Power System Equipment Research Articles

Algorithms of training programs for simulator training of ship's engine room personnel

The ship's engine room personnel (mechanics, electromechanics) ensure the technical operation and safe functioning of the ship's power systems. Professional training and retraining of marine specialists of ship's engine rooms is necessary due to the constant improvement of equipment and automation of electric power systems of modern ships. To ensure high-quality training of the ship's engine room team, simulator training is used with a set of modern simulators of ship power plants and electric power systems that meet the standards of the International Convention on Standards of Training, Certification and Watchkeeping for Seafarers. In the course of the study, block diagrams of training algorithms have been developed; program features and examples of their operation have been shown. The obtained algorithms for training on simulators make it possible to implement both the initial training of cadets and the retraining of specialists when improving their qualifications. In the course of simulator practice, the opportunity to develop competencies for working with high-voltage equipment (3–10 kV) of modern ships in accordance with the requirements of the STCW Convention and the STCW Code is of particular importance. Algorithms for training in high-voltage equipment maintenance should be introduced into the educational process to improve the level of qualification of specialists.

Research on the abnormal identification method of remote real-time monitoring of power system equipment based on artificial intelligence

ABSTRACT This study addresses the critical challenge of anomaly detection in modern power systems, proposing a novel hybrid approach that combines supervised and unsupervised machine learning techniques. The increasing complexity of power grids and integrating renewable energy sources and smart technologies necessitate more sophisticated methods for identifying and mitigating potential threats to system stability and security. Our model leverages deep learning architectures, including Long Short-Term Memory networks and Convolutional Neural Networks, to process high-dimensional time-series data from various grid sensors. The system demonstrates exceptional performance in detecting diverse anomalies, including cyber-attacks, equipment failures, and environmental disturbances, with an overall accuracy of 97.8% and an F1-score of 0.956. Real-time processing capabilities enable anomaly detection within 1.2 seconds, crucial for preventing cascading failures. A novel interpretability framework provides insights into the model’s decision-making process, enhancing trust and actionable intelligence for operators. Extensive case studies and robustness analyses validate the system’s effectiveness across various operational scenarios. This research significantly enhances power grid resilience and reliability, offering a robust foundation for future advancements in smart grid technologies and adaptive anomaly detection systems.

An approach to improve grounding resistance characteristic in existing 115 KV transmission towers

Effective grounding can attenuate fault currents and reduce damage to power system equipment, making the grounding system a crucial protection scheme. Generally, the ground resistance is set to a value less than 10 Ω. However, in practice, it is difficult to achieve this in certain areas, such as mountains, plains, and swamps. To address this problem, this study analyses the (1) actual footing resistance, (2) ground resistivity, and (3) ground length of transmission towers located in specific areas. Non-linear factors, impedance, and coupling effect are considered instead of traditional variables. A grounding design algorithm is proposed based on the ATPDraw program. Simulation results are applied to the 115 kV real-world system of the Electricity Generating Authority of Thailand. The grounding system exhibits a satisfactory performance improvement, as evidenced by comparisons between simulation and actual measurements. The findings are useful for implementing future improvements in grounding protection schemes.

Simulation of Faults in the Yeywa-Thaphaywa High Voltage Transmission Line using Fuzzy Logic

Faults in transmission networks frequently undermine the reliability and stability of electrical power supply, causing load shedding and posing risks to power system equipment. Effective fault classification is crucial to minimize power outages and ensure timely restoration. This study presents a fault classification method for transmission lines using fuzzy logic. The classification utilizes three-phase feeder currents, neutral current, and phase voltages as inputs to the Fuzzy Inference System (FIS). The proposed method accurately classifies the various types of faults in a high voltage transmission network, including line-to-ground fault, double line-to-ground fault, line-to-line fault, and three-phase faults. Numerous test cases demonstrate the technique's high accuracy. The simulations are carried out in MATLAB/SIMULINK using Sim Power Systems and the Fuzzy Logic Toolbox (MATLAB 2016a).

Fault detection and treatment method of power converter station based on man-machine cooperative intelligence

Abstract Aiming at the problems of low efficiency of human operation and maintenance and high cost of resources faced by converter station equipment in the current new power system, this work studies and proposes an intelligent converter station anomaly collaborative processing method. Based on the data set of the existing data structure of the converter station, the operation of the power equipment is monitored by the sensor equipment. According to the abnormal signal and the designed fault analysis technology and knowledge graph, the fault intelligent analysis and fault disposal strategy are realized. The experimental results show that the proposed method can effectively monitor the abnormal data in the intelligent converter station and realize efficient fault collaborative processing.

Infrared thermal measurement in power system considering complex circumstances

Abstract Infrared thermal measurement is a vital tool in power systems for monitoring the temperature of electrical equipment without direct contact. This non-invasive technique helps detect hot spots, identify potential issues, and evaluate the performance of power system components. However, the accuracy of the thermal measurement for power system equipment suffers from complex situations and stochastic circumstances. This paper uses a machine-learning-based method to calculate the accurate surface temperature of equipment, which is mainly based on the feedforward network and backpropagation algorithm. At the same time, multiple regression algorithms are implemented and compared. The simulation results and the experiments show that the infrared thermal measurement based on the proposed method reduces the measurement error to 0.1%.

Flexible assessment of line polymer insulators based on condition indexes

Line polymer insulators, as a kind of power system equipment which are faced with many of the environmental stresses, need to have a condition assessment strategy with the aim of predictively protection of lines against any failures and outages. This paper, according to some experimental test process results, proposed a flexible condition assessment method based on defined values of indexes which consist of elongation percent, hardness, leakage current, corrosion, dye penetration, water diffusion, thermo-gravimetric analysis and Fourier transform infrared spectroscopy with regard to visual inspection and hydrophobicity classification. Also, estimations of operation life time have been done for unknown-years-old insulators of the Yazd utility of Iran power network as a case study by defining relative factors. Finally, with regard to environmental pollution measurement, by considering the assessment results, a series of next actions have been suggested according to lines importance.

Development of an intelligent-based telemetry hexapod robotic system for surveillance of power system components

A robot is an artificial system that performs specific programmed tasks to aid day-to-day human activities. Robot finds applications in industries for large-scale production, healthcare for computer-aided surgeries, security for surveillance of electrical power system equipment, and the military for carrying out dangerous reconnaissance. Most robotic systems employed for different operations use a wheeled mode of locomotion which has limitations over rough terrains and the issue of communication setup of the system. This work developed a hexapod, a six-legged robotic system, to overcome these identified challenges. The robot was developed in tandem with a telemetry system for surveillance. Although, automating the surveillance process using robotic systems has been in the works for some time. However, this work developed a telemetry hexapod robotic system for enhanced surveillance for the security of power systems equipment and other critical infrastructures using a Raspberry Pi for fast, secure data transmission, and precise system synchronization. An inverse kinematics approach was used to determine joint configurations for better positions of each endpoint. A digital camera was integrated into the robot to relay real-time images of adversaries of power system components intelligently. In addition, the system incorporates data processing capability. In the result obtained, the developed telemetry hexapod robotic system receives instructions over a distance slightly above 300 m. It establishes effective control of the telemetry system without any hindrance of a limited range as observed in Bluetooth-controlled systems. The system's performance includes an average efficiency of 98.2 %, latency of 0.48 s at the peak distance, and an endurance of 4 h. Further analysis of the system shows that the corresponding increase in the latency with the distance is negligible. The system performed better than other related systems considered in the literature. The correct implementation of the developed telemetry hexapod robotic system further enhances mobility, stability in rough terrain, reliable communication system. Also, it brings about a notable reduction in component theft in electrical power systems.

Power equipment defect prediction based on time series knowledge graph

With the increasing scale of power facilities, the operation monitoring of power equipment has become an important part of power system management. Defect prediction of power equipment is a key step in power system operation monitoring. In order to solve the problem of defect prediction of power equipment in large-scale power systems, this paper proposes a power equipment defect prediction model based on time series knowledge graph in combination with artificial intelligence technology. Multimodal information is fused through the attention mechanism, and then the time series representation of entities and relations is obtained by using the relationship-aware graph neural network and recurrent neural network. Finally, the defects of power equipment are predicted based on the time series representation. The method proposed in this paper can make full use of multimodal information and improve the accuracy of power equipment defect prediction. Experiments show that the performance of the model proposed in this paper is better than that of the baseline model.

Role of artificial intelligence in transmission line protection: A review of three decades of research

The optimal functioning of the power system is crucially dependent upon the sound protection of its major stakeholder, i.e., the transmission line, as it is prone to fault. To maintain the integrity of the power system and protect costly power system equipment, protective relaying is necessary to provide a steady and affordable supply of electricity. Relays recognize, classify, and identify transmission line faults using input signals of voltage and current. Many artificial intelligent methods based on Expert Systems, Artificial Neural Networks, Fuzzy Logic, Support Vector Machines, Wavelet-based systems, and deep learning techniques are being investigated to improve modern digital relays’ consistency, speed, and accuracy. This paper is a comprehensive and all-inclusive survey that reviews and incorporates Phasor Measurement Unit (PMU) and Global Positioning System (GPS) approaches together with all of these intelligent transmission line safety strategies and concepts. Initial investigators will benefit from this study by being able to examine, evaluate, and analyze a variety of approaches with references for all relevant contributions.

Research on new method of transformer winding deformation detection

As one of the most important electrical equipment in power system, the stable operation of transformer is the key factor to ensure the safety of power grid system. Winding deformation is the most common fault of transformer, which will seriously affect the reliable operation of the whole power system. Therefore, the detection of transformer winding deformation is of great significance to prevent transformer accidents. In order to further improve the reliability of transformer winding deformation detection, this paper proposes a transformer winding deformation detection method based on oscillation wave method. Firstly, the equivalent circuit model of transformer winding is established, and the transformer winding fault is simulated by changing the inductance and capacitance parameters in the model, and the law between the change of oscillation wave curve and the change of related performance indexes and equivalent parameters is obtained. Finally, the field test verification is carried out on a 10 kV transformer in the laboratory. The results show that the oscillation wave method can diagnose the transformer winding deformation and can be used for the actual transformer winding deformation detection.

Аналіз факторів впливу на споживання електроенергії при запровадженні «Концепції «зеленого» енергетичного переходу України до 2050 року»

On the way to EU membership, Ukraine joined the "European Green Deal", which aims to achieve zero greenhouse gas emissions and zero environmental pollution by transitioning from the use of fossil fuels to renewable sources of energy and raw materials in the member states of the European Union by 2050. The concept of the "green" energy transition of Ukraine by 2050 was formulated, which involves a change in approaches to the development of energy with an emphasis on the problem of combating climate change and sustainable economic development. When achieving the goals of the "green" transition, it is important to understand which factors have the greatest impact on electricity consumption. It is promising to include such factors in electricity demand forecasting models. These models will become increasingly important to ensure network reliability and efficiency. Identifying the factors that affect energy consumption can drive innovation in energy-efficient technologies and practices. The article concludes that electricity consumption in Ukraine is affected by the same main factors as in other countries of the world. The desired reduction in electricity consumption can be achieved both by purely economic measures (increasing tariffs) and by more acceptable, technological measures. However, in the conditions of war, there are additional difficulties, such as the difficulty of forecasting the structure, state, and development of the energy system on the energy market. A significant part of the population now has more urgent problems than energy conservation, so the role of the state in this issue is increasing. Ukraine's energy system has already suffered significant destruction, and this process is ongoing and may continue for an indefinite period of time. Despite the difficulties, the lost power system equipment is gradually being replaced with new, more technologically advanced equipment. Some of the enterprises that had significant consumption were either lost or failed. The most adapted to new problems turned out to be the trade sector and the service sector and part of the population. Here, there is a rapid transition to the use of own generators, solar panels and electric transport, which reduces the consumption of electricity from the state power system. Industry, taking into account the funds required for this, will replace technologies with more energy-efficient ones after the end of the active phase of the war. Keywords: "green" transition, energy efficiency, electricity consumption, electricity demand forecasting, energy system.

Exploration of SF6 and its decomposed gases in adsorption and sensing (four modified WSe2 monolayers at quantum level)

As SF6 is the most widely used high-voltage insulating gas, the monitoring of its decomposition products to ensure the stable operation of GIS equipment and the treatment of leakage have been two important research topics. This work is based on the first principles of adsorption and sensing characterization of SF6 and its decomposed gases (HF, H2S, SO2, SOF2, SO2F2) using four modification strategies on the surface of WSe2. The energy parameters such as transfer charge, density of states, adsorption energy, band gap, and work function are calculated for each system. The results show that the electrical conductivity of the system is greatly enhanced when defect modification is employed, and the band gap is changed from 2.018 eV to 0.008 eV (W@WSe2) and 1.271 eV (Se@WSe2). The CoO and TiO2 doped WSe2 showed excellent adsorption of SF6 (−7.818 eV) and SOF2 (−14.263 eV), respectively, which is of great significance for SF6 gas leakage treatment. In addition, the results of work function calculations indicate that CoO-WSe2 has potential as a work function type sensor for these six gases. Finally, by calculating the desorption time (τ), we systematically explored whether the four modified materials have the characteristics to be used as recyclable resistive type sensors under different temperature environments. Our work will help to explore the adsorption performance and sensing characteristics of WSe2 monolayers with different modifications for SF6 and its decomposed gases, which is crucial for the protection of GIS equipment in power systems.

An Approach to Remaining Life Estimation for Power Transformers with Biased Data

Abstract Power transformers are among the most important equipment in power systems. Estimating the remaining life of transformers is an important issue for energy companies because of the need for planning maintenance and capital expenditures. Traditional methods fail to provide accurate results mainly because data is incomplete, and there is a significant lack of records of equipment scrapped before 2015. Given this, the study provides a new approach to life estimation for power transformers based on survival analysis. First, the data was selected from only the transformers that were still operating after 2015. Then, a Bayesian survival model with a truncated Weibull distribution is developed to fit the biased data. Parameters are, at last, estimated through maximum likelihood estimation. The experimental results indicate that the proposed method performs well.

Multivariate impedance spectroscopy method for the measuring of quality and status of electric power oils

With the rapid development of power technology and the complexity of power system equipment, efficient and accurate assessment of the quality and condition of electric power equipment oil (EPEO) has become particularly critical. EPEO is an important factor to ensure the stable operation of power equipment, and its quality and state directly affect the safety and reliability of equipment. However, there are many challenges with traditional oil measuring techniques, which often rely on destructive testing, which not only increases maintenance costs, but can also cause damage to the equipment itself. In the face of these limitations, there is an urgent need to study new oil detection technologies and methods to meet the high standards of modern power systems for high efficiency, non-destructive and comprehensive analytical capabilities. In this paper, a new EPEO measuring technique based on multivariable impedance spectroscopy (MIS) is proposed. Through in-depth analysis of oil's impedance response characteristics under electric field excitation with different frequency., a new approach is provided for the comprehensive evaluation of oil's performance. MIS technology not only has the characteristics of non-destructive testing, ensuring the non-destructive measuring of EPEO, but also its rapid response and real-time analysis ability significantly improves the monitoring efficiency. Based on the proposed MIS detection method, a detection system and experimental prototype which can detect and evaluate the performance and quality of power oil more accurately are designed. Compared with the traditional measuring device, the measuring device utilized in this method can employ three variables. Specifically, it covers a frequency range for the detectable excitation signal spanning from 1 to 100 kHz, an amplitude range from 0.1 to 11.7 V, and a temperature range from −100 °C to 100 °C. The MIS detection method has the capability to identify a variety of parameters, including the dielectric constant, volume resistivity, and dielectric loss factor, among others. This method encompasses a broader spectrum of parameters compared to traditional detection methods, which typically focus on one or two detectable indicators. The correctness and feasibility of the proposed multivariable impedance spectrum detection technique are verified, which provides a new way for the comprehensive evaluation of oil's performance.

Research on Vibration Characteristic Analysis and Fault Diagnosis Method of Oil‐Immersed Transformer Based on Multi‐Physics Coupling

The oil‐immersed transformer is a crucial power equipment in power system, which is prone to abnormality or failure under long running. In order to improve the level of operation and maintenance and ensure the safe and reliable operation of the power grid, it is necessary to diagnose the fault of the oil‐immersed transformer in time. Most of the traditional fault vibration methods are off‐line detection, and are easily disturbed by mechanical deterioration. In order to solve the above problems, this paper established a 3D electromagnetic and stress coupling simulation model of oil‐immersed transformer by finite element simulation software. The vibration characteristics of oil‐immersed transformer core winding are obtained, and at the same time, this paper obtained the vibration signals at different positions on transformer core winding and oil tank. According to the vibration signal of the transformer, the best measuring point position of the tank wall is proposed to accurately reflect the vibration characteristics of the transformer. The signal data is preprocessed by CEEMDAN decomposition method, and the signal data is classified by GWO‐BP composite classification algorithm. This paper proposed an online transformer fault diagnosis method based on vibration signal, which achieves the purpose of accurately diagnosing oil‐immersed transformer faults. The test results show that in the case of oil‐immersed transformers the accuracy of the proposed transformer fault diagnosis method is more than 93%, which provides important guiding significance for the safe and stable operation of the transformer. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Research and application of power equipment monitoring and fault diagnosis

Power equipment monitoring and fault diagnosis are important parts of power system operation and maintenance. By monitoring the status and performance parameters of power equipment in real time, and timely discovering and diagnosing potential fault problems, the reliability, safety and economy of the power system can be improved. At present, this research has made significant progress, but there are still some research gaps that need to be further explored and resolved. Through the research and application of power equipment monitoring and fault diagnosis, this paper aims to improve the reliability, safety and economy of power grid, reduce the risk of failure, optimize equipment operation and maintenance strategies, and improve power supply quality and user satisfaction. Condition monitoring and fault diagnosis are very important for the normal operation of the power system equipment, which can not only strengthen the fault diagnosis of the power system equipment, but also avoid the problem of large-scale paralysis caused by the failure of the power system equipment.

A Deep Learning-Based Fault Diagnosis Approach for Power System Equipment via Infrared Image Sensing

Automatic fault diagnosis for power system equipment has always been an essential concern in this industry. Conventionally, such works are conducted by manual patrol inspection, which consumes much human labor and expert knowledge. Fortunately, infrared images can present diagnosis areas inside the equipment via the thermal sensing function. In such context, this work utilizes deep neural network to construct a specific infrared image processing framework that can realize automatic fault diagnosis. Thus in this paper, a deep learning-based fault diagnosis approach for power system equipment via infrared image sensing is proposed. First, a pulse-coupled neural network structure is employed to enhance feature representation for infrared images of the equipment. Next, a fuzzy [Formula: see text]-means (FCM)-based segmentation method is developed to filter diagnosis areas from the infrared images. Finally, a convolution operation-based fault diagnosis operator is adopted to identify the diagnosis types. After that, some simulation experiments are conducted on real-world infrared images on the power system equipment, in order to make the performance evaluation of the proposed approach. The proposal realizes the end-to-end process of feature extraction and fault detection and identification, and avoids the problems of single feature. It is due to manual extraction of fault features, and the inability to detect and identify faults effectively in specific situations and scenarios.

YOLO‐DFT: An object detection method based on cloud data fusion and transfer learning for power system equipment maintenance

YOLO‐DFT: An object detection method based on cloud data fusion and transfer learning for power system equipment maintenance

A novel solution for the power transmission congestion of deregulated power system using TCSC and TLBO algorithm

The expansion of the electrical power sector has introduced heightened competition in electric markets, compelling utilities to enhance the efficiency of their power system equipment to provide customers with cost-effective and premium-quality power. Deregulation has, however, led to challenges such as congestion in transmission lines, adversely affecting power quality, voltage profiles, and system security. The congestion can be alleviated either by technical or non-technical methods. To address this issue, the manuscript proposes the implementation of Thyristor Control Series Capacitors (TCSC) as a technical solution for congestion mitigation. TCSC is used as a variable reactance device. The study employs line utilization factors (LUF) and disparity line utilization factors (DLUF) to obtain the optimal locations for TCSC installation. Additionally, the Teaching-Learning-Based Optimization (TLBO) algorithm is employed for the optimization of TCSC reactance. The manuscript also suggests another approach by simultaneously optimizing the location and parameters of multiple TCSCs using the TLBO algorithm without using the sensitivity factors. Comparative analyses with other optimization algorithms like gray Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) are conducted to determine the superior method for ensuring stable system operation. By application on the standard IEEE-30 bus system, the TLBO algorithm is validated, demonstrating its effectiveness in mitigating congestion by minimizing power loss, and voltage deviation, and enhancing security margins. The findings contribute valuable insights for practitioners seeking robust solutions in managing congestion within deregulated electric markets.