Search

The ability of fast restoration reflects power system resilience and safety. This paper proposes a partitioning decision-making method for parallel restoration based on the label propagation algorithm and the cooperative game theory. The topological and physical characteristics of blackout systems are considered as the edge weight and the node importance simultaneously for formulating the partitioning model. By the label propagation algorithm, various labels representing different subsystems mark all buses. To accelerate the speed of convergence and prevent the label oscillation, this paper proposes a game strategy of cooperation between buses and subsystems by evaluating the Shapley value of buses. The partitioning constraints are integrated into each label propagation process to assess the feasibility of the partitioning strategy. Finally, case studies on the IEEE 39- and 118-bus test systems and an actual urban power system in South China verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Due to the significant gap between demand and supply of electricity, power utility companies employ rolling blackouts, in which the power supply to different regions is cut-off periodically for a specified duration. However, this causes increased user inconvenience as consumers are left without power; also, a large undesirable blackout may occur if the circuit breakers are not opened in time. To solve this problem, this paper has developed a more pragmatic approach in which a power threshold limit is imposed on households during times of deficit power supply. This paper presents five algorithms designed to allocate load thresholds to households in a fair manner. Some of these algorithms perform the worst (high violations) in some time slots and best (minimal violations) in other slots, but none guarantees minimal mean violation across all the time slots. In this regard, this paper has also developed a novel optimal algorithm that ensures a minimal violation percentage of the allotted thresholds across all time slots in all the households of a neighbourhood. It also employs multiple heuristics for threshold allocation, thereby preventing household starvation. The algorithms are evaluated on a real-world dataset. Its mean violation across all the houses goes as low as nearly 45% and is minimum across all the time slots. It performs all the algorithms used for power limit distribution. This result demonstrates the effectiveness of the developed technique in the implementation of the brownout scheme in practise. [ABSTRACT FROM AUTHOR]

To enhance the power supply reliability of the microgrid cluster consisting of AC/DC hybrid microgrids, this paper proposes an innovative structure that enables backup power to be accessed quickly in the event of power source failure. The structure leverages the quick response characteristics of thyristor switches, effectively reducing the power outage time. The corresponding control strategy is introduced in detail in this paper. Furthermore, taking practical considerations into account, two types of AC/DC hybrid microgrid structures are designed for grid-connected and islanded states. These microgrids exhibit strong distributed energy consumption capabilities, simple control strategies, and high power quality. Additionally, the aforementioned structures are constructed within the MATLAB/Simulink R2023a simulation software. Their feasibility is verified, and comparisons with the existing studies are conducted using specific examples. Finally, the cost and efficiency of the application of this study are discussed. Both the above results and analysis indicate that the structures proposed in this paper can reduce costs, improve efficiency, and enhance power supply stability. [ABSTRACT FROM AUTHOR]

The decision-making of generator start-up sequence plays a pivotal role in the power system restoration process following the blackout. In this paper, an optimized deep Q-learning network (DQN) algorithm is proposed to address this challenge. The generator start-up process is modeled as a Markov Decision Process (MDP) based on its characteristics. The DQN is tasked with deciding both the generator start-up sequence and the corresponding restoration path. To address the limitations of DQN, such as low exploration efficiency and slow convergence, the study incorporates the Artificial Potential Field (APF) algorithm to refine the reward function of it. This integration results in the development of the F-DQN (APF-DQN) algorithm, which enhances training efficiency. The effectiveness of this proposed method is demonstrated through the IEEE 39-bus test system. The results reveal that the DQN algorithm is capable of efficiently solving the model of the generator start-up sequence after the blackout. Moreover, the F-DQN algorithm exhibits superior learning efficiency, faster convergence, and higher-quality optimal solutions compared to the DQN. This paper also discusses the applicability of this method under partial blackouts. When compared to other decision-making algorithms, the proposed method offers a restoration scheme that is both time-efficient and results in increased electricity generation. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the face of increasing climate variability and the complexities of modern power grids, managing power outages in electric utilities has emerged as a critical challenge. This paper introduces a novel predictive model employing machine learning algorithms, including decision tree (DT), random forest (RF), k-nearest neighbors (KNN), and extreme gradient boosting (XGBoost). Leveraging historical sensors-based and non-sensors-based outage data from a Turkish electric utility company, the model demonstrates adaptability to diverse grid structures, considers meteorological and non-meteorological outage causes, and provides real-time feedback to customers to effectively address the problem of power outage duration. Using the XGBoost algorithm with the minimum redundancy maximum relevance (MRMR) feature selection attained 98.433% accuracy in predicting outage durations, better than the state-of-the-art methods showing 85.511% accuracy on average over various datasets, a 12.922% improvement. This paper contributes a practical solution to enhance outage management and customer communication, showcasing the potential of machine learning to transform electric utility responses and improve grid resilience and reliability. [ABSTRACT FROM AUTHOR]

Nonvolatile computing have been shown to be effective in the face of the sudden power outage for wireless sensor networks, Internet-of-Things applications, data converters, and emerging energy-harvesting circuits. It also plays a significant role in power-gating to minimize the leakage power for improving the energy efficiency. However, using on-chip backup module for D flip--flop has a bottleneck, and result in an increase in total power consumption, occupied area, and reduced calculation speed. Furthermore, the backup module needs external control signals, which increases the complexity of the circuit. This paper proposes a novel nonvolatile flip-flop with simultaneous data backup capability, which uses NCFET ferroelectric transistor to fundamentally advance the non-volatile computing paradigm. Proposed NVFF exhibits 0.8% faster and 5.0% smaller energy than previous works, while uniquely providing Radiation- Hardened feature. [ABSTRACT FROM AUTHOR]

As the finite nature of non-renewable energy resources is realised and climate change concerns become more prevalent, the need to shift to more sustainable forms of energy such as the adoption of renewable energy has seen an increase. More specifically, wind energy conversion systems (WECS) have become increasingly important as a contribution to grid frequency support, to maintain power at the nominal frequency and mitigate power failures or supply shortages against demand. Therefore, limiting deviations in frequency is imperative and, thus, the control methods of WECS are called to be investigated. The systematic literature review methodology was used and aimed at investigating these control methods used by WECS, more specifically variable-speed wind turbines (VSWT), in supporting grid frequency as well as the limitations of such methods. The paper identifies these to be de-loading, energy storage systems and emulated inertial response. Further classification of these is presented regarding these control methods, which are supported by literature within period of 2015–2022. The literature indicated a persistent interest in this field; however, a few limitations of VSWTS were identified. The emulated inertial response, specifically using a droop control-based frequency support scheme, was the primary means of providing frequency support. This systematic literature review may be limited by the number of papers selected for the study. Results and conclusions will not only be useful for WECS development but also in assisting with the security of the transmission grid's frequency stability. Future work will focus on further studying the limitations of WECS providing frequency support. [ABSTRACT FROM AUTHOR]

Temporary microgrids containing a high proportion of distributed renewable generations can offer great benefits during the recovery stage after large‐scale power system blackout, by picking up critical load and speeding up the entire grid recovery process. To solve the frequency security problem of temporary microgrids faced in the load restoration process, this paper proposes an adaptive fuzzy model predictive control (adaptive fuzzy‐MPC) strategy to obtain effective frequency regulation to suit the special characteristics of this process. First, the load restoration plan within a short time frame is regarded as the feedforward information and assists in the establishment of the MPC model. Then, considering the uncertainty of renewable distributed generations, the regulation resources are cooperated by an adaptive fuzzy controller which provides the weights of different regulation resources adopted in MPC. Specifically, the base weights of the energy storage system and renewable distributed generations are obtained from fuzzy rules, and then the weight for each regulation resource is modified by the designed adjustment rules. Simulation results illustrate that the proposed strategy can efficiently improve frequency performance while making various frequency regulation resources with different regulation capacities cooperate properly, which can further accelerate the load restoration progress and enhance system resilience. [ABSTRACT FROM AUTHOR]

This paper presents an approach for using a long short-term memory (LSTM)-based recurrent neural network with various configurations to construct a forecasting model for electrical load prediction of a 110 kV substation. The issues of unbalances arising in energy management systems due to discrepancies between generated and consumed energy can lead to power outages and blackouts. With the introduction of the most accurate forecasts, the task of maintaining electrical reliability for grid operators can be greatly simplified. Through an investigation into 81 different parameter combinations, the research revealed the optimal setup for an LSTM model in the task of electrical load forecasting. This configuration comprised 15 neurons, a batch size of 16, and employed the Adamax optimization algorithm. Applying this specific setup yielded a mean squared error (MSE) of 5.584 MW² and an R² value of 0.99. High R² values and low MSE values indicate that the LSTM model accurately captures changes in electricity consumption with minimal deviation between predicted and actual consumption values. Selection of appropriate parameters significantly enhances the performance of the predictive model and resulted in a reduction of the MSE error from 12.706 to 5.584 MW². The optimized configuration of the LSTM model, tailored through extensive experimentation, enhances its predictive capabilities. The proposed LSTM model holds practical utility for integrating into systems for monitoring and forecasting mode reliability of electrical networks, particularly in the Western energy hub of the Republic of Kazakhstan. Its accuracy and reliability make it valuable for energy resource management and infrastructure planning [ABSTRACT FROM AUTHOR]

The energy harvesting of ultra‐high voltage transmission lines and wireless power transmission technology are effective means to solve the stable energy supply of outdoor tower equipment. However, there are inevitably technical problems such as long transmission distance, weak coupling, and parameter sensitivity. In this paper, an effective design approach for a power demand‐oriented long‐distance wireless power transmission system is proposed. With the goal of transmitting power, a system equivalent parameter model is established to address the sensitivity of design parameters for the point‐to‐point long‐distance wireless power transmission system, and the main influencing factors that lead to system power transmission failure are analyzed. To effectively address the impact of high‐sensitivity parameters, a fast‐tuning method with redundant capacitance matrix compensation is proposed, which improves the deviation problem in system parameter design, improves the efficiency of offset parameter matching, and analyzes the impact of wireless energy transmission systems on high‐voltage insulator strings. Finally, an experimental platform was constructed to achieve power transmission at a distance of not less than 5 m at a resonant frequency of 50 kHz. The transmission efficiency met the design expectations and verified the effectiveness of the analysis. [ABSTRACT FROM AUTHOR]

The growing use of gas-fired power generators and electricity-driven gas compressors and storage has increased the interdependence between electric power infrastructure and natural gas infrastructure. However, the increasing interdependence may spread the failures from one system to the other, causing subsequent failures in an integrated power and gas system (IPGS). This paper investigates the structural vulnerability of a realistic IPGS based on complex network theory. Different from the existing works with a focus on the static vulnerability analysis for an IPGS, this paper considers both static and dynamic vulnerability analysis. The former focuses on vulnerability analysis under random and selective failures without flow redistribution, while the latter concentrates on vulnerability analysis under cascading failures caused by flow redistribution. Also, different from the existing works with a focus on the IPGS as a whole, we not only analyze the vulnerability of the IPGS but also analyze the vulnerability of the power subsystem (PS) and gas subsystem (GS), in order to understand how the vulnerability of the IPGS is affected by its PS and GS. The analysis results show that (1) if the PS and GS are more susceptible to cascading failures than selective and random failures, the IPGS as a whole is also more vulnerable to cascading failures. (2) There are different dominant factors affecting the IPGS vulnerability under cascading failures and selective failures. Under cascading failures, the GS has a more significant impact on the IPGS vulnerability; under selective failures, the PS has a more important impact on the IPGS vulnerability. (3) The IPGS is more vulnerable to failures on the critical nodes, which are identified from the IPGS as a whole rather than from the individual PS or GS. The results provide insights into the design and planning of IPGSs to improve their overall reliability. [ABSTRACT FROM AUTHOR]

Writing centres have had to adapt to many challenges, including the move to online learning during the Covid-19 pandemic. In South Africa this move was complexified by differential access to digital environments and contextual issues such as lack of electricity and rolling blackouts. Writing centres also need to consider the increasing massification of higher education and ways of harnessing diverse resources to enrich communication. This paper explores writing centre approaches to research and pedagogy within a social justice agenda, including combining an academic literacies approach with a multimodal social semiotic approach. It also investigates innovative approaches to teaching writing in diverse and multilingual contexts, including expanding the repertoire of resources used in writing consultations. It argues that the explicit utilisation of a range of modes can enhance writing production and enable writers to actively explore different modes for constructing meaning. The paper reflects on developing inclusive multimodal writing centre materials. It then explores the changing function of modes in online and face-to-face consultations (post-pandemic lockdowns), including talk as mode, silence as mode, visual modes, and the use of time. The aim is to interrogate our academic practices concerning the diverse languages and various forms of communication that students possess, whether in virtual or physical learning environments. [ABSTRACT FROM AUTHOR]

When the gearbox fails, the impulsive fault feature is usually submerged in other frequency component. The recognition accuracy of the network model will be seriously affected when directly using the vibration signal as the sample input of Convolutional Neural Network (CNN). To solve this problem, a state recognition method based on vibration signal fusion feature convolutional learning network (FF-CNN) is proposed to conduct the gear fault diagnosis. This method uses the Resonance-based Sparse Signal Decomposition (RSSD) algorithm to separate the periodic and impulsive components that characterize different fault characteristics of the vibration signal. The impulsive feature is amplified by the Teager energy operator. And then the signal periodic and impulsive components are input into CNN to perform the targeted deep learning. Finally, the efficiency of this method is validated using the gear power transmission failure simulation experimental setup. The research in this paper improves the effectiveness of the feature learning, reduces the complexity of deep learning model, and ensures the accuracy of state recognition. Through experimental research, it is found that the recognition accuracy of the recognition model based on the method in this paper reaches more than 95.6%. Highlights: The experiments were carried out in the dynamic engineering laboratory. An FF-CNN method is proposed to conduct the fault diagnosis. T-SNE tool in the manifold learning method was used to visualize the results. FF-CNN can improve the accuracy of fault diagnosis by separating fault features. [ABSTRACT FROM AUTHOR]

The authors of this paper analyze the use of biogas-powered micro-Combined Heat and Power (micro-CHP) systems for residential microgrids to enhance their resilience during blackouts. With the increasing frequency of natural disasters, ensuring power system reliability has become a critical concern. Microgrids can provide a solution to this problem by integrating distributed energy resources and operating them independently of the grid. The authors of this paper investigate the design and implementation of biogas-powered micro-CHP systems for residential microgrids. The paper concludes with a discussion of the potential of biogas-powered micro-CHP systems as a key component of resilient and sustainable energy systems in the future. [ABSTRACT FROM AUTHOR]

Unpredictable variations in load demand and unanticipated component failures are progressively impacting the operation of modern power systems, making system evaluation more stochastic in nature. Although deterministic approaches were formerly the norm for determining system status, probabilistic approaches have greatly improved the capacity to capture the stochastic behavior characteristic of power system operations. The presented work in the paper recommends the use of probabilistic modelling approaches with deterministic approaches, highlighting their crucial function in augmenting the reliability and security of contemporary power systems to unanticipated failures. In this paper, N − 1 security criteria based reliability of the composite power system (CPS) is proposed using an integrated deterministic and probabilistic framework (D-P) considering outage of the transmission line. For the deterministic approach (DA), line overloading on available lines is determined using the static security index (SSI). For the probabilistic approach (PA), reliability indices such as expected loss of power (ELOP), expected frequency of contingency (EFOC), expected loss of load (ELOL), probability of load curtailment (PLC), and expected duration of load curtailments (EDLC) are calculated. Further, for each contingency, a performance index is determined using both approaches to assess the severity of the contingency that occurred on the power system. Based on the N − 1 security criteria based reliability analysis using an integrated D-P framework, a credible critical set of transmission lines is obtained, which can serve as important information to system operators. The proposed techniques have been tested on IEEE 24 bus reliability test system (RTS). [ABSTRACT FROM AUTHOR]

The complexities in modern power systems are increasing daily with the integration of renewable sources and an increasing number of power system components such as lines, buses, transformers, FACTs devices, and many more. The complexity and the limitations in power transfer capabilities for meeting the power consumptions under the contingency condition may occur in the system. The power system contingencies may lead to blackouts and complete system failure. Therefore, power system contingency analysis is a very emerging topic in power system analysis. This paper identifies some previous contingency analysis research and finds the different research persons and journal sources that publish related articles in this field through a bibliometric analysis. [ABSTRACT FROM AUTHOR]

Efficient bandwidth utilisation is significant in new communication systems where secondary users can be used besides primary users considering interference issues and idle state of primary users. Using secondary users as relays to transmit their own signals in addition to the primary signals can be applied for more reliability of the system where opportunistic relay selection can significantly enhance the performance of the system. The best-condition secondary user is selected as the optimum relay for retransmission of primary/secondary signal. Outage probability is analysed in this paper based on decode and forward technique in secondary users while the closed-form statement for outage probability is provided and verified by numerical evaluations. [ABSTRACT FROM AUTHOR]

The volume of flow demand in cyber-physical power systems (CPPSs) fluctuates unevenly across coupled networks and is susceptible to congestion or overload due to consumers' energy demand or extreme disasters. Therefore, considering the elasticity of real networks, communication links with excessive information flow do not immediately disconnect but have a certain degree of redundancy. This paper proposes a dynamic cascading failure iterating model based on the distribution of information flow overload in a communication network and power flow betweenness in the physical power grid. First, a nonlinear load capacity model of a communication network with overload and weighted edges is introduced, fully considering the three link states: normal, failure, and overload. Then, flow betweenness substitutes for branch flows in the physical power network, and power flow on failed lines is redistributed using the load capacity model, simplifying the calculations. Third, under the influence of coupling relations, a comprehensive model based on improved percolation theory is constructed, with attack strategies formulated to more accurately assess the coupled networks. Simulations on the IEEE-39 bus system demonstrate that considering the overload capacity of communication links on a small scale enhances the robustness of coupled networks. Furthermore, deliberate link attacks cause more rapid and extensive damage compared to random attacks. [ABSTRACT FROM AUTHOR]

This paper presents the topology and machine learning-based intelligent control of high-power PV inverter for maximum power extraction and optimal energy utilization. Modular converters with reduced components economic and reliable for high power applications. The proposed integrated intelligent machine learning based control delivers power conversion control with maximum power extraction and supervisory control for optimal load demand control. The topology of the inverter, operating modes, power control and supervisory control aspects are presented. Simulation is carried out in MATLAB/SIMULINK to verify the feasibility of the proposed inverter and control algorithm. The experimental study is presented to validate the simulation results. The operational performance of the proposed topology is evaluated in terms of operational parameters such as regulation of output power, and load relay control and is compared to existing topologies. The economic performance is also evaluated in terms of power switch sizing and reliability in power delivery concerning switch or power sources failure. [ABSTRACT FROM AUTHOR]

Against the background of the construction of new power systems, power generation, transmission, distribution, and dispatching services are open to the outside world for interaction, and the accessibility of attack paths has been significantly enhanced. We are facing cyber-physical cross-domain attacks with the characteristics of strong targeting, high concealment, and cross-space threats. This paper proposes a quantitative analysis method for the influence of power cyber-physical cross-domain attack paths based on graph knowledge. First, a layered attack graph was constructed based on the cross-space and strong coupling characteristics of the power cyber-physical system business and the vertical architecture of network security protection focusing on border protection. The attack graph included cyber-physical cross-domain attacks, control master stations, measurement and control equipment failures, transient stable node disturbances, and other vertices, and achieved a comprehensive depiction of the attack path. Second, the out-degree, in-degree, vertex betweenness, etc., of each vertex in the attack graph were comprehensively considered to calculate the vertex vulnerability, and by defining the cyber-physical coupling degree and edge weights, the risk of each attack path was analyzed in detail. Finally, the IEEE RTS79 and RTS96 node systems were selected, and the impact of risk conduction on the cascading failures of the physical space system under typical attack paths was analyzed using examples, verifying the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

As a classic cyber-physical system, smart grids often suffer from various types of attacks, one of which the most threatening attacks is coordinated cyber-physical attack (CCPA). In order to improve the robustness of the smart grids against CCPA, we predict and simulate various possible attack scenarios, and propose three attack strategies, such as optimal attack strategy (OAS), saturation attack strategy (SAS) and decentralized random selective attack strategy (DRSAS). The OAS is used to get the maximum attack effect by attacking a small of number of targets in the case of limited resources. The SAS is designed to destroy the entire network with sufficient resource while avoiding repeated or redundant attacks. The DRSAS is proposed to random select decentralized cyber and physical targets according to attacker acknowledgement, real-time vulnerabilities, network characteristics, which is divided into implicit attack and explicit attack. In this paper, attack goals (AGs) function is firstly constructed to identify important cyber and physical targets. Secondly, based on these AGs, OAS and SAS are designed and applied to CCPA for analyzing the effect of those attack strategies on smart grids. Thirdly, The DRSAS is used to study the influence of decentralized random attack on cascading failure of smart grid, which applied to CCPA is to compare with OAS and SAS. Finally, node loss is used as an evaluation index to compare the attack effect of cyber attack (CA), physical attack (PA), CCPA based on OAS, SAS and DRSAS. The experimental results show that: (I) when the same proportion of nodes are removed, the CCPA based on the OAS or SAS has better attack effect rather than CA and PA; (II) CCPA based on DRSAS has different effects at different times, and explicit attack has better effect than implicit attack regardless of α = 0.3 , 0.4 , 0.5 and times t1, t2, t3. [ABSTRACT FROM AUTHOR]

A double-volute centrifugal pump is a very important pump type; the internal flow field of a centrifugal pump will change drastically during the transition process of power failure, which will affect the safety and stability of the pump's operation. In this paper, the CFD numerical simulation method is used, and the UDF procedure is developed to realize the continuous update of the impeller speed at each time step. The working parameters, such as the torque and flow rate at the instantaneous moment, are obtained through the sequential iteration of each small step, and a numerical simulation of the power-off transient is carried out on a double-volute centrifugal pump; additionally, the changes in the external characteristic parameters and the internal flow field of the centrifugal pump are analyzed in detail. The results show that the double-volute centrifugal pump experienced four different modes after power failure, namely pump mode, braking mode, turbine mode, and runaway mode, and the absolute values of the runaway speed and runaway flow rate are 1.465 times and 1.21 times the initial values, respectively. Through the analysis of the flow field in different regions, the change processes of the generation, development, and disappearance of the vortex at each position of the centrifugal pump are obtained, and the change and development processes of the internal velocity gradient of the centrifugal pump are obtained. In addition, it is found that the high-speed area located in the second volute runner is larger than that of the first volute runner because the second volute runner is shorter and narrower than the first volute runner. [ABSTRACT FROM AUTHOR]

With the continuous development of the power grid, its structure is becoming increasingly complex. The occurrence of faults in transmission lines may lead to cascading failures in the power grid, ultimately resulting in widespread power outages. The transmission of equipment information and the sending of fault reports in the power grid rely on the power communication network. This network is crucial for ensuring the safe, stable, and economical operation of the power grid. As the number of devices in the power grid increases and sensor technology becomes more widespread, the volume of data generated by both the power grid and the power communication network has increased sharply. However, relational databases have limited scalability and struggle to meet the growing volume of data and user demands. This paper proposes a graph mapping method based on the power grid and communication network, utilizing data from both networks to construct a unified data plane in a graph database. Taking power transfer operations as an example, a unified standard data model and monitoring indicator system are established for both networks, enabling faster response and power restoration to blackout areas in the event of power grid faults. Simulation results demonstrate that compared to traditional relational databases, graph databases exhibit significantly improved efficiency in handling large-scale, highly connected data, making them more suitable for future power grids. [ABSTRACT FROM AUTHOR]

The switching power supply is widely used in the secondary equipment of the power grid. The failure of the switching power supply will cause the protection device to malfunction, leading to a risk of large‐scale power failure. In this paper, a real‐time monitoring method based on only output voltage information is proposed. The experiment shows that this parameter‐reduced approach, relying only on voltage information, outperforms the existing method in detection accuracy. [ABSTRACT FROM AUTHOR]

In order to explore the stability of multi-step high steep slope, this paper considers layer distribution of soil and builds a slope failure mechanism which meets requirements of velocity separation based on plastic mechanics principles, then derives the calculation method of external force power and internal energy dissipation power in failure area using numerical analysis theory, and proposes a loop algorithm for slope stability analysis combining upper bound limit analysis theorem and strength reduction principle. At the same time, in order to enhance the applicability of the upper bound limit analysis method, this paper develops a slope stability calculation system of upper bound limit analysis combined with computer programming technology and evaluates the accuracy of the calculation system through slope engineering practice in open-pit mine. The results show that stability factor by calculation system of upper bound limit analysis is an upper limit solution compared with limit equilibrium method, but the error rate of two methods is less than 5%, which indicates that the slope stability calculation system established in this paper has high accuracy in stability analysis of multi-step high steep slope in the practice of open-pit mine. On the other hand, the most dangerous sliding surface searched by upper bound limit analysis can fully solve velocity problems of multi-step high steep slope failure, so it has stronger reference values in engineering practice. [ABSTRACT FROM AUTHOR]

Deteriorated power system components have a higher probability of failure than new components. Still, the reliability of supply analyses traditionally models all components of the same type with the same probability of failure, and thus neglects the effect of deteriorated components. This paper presents a methodology to integrate a condition‐dependent component probability of failure model into a power system reliability analysis. The component state is described by a semi‐Markov process, and the paper shows how this, under reasonable assumptions, can be approximated by a Markov process. The Markov assumption simplifies the analysis and allows the model to include preventive retirement and be calibrated to statistical data. A case study using statistical data for Norwegian power transformers shows that, in the Norwegian power system, the proportion of failures that are due to the poor condition is small, partly due to the common strategy of preventive retirement. However, if the condition of the transformers were worse, the impact of poor conditions can be considerable. The methodology further enables the identification of the transformers that contribute most to the risk to the reliability of supply. The paper thus highlights the importance of accounting for the component condition in strategic decisions such as long‐term renewal planning [ABSTRACT FROM AUTHOR]

At present, the proportion of new energy in the power grid is increasing, and the random fluctuations in power output increase the risk of cascading failures in the power grid. In this paper, we propose a method for identifying high-risk scenarios of interlocking faults in new energy power grids based on a deep embedding clustering (DEC) algorithm and apply it in a risk assessment of cascading failures in different operating scenarios for new energy power grids. First, considering the real-time operation status and system structure of new energy power grids, the scenario cascading failure risk indicator is established. Based on this indicator, the risk of cascading failure is calculated for the scenario set, the scenarios are clustered based on the DEC algorithm, and the scenarios with the highest indicators are selected as the significant risk scenario set. The results of simulations with an example power grid show that our method can effectively identify scenarios with a high risk of cascading failures from a large number of scenarios. [ABSTRACT FROM AUTHOR]

Air valves are protective devices often used in pressurised water pipelines, ideally admitting air to limit sub-atmospheric pressures and controlling the release of entrapped air. This work summarises a comprehensive sensitivity analysis of the transient behaviour in a rising water pipeline with an air valve following a pump trip. The paper examines the water hammer stages associated with a pump trip, namely, the initial depressurisation, followed by air admission, then air expulsion, and finally the creation of a secondary pressure wave. For each air valve location and specific set of design conditions, the relationship between the transient magnitude and air valve outflow capacity is found to be non-linear, but to roughly follow the shape of a logistic curve having a lower left plateau for attenuated (type 1) behaviour and transitioning through type 2 behaviour to a higher right plateau for water-hammer-dominated (type 3) behaviour. Through an extensive set of simulations covering a wide range of conditions, the study identifies the size of the critical outflow orifices associated with both type 1 and type 3 responses and assesses the influence of the location of the air valve on the transient magnitude and on the timing of air pocket collapse. Furthermore, the paper highlights that a non-slam air valve is capable of effectively mitigating transient magnitudes provided that its design parameters are judiciously chosen and account for both the system's attributes and the characteristics of the transient event. [ABSTRACT FROM AUTHOR]

Aiming at the problems of the low integration of the current power information system security assessment method with classified protection 2.0, strong subjectivity, and the vague quantification of objective indexes, this article proposes to establish a safety index system suitable for the electric power information system with the existing safety inspection standards for power systems on the basis of the classified protection 2.0 evaluation system. Additionally, it quantifies each index through a quantitative method combining subjective and objective methods and uses the analytic hierarchy process entropy method to obtain the combined weight of the evaluation index system. Finally, this paper summarizes the expert scores and the graded protection evaluation reports of the six systems into the original data for comprehensive weight calculation and analyzes the rationality of the weight change. The comparison and analysis of the closeness of the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) and the evaluation score of the equal guarantee have confirmed the scientificity and rationality of the evaluation model. It provides a reasonable logical idea for the implementation of the classified protection 2.0 system in the power information system. [ABSTRACT FROM AUTHOR]

This paper describes how Hurricane Ida exposed gaps in emergency preparedness planning and coordination in New Orleans, particularly related to the health and safety of the residents of multi-storey independent living facilities designated for seniors and persons with disabilities, where at least ten lives were lost due to the power outages and extreme heat that occurred following the storm. As this paper discusses, New Orleans Health Department leaders responded by taking swift policy action, working with the mayor, city council and community stakeholders to ensure better coordination, preparation and accountability for owners and operators of certain independent living facilities. The article recommends that states and localities with individuals living in independent living facilities should consider similar policy interventions as part of their disaster cycle planning activities. [ABSTRACT FROM AUTHOR]

Since modern smart grids have various and deeply coupled cyber-physical components, they are vulnerable to malicious cyber attacks. Although regular defenses including firewall and IDS are deployed, they may be weakened by zero-day vulnerabilities and sophisticated attack schemes. Therefore, defense strategies to mitigate the risk of blackouts during cyber attacks are necessary. This paper proposes a cyber-physical coordinated defense strategy to overcome the disruption and minimize the risk as much as possible. At the cyber layer, a zero-sum multilevel Markovian Stackelberg game is proposed to model sequential actions of the attacker and the defender. The defender distributes defensive resources to protect lines in a real-time manner, according to the attacker's action. If cyber attacks should result in physical outages, defense at the physical layer is then employed. A security-constrained optimal power flow reserving security margin of critical components will be performed to minimize the blackout scale and potential future risk. To solve the corresponding optimization problem and further get the optimal defense strategy, this paper devises a novel “water-pouring” algorithm. Lastly, test results show that the proposed dynamic defense strategy mitigates risk significantly and outperforms existing methods. [ABSTRACT FROM AUTHOR]

This paper introduces a post-disaster load restoration approach for the distribution grid, utilizing network reconfiguration (NR) and dispatching of repair crews (RCs) to significantly enhance grid resilience. We propose an RC-NR coordinated model that leverages diverse flexible resources within the active distribution network (ADN), aimed at not only enhancing the grid's resilience level but also efficiently mending the fault lines. The model introduces fault repairing and sequential NR coupled constraints to devise an optimal resilience strategy within temporal domain cooperation, focusing on minimizing repair and penalty costs associated with the restoration process. To tackle the challenge of computational complexity, the nonlinear model is reformulated into a mixed-integer second-order cone programming model. The efficacy of the approach is validated through case studies on an IEEE 33- bus system, in which simulation results demonstrate a considerable improvement in grid resilience, achieving optimal load recovery with reduced restoration time and costs. The proposed approach outperforms traditional methods with optimal repair sequence and RC scheduling, aligned with NR efforts, and contributes to an improved system resilience level. [ABSTRACT FROM AUTHOR]

Large geomagnetic storms are a space weather hazard to power transmission networks due to the effects of Geomagnetically Induced Currents (GICs). GIC can negatively impact power transmission systems through the generation of even‐order current and voltage harmonics due to half‐cycle transformer saturation. This study investigates a decade of even‐order voltage total harmonic distortion (hereon referred to as Even‐Order Total Harmonic Distortion (ETHD)) observations provided by Transpower New Zealand Ltd., the national system operator. We make use of ETHD measurements at 139 locations throughout New Zealand, monitored at 377 separate circuit breakers, focusing on 10 large geomagnetic disturbances during the period 2013–2023. Analysis identified 5 key substations, which appeared to act as sources of ETHD. The majority of these substations include single phase transformer banks, and evidence of significant GIC magnitudes. The ETHD from the source substations was found to propagate into the surrounding network, with the percentage distortion typically decaying away over distances of 150–200 km locally, that is, at a rate of −0.0043 %km−1. During the study period some significant changes occurred in the power network, that is, removal of the Halfway Bush (HWB) single phase bank transformer T4 in November 2017, and decommissioning of the New Plymouth substation in December 2019. Decommissioning of these two assets resulted in less ETHD occurring in the surrounding regions during subsequent geomagnetic storms. However, ETHD still increased at HWB with increasing levels of GIC, indicating that three phase transformer units were still susceptible to saturation, albeit with about 1/3 of the ETHD percentage exhibited by single phase transformers. Plain Language Summary: Space weather, triggered by solar storms, can lead to significant variations in the Earth's magnetic field. The magnetic variations are termed geomagnetic disturbances. Changing magnetic fields induce electric fields at the Earth's surface, causing unwanted currents to flow in electrical power transmission networks. These Geomagnetically Induced Currents (GICs), pose a hazard to network operations as they can cause networks to become destabilized, leading to blackouts and damage to transformers. One negative impact of GIC in a power network is the production of harmonics of the power transmission frequency (typically 50 or 60 Hz); such harmonics contributed to the blackout of the Québec power system in March 1989. In our paper we study harmonic distortion occurrence in the New Zealand power grid using measurements from 139 substations across the country during 10 geomagnetic storms. Analysis identified 5 key substations, which appeared to act as sources of harmonic distortion. The majority of these substations include a particular transformer design which is known to be more susceptible to GIC issues, as well as evidence of significant GIC. Key Points: A decade of even‐order harmonic distortion data are used to investigate the impact of space weather on a power networkIn New Zealand four key substations containing single phase transformers act as sources of enhanced harmonic distortionThe harmonic distortion is found to propagate into the nearby power network over 150–200 km distances, decaying at a rate of −0.0043 %km−1 [ABSTRACT FROM AUTHOR]

A grid blackout is an intractable problem with serious economic consequences in many developing countries. Although it has been proven that microgrids (MGs) are capable of solving this problem, the uncertainties regarding when and for how long blackouts occur lead to extreme difficulties in the design and operation of the related MGs. This paper addresses the optimal design problem of the MGs considering the uncertainties of the blackout starting time and duration utilizing the kernel density estimator method. Additionally, uncertainties in solar irradiance and ambient temperature are also considered. For that, chance-constrained optimization is employed to design residential and industrial PV-based MGs. The proposed approach aims to minimize the expected value of the levelized cost of energy ( L C O E ), where the restriction of the annual total loss of power supply ( T L P S ) is addressed as a chance constraint. The results show that blackout uncertainties have a considerable effect on calculating the size of the MG's components, especially the battery bank size. Additionally, it is proven that considering the uncertainties of the input parameters leads to an accurate estimation for the LCOE and increases the MG reliability level. [ABSTRACT FROM AUTHOR]

This paper conducts a comprehensive study on intermittent computing within IoT environments, emphasizing the interplay between different dataflows—row, weight, and output—and a variety of non-volatile memory technologies. We then delve into the architectural optimization of these systems using a spatial architecture, namely IDEA, with their processing elements efficiently arranged in a rhythmic pattern, providing enhanced performance in the presence of power failures. This exploration aims to highlight the diverse advantages and potential applications of each combination, offering a comparative perspective. In our findings, using IDEA for the row stationary dataflow with AlexNet on the CIFAR10 dataset, we observe a power efficiency gain of 2.7% and an average reduction of 21% in the required cycles. This study elucidates the potential of different architectural choices in enhancing energy efficiency and performance in IoT systems. [ABSTRACT FROM AUTHOR]

One of the most challenging events for distance relays is a transient phenomenon known as power swing, which can have damaging results to the system. The occurrence of power swing can cause the impedance trajectory to enter protective zones of distance relays that may lead them to the wrong operation. Malfunction of distance relays can create instability in power systems, and a large blackout may occur in these networks. The purpose of this paper is to introduce a new method based on using the mean value of sampled data from the current signal to recognize power swing from simultaneous faults. This method has been tested using DIgSILENT and MATLAB software on a two-bus network with parallel lines. The obtained results demonstrate that the proposed scheme is capable of detecting various types of power swings, including both unstable and stable power swing. The presented method in this paper also has an accurate operation to detect simultaneous symmetric faults. Besides, this method is planned for a protection system with a central controller using the IEC-61850 standard. [ABSTRACT FROM AUTHOR]

Power transformers are essential assets in power system networks that must be meticulously monitored throughout their operational life cycle. Given that a substantial percentage of in-service power transformers around the world have reached the end of their projected technical lifespan, utilities are adopting various transformer condition-based maintenance strategies to minimize potentially devastating equipment failure. Thus, further usage of aging fleet of power system assets can be improved by gaining a better understanding of life threatening factors. In-service power transformers and their auxiliary components are susceptible to a variety of operational risks, which can compromise their performance and efficiency, thus resulting in devastating failures, financial losses, and power outages. Hence, it is necessary to investigate transformer life time issues so as to fully grasp the operational and performance status in order to reduce failures and operating costs. This paper presents a unified framework of attributes pertaining to life time issues for transformer operation evaluation together with a summary of recent findings in order to explore the current status and progress of this rapidly progressing field. The failure statistics analysis is presented first, followed by an examination of the transformer's insulation degradation and aging mechanism. Furthermore, emphasis was on detailing the commonly adopted models and strategies in transformer condition evaluation, fault diagnostics, and remnant life estimation. Despite significant advancements in ascertaining transformer health diagnosis and prognosis technologies, including test accuracy, quick, precise fault localization, and fault type classification, there are still several shortcomings that require additional research, and thus future research perspectives are also discussed. [ABSTRACT FROM AUTHOR]

Purpose: Coworking spaces are recent developments in commercial property investment portfolio in Nigeria. Given the user-centered nature of coworking space practices, the purpose of this paper is to examine the factors influencing users' preference for coworking, and the challenges associated with the use of coworking spaces in the emerging Nigeria property market. Design/methodology/approach: This study used a quantitative research approach. This study sampled nine (9) traditional coworking spaces in Ibadan property market, from which 15 coworking space users were randomly selected in each of the coworking hubs. From a total of 135 respondents, only 45 (33.33%) questionnaires were retrieved and found suitable for analysis. The data collected were analyzed using descriptive and inferential statistics. Findings: The results showed that economic and social motivators were significant drivers influencing coworkers' decision to cowork. Personal factors were the least rated. Also, top-rated features/facilities that users prefer in the workstations include a neat environment and conveniences, uninterrupted power supply, serenity of the neighborhood and ease of signing up. The findings also showed that the challenges being faced by the users were predominantly about disturbance/noise from guests and/or other users, lack of privacy, poor internet connectivity, power failure and inability to personalize workspace (in decreasing order). Practical implications: This study has implications for investors and users of coworking spaces in comparable developing markets. Also, the study will influence strategies and decisions of private firms/companies, as it relates to the work pattern of their employees. Originality/value: This paper is relevant given the emergent behavioral changes, necessitated by the changing work practices. This compels the need for a reevaluation of the preferences and challenges associated with coworking spaces, especially as it relates to the users of flexible office spaces in emerging economies. [ABSTRACT FROM AUTHOR]

With the rapid development of China's economy, power demand has been closely linked with economic development in order to analyze and predict the future power situation in China. Based on the historical data of China's electricity consumption, this paper analyzes the data characteristics of China's electricity consumption by using Eviews software. The long-term trend of power consumption sequence is eliminated by fitting the regression curve, and then the residual sequence is analyzed and identified according to the relevant theory of time series. Using the data of China's power demand from 2004 to 2019, the ARMA time series model is used to analyze China's future power situation finally according to the test statistics of the model. Finally, the exponential regression ARMA model is selected to predict China's electricity consumption. The index regression analysis method extracts the long-term characteristic information of the sequence, which is conducive to the stability processing and empirical analysis of long-term prediction. The result predicts China's power demand in 2020 more accurately, which can provide a reference for the future productivity layout of China's power. [ABSTRACT FROM AUTHOR]

The rotor of a magnetic suspension centrifugal compressor is supported by magnetic bearing, and a power failure will cause the high‐speed rotor to fall directly onto the protective bearings, affecting the reliability of the compressor. To solve this problem, this paper proposes an abnormal power failure shutdown control method with semi‐switching modulation of the inverter. Firstly, the overall scheme of abnormal power failure shutdown control is designed, and its feasibility is analysed in detail through mathematical modelling. Secondly, aiming at the non‐linear, time‐varying and gain uncertainty characteristics of the control system, a voltage control strategy based on parametric self‐regulating sliding‐mode variable structure is proposed, and the stability is analysed. Finally, the method proposed in this paper is experimentally verified, the results show that this method does not depend on the electric angle of the motor compared to existing methods, and the rotor can achieve a zero‐drop speed shutdown, which greatly improves the reliability of the magnetic suspension centrifugal compressor. [ABSTRACT FROM AUTHOR]

With the continuous development of society, power grid has become a major guarantee for social and economic development, and its coverage is also expanding. The power grid not only covers some developed areas, but also extends to remote mountainous areas with complex changes in geological and climatic conditions. However, at the same time, line failures and power outages caused by mountain fires are becoming increasingly frequent, which poses a significant threat to the safety and stable operation of the power system. Therefore, how to solve the problem of mountain fire on transmission lines has become an important topic today. The main purpose of this study is to achieve effective monitoring of transmission line mountain fires and provide more assurance for the stable operation of the power system. This article discussed the transmission line and traditional mountain fire prevention monitoring system, and combined the current hot machine vision algorithm to design the overall mountain fire prevention monitoring system, focusing on analyzing the differences between this system and traditional systems. Through experiments, it was found that the monitoring accuracy of the mountain fire prevention monitoring system designed in this article has been improved by 10.6 % compared to the traditional system at location 4, 7.6 % at location 6, and 7.9 % at location 7. At the same time, the real-time performance and anti-interference performance of this system were also better than traditional systems, and the overall performance of the system was better. Practice has proved that the system can effectively overcome the interference of external environment, and monitor and identify the fire situation in real time and accurately, ensuring the safety of transmission lines. At the same time, the research on machine vision algorithms in this paper can also help promote the more perfect development of power systems. Practice has proved that this system can effectively overcome the interference of the external environment, and continuously and accurately detect and identify the fire situation. [ABSTRACT FROM AUTHOR]

In some special geological areas, the inclination and displacement of transmission line towers are relatively common, which should be analyzed from various factors. Transmission towers are the support structure for overhead transmission lines and play a pivotal role in the safe operation of the power grid. Transmission lines are widely distributed. Transmission poles and towers are generally built in areas with poor geological conditions such as goaf, river bed and slope. Under severe weather conditions, the force on the tower may be affected to a certain extent, causing the tower to tilt, deform, or even collapse, and thus causing a wide range of power system failures, which have a great impact on people's production and life. Based on this objective problem, this paper has mainly studied the intelligent recognition methods and key technologies based on machine learning. In the experimental study of support vector machine (SVM) model based on machine learning, the average training time of genetic algorithm support vector regression (GA-SVR) was the longest, reaching 1.462 s. The average training duration of double chain quantum genetic algorithm-least squares support vector regression (DCQGA-LSSVR) was the shortest, with 0.156 s. The average pose error of double chain quantum genetic algorithm-support vector regression (DCQGA-SVR) was the smallest, only 0.136, while the average attitude error of genetic algorithm-least squares support vector regression (GA-LSSVR) was the highest, reaching 0.45. Therefore, it is of the great significance to analyze abnormal vibration of the transmission towers based on machine learning method. [ABSTRACT FROM AUTHOR]

With the arrival of the digital era, the substation has got rid of the previous operation mode and gradually turned to digitalization and modernization, which reduces the risk brought by manual operation and improves the efficiency and quality of substation operation. But substation in electric power equipment failure problems will not be able to avoid, need regular maintenance/repair, and internal defects for some electric power equipment, it is hard to recognize by the naked eye or experience, even if can preliminarily judge the failure problems, also can’t accurate positioning, need to use modern methods to solve. Based on this, this paper takes the sample of transformer oil tank as the research object, diagnoses the internal defects of the sample with the help of laser focusing enhancement technology, and obtains the imaging results of internal defects and specific defect parts. The verification results show that the imaging results and specific defect parts of the sample can be accurately obtained through this technology. The feasibility of the application of this technology in the fault diagnosis of substation power equipment is confirmed, and the fault can be detected in time to ensure the safety of the whole operation of the substation. [ABSTRACT FROM AUTHOR]

Wildfires are one of the most hazardous natural disasters in Australia in terms of fatalities, property damage and financial losses. Events of catastrophic wildfires are recorded across the world including in the United States and Canada. Failures along power distribution infrastructure and network faults have been identified as some of the causes for the initiation of wildfires. Thus, it is critical to better understand the mechanisms and the potential prevention strategies for wildfires caused by power distribution system faults. In this light, this paper presents how the power distribution network faults cause wildfires highlighting the main mechanisms. Further, this paper reviewed studies on recent advancements for the prediction, detection and prevention of wildfires. Condition assessment of power distribution infrastructure including poles, crossarms, overhead cables and other attachments are paramount to detect potential defects and to carry out timely replacements which can subsequently mitigate the possibility of wildfire initiation. Therefore, this paper summarized the studies on condition monitoring and surveillance techniques for power distribution infrastructure. Altogether, this paper aimed to enhance the awareness about the prevention strategies for wildfires caused by power distribution system faults. [ABSTRACT FROM AUTHOR]

Smart electric grids are practising flexible, reliable and robust operations during delivery and consumption of power. However, these grids are highly vulnerable to a wide range of cyber attacks due to the deployment of an extensive communication network. In this paper, the nature of cyber attack on given power system based on proposed cyber attack models and theorems is analysed by utilizing steady state voltage stability (L index). Further, a cyber attack factor is introduced which may mislead the bus voltage stability virtually. The proposed cyber attack models and theorems are validated by executing cyber attacks on WSCC 9 bus and IEEE 14 bus test systems by using Siemens PSS/E and MATLAB softwares. Through the proposed theorems, the paper exposes and quantifies the threat of cyber attacks in the electric power grid. The simulation results reveal that the proposed cyber attack models may misrepresent the bus voltage stability, thereby misleading the energy management centre (EMC) operator into taking incorrect countermeasures. The above incorrect actions may force voltage instability which further leads to major interruptions in the electric power supply and possible cascading failure of the electric power grid. Moreover, the proposed theorems and rigorous simulations presented in the paper support the EMC operator in intelligently identifying a cyber attack, thereby enabling development of appropriate corrective actions during such cyber attacks on the smart electric grid. Thus, the concept of proposed methodology could best assist the power system operator to build detection algorithms for discrimination of cyber attacks from electrical faults towards strong electric grid resilience character. [ABSTRACT FROM AUTHOR]

In electrified transportation systems, power system failures can lead to greater disasters. Therefore, the reliability of converters in transportation systems has been a concern. Fault-tolerant techniques are widely applied to ensure that converters can continue to supply loads under fault conditions. Fault diagnosis as a prerequisite for fault tolerance has also become a research hotspot. This paper proposes a fast method for fault diagnosis of high-frequency LLC converters. The proposed fault diagnosis method is based on the observation of the voltage across the resonant capacitor to determine and locate the faulty power switch, providing a basis for fault tolerance. This diagnosis method requires a voltage sensor, which is also necessary for some control methods. When applying these control methods, the proposed fault diagnosis method can be used without additional sensors, beneficial for cost reduction. A full-bridge LLC converter controlled by a digital signal processor was used as an experimental platform to verify the effectiveness and speed of the proposed diagnostic method. The results show that the proposed fault diagnosis method can achieve the fast diagnosis of high-frequency LLC converters in a short time and with only minimal computational resources. [ABSTRACT FROM AUTHOR]

Modern information technology has been constantly evolving, transforming the traditional power grid into a network that couples both power and information layers. Understanding the cascade failure behavior of such power communication interdependent networks is essential for effectively controlling catastrophic network failures, preventing system collapse, and ensuring normal network operation. This research can contribute to the development of tools to predict and prevent such failures, and restore normal network functions in a timely manner. This paper focuses on the modeling method and cascading fault analysis of the power-information double-layer coupling network. We construct power information interdependent networks based on IEEE30 system and England39 system, and evaluate the cascade failure results using load distribution cascade failure model and HITS algorithm. The evaluation criteria include network efficiency, residual network size, and residual network load. By analyzing these parameters, we can gain insights into the performance of the power-information interdependent networks during cascade failures. Through simulation results, we demonstrate that the type i attack proposed in this paper renders the network structure unstable and less robust compared to the degree attack, intermediate attack, and random attack. These findings provide valuable references for developing strategies to mitigate the cascading failure of power-information interdependent networks. [ABSTRACT FROM AUTHOR]