Operation Of Electrical Equipment Research Articles

Identification of abnormal operation of electric energy metering equipment based on multi-dimensional and multi-layer association rule mining algorithm

Abstract In order to comprehensively and deeply explore the potential correlations and patterns in operational data, this paper proposes a method for identifying abnormal operation of electric energy metering equipment based on multi-dimensional and multi-layer association rule mining algorithms. A multi-level association rule mining model has been constructed to automatically discover potential correlations and abnormal patterns between different indicators, thereby accurately identifying abnormal operation of energy metering equipment. The raw running data is converted into a form suitable for association rule mining, and evaluation metrics such as support and confidence are calculated for each rule. Abnormal operation of energy metering equipment is identified based on these evaluation indicators. The experimental results show that it can effectively identify the abnormal operation of energy metering equipment, providing strong technical support for equipment maintenance and troubleshooting of power companies.

Power quality disturbances categorization using Identity Feature Vector and Extreme Learning Machine

Power quality disturbances are variations or anomalies in the voltage, current, or frequency of electrical power that can affect the proper operation of electrical equipment. These disturbances are usually classified into different categories based on their attributes and effects. This article presents an intelligent technique based on an Identity Feature Vector and an Extreme Learning Machine (ELM). This study first derives a constant length vector for each disturbance signal. A wavelet transform is applied to derive attributes from the input disturbance signal, and the identity vector is formed using the approximation coefficients. After the required normalization procedures, the normalized identity vector is classified using an ELM. To assess the productivity of the suggested approach, 12 types of disturbances, single and combined, are generated, and the system's efficiency is studied. The results indicate that ten out of 12 combinations, including Harmonic, Sag, and Flicker, were detected with 100% accuracy. Additionally, the combination "Harmonic + Swell" exhibited the lowest accuracy, identified with 98% accuracy. The total average accuracy of this method is 99.75%. The outcomes demonstrate the highly favorable performance of this approach. This study evaluated the analyzed algorithm under noisy conditions with three different noise levels: 30 dB, 40 dB, and 50 dB, respectively. The average prediction accuracy for these three noise levels is 99.16%, 99.25%, and 98.91%. The outcomes demonstrate that the evaluated algorithm accurately detects power quality disturbances across various noisy conditions.

Multi-scale analysis of voltage curves for accurate and adaptable lifecycle prediction of lithium-ion batteries

Health status prediction of lithium-ion batteries is critical for the stable operation of electrical equipment. The data-driven approach can fit the degradation laws based on the historical cyclic data and identify potential problems in time. However, existing prediction methods mainly focus on acquiring various cyclic degradation features, and excessive battery-related data types increase the complexity and indeterminacy, posing a challenge for efficient and accurate prediction during practical application scenarios. Herein, this study only uses cyclic voltages and proposes a novel multi-scale cyclic voltage data preprocessing technique, involving the selection of highly correlated feature parameters with battery life in both the time and frequency domains, and constructing graphical samples with dynamic node connectivity properties. The proposed multi-scale graph convolutional network model adeptly captures and amalgamates the evolving features among graphical samples with efficacy. The one-step prediction experiments demonstrate the capability to predict subsequent capacity degradation solely based on the voltages of any consecutive 20 cycles, with a root mean square error of 0.173 %, exhibiting adaptable capability across different battery datasets. Compared to existing methods that transform data into images for health status prediction, this study highlights the significance of a multi-scale approach to voltage analysis in cyclic data, leveraging advanced feature engineering and modeling techniques, not only to enhance the accuracy and adaptability of battery lifecycle predictions but also to offer a robust framework for overcoming prevailing challenges in monitoring accuracy and cost-effectiveness.

A frequency control strategy with DC microgrid systems for EV stations

ABSTRACT Fluctuations in power supply and demand lead to frequency deviations, which have detrimental effects on the operation of electrical devices and equipment. Therefore, the implementation of effective frequency control mechanisms is essential for enhancing the strength and dependability of microgrid systems. Research aims to develop a frequency control mechanism for an electrical grid system that integrates both wind and photovoltaic (PV) energy sources. The proposed system utilises a Doubly Fed Induction Generator (DFIG) system with frequency controller for regulating frequency and balancing source and demand of power in the grid. To optimise the solar power output, a Luo converter is implemented, which offers high efficiency with improved conversion of power. Additionally, an adaptive neuro fuzzy inference system (ANFIS) maximum power point tracking (MPPT) controller is engaged to ensure optimal regulation of photovoltaic system. DC link obtained from the PV system is then utilised for powering DC loads and charging electric vehicles (EVs) effectively. In order to accomplish this, a bidirectional converter is used, which permits power flow in both ways and facilitates effective EV charging from the microgrid system. The validation of research is conducted on MATLAB software, and the results proves greater converter efficiency of 94.93%, with reduced total harmonic distortion (THD) of 1.24%.

Effects of wire rope isolators on seismic life-cycle cost of UHV bypass switch

The seismic isolation devices reduce the seismic vulnerability of the electrical equipment. Accurately assessing the seismic life-cycle cost (SLCC) of the electrical equipment is beneficial in guiding the design and enhancing the seismic resilience of electrical substations and converter stations. To evaluate the effects of the isolator devices on the seismic life-cycle cost of electrical equipment, a SLCC evaluation model was proposed in this study, and the evaluation was conducted on an ultra-high voltage (UHV) bypass switch (BPS) with wire rope isolators (WRI). The model takes into account equipment purchase, maintenance, transportation and installation costs and indirect losses caused by power outages. Afterward, the SLCC and break-even time of the UHV BPS with and without WRIs in different regions were analyzed. The results indicate that beyond the break-even time, the BPS with WRIs becomes more economically viable. Moreover, its economic viability increases as the service life extends. Therefore, in high seismic cost risk areas, it is recommended to adopt seismic isolation devices to ensure the secure and economically efficient operation of electrical equipment.

Effect of SF6 circuit breaker parameters on switching overvoltages evaluated based on nominal modes of converter transformers

In this work, we set out to investigate the influence of operating currents of SF6 circuit breakers on the magnitude of overvoltages during switching of converter transformers. The experience of electrical equipment operation is analyzed with a focus on the problems associated with the natural aging of insulation, overloads, and switching overvoltages. The methods of equipment diagnostics were used. According to statistical data, at present, about 37% of short circuits between phases and 42% of single-phase earth faults in electrical networks of industrial enterprises occur due to switching overvoltages. The conducted tests showed that the overvoltages emerging during switching by SF6 circuit breakers of converter transformers, which are used in aluminum smelters, may reach a three-fold rated voltage of the network. This threatens the insulation of transformer windings, cable line, and the switch itself. It was established that an increase in operating currents of an LF2 circuit breaker, not exceeding 27.8% of the rated current of the switch, leads to an increase in overvoltages during switching of converter transformers. At a further increase in the operating currents of the circuit breaker by more than 27.8% of the rated current of the circuit breaker, a sharp decrease in the value of switching overvoltages is observed. Therefore, the operating currents of SF6 circuit breakers were established to affect the level of switching overvoltages of converter transformers. The underlying mechanism of this phenomenon was determined, which should be taken into account when improving the reliability of power supply of converter transformers and, consequently, the reliability of aluminum production. The feasibly of resistive-capacitive dampers as the most effective means for limiting overvoltages during switching of converter transformers was confirmed.

Significantly Enhanced Corona Resistance of Epoxy Composite by Incorporation with Functionalized Graphene Oxide

Enhancing the corona resistance of epoxy resin (EP) is crucial for ensuring the reliable operation of electrical equipment and power systems, and the incorporation of inorganic nanofillers into epoxy resin has shown significant potential in achieving this. In this study, functionalized graphene oxide (KHGO) was synthesized via a sol-gel method to enhance the corona resistance of EP with electrochemical impedance spectroscopy (EIS) used to assess the properties of KHGO/EP composites. Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS) verified the successful grafting of epoxy groups onto the GO surface. The thermal conductivity and stability of the KHGO/EP composite initially increased with KHGO content but declined when the content exceeded 1.2 wt.%. Positron annihilation lifetime spectroscopy (PALS) indicated that KHGO improved interfacial compatibility with EP compared to GO, with agglomeration occurring when KHGO content exceeded a threshold value (1.2 wt.%). EIS analysis revealed that the corona resistance of the KHGO/EP composite was optimal at a filler content of 0.9 wt.%. After corona treatment, the saturation water uptake of the 0.9 wt.% KHGO/EP composite decreased by 15% compared to pure EP with its porosity reduced to just 1/40th of that of pure EP. This study underscores that well-dispersed KHGO/EP composite exhibits excellent corona resistance property suggesting the potential for industrial applications in high-voltage equipment insulation.

Application of artificial intelligence technologies at substantiation of effective control algorithms for the electrical engineering complex of urban electric transport traction electrical equipment

Background. Studies on the current state and development trends of urban ground public electric transportation in Russia highlight the urgent need for innovative technologies. These innovations should focus on designing and operating new types of rolling stock, traction electrical equipment, and promising types of electric traction. At the same time, special attention should be paid to building autonomous control systems for electric transportation using artificial intelligence technologies. Aim. This study aims to explore the application of neural networks to develop algorithms for effectively controlling the electrical engineering complex of traction electrical equipment in urban ground rail electric transport. Materials and Methods. The research utilized data from studies on the traction electrical equipment modes of rolling stock. These studies were conducted through both computer simulations and field experiments under real operating conditions of urban electric transport using different control algorithms. By applying probability theory and mathematical statistics, the analysis identified the correlations between operational and energy parameters of rolling stock movement and the operational mode of its traction electrical equipment. Results. The identified correlation dependencies informed the design of an effective network architecture, including its size and complexity, as well as the composition of their training samples. This led to the development of an original, simplified algorithm for determining effective control parameters for the electrical complex of traction electrical equipment during the movement of a vehicle on a given section of track. Conclusion. The research concluded that using “simple” neural networks for calculating the parameters of effective control of traction electrical equipment operation modes in urban electric transport provides higher speed and sufficient accuracy compared to complex neural network models. These results are valuable for developers of intelligent control systems for streetcar transportation.

Dielectric behavior and breakdown strength of glass fiber reinforced epoxy composites under dynamic mechanical fatigue

Glass fiber reinforced polymer (GFRP), used in insulating components like insulation rods, needs to withstand both high voltage and large dynamic mechanical fatigue during operation. In this paper, the effects of tension–compression fatigue loads on the dielectric properties of GFRP under various fatigue cycles and stress levels are investigated. The results show that DC conductivity has a strong negative correlation with stiffness, while breakdown strength is showing a positive correlation. Fatigue-induced internal damage could cause continuous charge accumulation and enhanced interfacial polarization, leading to the increase of dielectric constant by 46.89% and the reduction of breakdown strength by 15.05%, when the fatigue span ratio reaches 80% under 40% stress level. Understanding the evolution of dielectric properties of GFRP under dynamic mechanical fatigue conditions is helpful for ensuring the safe and stable operation of electrical power equipment subjected to both high voltage and fatigue loads.

Influence mechanisms of ultraviolet irradiation aging on DC surface discharge properties of silicone rubber in dry and humid air

In the modern DC electrical transmission system, high-energy photons interact with silicone rubber (SIR) chains during long-term ultraviolet (UV) irradiation, it degrades the surface properties and triggers surface discharge (flashover) along the SIR-air interface, threatening the secure operation of the advanced electrical equipment. To further comprehend how UV-aging influences the flashover performance, in this work, the chemical bonds, surface charge transport parameters, and flashover performance of UV-aged SIR samples are characterized, and the influence mechanisms of UV-aging on surface discharge in dry and humid air are comprehensively clarified through the investigation from “molecular-mesoscopic-macroscopic” scales. The results indicate both degradation and oxidization reactions occur during UV-aging. The degraded molecular chains dominate the discharge in dry air, while oxidization reactions determine the discharge in humid environment: The degradation reactions reduce the surface trap depth and increase surface conductivity, leading to large surface leakage current and surface charge dissipation rate, causing flashover voltage in dry air increases and decreases as UV-aging time increasing. The oxidization of the chains introduces –OH and –COOH groups into chains, enhancing permittivity and adsorbing H2O molecules, significantly increasing surface leakage current, causing surface discharge voltage in humid air to decrease as UV-aging time increases.

Iron Loss and Temperature Rise Analysis of a Transformer Core Considering Vector Magnetic Hysteresis Characteristics under Direct Current Bias.

Direct current (DC) bias induced by the DC transmission and geomagnetically induced current is a critical factor in the abnormal operation of electrical equipment and is widely used in the field of power transmission and distribution system state evaluation. As the main affected component, the vector magnetization state of a transformer core under DC bias has rarely been studied, resulting in inaccurate transformer operation state estimations. In this paper, a dynamic vector hysteresis model that considers the impact of rotating and DC-biased fields is introduced into the numerical analysis to simulate the distribution of magnetic properties, iron loss and temperature of the transformer core model and a physical 110 kV single-phase autotransformer core. The maximum values of B, H and iron loss exist at the corners and T-joint of the core under rotating and DC-biased fields. The corresponding maximum value of the temperature increase is found in the main core limb area. The temperature rise of the 110 kV transformer core under various DC-biased conditions is measured and compared with the FEM (Finite Element Method) results of the proposed model and the model solely based on the magnetization curve B||H. The calculation error of the temperature rise obtained by the improved model is approximately 3.76-15.73% and is much less than the model solely based on magnetization curve B||H (approximately 50.71-66.92%).

Intelligent Diagnosis Method of Transformer Based on Oil Chromatographic Data

Abstract As one of the most important equipment in the operation of power system, ensuring the safe and stable operation of power transformer is a prerequisite for ensuring the normal supply of power grid. The failure of the power transformer will lead to the interruption of the power supply of the power grid and cause huge losses to the national economy. With the rapid development of China’s power grid scale and the continuous improvement of the intelligent construction of modern power system, the number of power equipment such as transformers is increasing. Therefore, it is necessary to make timely and accurate judgment on the status of key power transformation and distribution equipment in the power system, and grasp the operation of electrical equipment in real time, so as to ensure the reliability of power supply in the power system. This paper takes 220kV oil-immersed transformer as the research object. This paper considers the problems and characteristics of the current transformer fault diagnosis methods. By making full use of dissolved gas analysis (DGA) information in oil, a method for diagnosing faults in electrical transformers based on particle swarm optimization and generalized regression neural networks has been proposed. The simulation comparison experiment is carried out to select the most suitable transformer intelligent diagnosis method based on oil chromatographic data.

Laser-Induced Breakdown Spectroscopy Analysis of Sheet Molding Compound Materials

Sheet Molding Compound (SMC) materials are extensively utilized as high-voltage insulation materials in electrical equipment. SMC materials are prone to aging after long-term operation. Conducting non-destructive testing to assess their electrical and physicochemical properties is crucial for the safe operation of electrical equipment. This study identifies the optimal equipment parameters for testing SMC materials using Laser-Induced Breakdown Spectroscopy (LIBS) technology through experimental investigation and also explores the ablation characteristics of SMC under various laser parameters. The results indicate a significant positive correlation between the ablation depth and laser pulse number, while there is no correlation with single laser pulse energy. However, the ablation area demonstrates a strong positive correlation with both single laser pulse energy and laser pulse number. Additionally, LIBS spectral analysis provides elemental results comparable to Energy Dispersive Spectroscopy (EDS), facilitating the examination of variations in Na, Ti, Fe, Mg, Ca, and C elemental contents with depth. Moreover, an enhanced iterative Boltzmann plot method is suggested for calculating the plasma temperature using 21 Fe I spectral lines and the electron density using the Fe II 422.608 nm line. The variations of these plasma parameters with laser pulse number are documented, and the results show consistent trends, confirming that the laser-induced SMC plasma adheres to local thermodynamic equilibrium.

Novel hybrid arithmetic optimization algorithm-recursive least square approach for power system harmonic estimation

Abstract Accurate harmonic estimation is essential for effective power quality assessment, designing appropriate harmonic filters, and ensuring the reliable operation of electrical equipment. This article proposes a novel hybrid harmonic estimation technique combining recursive least square (RLS) and arithmetic optimization algorithm (AOA) for accurate estimation of harmonics, inter-harmonics and sub-harmonics. AOA is a new meta-heuristic method based on distribution behaviour of main arithmetic operators such as addition, subtraction, multiplication and division. RLS is used for estimation of amplitude of harmonics, whereas phase is estimated by AOA. The performance of AOA–RLS is investigated in detail for estimation of power system signals using two set of test signals buried with noise. The proposed AOA–RLS is proved to be efficient for estimating both phase and amplitude parameters under different signal to noise ratio (SNR) conditions with an estimation of error of E−3. The efficacy of AOA–RLS technique is demonstrated by comparing with competitive existing techniques. The performance of AOA–RLS also verified in experimental studies.

PMSNet: Multiscale Partial-Discharge Signal Feature Recognition Model via a Spatial Interaction Attention Mechanism.

Partial discharge (PD) is a localized discharge phenomenon in the insulator of electrical equipment resulting from the electric field strength exceeding the local dielectric breakdown electric field. Partial-discharge signal identification is an important means of assessing the insulation status of electrical equipment and critical to the safe operation of electrical equipment. The identification effect of traditional methods is not ideal because the PD signal collected is subject to strong noise interference. To overcome noise interference, quickly and accurately identify PD signals, and eliminate potential safety hazards, this study proposes a PD signal identification method based on multiscale feature fusion. The method improves identification efficiency through the multiscale feature fusion and feature aggregation of phase-resolved partial-discharge (PRPD) diagrams by using PMSNet. The whole network consists of three parts: a CNN backbone composed of a multiscale feature fusion pyramid, a down-sampling feature enhancement (DSFB) module for each layer of the pyramid to acquire features from different layers, a Transformer encoder module dominated by a spatial interaction-attention mechanism to enhance subspace feature interactions, a final categorized feature recognition method for the PRPD maps and a final classification feature generation module (F-Collect). PMSNet improves recognition accuracy by 10% compared with traditional high-frequency current detection methods and current pulse detection methods. On the PRPD dataset, the validation accuracy of PMSNet is above 80%, the validation loss is about 0.3%, and the training accuracy exceeds 85%. Experimental results show that the use of PMSNet can greatly improve the recognition accuracy and robustness of PD signals and has good practicality and application prospects.

Reactive Power Optimization in Distribution Networks of New Power Systems Based on Multi-Objective Particle Swarm Optimization

The new power system effectively integrates a large number of distributed renewable energy sources, such as solar photovoltaic, wind energy, small hydropower, and biomass energy. This significantly reduces the reliance on fossil fuels and enhances the sustainability and environmental friendliness of energy supply. Compared to distribution networks in traditional power systems, the new-generation distribution network offers notable advantages in improving energy efficiency, reliability, environmental protection, and system flexibility, but it also faces a series of new challenges. These challenges include potential harmonic issues introduced by the widespread use of power electronic devices (such as inverters for renewable energy generation systems and electric vehicle charging stations) and the voltage fluctuations and flickering caused by the intermittency and uncertainty of renewable energy generation, which may affect the normal operation of electrical equipment. To address these challenges, this study proposes an optimization model aimed at minimizing network losses and voltage deviations, utilizing traditional capacitor adjustments and static var compensators (SVCs) as optimization measures. Furthermore, this study introduces an improved version of the multi-objective particle swarm optimization (MOPSO) algorithm, specifically enhanced to address the unique challenges of reactive power optimization in modern distribution networks. The test results show that this algorithm can effectively generate a large number of Pareto optimal solutions. The application of this algorithm on a 33-node network case study demonstrates its advantages in reactive power optimization. The optimization results highlight the effectiveness and feasibility of the proposed improved algorithm in the application of distribution network reactive power optimization, offering users a uniform and diverse range of reactive compensation solutions.

Adsorption and gas sensitivity properties of graphene and Rh-decorated graphene towards SF6 decomposed gases: A DFT study

In order to solve the serious threat of SF6 decomposed gases (H2S, SO2, SOF2, SO2F2) to the stable operation of electrical equipment. In this study, the geometric structure, adsorption energy, density of states (DOS), sensitivity based on band structure and recovery time of H2S, SO2, SOF2, SO2F2 on graphene and Rh-modified graphene (Rh-graphene) were investigated computationally by density functional theory (DFT). The geometric structure of Rh-graphene was optimized with an average Rh-C bond length of 1.891 Å. According to the analysis of DOS and band structure, the electrical conductivity has a little effect of the Rh-graphene compared with pristine graphene, but Rh modification can regulate the band gap of graphene. The adsorption energy (Ead) and the sensitivity (S) at work temperature indicate that graphene has ideal adsorption and sensing properties for SO2 and Rh-graphene reflects the excellent applicability as the SO2 and SO2F2 gases sensor with the sensitivity of approximately −99 % and −60 %, respectively. The Graphene/SO2 system owns the minimum recovery time of 2154.3 ps. The recovery time of Rh-graphene/H2S and SOF2 is several orders of magnitude smaller than Rh-graphene/SO2 and SO2F2. The analysis of DOS and band structure was used to explain the sensing mechanism. The study would lay a theoretical foundation for the development of SF6 decomposed gases sensor for electrical equipment insulation.

Research on electromagnetic shielding device for pulse inductor in CRAFT quench protection system

The quench protection system is the most important safety equipment of the Comprehensive Research Facility for Fusion Technology (CRAFT) project, which is used to protect asset security in the event of superconducting device quenching. Since the pulse inductor of the quench protection system works under extreme conditions, a strong induced magnetic field will be generated around the equipment, which will affect the normal operation of other electrical equipment in the inductor cabinet. Aiming at this situation, it is necessary to analyze the changing magnetic field of the inductor. This paper proposed a new shielding structure that can effectively reduce the magnetic induction around the inductor, and the effectiveness of the design was verified through simulation.

СТРУКТУРА ПРИЧИН ВОЗНИКНОВЕНИЯ ПОЖАРОВ НА ОБЪЕКТАХ ПРОМЫШЛЕННОСТИ

The causes of fires at industrial facilities of the Russian Federation in general and in individual industries were studied on the basis of statistical information. It has been shown that most often – in 36 % of cases – the cause of a fire is a violation of the rules for the design and operation of electrical equipment. In 22 % of cases, a fire occurs due to careless handling of fire, in 11 % of cases – due to violation of the rules for the design and operation of vehicles. The distributions of the frequency of fire occurrence for various reasons for enterprises in different industries are given. An analysis of the most common causes of fire in general for industrial enterprises and for individual industries was carried out. Measures are proposed that will minimize the likelihood of fires and explosions in industrial enterprises.

Research on Insulation Configuration of 220 kV Bushing in High-Altitude Polluted Areas

The external insulation strength of the power equipment sleeve in the substation and converter station will be reduced with the increase in altitude and atmosphere pollution. The weakness of external insulation seriously threatens the safe operation of electrical equipment. At present, there are few studies and conclusions that focus on the flashover characteristics of power equipment bushing in high-altitude polluted areas. Therefore, this paper takes the 220 kV voltage level bushing as the research object and studies the Alternating Current (AC) pollution flashover characteristics and lightning and switching impulse flashover characteristics at low pressure. The insulation configuration at high-altitude polluted areas is analyzed. Finally, the insulation configuration scheme, which is suitable for high-altitude polluted areas, is proposed. The results show that the AC pollution flashover voltages of the bushing decrease with the decrease in the atmospheric pressure and the increase in the salt density as a power exponent function. The impulse flashover voltages also decrease with the decrease in the atmospheric pressure as a power exponent function. The atmospheric pressure/pollution impact characteristic index is related to the type of voltage, the value of the atmospheric pressure, and the value of the salt density. The test bushing does not meet the insulation configuration requirements in some high-altitude polluted areas. Through the analysis and calculation of the test results, the bushing insulation configuration scheme in high-altitude polluted areas is proposed. The research results can provide a reference for the external insulation design of bushings in substations and converter stations in high-altitude polluted areas.