporting regions but boosted firm activity in the northern corridor. Additionally, WEETP exacerbated infrastructure overbuilding and environmental damage in energy-exporting regions, further weakening entrepreneurial dynamism (ED) and widening the development gap with energy-importing regions. Our study provides insights into the real impact of national energy strategies on regional development and highlights the institutional factors contributing to the resource curse. The findings demonstrate the limitations of non-market pricing approaches to resource allocation, thereby offering an empirical basis for narrowing the development gap and promoting developmental affirmative action.

The impact of hydrogen on decarbonisation and resilience in integrated energy systems

The lack of clarity and uncertainty about hydrogen's role, demand, applications, and economics has been a barrier to the development of the hydrogen economy. In this paper, an optimisation model for the integrated planning and operation of hydrogen and electricity systems is presented to identify the role of hydrogen technologies and linepack in decarbonising energy systems, improving system flexibility, and enhancing energy system security and resilience against extreme weather events. The studies are conducted on Great Britain's (GB) 2050 net-zero electricity and gas transmission systems to analyse the hydrogen transport and capacity requirements within the existing infrastructure under different scenarios. This includes sensitivities on the level of flexibility, high gas prices, hydrogen production mixes, enabled reversibility of electrolysers, electricity generation cost, and hydrogen storage facilities. In all sensitivity scenarios, efficient hydrogen transport within the existing infrastructure is enabled by the optimal allocation of green and blue hydrogen sources, distributed storage facilities, and the intra-day flexibility provided by linepack. The findings highlight that increased renewable deployment transfers intermittency to the hydrogen network, requiring greater linepack flexibility compared to the current paradigm (up to 83%). Furthermore, the necessity of synergy between different gas and electricity systems components in providing flexibility, security, and resilience is quantified.

Analysis and Modeling of Four-Quadrant PWM Rectifier

Different from the traditional AC/DC converters, the four-quadrant converters are widely used in modern AC power transmission systems because of their advantages such as two-way flow of energy, adjustable power factor on the grid side, and stable voltage on the DC side [1]. In this paper, the mathematical model of four-quadrant PWM rectifier is presented, and on this basis, the constant-frequency double closed-loop control strategy is adopted to control the DC voltage and the power factor of the grid side. The determination method of voltage and current PI regulator parameters is analyzed, and the influence of ac side inductance and DC side capacitance on the whole system is pointed out, and the design method is given. Through the establishment of simulation model under MATLAB/ Simulink, it is proved that this model can realize the DC voltage and power factor control at the side of the network, has a good load regulation ability, and can switch between the rectifier mode and the inverter mode, realizing the two-way flow of energy. This paper has some guiding effect on the design of four-quadrant PWM rectifier.

The impact of high‐voltage circuit breaker condition on power system reliability indices

AbstractCondition monitoring data can be used to assess the health of a power system component but is rarely used to assess the reliability of the power system they are part of. For high‐voltage circuit breakers (HVCBs), the susceptibility to various failures can be assessed by examining trip coil current (TCC) measurements. HVCBs have two failure modes, failure to trip on command and tripping without a command, which are triggered by various failure mechanisms that in turn may depend on the technical condition of the HVCBs. The aim of this article is to demonstrate a methodology for quantifying the impact that the technical condition of HVCBs has on the power system reliability indices. An equivalent transmission line failure rate considering protection system failures, including failures related to HVCBs, can be calculated by computing contributions from various fault types (FTs). This article proposes a framework that can quantify the frequency of the FTs that are affected by HVCB condition. The system‐level effects are then evaluated using approximate methods for power system reliability evaluation. To demonstrate the principles and benefits of this methodology, a case study is presented that incorporates HVCB condition data from the Icelandic transmission grid into an illustrative 4‐bus test system; a dataset of 83 TCC measurements from 38 HVCBs from the Icelandic Transmission System Operator is used, together with an outage database of life histories of 464 HVCBs from the Icelandic transmission grid. Results indicate that the condition deterioration associated with the probability of an HVCB failing to trip on command can significantly degrade the reliability indices.

A Self‐Powered and Self‐Absorbing Wireless Sensor Node for Smart Grid

The health monitoring of electricity transmission systems has been increasingly attracting the scientific community's attention, especially those power transmission towers built in remote and deserted areas. The smart grid provides a realistic solution to the health monitoring problem, but there is a problem that the energy supplies are still constrained by batteries. This article proposes a novel vibration energy harvester to address the power supply challenges of auxiliary equipment mounted on electricity towers or transmission lines. The proposed harvester not only harvests vibration energy when working but also attenuates the vibration amplitude of the transmission line. The structure of the device comprises three modules: a module for capturing vibration, a module for motion conversion, and a power management module. The analytical response under sinusoidal and random excitation is investigated, and the performance of energy harvesting and the effect on vibration is tested. The prototype achieves a maximum power of 183.96 mW when tested using the servo hydraulic mechanical testing and sensing system, and the wireless data transmission experiment proves the power generation ability of the prototype. The experimental results show that the acceleration of the transmission line decreases when the prototype is working.

Electrical shock injuries: an analysis of voltage, frequency, and contact mode determinants

This article examines the precision of medical terminology commonly used to diagnose and understand the pathogenesis of electrical shock injuries. As everyday technology increasingly depends on advanced electrical mechanisms that utilize more efficient modes of electrical energy transmission, waveforms, and frequencies, emergency and trauma physicians will continue to encounter a broader array of electrical injury manifestations. This phenomenon prompts a closer examination of the diagnostic terminology associated with electrical shocks. The pathogenesis of electrical injury depends on the tissue electric field strength, frequency, current duration, and tissues involved. Some traditional diagnostic terms, for example, “entry” and “exit” wounds, arc-flash burns, and “high-voltage” and “low-voltage” electrical injuries, obscure the complexity of this pathogenesis, likely impeding medical management and advances in electrical safety science. This article presents the scientific rationale for suggested changes to medical terminology and aims to encourage future refinement.

A Fractional Reduced Differential Transform Method for Solving Multi-Fractional Telegraph Equations

This paper presents a novel modification of the Fractional Reduced Differential Transform Method (FRDTM) to solve space-time multi-fractional telegraph equations. The telegraph equation is crucial in modeling voltage and current distribution in electrical transmission lines, and its solutions have applications in physics, economics, and applied mathematics. The proposed method effectively simplifies the fractional differential equations by omitting one fractional derivative term, allowing for the transformation of the remaining terms using the FRDTM. The solutions demonstrate the method’s accuracy and efficiency in fractional partial differential equations. This study advances the analytical solutions of fractional telegraph equations by providing a straightforward yet powerful approach to fractional differential problems.

Synergetic Optimization via Indium and Rare Metal Yttrium Co-doping in GeTe Results in High Power Factor and Excellent Thermal Performance.

Excess intrinsic Ge vacancies in GeTe materials lead to excessively high hole concentration and high thermal conductivity, producing poor thermoelectric performance. Here, synergistic control and optimization of the thermoelectric transport properties and microstructure of GeTe-based materials were achieved through co-doping with In and rare earth element Y, resulting in a significant enhancement of thermoelectric performance. The Ge0.94In0.03Y0.03Te sample reached a ZTmax of 1.84 at 773 K, representing an increase of around 91% compared to the GeTe matrix. The experimental results indicate that the doping of In optimizes the band structure by introducing resonant levels and increasing the degeneracy of the valence band. Y doping introduces in situ nanoscale secondary phases and lattice distortions due to defect generation, enhancing phonon scattering and significantly reducing the κlat. This work elaborates on how co-doping with In and Y achieves the optimization of the thermoelectric performance of GeTe-based materials. While the electrical transmission characteristics are improved, the thermal conductivity is significantly reduced. For the Ge0.94In0.03Y0.03Te sample, κlat decreased to ∼0.56 W m-1 K-1 at 573 K, resulting in a ZTave of ∼0.99 over the entire temperature range, representing over 140% improvement compared to undoped GeTe. This improvement is significantly higher compared with other works on GeTe and PbTe.

Evaluation of Low Frequency Electrical and Magnetic Fields in a Electrical Transmission Substation

Evaluation of Low Frequency Electrical and Magnetic Fields in a Electrical Transmission Substation

Nash Bargaining-Based Coordinated Frequency-Constrained Dispatch for Distribution Networks and Microgrids

As the penetration of distributed renewable energy continues to increase in distribution networks, the traditional scheduling model that the inertia and primary frequency support of distribution networks are completely dependent on the transmission grid will place enormous regulatory pressure on the transmission grid and hinder the active regulation capabilities of distribution networks. To address this issue, this paper proposes a coordinated optimization method for distribution networks and microgrid clusters considering frequency constraints. First, the confidence interval of disturbances was determined based on historical forecast deviation data. On this basis, a second-order cone programming model for distribution networks with embedded frequency security constraints was established. Then, microgrids performed economic dispatch considering the reserves requirement to provide inertia and primary frequency support, and a stochastic optimization model with conditional value-at-risk was built to address uncertainties. Finally, a cooperative game between the distribution network and microgrid clusters was established, determining the reserve capacity provided by each microgrid and the corresponding prices through Nash bargaining. The model was further transformed into two sub-problems, which were solved in a distributed manner using the ADMM algorithm. The effectiveness of the proposed method in enhancing the operational security and economic efficiency of the distribution networks is validated through simulation analysis.

High Gradient Magnetic Fields Generated in Events on the 230 kV Electric Power Transmission Infrastructure: Human Exposure Analysis and Risk

This work presents a process for analyzing human exposure to high-gradient magnetic fields based on protection models for patients undergoing magnetic resonance examinations developed by the International Commission on Non-Ionizing Radiation Protection (ICNIRP). A series of events presented in the electrical energy transmission infrastructure is taken, with typical currents and front times, a case of extreme failure is added, and results are presented. Subsequently, with a classic lightning signal, compliance with the exposure to this type of field in the right-of-way strip of a transmission line is verified and validated in the event of a hypothetical atmospheric discharge impact event. Finally, a simulation is implemented with space discretization techniques, establishing the signal resulting from the magnetic field generated as a function of time. The exposure is evaluated using the presented model. Only in one of the analysis cases was it found that the perception threshold is exceeded at distances less than 0.6 cm, virtually a situation in which the worker has contact with the down conductor. With the results obtained and the cases analyzed, it is concluded that failures of this type do not generate significant exposure to high-gradient magnetic fields and do not exceed the determined perception thresholds.

Security constrained optimal power flow solution for practical transmission grid using hybrid use of generating plant and network restructuring

This paper presented a study on the security constrained optimal power flow (SCOPF) of a practical transmission utility grid network (TUGN). The objectives of reduction in the transmission losses of TUGN (TLGN) and to minimize the bus voltage deviations (BVD) are achieved by addition of thermal power plant (TPP) and network restructuring (NR). Identification of best fit node for generation injection is achieved using the method based on hybrid combination of analytic approach and genetic algorithm (GA). Efficacy of the proposed study is evaluated using the computation of energy equivalent to network loss saving, percentage derating of the load (PDOL), voltage profile, bus voltage deviations, and financial analysis (FA). Study is performed without and with contingency (N-1) for the base case network, TUGN with addition of TPP and TUGN with addition of TPP and NR. This is established that addition of TPP, addition of TPP with NR are effective to reduce the TLGN, improve the voltage profile and minimize the bus voltage deviations. This study is more efficient compared to various studies reported in literature.