To address the challenges posed by frequent source and load fluctuations in existing loop closing current calculation methods, this paper proposes an online loop closing current calculation method that considers source and load uncertainties. First, a dual-stack dynamic monitoring system is utilized to obtain real-time voltage and current variations before and after disturbances. Second, Thevenin's theorem is employed to build an equivalent model of the distribution network, simplifying the complex network into a combination of an independent voltage source and a series impedance. Then, the steady-state loop closing current is calculated based on the open-circuit voltage and equivalent impedance at both sides of the loop closing point. Next, the optimal frequency method is applied to determine the equivalent impedance and attenuation time constant at a specific frequency, achieving accurate calculation of the transient loop closing current. Finally, simulations are conducted to model the fluctuations in distributed generation and load, analyzing the steady-state and transient loop closing currents. The simulation results demonstrate that the proposed method accurately captures the effects of source and load fluctuations on the loop closing current in dynamic environments, with minimal calculation error, indicating its high practicality.

The inherent intermittency and fluctuation of renewable energy generation can lead to uncertainty in electricity carbon emission intensity. This uncertainty poses challenges to the low-carbon scheduling of integrated energy systems and impacts the economic benefits of system operation. To this end, this paper proposes an optimization model for integrated electricity-heat-gas energy system operation considering uncertain indirect carbon emission intensity. First, a regional integrated electricity-heat-gas energy system model considering the tiered carbon trading mechanism is formulated. Then, polyhedral uncertainty sets are introduced to analyze and qualify the uncertainties in renewable energy and indirect carbon emission intensity. Finally, a stochastic optimization method is introduced to deal with the uncertainty in the integrated energy scheduling model. Extensive case studies are conducted to verify the competence of the proposed models in enhancing the environmentally friendly and economic benefits of integrated energy systems.

javascript:void(0) Grid-forming (GFM) inverters are anticipated to play an essential role in facilitating the integration of renewable energy in bulk power systems. The fault-ride through (FRT) of GFM inverters and its impact on traditional protection schemes is an ongoing research topic. Distance protection is today one of the most commonly applied protection schemes and depends on multiple system preconditions for reliable operation — many of which may no longer hold in systems with a high penetration of inverters. This paper investigates the impacts of GFM inverters on distance protection, with the main objective to provide an improved understanding of the topic. Important interoperability issues are highlighted with simulation results and elaborated upon based on the theory behind the distance relay model and the behaviors of GFM inverters during faults. The simulations consider numerous fault types and two GFM inverters employing different current-limiting control techniques in their FRT strategies.

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.

Equitable carbon responsibility distribution is pivotal for the low-carbon transformation of the power system and the realization of dual-carbon goals, fostering fair benefits and balanced development for all entities within the system. Addressing the current practice of a fixed ratio sharing between generation and load sources, which deviates from the principle of “the polluter pays”, this paper proposes a coordinated planning method for generation, storage, and transmission, considering carbon responsibility allocation from a planning perspective. Firstly, a adhering to the fundamental principle of ” who causes, who bears, ” the various stakeholders are classified and held accountable, and the reasonable and fair sharing principle and reward and punishment index are established. Then, on this basis, a two-layer carbon responsibility cost allocation rule for power system power balance and power balance is established. Secondly, a source-storage-network joint planning model considering the investment cost of conventional thermal power installation, energy storage investment cost, wind curtailment penalty cost, transmission line expansion cost and carbon emission responsibility cost is constructed. Finally, the effectiveness of the proposed method is verified by an example of a regional power grid transmission project system in Northeast China, and compared with the source-storage-transmission planning method without considering carbon responsibility allocation, the necessity of considering carbon responsibility allocation is further explained.

The operating environment of transformers is getting more complex with the emergence of new energy sources and power electronic devices. This complexity can cause minor internal faults in transformer windings. Under the cumulative effect, minor faults gradually develop into serious faults, resulting in transformer damage. Conventional differential protection systems may have difficulty detecting these glitches and require avoiding the problem of protection false activation caused by inrush currents. This paper proposes a new online monitoring method for transformer windings based on longitudinal branch circuit conductance to address this issue. First, a unified transformer equivalent circuit is proposed to represent transformers under normal conditions, inrush currents, and internal faults. Then, an online transformer monitoring method based on branch conductance is proposed, which is immune to inrush currents. This method aims to prevent delayed detection of faults during inrush currents, improving sensitivity and response speed, especially for minor turn-to-turn faults hidden in inrush currents. The proposed method also provides higher sensitivity to minor turn-to-turn faults and larger protection margins. Simulation and experimental results validate the effectiveness of this method.

During prolonged extreme meteorological events such as cyclones, low-level rapids, thunderstorms, etc., the output of wind farms may undergo noticeable fluctuations within short timeframes, leading to wind power ramp events (WPREs). This paper proposes an optimization method for allocating electrochemical energy storage (EES) to tackle WPREs considering the correlation among offshore wind farms. Firstly, the correlation of offshore wind farms’ output is verified and analysed. Multiple scenarios are sampled using Latin Hypercube Sampling (LHS) and then rearranged using Cholesky decomposition ordering method (CDOM) to obtain output scenarios that incorporate the varying correlations among adjacent wind farms. Secondly, an improved multi-parameter segmentation algorithm is proposed to detect WPREs for the different correlation scenarios and assess the demand of EES for WPREs. Following this, a bi-level optimization model of EES coordinated with thermal power units (TPUs) for WPREs is formulated. Finally, through an example analysis, the effectiveness of the proposed model is verified. The simulation results show that stronger correlation among wind farms lead to fewer WPREs and lower requirements for TPUs and EES capacity, thereby reducing the investment and operation costs.

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.

Power system protection has evolved over decades. A step change occurred when communication aspects were introduced as part of IEC 61850 during the late nineties. Renewable energy penetration into power network especially into high voltage levels has been yet another big step. Protection engineers are facing challenges with traditional relay algorithms and the way relays are set in the substation. The primary reason could be attributed to the characteristics of renewable source which differ from the characteristics of traditional grid in many ways. Studies and field analysis are on-going, and engineers are continuously striving to optimize relay design, practices, as well as settings considering the aspects related to inverter design. The detection of directionality in distance relay and response for various type of system faults is a key area to be considered. The objective of this paper is to analyze the performance of directional elements in a distance protection for a DER (Distributed Energy Resource) connected system in India. In addition, the aim is to understand the gaps from existing research to the behaviors of the current directional protections which would help in identifying mechanisms to ascertain directionality for similar systems in the future.

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.