Power System Applications Research Articles

Optimal PMU Placement for Fault Classification and Localization Using Enhanced Feature Selection in Machine Learning Algorithms

Machine learning (ML) algorithms are increasingly used in power systems applications. One important application is the classification and localization of various types of transmission line faults. Using voltage and current measurements from phasor measurement units (PMUs), a number of useful features can be extracted, which can form the basis of a ML-based prediction of the fault type, line, and distance on the line. This paper proposes a technique to find the optimal number and placement of PMUs by performing thorough feature selection. The features are selected to maximize the accuracy of the ML classification and regression algorithms. The results show that for the IEEE 14 bus system, the use of only five PMUs is sufficient to obtain high levels of accuracy. For example, a testing accuracy of 99.0% and 97.1% can be achieved for the fault type and fault line location, respectively. As for the fault distance along the line, the testing MAE of 3.1% can be obtained along with an R2 score of 94.4%. Adding more PMUs does not provide any additional value in terms of accuracy.

Enhanced energy-storage properties in (Bi0.5Na0.5)TiO3 ceramics by doping linear perovskite materials Ca0.85Bi0.1(Sn0.5Ti0.5)O3

For energy storage applications in Bi0.5Na0.5TiO3 (BNT)-based materials, the key challenges are the premature polarization saturation and low breakdown electric field (Eb), which confine the energy storage capacity of BNT and significantly restrict progress in advancing pulsed power capacitors. Hence, the cooperative optimization strategy of band structure and defect engineering was proposed, which successfully obtained a high recoverable energy density of 3.83 J/cm3 and an energy efficiency of 77.9 % in the Bi0.5Na0.5TiO3-0.12Ca0.85Bi0.1(Sn0.5Ti0.5)O3 (BNT-0.12CBST) ceramics. Doping the BNT matrix with CBST has been shown to not only reduce grain size but also enhance relaxor behavior, which collectively improves breakdown strength and delays polarization saturation. Furthermore, the x = 0.12 ceramic also exhibits a commendable discharge power density of 91.9 MW/cm3 and a transient discharge time of 40 ns. The higher resistivity and lower oxygen vacancy concentration are conductive to acquire superior breakdown electric field and energy storage performance. We determine the crystal structure, dielectric and ferroelectric properties of the studied ceramics in a wide range of temperatures and concentrations, and a phase diagram is constructed, which illustrates the complex phases present and their transformation behavior. Our work provides an effective strategy to optimize the energy-storage of dielectric ceramics, and shows great potential for practical applications in pulse power systems.

Achieving High Energy Storage Performance under a Low Electric Field in KNbO3-Doped BNT-Based Ceramics.

Ceramic capacitors have great potential for application in power systems due to their fantastic energy storage performance (ESP) and wide operating temperature range. In this study, the (1 - x)Bi0.5Na0.47Li0.03Sn0.01Ti0.99O3-xKNbO3 (BNLST-xKN) energy storage ceramics were synthesized through the solid-phase reaction method. The addition of KN disrupts the long-range ferroelectric order of the BNLST ceramic, inducing the emergence of polar nanoregions (PNRs), which enhances the ESP of the ceramics. The BNLST-0.2KN ceramic demonstrates a high recovered energy density (Wrec ∼ 3.66 J/cm3) and efficiency (η ∼ 85.8%) under a low electric field of 210 kV/cm. Meantime, it exhibits a large current density (CD ∼ 831.74 A/cm2), high power density (PD ∼ 78.86 MW/cm3), and fast discharge rate (t0.9 ∼ 0.1 μs), along with good temperature stability and excellent fatigue stability. These properties make the BNLST-0.2KN ceramic a promising candidate for energy storage applications in low electric fields.

Analysis of Characteristics on a Compressed Air Power System Generating Supercavitation Drag Reduction for Underwater Vehicles

An unmanned underwater vehicle (UUV) powered by a compressed air power system is proposed to address challenges for battery/motor-powered vehicles under high-speed navigation, long endurance, and high mobility. These vehicles actively utilize supercavitation drag reduction by the exhausted gas from the compressed air power system. MATLAB/Simulink and FLUENT are used to establish theoretical models of the compressed air power system and ventilation supercavitation. The relationship between system power and navigation resistance is examined with different air flows, along with a comparison of endurance of different power vehicles at various speeds. The issue of the endurance-enhancing effect of supercavitation at high speed is investigated. The results demonstrate that increasing the air flow leads to higher power and reduced navigation resistance, and there is a balance between them. Furthermore, compared to the battery-powered vehicles with equal energy storage capacity, the compressed air power system shows 210.08% to 458.20% longer endurance times at speeds of 30 kn to 60 kn. Similarly, considering equal energy storage mass, it achieves 42.02% to 148.96% longer endurance times at high speeds (30 kn to 60 kn). The integration of supercavitation and air-powered systems can greatly enhance the endurance and maneuverability of the vehicle at high speeds while ensuring a compact system structure. The investigations could offer valuable ideas for the development and application of compressed air power systems for UUV at 30 kn to 60 kn or higher maneuvering.

Mixed Algorithm of SINDy and HAVOK for Measure-Based Analysis of Power System with Inverter-based Resource

Artificial intelligence and machine learning is enhancing electric grids by offering data analysis tools that can be used to operate the power grid more reliably. However, the complex nonlinear dynamics, particularly when coupled with multi-scale interactions among Inverter-based renewable energy Resources, calls for effective algorithms for power system application. This paper presents affective novel algorithm to detect various nonlinear dynamics, which is built upon: the Sparse Identification of Nonlinear Dynamics method for nonlinear dynamics detection; and Hankel Alternative View of Koopman method for multi-scale decomposition. We show that, by an appropriate integration of the strengths of the two, the mixed algorithm not only can detect the nonlinearity, but also it distinguishes the nonlinearity caused by coupled Inverter-based resources from the more familiar ones caused synchronous generators. This shows that the proposal algorithm can be a promising application of artificial intelligence and machine learning for data measure-based analysis to support operation of power system with integrated renewables.

Supercritical carbon dioxide cycle thermodynamic and exergoeconomic improvements using a bidirectional coupling strategy

Supercritical carbon dioxide (SCO2) combined cycle power system, one of the potentially effective ways to achieve energy grade utilization of the medium and high-temperature heat sources, is vulnerable to ambient temperatures as the system performance is susceptible to the compressor inlet temperature. So far, most studies have been focused on the system performance analysis and optimization under ideal operation conditions while the mitigation of unfavorable ambient temperature is missed. In this work, a bidirectionally-coupled system is proposed to reduce the compressor inlet temperatures in the original SCO2 cycle power system so that the adverse effects of ambient temperature are alleviated. The proposal utilizes the low-grade waste heat in a SCO2 recompression reheat Brayton cycle (i.e., topping cycle) to drive an absorption refrigeration cycle (i.e., bottoming cycle) which in turn precools the compressor inlet of the topping cycle. The thermodynamic and exergoeconomic analyses have been conducted for the proposed bidirectionally-coupled combined SCO2 RRBC and ARC system, with the attention to the effects of ambient temperatures on the system performance. Our results show that the combined SCO2 RRBC and ARC system increases the energy efficiency relative to the standalone cycle power system by over 5% at the ambient temperatures of 303.15 K or above, while the exergy efficiency improves by 1.124%–3.574%. In terms of the exergoeconomic factor, the combined SCO2 RRBC and ARC system also exhibits superiority against standalone cycle power system. The current work proves that a conventional recuperated SCO2 cycle system can be improved both thermodynamically and economically by coupling it to an absorption refrigeration cycle, despite of the additional upfront costs for the components. The discovery could further promote the commercial application of the SCO2 cycle power systems in new generation plants.

Improving the recovery performance of superconducting fault current limiters using nitrogen/tetrafluoromethane mixed coolants

The superconducting fault current limiter (SFCL) improves the power grid safety by restricting the drastically increased current when the circuit fault occurs. However, the SFCL suffers from a long recovery time and can hardly recover to the superconducting state before the breaker attempts to reclose, which hinders its practical application. This paper presents the implementation of the rapid-recovery SFCL with efficient heat dissipation even if encountering a large fault current. This is enabled by thermal performance regulation of the working coolant through adding tetrafluoromethane (CF4) into the liquid nitrogen (LN2). The proposed mixed coolant features a high critical heat flux that suppresses the onset of film boiling. Controlled experiments were performed with the fabricated current-limiting coil immersed in the N2/CF4 mixed coolants containing 0, 20 and 40 mol% of CF4. Measured results regarding both quenching and recovery processes are reported, demonstrating an 85.4% reduction in recovery time for the 40 mol% composition compared to the conventionally used pure LN2. Analyses of the electrical parameters of the coil verify its effective quenching performance in the proposed immersion cooling system with an overall current limiting rate of over 55%. These findings lay a foundation for designing the rapid-recovery-type SFCL and facilitating its application in power systems.

Enhancing energy storage performance of polyethylene via passivation with oxygen atoms through C–H vacancy carbonylation

Low energy density of polymer film capacitors is regarded as one of the most serious drawbacks facing growing demands for equipment integration and miniaturization. Herein, ultraviolet light and ozone (UVO) surface modification is utilized to simultaneously improve dielectric constant and breakdown strength of polyethylene (PE) films. As a result, after 3 min of UVO treatment, an enhanced recoverable energy density of 4.79 J/cm3 with a charge-discharge efficiency of >95% is obtained under 650 MV/m at room temperature (RT). Significantly, stable energy storage performance under 200 MV/m is maintained throughout a broad temperature range from −90 °C to 90 °C and during 20,000 cycles of charge-discharge procedures. According to first-principles calculations and thermally stimulated depolarization current measurements, formation of carbonyl groups (C=O) after UVO treatment could effectively passivate initial deep-level defect states caused by H vacancies, which explains the improvement in capacitive energy storage. Moreover, the metalized UVO-modified PE exhibits valuable breakdown self-clearing ability, and the self-cleared specimen maintains stable energy storage performance over 20,000 cycles at 200 MV/m and RT. This work offers an effective and user-friendly method for enhancing comprehensive dielectric characteristics of PE-based materials and potential for applications in modern power systems and electronic devices.

On Credibility of Adversarial Examples Against Learning-Based Grid Voltage Stability Assessment

Voltage stability assessment is essential for maintaining reliable power grid operations. Stability assessment approaches using deep learning address the shortfalls of the traditional time-domain simulation-based approaches caused by increased system complexity. However, deep learning models are shown to be vulnerable to adversarial examples in the field of computer vision. While this vulnerability has been noticed by the power grid cybersecurity research, the domain-specific analysis on the requirements imposed upon effective attack implementation is still lacking. Although these attack requirements are usually reasonable in computer vision tasks, they can be stringent in the context of power grids. In this paper, we conduct a systematic investigation on the attack requirements and credibility of six representative adversarial example attacks based on a voltage stability assessment application for the New England 10-machine 39-bus power system. We show that (1) compromising about half the transmission system buses' voltage traces is a rule-of-thumb attack requirement; (2) the universal adversarial perturbations regardless of the original clean voltage trajectory possess the same credibility as the widely studied false data injection attacks on power grid state estimation, while the input-specific adversarial perturbations are less credible; (3) the prevailing strong adversarial training thwarts the universal perturbations but fails in defending certain input-specific perturbations. To advance defense to cope with both universal and input-specific adversarial examples, we propose a new approach that simultaneously estimates the predictive uncertainty of any given input of voltage trajectory and thwarts the attacks effectively.

LCC-HVDC Frequency Robust Control Strategy Based on System Parameter Identification in Islanded Operation Mode

To enhance the stability of the frequency at the sending terminal of the HVDC island during operation, a novel DC supplemental frequency robust controller is proposed in this paper. The proposed controller utilizes the fast controllability of a DC power supply to maintain system frequency stability. The identification of a low-order linearized model of the system can be obtained from a high-precision Prony algorithm based on the second derivative method (SDM). Subsequently, utilizing a robust design methodology based on linear matrix inequalities, an additional frequency robust controller is devised, striking a balance between optimal performance and robustness. This supplementary frequency robust controller exhibits a straightforward control structure with a modest order, making it readily implementable. Simulation experiments conducted within the PSCAD/EMTDC framework substantiate that the designed supplemental frequency robust controller significantly enhances the frequency stability of the sending terminal system. Furthermore, when compared with traditional proportional integral (PI) controllers, it demonstrates superior control efficacy and robustness against various types of faults under different operational modes. Even in interconnected operational modes, it continues to operate effectively. The research findings offer valuable insights for practical applications in islanded power systems.

Preface

It is with great pleasure and enthusiasm that we present the proceedings of the 2023 3rd International Conference on Frontiers of Electrical Power & Energy Systems. This conference, held from December 23-24, 2023, brought together researchers, scholars, engineers, and industry professionals from around the world to share their latest advancements and discoveries in the dynamic field of electrical power and energy systems.Electrical power and energy systems form the backbone of modern society, enabling the seamless functioning of industries, homes, and technological advancements. The conference theme, “Advancing Innovation in Power and Energy Systems,” underscored the importance of embracing cutting-edge technologies, novel methodologies, and innovative solutions to address the challenges and opportunities in the realm of electrical power and energy. The papers selected for inclusion cover a wide spectrum of topics, spanning Electrical and Electronic Engineering, Energy and Power System, Automation and Engineering Application and so on.One of the highlights of this conference was the international collaboration and knowledge exchange that took place among participants. Researchers from different continents and cultural backgrounds shared their unique perspectives, fostering a global understanding of the issues at hand. This interconnectedness is vital as we collectively strive towards sustainable and efficient solutions for the world’s growing energy needs.The diversity and depth of the papers included in these proceedings reflect the richness of the discussions and presentations that took place during the conference. We extend our sincere appreciation to all the authors for their valuable contributions, which have significantly enriched the body of knowledge in this rapidly evolving field. We also extend our gratitude to the dedicated reviewers who rigorously evaluated each submission. Their commitment to academic excellence has ensured the high quality and relevance of the papers included in these proceedings. Special thanks are due to the organizing committee, session chairs, keynote speakers, and sponsors for their invaluable contributions to the success of the conference. Their dedication and support have played a pivotal role in creating a vibrant and intellectually stimulating environment.Once again, we welcome you to the proceedings of the 2023 3rd International Conference on Frontiers of Electrical Power & Energy Systems and hope that the insights gained from this conference will contribute to the development of sustainable and resilient solutions that address the evolving challenges in our ever-changing world. We look forward to continued collaboration and the prospect of meeting again at future conferences to further advance the frontiers of electrical power and energy systems. EPES 2023 Organizing Committee List of Committee Members is available in this pdf.

A novel hybrid forecasting model with feature selection and deep learning for wind speed research

AbstractAccurate wind speed prediction is of great importance for the operation of wind farms, and extensive efforts have been made to develop effective forecasting methods in this regard. However, the feature selection of data input as well as optimization of deep learning models have received comparatively less attention, leading to unreliable forecasting results. This research proposes a novel hybrid model that integrates data preprocessing, feature selection, and optimized forecasting for improved wind speed prediction. Specifically, a powerful preprocessing technique is utilized to reduce data noise disturbances, while an innovative two‐stage feature selection is designed to achieve the optimal input data format for forecasting purposes. Moreover, a hybrid forecasting module based on long‐short term memory, which is optimized by the Bayesian optimization algorithm, has been developed to enhance the efficiency and reliability of the model. The empirical study used 10‐min interval wind speed data of four seasons for presentation and evaluation results demonstrated its superior performance in effectively learning the volatility and irregularity features of wind speed series, which established a solid foundation for practical applications in wind power systems.

Polyimide composites crosslinked by aromatic molecules for high-temperature capacitive energy storage

High-temperature polymer-based dielectric capacitors are crucial for application in electronic power systems. However, the storage performance of conventional dielectrics polymer dramatically deteriorates due to the thermal breakdown under concurrent high temperatures and electric fields, and there are hardly reports on the causes of thermal breakdown from the aspects of the high-temperature conduction loss and Joule heat dissipation. Herein, a combined strategy of crosslinking and compositing for polyimide-based composites is proposed, which minimizes the thermal breakdown by significantly inhibiting the high-temperature conduction loss and enhancing the thermal conductivity. Furthermore, the rationale of the strategy was theoretically and experimentally verified from multiple perspectives. The charge-trapping effect is directly observed by Kelvin probe force microscopy probed (KPFM) with nano-level resolution and quantitatively by thermally stimulated depolarization current measurements, indicating that the crosslinking network introduces local deep traps and effectively suppresses the charge transport. The thermal conductivity of the composites inhibits the high-temperature thermal breakdown, which is confirmed by phase-field simulations. Consequently, the optimized composites possess an ultra-high discharge energy density (Ud) of 5.45 J/cm3 and 3.54 J/cm3 with a charge–discharge efficiency (η) of 80 % at 150 and 200 °C, respectively, which outperforms the reported polyimide-based dielectric composites. This work provides a scalable direction for high-temperature polymer-based capacitors with excellent performance.

Design and experimental study of electromagnetic ultrasonic single-mode guided wave transducer for small-diameter stainless steel tubes

ABSTRACT Small-diameter stainless steel pipes find widespread application in nuclear power systems. In order to detect surface axial wear defects that may occur during their service life, a circular fully enclosed probe has been developed for inspecting these small-diameter stainless steel pipes. The probe is composed of a periodic permanent magnet (PPM) and a racetrack-shaped coil. Through the optimised design of the PPM structure, two probes were devised to excite single flexural and single torsional modes, respectively. Furthermore, employing the non-equivalent concept, the torsional mode guided wave probe underwent optimisation, resulting in a 54% increase in the received signal amplitude. Finally, two types of guided wave probes were utilised to conduct detection experiments on artificially induced defects in an outer diameter 10 mm sample pipe. The results indicate that both T(0,1) and F(1,2) mode guided waves can detect axial defects. Moreover, the detection amplitude of the T(0,1) mode guided wave surpasses that of the F(1,2) mode guided wave by a maximum of 53%.

A Review of the Literature on Multi Agent Based Power Distribution System Restoration

One of the most popular research areas is multi agent systems (MAS), which consists of multiple agents interacting with one another to accomplish a shared goal. MAS has been created with a variety of power system applications in mind. One major use for such is power system restoration. A number of architectures for fault isolation, problem identification, and power system restoration are presented by researchers. The available architectures for power distribution restoration and upcoming developments in MAS-based power system restoration are thoroughly reviewed in this research.

Perception of power quality disturbances using Fourier, Short-Time Fourier, continuous and discrete wavelet transforms

Electric power utilities must ensure a consistent and undisturbed supply of power, with the voltage levels adhering to specified ranges. Any deviation from these supply specifications can lead to malfunctions in equipment. Monitoring the quality of supplied power is crucial to minimize the impact of fluctuations in voltage. Variations in voltage or current from their ideal values are referred to as "power quality (PQ) disturbances," highlighting the need for vigilant monitoring and management. Signal processing methods are widely used for power system applications which include understanding of voltage disturbance signals and used for retrieval of signal information from the signals Different signal processing methods are used for extracting information about a signal. The method of Fourier analysis involves application of Fourier transform giving frequency information. The method of Short-Time Fourier analysis involves application of Short-Time Fourier transform (STFT) giving time–frequency information. The method of continuous wavelet analysis involves application of Continuous Wavelet transform (CWT) giving signal information in terms of scale and time where frequency is inversely related to scale. The method of discrete wavelet analysis involves application of Discrete Wavelet transform (DWT) giving signal information in terms of approximations and details where approximations and details are low and high frequency representation of original signal. In this paper, an attempt is made to perceive power quality disturbances in MATLAB using Fourier, Short-Time Fourier, Continuous Wavelet and Discrete Wavelet Transforms. Proper understanding of the signals can be possible by transforming the signals into different domains. An emphasis on application of signal processing techniques can be laid for power quality studies. The paper compares the results of each transform using MATLAB-based visualizations. The discussion covers the advantages and disadvantages of each technique, providing valuable insights into the interpretation of power quality disturbances. As the paper delves into the complexities of each method, it takes the reader on a journey of signal processing complexities, culminating in a nuanced understanding of power quality disturbances and their representations across various domains. The outcomes of this research, elucidated through energy values, 3D plots, and comparative analyses, contribute to a comprehensive understanding of power quality disturbances. The findings not only traverse theoretical domains but also find practical utility in real-world scenarios.

Applications of Artificial Intelligence in Power Systems

A continuous and reliable supply of electricity is necessary for the functioning of today’s modern and advanced society. Since the early to mid 1980s, most of the effort in power systems analysis has turned away from the methodology of formal mathematical modeling which came from the areas of operations research, control theory and numerical analysis to the less rigorous and less tedious techniques of artificial intelligence (AI). Power systems keep on increasing on the basis of geographical regions, assets additions, and introduction of new technologies in generation, transmission and distribution of electricity. AI techniques have become popular for solving different problems in power systems like control, planning, scheduling, forecast, etc. These techniques can deal with difficult tasks faced by applications in modern large power systems with even more interconnections installed to meet increasing load demand. The application of these techniques has been successful in many areas of power system engineering.

GÜÇ TRANSFORMATÖRLERİNDE MİNERAL YAĞLARA ALTERNATİF DOĞAL ESTERLERİN UYGULAMALARI

The electrical and environmental disadvantages of mineral oils, traditionally used as insulating fluids in power systems, lead to the search for alternatives. Natural esters produced from vegetable seeds are one of the most important alternatives. Natural esters with high dielectric strength can meet ultra-high voltage transformer design requirements. In addition, they can respond to environmental concerns caused by transformer oils with their biodegradability. This study first compares natural esters with other trans-former oils and explains why natural esters stand out. The basic properties of natural esters are defined in terms of advantages and disadvantages, and power system applications are exemplified. Esterification and synthesis of nanoparticle-added nanofluids, widely used in the literature to improve natural esters' electrical and thermal properties, are presented with experimental applications. Natural esters can have a better oxidation stability with the esterification process. The AC, DC, and lightning impulse (LI) break-down voltages of nanofluids based on natural esters also enhanced by 10% on average. This substation and experimental applications in which natural esters are used show that these environmentally friendly oils can provide insulation requirements in many power system equipment, especially transformers.

Deep reinforcement learning approach to estimate the energy-mix proportion for secure operation of converter-dominated power system

The dynamic evolution of the modern energy landscape has forced the integration of renewable energy sources (RESs) into power generation, catalyzing a paradigm shift towards converter-dominated power systems. This paper addresses the imperative challenge of optimizing power-generating technologies within these complex systems to ensure both stability and sustainability. The underlying issue lies in effectively managing the energy-mix proportion—balancing the total electricity generated with the overall system load—while accounting for the intermittent nature of RESs. Motivated by the insistent need for adaptive and efficient power system management, this paper presents a pioneering deep reinforcement learning (DRL) approach within modern power system applications. Leveraging the Deep Q-Network (DQN) paradigm, the proposed methodology estimates the energy-mix proportion through a data-driven lens. By integrating MATLAB/Simulink and Python libraries, offline training and online testing validate the approach's applicability in real-world scenarios. Results from comprehensive experiments on an IEEE-9 bus system underscore the efficacy of the DRL-based framework. Notably, the online short circuit level (SCL) emerges as a robust indicator of power system security, a significant innovation in stability assessment. The model demonstrates remarkable responsiveness to load fluctuations, optimizing energy generation and respecting operational constraints. Furthermore, the adaptability of grid-forming (GFM) and grid-following (GFL) converters is showcased, highlighting their resilience in converter-dominated power systems. The study offers a promising avenue for future research and underscores the potential of DRL in power system optimization and operation for a sustainable energy future.

Enhanced Absolute Recovered Energy under Low Electric Field in All-Inorganic 0-3 Nanocomposition Thick Films.

Inorganic thick-film dielectric capacitors with ultrahigh absolute recovered energy at low electric fields are extremely desired for their wide application in pulsed power systems. However, a long-standing technological bottleneck exists between high absolute energy and large recovered energy density. A new strategy is offered to fabricate selected all-inorganic 0-3 composite thick films up to 10µm by a modified sol-slurry method. Here, the ceramic powder is dispersed into the sol-gel matrix to form a uniform suspension, assisted by powder, therefore, the 2µm-thickness after single layer spin coating. To enhance the energy-storage performances, the composites process is thoroughly optimized by ultrafine powder (<50nm) technique based on a low-cost coprecipitation method instead of the solid-state and sol-gel methods. 0D coprecipitation powder has a similar dielectric constant to the corresponding 3D films, thus uneven electrical field distributions is overcome. Moreover, the increase of interfacial polarization is realized due to the larger specific surface area. A maximum recoverable energy density of 14.62Jcm-3 is obtained in coprecipitation thick films ≈2.2 times that of the solid-state powder and ≈1.3 times for sol-gel powder. This study provides a new paradigm for further guiding the design of composite materials.