The current overhead work robot for transmission line faces issues related to its compact structure and high target localization requirements. To address these challenges, this paper proposes a lightweight bolt detection algorithm based on improved YOLOv8 (You Only Look Once v8) model. Firstly, the C2f module in the feature extraction network is integrated with the Self-Calibrated Convolution (SCConv) module, and the model is streamlined by reducing spatial and channel redundancies of the network through the SRU and CUR mechanisms in the module. Secondly, the P2 Small Object Detection Layer is introduced into the neck structure and the BiFPN network structure is incorporated to enhance the bidirectional connection paths, thereby promoting the upward and downward propagation of features. It improves the accuracy of the network for bolt-small target detection. The experimental results show that, compared to the original YOLOv8 model, the proposed algorithm demonstrates superior performance on a self-collected dataset. In this paper, the mAP accuracy is improved by 9.9%, while the number of model parameters and the model size is reduced by 0.973×106 and 1.7MB, respectively. The improved algorithm improves the accuracy of the bolt detection while reducing the computation complexity to achieve more lightweight model.

Condition monitoring data can be used to assess the health of a power system component but is rarely used to assess the reliability of the power system they are part of. For high-voltage circuit breakers (HVCBs), the susceptibility to various failures can be assessed by examining trip coil current (TCC) measurements. HVCBs have two failure modes, failure to trip on command and tripping without a command, which are triggered by various failure mechanisms that in turn may depend on the technical condition of the HVCBs. The aim of this paper is to demonstrate a methodology for quantifying the impact that the technical condition of HVCBs has on the power system reliability indices. An equivalent transmission line failure rate considering protection system failures, including failures related to HVCBs, can be calculated by computing contributions from various fault types (FTs). This paper proposes a framework that can quantify the frequency of the FTs that are affected by HVCB condition. The system-level effects are then evaluated using approximate methods for power system reliability evaluation. To demonstrate the principles and benefits of this methodology, a case study is presented that incorporates HVCB condition data from the Icelandic transmission grid into an illustrative 4-bus test system; a dataset of 83 TCC measurements from 38 HVCBs from the Icelandic Transmission System Operator is used, together with an outage database of life histories of 464 HVCBs from the Icelandic transmission grid. Results indicate that the condition deterioration associated with the probability of an HVCB failing to trip on command can significantly degrade the reliability indices.

This study developed a damped alternating current (DAC) testing methodology, composed of an improved DAC generator and a pulse-based distributed PD-detection technique. The improved DAC generator lacks high voltage (HV) solid-state switches and is thus a cost-efficient solution for field-testing voltage generation via a direct current–alternating current (DC-AC) conversion technique. A capacitor bank is used for power supply to achieve the requirement for high instantaneous power. In addition, the use of a distributed PD detection technique enables high sensitivity and makes the method free from restrictions of the cable length. To reduce the construction cost of the distributed PD-detection system, a pulse synchronization technique is used. Attempts was made to reduce the weight based on the simulations, and a prototype with the total weight of 850kg was designed and developed for a 64/110kV power cable. Laboratory and field tests validated the effectiveness of the cost-efficient DAC testing methodology.

Faults in power systems occur for various reasons, such as aging or natural disasters. Detecting, locating, and promptly clearing these faults is crucial for maintaining the safety and reliability of transmission lines. Distance relays, which protect transmission lines, detect faults, estimate their location, and send the required commands. However, these relays may experience mis-detection due to manipulated impedance arising from both internal and external factors. These factors include measurement device errors, network topology changes, the presence of fault resistance, and injected currents from remote line terminals. To address this challenge, we propose an innovative adaptive protection scheme that considers fault resistance, changes in network topology, and injected current from the opposite end of the line. By estimating the equivalent circuit impedances of the network connected to the terminal of the transmission line, this protection scheme utilizes impedance estimation techniques at the line terminals and offline network information. Simulation studies (tested on the IEEE 39-bus standard network) show that the proposed scheme accurately estimates fault location and fault resistance with high precision. The simulation results demonstrate its effectiveness in improving the performance of conventional distance protection relays in both the first and second protection zones (Z_1 and Z_2).

The correct design of the generator circuit breaker (GCB) is essential for the safe and reliable operation of a power plant or generation system. The design is usually based on standardized calculation methods according to standards (IEC standard 60909-0, IEC/IEEE standard 62271-37-013), often supplemented by selected transient calculations. A non-systematic approach can often be observed here, which does not adequately take into account significant influencing variables or operating states of the generator. This article therefore systematically examines various parameters that influence the short-circuit current components of the generator and are relevant for the design of the generator circuit-breaker: Short-circuit angle, operating point, phase clearing, switching arc, and fault arc. The results of the current parameters most relevant to the dimensioning of the GCB were then compared for different calculation methods. Particular attention was paid to the influence of the switching arc and fault arc, which is modeled in a transient calculation, and its effect on the short-circuit currents is systematically recorded. This work aims to summarize all relevant variables that influence the generator short-circuit current and are relevant for the dimensioning of the GCB and to present the different results based on a short-circuit calculation according to the standard and transient calculation to create a basis for a proper dimensioning of the generator circuit breaker.

Wind energy systems require fault diagnosis that identifies faults despite data inconsistencies. This study addresses challenges in supervisory control and data acquisition (SCADA) systems for monitoring wind turbine conditions from imbalanced data representation and error vulnerability. It examines the efficacy of Adaptive Elite-Particle Swarm Optimization (AEPSO)-tuned Extreme Gradient Boosting (XGBoost) on an imbalanced SCADA dataset for wind turbine fault classification. The methodology integrates the resampled SCADA dataset with t-distributed Stochastic Neighbour Embedding represented deep learning features. Employing AEPSO-XGBoost classifier trained on merged SCADA and deep learning data from a physics-informed deep convolutional neural network forms the basis of the fault classification model. The AEPSO-XGBoost regressor is validated across three distinct rear bearing temperature datasets, facilitating parameter optimization and model robustness. Also, this study explores supervised and unsupervised anomaly detection models using PDCNN and AEPSO-XGBoost with rear-bearing temperature data. Findings exhibit substantial fault classification and prediction enhancements by merging resampled SCADA data with deep learning features. Moreover, results show that applying AEPSO-XGBoost can significantly improve anomaly detection metrics. Through AEPSO-XGBoost’s efficacy in enhancing fault prediction within imbalanced SCADA datasets, the study proposes an integrated framework for fault classification and anomaly detection as an innovative predictive maintenance system for wind energy systems.

With the development of the digital economy, the power demand for data centers (DCs) is rising rapidly, which presents a challenge to the economic and low-carbon operation of the future distribution network. To this end, this paper fully considers the multiple flexibility of DC and its impact on the distribution network, and establishes a collaborative planning model of DC and distribution network. In this model, the interval method is utilized to capture the inherent uncertainties within the system (such as the renewable energy source (RES) output, wholesale market price, power load demand, carbon emission factor and workloads), and the planning model is transformed into a multi-objective optimization problem with interval parameters problem (IMOP) to minimize economic cost and carbon emission. On this basis, an interval multi-objective optimization evolutionary algorithm based on decomposition (IMOEA/D) is proposed to solve the IMOP and obtain the Pareto optimal solution while retaining all the uncertainty information. Finally, an improved IEEE 33-node distribution network is utilized as an example for simulation and analysis to confirm the efficacy of the proposed approach.

The global energy landscape is undergoing a profound transformation, marked by an unprecedented integration of renewable sources. This paradigm shift brings forth the challenge of low inertia in power systems, posing significant uncertainties to grid stability and reliability. This paper addresses these challenges and proposes innovative solutions to ensure the resilience of future transmission networks. The paper leverages advanced modeling techniques, including dynamic simulation models and control methods to analyze real-world case studies, mainly focusing on wind power plants operating as hybrid plants with integrated energy storage systems and participating in reserves markets to provide frequency response. The analysis includes adapting the Nordic equivalent power system model, allowing a deeper understanding of the dynamics of low-inertia environments and the impact of renewable energy integration. The aim is to provide valuable insights into the complex interactions within low-inertia power systems and highlight the importance of adapting power systems to ensure resilience in evolving energy scenarios. The paper contributes to the ongoing discourse on building sustainable and reliable future transmission networks through empirical analysis and theoretical modeling. It emphasizes technical strategies, operational advancements, and policy considerations essential for navigating the challenges posed by the transition to renewable energy sources.

To address the key challenges of insufficient comprehensive extraction and fusion of meteorological conditions, temporal features, and periodic characteristics of power in short-term power prediction of distributed photovoltaic (PV) stations, a TPE-CBiGRU model prediction method based on multi-scale feature fusion is proposed. Firstly, multi-scale feature fusion of meteorological features, temporal features, and hidden periodic features in PV power is performed to construct model input features. Secondly, CNN and Bi-GRU are utilized to model the feature relationships between PV power and its influencing factors from spatial and temporal scales, respectively, and the spatial-temporal features extracted are fused through an Add network. Finally, the Bayesian hyperparameter optimization method is adopted to further optimize network parameters, achieving the prediction of single-station PV power. Validation using measured data from a certain PV station shows that the proposed method enhances the comprehensiveness of feature information extraction from both data and model layers, significantly improving the accuracy of short-term PV power prediction. Compared with other prediction models, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are reduced by 26.11% and 35.64%, respectively, and the R-squared (R2) is increased by 3.07%.

In order to improve the measurement accuracy of mutual inductance parameters of transmission lines in the en-vironment of double circuit power lines without power outage, this paper establishes simulation models for measuring mutual inductance parameters of transmission lines using the different frequency method in two modes: equal and unequal zero sequence self-parameters of two circuit lines, using a parallel zero sequence coupling model of double circuit power lines. In the article, simulation analysis is conducted on the line parameters with a coupling coefficient between 0.4 and 0.6 and a line length of 20km-50km.In order to further verify the correctness of the simulation model and measurement methods, a test bench was established based on the principle of line mutual inductance parameter testing in a laboratory dual circuit line without power outage environment for experimental testing. By comparing the test values under experimental conditions with the standard values, it has been proven that the model and method can meet the actual measurement requirements of engineering.

The use of biomass as a renewable energy source for electricity generation has gained attention due to its sustainability and environmental benefits. However, the intermittent electricity demand poses challenges for optimizing electricity generation in thermal systems. Time series forecasting techniques are crucial in addressing these challenges by providing accurate predictions of biomass availability and electricity generation. In this paper, convolutional neural networks (CNN) are used to extract features of the time series, and long short-term memory (LSTM) is applied to perform the predictions. The result of the mean absolute percentage error equal to 0.02562 shows that the CNN-LSTM is a promising machine learning methodology for electricity generation forecasting. Additionally, this paper discusses the importance of model evaluation techniques and validation strategies to assess the performance of forecasting models in real-world applications. Finally, future research directions and potential advancements in time series forecasting for biomass thermal systems are outlined to foster continued innovation in sustainable energy generation.

Transmission line planning is affected by a variety of geographic information factors, and traditional path planning methods are difficult to meet the future intelligent planning needs. In this paper, based on the GIS platform for geographic information data processing, the triangular fuzzy number analytic hierarchy pro-cess (TFN-AHP) is utilized for weight analysis to construct the cost layer. Meanwhile, guided mechanism, dimension-reducing mechanism, and avoidance mechanism are proposed to improve the ant colony opti-mization (ACO) algorithm, to shorten the planning time and improve the route planning effect. Finally, a local area in Guizhou Province is selected for case study and compared with the results of traditional ACO, and the results prove the effectiveness of the transmission line planning model proposed in this paper.

Compared to offline measurement (interrupting the applied voltage before measurement), online measurement of surface charge on gas-insulated transmission line (GIL) insulators effectively avoids the impact of spontaneous dissipation of charges. Thus, the accuracy of results can be improved. In this paper, a measurement model of capacitive electrostatic probes for surface charges on ±320kV GIL tri-post insulator is established. The geometry of probes is optimized. The feasibility of online measurement of surface charges is analyzed. Results indicate that the surface leakage distance of induced charges is positively related to the length. Furthermore, as the diameter of probes increases, the maximum electric field on the sensitive electrode surface rises, while the radius of the sensitive electrode has the opposite effect. Hence, the diameter of the probe is 6mm. The length and radius of sensitive electrodes are 50mm and 1.2mm, respectively. For online measurement of surface charges on ±320kV tri-post insulators, the induced potential distribution of the probe is consistent with surface charges. Therefore, the capacitive electrostatic probe in the embedded electrode is appropriate for the online measurement of surface charges on tri-post insulators. It provides a reference for avoiding flashover triggered by surface charge accumulation on DC GIL insulators.

The future multi-terminal direct-current (MTDC) grid will require the interconnection of point-to-point high-voltage (HV) DC links with different specifications such as DC voltage level, system grounding configuration and HVDC technology. To adapt these differences, DC/DC converters are obligatory to interconnect HVDC links. Additionally, they are capable of providing supplementary functionalities as they are highly controllable devices. In this paper, a primary virtual resistance DC voltage controller associated with DC/DC converter is proposed for managing DC grid voltages of the interconnected HVDC grids, increasing the reliability of the system. The commonly known topology, Front-to-Front Modular Multilevel Converter (F2F-MMC) is adopted for DC/DC converter. Time-domain simulations are performed using EMTP software for validating the controller behaviour under power disturbances and large events of loss of one converter in a MMC-based MTDC system. The converters are modelled using reduced order modelling (ROM) methodology. Apart from this, dynamic studies have been carried out using a linear state space model for small-signal stability analysis of a HVDC system integrating DC/DC converter with a virtual resistance DC voltage controller. The results are examined through parametric sensitivity analysis.

This paper presents a decentralized prediction-based algorithm designed to address out-of-step (OOS) conditions in power systems. The algorithm utilizes generator data obtained from phasor measurement units. The transient stability of a multi-machine power system is evaluated using the equal-area criterion (EAC). The proposed algorithm calculates the characteristics of the P-δ curves within the EAC framework after a large disturbance. The critical P-δ trace is determined by analyzing the cumulative energy in the acceleration area following fault clearance. The stability margin of the rotor angle is then computed based on the actual active power and its relationship with the critical curve. The algorithm predicts the occurrence of OOS by comparing the measured active power with the corresponding value on the critical curve. The effectiveness of the proposed algorithm is validated through simulations conducted on the 73-bus IEEE test power system.

Focusing solely on enhancing the resilience of power systems at a systemic level would lead to a significant underestimation of the actual impact of extreme disasters. Equally vital is the assurance of livelihood security amidst such extreme conditions, which is crucial for the development of a truly resilient power system. Hence, this paper attempts to incorporate quantifiable metrics assessing public safety impacts into resilience enhancement works, thereby guiding precise allocation of funds. Considering that the residents’ intuitive feelings are the most direct reflection of the severity of the disaster, this paper employs the modified prospect theory to formulate functions representing residents’ psychological risk perception and risk-taking willingness to tolerate risks during disruptions in power, gas, and water supplies. Meanwhile, in order to accurately calculate the energy loss duration for each residential customer, a resilience enhancement method for post-disaster collaborative dispatch of electricity-gas-water systems (EGWSs) is proposed. With the objective of minimizing the public safety and economic impact of disasters, the optimal multi-source collaborative emergency restoration strategy is developed. The significant necessity and efficiency of the proposed strategy are verified with exhaustive case studies. Numerical results evince the resilience enhancement by considering the livelihood security in the post-disaster restoration stage.

The planning and operation of radial distribution networks (RDN) face increasing challenges such as active power losses and voltage instability, prompting a focus on integrating renewable energy resources (RER) to mitigate these issues. This study presents a techno-economic optimization framework leveraging the Cheetah optimizer (CO), a recently introduced metaheuristic technique, to optimize the accommodation of distributed energy resource (DER) units within the IEEE 33-bus radial distribution networks (RDN) utilizing MATLAB environment. Both single and multi-objective perspectives are explored, demonstrating significant reductions in active power losses, minimized voltage deviation, improved stability, and maximized economic benefits. The Cheetah optimizer (CO) efficacy is showcased through notable achievements, including a 94.20% reduction in active power losses and annual savings of up to $77,933 for optimal power factor (OPF) mode in multi-objective optimization, surpassing existing literature. Additionally, reliability analysis conducted with ETAP software underscores the effectiveness of DER integration, particularly with wind turbine systems, in enhancing network reliability.

Recently, distributed generators (DGs) have been widely integrated into distribution network, so that the distribution network is gradually transforming into an active distribution network (ADN). Due to the influence of meteorological conditions, the output of DGs has high uncertainty. At the same time, considering the increasing variety of loads in ADNs, the uncertainty of load demand of user side is also increasing. In order to fully consider the uncertainty of measurement and quantitatively evaluate the operational status, this paper proposes a steady-state analysis method for ADNs under uncertain conditions. Firstly, this paper proposes a steady-state analysis method including power flow analysis model and evaluation indicators for the operation status from the perspectives of node and network. Secondly, the uncertainty factors are elaborated from three aspects: sources, impact on evaluation index and impact on scheduling. The evaluation indicators considering uncertain conditions, the impact on system security and scheduling of network are further discussed. Finally, through the simulation analysis of the modified IEEE 33-node test system, The effectiveness of the proposed method are verified.