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• Proposes an improved YOLOv5s model which combines the GIoU, Mish, and CBAM modules. • The improved algorithm can effectively detect different-size insulator defects under different environmental backgrounds. • The AP value in detecting insulator defects can be improved by 5 %. As one of the most important components on the transmission line, the insulator is prone to failure, which affects the safe operation of electrical power system. Hence, it is crucial to accurately detect the insulator defects for further maintenance in time. Recently, with the development of artificial intelligence and target detection algorithms, the insulator defect detection has received more and more attention. However, there are still existing some difficulties: insufficient samples and low detection accuracy. To improve the accuracy of insulator defect detection, this paper proposes an auto-detection method based on an improved lightweight YOLOv5s model. First, this paper introduces the basic network frame of YOLOv5s and proposes an improved algorithm by utilizing the GIoU loss function, Mish activation function, and CBAM module. Then, performs data enhancement in the insulator dataset to enhance the robustness of the model. Finally, trains and tests the improved YOLOv5s model, and compares it with traditional target detection algorithms. Compared with traditional target detection algorithms, the AP value of the proposed algorithm in detecting insulator defects can be improved by 5 %. The results demonstrate the improved algorithm proposed in this paper can effectively identify and position the insulator defects. [ABSTRACT FROM AUTHOR]

• Electrical Power Systems Research. • This paper proposes a joint planning scheme for soft open points and energy storage to address the issue of unbalanced supply and demand in distribution networks, aiming to enhance the system's economic efficiency and stability. • This paper evaluates the proposed scheme in comparison with other literature under different electricity price conditions. The results indicate that as the electricity price increases, the economic growth rate of the proposed scheme in this paper is lower than that of the schemes in other literature, further confirming the advantages of this scheme. • This paper takes full account of the uncertainty of distributed power sources, adopts a two-stage robust model, and provides reasonable values for robust control parameters based on the planning results. With the increasing penetration rate of renewable energy, the uncertainty of its output has exacerbated power imbalances among different distribution networks. In this paper, we propose collaborative planning of soft open points and energy storage systems to balance a distribution network with source-load imbalance, aiming to improve the economy and reliability of the distribution network. Firstly, considering that distributed generation has uncertainty, the K-means is used to construct a typical day scenario, on the basis of which a collaborative planning model is established with the objective function of minimizing the annual integrated cost. Then, the original nonlinear nonconvex model is transformed into a mixed-integer second-order conical planning model by Big-M and second-order conical relaxation, and a two-stage robust optimization is used to seek the planning solution with the lowest operational cost under the worst-case scenarios, aiming to enhance the robustness of the planning model. Finally, the feasibility of the proposed planning model is verified on two source-load unbalanced IEEE 33-node active distribution network arithmetic cases. [ABSTRACT FROM AUTHOR]

The electric power sector is experiencing a rapid shift towards renewable energy resources. To accommodate this transition, the transmission system requires significant upgrades. Although enhancing grid capacity through transmission network expansion is always a solution, it can be very costly and requires a protracted permitting process. Grid-enhancing technologies (GETs) encompass a broad range of hardware and software tools that enable reconfiguration of the transmission grid and adjustment of its parameters. The proliferation of such technologies enhances transfer capability over the current transmission network, thus reducing the need for grid expansion. This paper offers a comprehensive review of grid-enhancing technologies. The paper discusses definitions of transmission flexibility and presents methods that are developed to quantify grid flexibility. The paper offers a comprehensive review of an extensive range of grid-enhancing technologies, including both principles of operation and state-of-the-art developments. Environmental impacts of grid-enhancing technologies, including renewable energy curtailment and carbon emission reduction, are also discussed. In addition to technical aspects of grid enhancing technologies, the paper also reviews the literature on the incentive challenges facing GETs adoption and efficient. Overall, the paper offers a comprehensive review of the advancements in grid-enhancing technologies, discusses the remaining critical challenges, and identifies directions for future research. • Grid-enhancing technologies (GETs) are necessary for the future grid. • GETs can enhance the transfer capability up to 50% over the existing grid. • While some GETs rely on mature technology, industry adoption remains rather limited. • GETs integration within energy management systems faces modeling challenges. • Current market and regulatory structures provide little to no incentive for adoption and efficient operation of GETs. [Display omitted] [ABSTRACT FROM AUTHOR]

• Blackouts bring major economic and societal repercussions. • Efficient black-start strategy is critical to address increasing risk of blackouts. • Variable speed hydro can provide a fast response and is used during black start. • Meet black start needs with renewables and cutting diesel use. • RES-BESS boosts power and fortifies an integrated renewable grid. A blackout refers to a total power outage that rapidly plunges the electrical power system into a severe crisis, posing significant challenges for subsequent restoration efforts. Blackouts can occur due to a range of factors, such as delays in clearing faults, generator malfunctions, equipment failures, and human errors. After a blackout occurs, all possible measures are taken to achieve the fastest power system restoration. To ensure preparedness for such emergencies, scheduled black start exercises are conducted using units capable of initiating power restoration from scratch, allowing authorities to assess their readiness in catastrophic scenarios. Numerous similar exercises have been conducted regularly generating to highlight the complexities of power system restoration of the grid with the black starts of hydro generating units. This has aided plant operators in improving restoration methods and finding additional remedial steps to enhance existing processes to achieve an effective black start exercise. This paper overviews the multiple mock black start exercises applied to hydroelectric generating units and discusses several blackouts worldwide. In addition, Tehri Hydro Power Plant is considered for the case study. Tehri HPP is linked to the 400 kV EHV grid, which was later upgraded to 765 kV transmission lines. Further, this paper observes and discusses various challenges encountered during mock black start exercises. Possible remedial actions are suggested to address these challenges. [Display omitted] [ABSTRACT FROM AUTHOR]

• This paper presents a review on the state-of-the-art electric vehicle charging technologies i.e., fast, super-fast, and ultra-super-fast charging stations that are under development. These have the potential to cause power quality issues such as charging transients, rapid voltage fluctuations, and harmonics in the power grids. • EVs can participate as mobile storage to provide vehicle-to-grid (V2G) support and ancillary services. There are still some barriers to the wide implementation of V2G systems. This paper has highlighted these issues and their impacts to power grids. • This paper investigates the impacts of random and fluctuating EV fast-charging loads on the electric power grids, mainly considering the random connection of EVs to the power grids through DC fast-charging stations as the principal source of fluctuating EV loads. • The charging stations are modeled for three different commercial DC fast charger connections (CHAdeMO, SAE CCS, and Chargepoint Express 20), with separate CC-CV charging modes of the DC fast chargers incorporated. To quantify the impacts, case studies are conducted on the simulation platform for two different scenarios. • The impact on the electric power grid is evaluated in terms of voltage drops, maximum power demand, current, and voltage total harmonic distortion (THD) experienced by the corresponding transformer connected to the charging stations. • The results have revealed that random and fluctuating EV fast-charging loads pose much more intense impacts on the power systems than the normal EV slow charging loads. The era of the electrified transportation system is fast approaching. Although the socioeconomic and environmental benefits of electric vehicles (EVs) have contributed to their large-scale utilization, it has also created a huge load demand on the existing power grids throughout the world. Moreover, fast, super-fast, and ultra-super-fast charging stations are under development, some of which are now in the markets. These have the potential to cause power quality issues such as charging transients, rapid voltage fluctuations, and harmonics in the power grids. Moreover, EVs can participate as mobile storage to provide vehicle-to-grid (V2G) support and ancillary services. There are still some barriers to the wide implementation of V2G systems. This paper addresses these issues and provides a review of the state-of-the-art EV technologies and their impacts on power grids. This paper also investigates the impacts of random and fluctuating EV fast-charging loads on the electric power grids, mainly considering the random connection of EVs to the power grids through DC fast-charging stations as the principal source of fluctuating EV loads. A practical electrical grid of Wollongong, New South Wales, Australia has been considered in this work to separately analyze the impacts of constant current (CC) and constant voltage (CV) charging modes upon the grid. Furthermore, design and modeling of three different commercial DC fast charger connections (CHAdeMO, SAE CCS, and ChargePoint Express 200), with separate CC-CV charging modes of the DC fast chargers have been incorporated. To quantify the impacts, two separate scenarios were examined using a simulation platform, with case studies conducted to determine the impacts on the power grid. The first scenario involved three fast charging stations, while the second scenario featured ten stations that were able to charge six and twenty electric vehicles respectively, with various load combinations considered. Each of these scenarios was analyzed under different conditions to evaluate their impact on the grid, using factors such as voltage drops, maximum power demand, current, and voltage total harmonic distortion (THD) for the transformer that was connected to the charging stations. The study results indicated that the power systems were affected more significantly by random and fluctuating EV fast-charging loads, compared to normal EV slow-charging loads. [ABSTRACT FROM AUTHOR]

The ampacity — a term that refers to the maximum current in amperes — of existing overhead lines can be increased using Dynamic Line Rating (DLR) systems. Based on ambient online conditions and electrical power flow measurements, together with a forecast of future ambient conditions, the ampacity of overhead lines can be dynamically adjusted. In essence, the ampacity of an overhead line is determined by the operating temperature of the conductor that still guarantees the required safe distances to the objects under the overhead line. This paper presents a calculation algorithm for the sag of a conductor that addresses an unequal temperature of the conductor along the line as a response to an expected problem in the future, when increasing numbers of DLR sensors will be installed on or even near overhead lines. A novel approach to the sag-temperature calculation useful for a DLR is developed based on the linearization of cable equations. As a result, the computational costs are greatly reduced, which is an important factor when the DLR system includes a large number of overhead lines and processes them simultaneously. The presented methods are open and can be adapted for an arbitrary DLR system. • A systematic computational algorithm for Dynamic Line Rating software solutions. • Addresses the problem of multiple overlapping DLR sensors installed in the same tension section. • A newly developed calculation method addresses unequal temperatures along the OHL. • A method based on the linearization of cable equations and the corresponding mechanics. • The computational costs of a large number of DLRs operating simultaneously can be reduced. [ABSTRACT FROM AUTHOR]

Electricity consumption forecasting provides a reference basis for electricity scheduling, it has become a research hotspot in the field of electric power. The fluctuation of electricity consumption is influenced by multiple factors, which has strong randomness and are strongly disturbed by noise. Single algorithms cannot ensure the stability and accuracy of electricity consumption forecasting. To address this problem, a two-stage framework for electricity consumption forecasting is proposed in this paper. In the first stage, multi-forecasting algorithms are considered to eliminate noise and forecast the electricity consumption trend. A hybrid model is constructed, which consists of three parts: Ensemble Empirical Mode Decomposition (EEMD), Gated Recurrent Unit (GRU) and Gradient Boosting Regression Tree (GBRT). The EEMD method is employed to decompose the electricity consumption signal into different frequency components. GRU and GBRT are adopted to forecast high-frequency and low-frequency components respectively. In the second stage, multidimensional meteorological factors are considered to further improve the accuracy of the electricity consumption forecasting. A structure for feature extraction is constructed to reduce the dimension of meteorological factors. The experimental results indicate that the two-stage framework model outperforms mainstream measurement models by an average reduction of 35.7% and 43.2% in MAE and RMSE. • A two-stage electricity consumption forecasting framework is proposed. • An architecture called "weight + network + meteorological scene" is established to reduce the redundancy of meteorological factor data. • The superiority of the proposed two-stage electricity consumption forecasting is verified in multiple dimensions. [ABSTRACT FROM AUTHOR]

• A broken overhead earth wire increases the risk of pole high earth potential rise. • Following a strict design diagram is crucial to achieving safety compliance. • Split factor in high-voltage transmission lines is crucial for safety compliance. An electrical high-voltage power system is essential to support everyday electrical demands. Due to the increase in electrical loads, a more rigid design is required to ensure system compliance with standards and policies. High-voltage safety is the number one priority when it comes to the design, installation, commissioning, and operation of the electrical power system network. Safety compliance is getting more complex due to the joining infrastructure between residential and high-voltage projects. Nowadays, high-voltage infrastructure, including substations and transmission lines, is constructed near residential properties. This neighboring setup increased the safety concerns of high voltage systems, especially under fault or system malfunction situations. Safety concerns are addressed during the design stage and verified during the commissioning stages. The paper discusses the minimum transmission line earthing system requirements. This document provides a clear and consistent approach to assess transmission line earthing system design. The work contents comply with current IEEE standards including rigid author research outcomes. The works reduce the complexity of the assessment and explain the critical role of different sectors within the transmission line network. The work introduces the boundaries when the fault current analysis should include the earth grid resistance. A case study is also included. [ABSTRACT FROM AUTHOR]

• The manuscript presents failures observed on voltage transformers (VTs) and CTs that operate at 150 kV and 400 kV, based on statistical data from real operating networks. • Following failure analysis, it is concluded that the percentage of failures for the VTs over the total installed parts on the 150 kV electricity network is higher than that for the CTs. • Even the number of failures for the VTs is larger than the corresponding number for CTs, the statistics show that finally the CTs present considerably less lifetime than the VTs. Electric power infrastructures are considered to be the most critical. The required monitoring of electricity networks is achieved using instrument transformers. The instrument transformer operating conditions must be kept steady, within safe limits. However, this entails comprehension of the several reasons that lead to failure. These reasons can introduce condition assessment monitoring techniques aiming to protect this type of electrical equipment and maybe contribute on their predictive maintenance. This paper describes the probable failures in high voltage instrument transformers with operating voltages of 150 kV and 400 kV, the rate and trend of failures as well as their basic sources. More specifically, this research has been applied for the 150 kV and 400 kV instrument transformers that are installed at the Hellenic Electricity Transmission Network. The results of this work will provide valuable insights about failure cause and operational life of measuring transformers in transmission networks allowing them to improve monitoring and resilience of the networks. [ABSTRACT FROM AUTHOR]

Electricity generation in Brazil heavily depends on hydroelectric power, making it vulnerable to fluctuations due to its reliance on weather patterns. Accurately forecasting water inflow into hydroelectric plants is vital for the National Electric System Operator to make decisions regarding the monthly scheduling and operation of the power system. In this paper, an evaluation of predicted flows for a 14-day horizon are evaluated for the Tucuruíhydroelectric plant, located in the Tocantins river in the North of Brazil. The temporal fusion transformer (TFT), long short-term memory (LSTM), and temporal convolutional networks (TCN) are compared. The findings demonstrate that the TFT is a more suitable alternative than LSTM and TCN models for predicting inflows for the next 14 days. The TFT model is hypertuned by Optuna to achieve an optimized structure (h-TFT). The h-TFT had a mean absolute percentage error of 13.1 and a Nash–Sutcliffe of 0.96, outperforming its initial version and even the bidirectional LSTM model in benchmarking. Based on the error results, h-TFT showed promise for flow forecasting and provides insights into decision-making processes in the Brazilian electricity sector. • Hypertuning the TFT improves its performance in median-term forecasting. • The h-TFT outperforms LSTM, and TCN models for time series forecasting. • Deep learning applied for scheduling operation of the electrical power system. • Energy planning based on the inflow forecasting of hydroelectric power plants. [ABSTRACT FROM AUTHOR]

• A multi-state reliability model addressing failure of composed components of pelamis as a commercial-scale wave converter and its produced power variation is developed. • The impact of wave period and wave height result in generated power variation on the reliability indices of power system containing pelamis park is investigated. • Adequacy assessment of electric network containing pelamis as large-scale wave converter by both analytical and sequential Monte Carlo simulation methods is examined. • A sensitivity analysis is performed to determine the suitable wave period and height for understudied site. Wave generators, as renewable sources, are increasingly applied for power generation in developed electric networks. With the development of wave generator technologies, large-scale devices such as oscillating water columns, pelamis, wave-dragon, oyster, buoy, sea wave slot cone converter, and tapchan have been invented to extract electrical power from wave energies. The output of wave generators relies on the height and period of waves, and because of the wide variation in these quantities, the produced power from them changes widely. Therefore, in electric networks incorporating large wave generators, reliability is influenced and should be analyzed. In this paper, for the first time, the reliability modeling of pelamis, as a kind of commercial-scale wave generator, is executed. In the proposed multi-state reliability model of pelamis, failures of composed components and changes in the production of power are addressed. To obtain the optimal number of power states in the model, the XB factor is computed. Then, to reduce the number of power states in the model, the fuzzy c-means clustering methodology is applied. The obtained model is used to examine the adequacy assessment of an electric network containing a pelamis farm. Numerical results show that wave converters can improve the reliability performance of electric systems. However, wave period and height affect the reliability indices of the power system. The effectiveness of the proposed method is validated by comparing the outcomes obtained by the analytical and Sequential Monte Carlo Simulation methods. [ABSTRACT FROM AUTHOR]

• Substation digitalisation is a growing field of academic research. • Advances in substation automation systems are accomplished with IEC61850. • Most research addresses specific technological matters, not holistic analyses. • A four-stage classification model of substation technologies is proposed. • New competencies are needed to manage and meet the challenges of smart substations. Advances in substation automation allowed more connected installations to incorporate additional functions aiming improved deployment, operations and maintenance. However, the characteristics that make substations smarter, how their technologies evolved over time, and its future challenges remain unclear. Beyond specific technological development, holistic analyses looking into the future substation, and how legacy systems are integrated into new ones, are essential for regulators and utilities plan, deploy, operate, maintain, upgrade and manage these assets. This paper presents a systematic literature review on the subject, and systematizes the key technologies and their time evolution in electrical power substations, incorporating bibliometric and content analyses from a sample of 253 suitable articles. A four-stage technological evolution model is proposed, classifying substation automation technologies from conventional to fully digital smart substations, comparing key hardware, software, and management characteristics between generations. The analysis reveals growing interest in the field; that the standard IEC 61850 is a critical enabler of digital substations; and that, despite frequent technical breakthroughs reports, few contextual studies investigate the benefits of smart substations. Furthermore, it provides a comprehensive academic understanding of the topic, identifies key research trends, and can also support the electricity industry's professional practice to better plan and operate substations. [ABSTRACT FROM AUTHOR]