State Grid Energy Research Institute

Fault detection becomes especially crucial for maintaining the dependability and safety of power equipment as power systems become more complicated. This work aims to increase the accuracy and efficiency of fault signal identification in power equipment by proposing a method based on nonlinear stochastic resonance and a Duffing chaotic oscillator to detect weak signals in power systems. First, this study develops a weak signal detection model based on the Duffing chaotic oscillator approach. By modifying the system parameters to put the signal in a critical condition, the model greatly increases the sensitivity of the signal to the tiny periodic sinusoidal waveforms. In the meantime, the stochastic resonance method’s high resistance to noise interference improves the dependability of signal identification. The experimental results demonstrate that the approach is more accurate and stable than the conventional method and can extract fault signal features when working with weak signals in high noise conditions. Ultimately, this paper’s research offers a fresh, efficient method for identifying power system faults.

There is high load density and multiple power supply demand of the urban power grids. Improving the emergency capacity of urban power grids in extreme condition can assist the quick recovery of the system, and guarantee continuous consumption of the critical consumer to enhance the anti-vulnerability of power grid. In this paper, the functional resonance analysis model (FRAM) and system dynamics (SD) were integrated to construct a novel anti-vulnerability improvement method for large urban power system, which considered the impact of emergency input factors on anti-vulnerability and the relationship between them. Firstly, identify the vulnerability factor of urban power grids in extreme events by functional resonance analysis model, and determine the critical factor based on the Monte Carlo simulation. Then put the critical factor into the system dynamic model as vulnerability subsystem, emergency capacity as anti-vulnerability subsystems. Finally, by changing the anti-vulnerability input ratio and total inputs, determine the optimal anti-vulnerability investment strategy, realize the anti-vulnerability income maximize. The simulation results show that the optimal investment ratio is 45% for material resource costs, 30% for human resource costs, 20% for opportunity loss costs, and 5% for financial resources. And in this case, anti-vulnerability benefits would be improved by 4%. If the total input is raised to 3.4 times, the power grid benefit will be increased by 62%. However, there is also a limiting effect between total inputs and benefits. As total inputs increase by 5 × 105, the rate of improvement in benefit decreases from 7.9 to 4.29%, resulting in a decline of 3.61%. The proposed anti-vulnerability model can balance the economic benefit and demand of extreme viability, provide the technical support of effective anti-vulnerability strategy for power grid enterprise.

With the expansion of high-rise building construction in China, tower cranes have become indispensable key equipment in the construction industry. To ensure the safety and structural reliability of tower cranes under complex working conditions, this paper takes a typical 40 m-high flat-arm tower crane as the research object. For the first time, the orthogonal test method was used to monitor the stress of key components (the root of the tower body and the root of the boom). The stress distribution characteristics of the tower crane structure under different working conditions were systematically analyzed. Then, based on the power spectral density analysis method, the natural frequency of the tower crane structure was identified. The influence of key structural parameters, such as lifting position, rope length, and lifting weight, on the stress of the tower crane was quantitatively studied through orthogonal experiments, revealing the multi-parameter coupling effect. The results show that the stress at the measuring point at the root of the tower body is significantly higher than that at the root of the boom. This indicates that the root of the tower body is the primary stress-bearing part of the tower crane structure, highlighting the need to focus on its fatigue performance and safety assessment. Based on the power spectral density analysis of the root stress of the tower crane, the natural frequencies of the tower crane structure were accurately identified. The first-order frequency was 0.10 Hz, and the second-order frequency was 0.20 Hz, providing data support for the study of the tower crane’s dynamic characteristics. The orthogonal test analysis shows that the influences of lifting position, rope length, and lifting weight on the stress of the tower crane are consistent, with no significant differences. The effects of lifting position and rope length on stress are dominant, while the influence of lifting weight is relatively small. These research findings provide an important basis for the lightweight design and safety assessment of tower cranes.

The classical Mohr-Coulomb criterion, widely used in geotechnical engineering, has been found to overestimate the tensile strength of materials such as clays and rocks. This overestimation leads to significant errors, particularly when structures are subjected to horizontal force components like seepage forces under water infiltration. To address this issue, tension cut-off or tensile cracks have been incorporated into the analytical upper bound limit analysis of slopes, yielding different conclusions. This paper proposes a rigorous three-dimensional numerical formulation for the upper bound analysis of slopes composed of Mohr–Coulomb materials with tension cut-off. The nonlinear strength envelope is represented by three semi-definite cones, and the resulting mathematical programming problem is solved using the optimization toolbox Mosek. Two- and three-dimensional numerical tests demonstrate the high numerical efficiency of the proposed method. The results show that, when full tension cut-off is considered, no energy is required for the formation of tension cracks at the slope’s crest. The influence of factors such as tensile strength, water infiltration, and preexisting cracks is analyzed in detail.

To address the issue of inaccurate power flow calculations in the asymmetric coupling system of a power grid and traction network, this paper proposes a dynamic power flow calculation method for the “renewable energy–power grid–transportation network” asymmetric coupled system. First, by utilizing the asymmetric characteristics of the traction transformer, the dynamic asymmetric nodal admittance matrix for the “renewable energy–power grid–transportation network” coupled system is established, which facilitates the construction of the mixed power flow equations for the coupling of the power grid and transportation network. Next, when analyzing the asymmetric coupling system of renewable energy, power grid, and transportation network in mountainous areas, it is necessary to allocate the power of electric multiple units (EMUs) to the three-phase (A, B, C) power distribution. To address this, a three-phase power balancing strategy is proposed, incorporating both the single-phase loads of EMUs and the output of renewable energy sources. Thus, a three-phase power balance strategy is proposed, incorporating the single-phase load of traction load units and renewable energy output. Finally, a simulation study is conducted using a real system of a regional power grid and traction network as a case example, demonstrating the suitability and effectiveness of the proposed model.

To ensure a smooth transition towards peak carbon emissions and carbon neutrality, one key strategy is to promote a low-carbon transition in the energy sector by facilitating the coordinated development of the electricity market, carbon market, and other markets. Currently, China’s national carbon market primarily focuses on the power generation industry. High-energy-consuming industries such as the steel industry not only participate in the electricity market but also play a significant role in China’s future carbon market. Despite existing research on market mechanisms, there remains a significant research gap in understanding how steel enterprises adjust their trading behaviors to optimize costs in multi-market coupling contexts. This study employs a system dynamics approach to model the trading interconnection between electricity trading (ET), carbon emission trading (CET), and tradable green certificates (TGC). Within this multi-market system, thermal power enterprises and renewable generators serve as suppliers of carbon allowances and green certificates, respectively, while steel companies must meet both carbon emission constraints and renewable energy consumption obligations. The results show that companies can reduce future market transaction costs by increasing the proportion of medium to long-term electricity contracts and the purchase ratio of green electricity. Additionally, a lower proportion of free quotas leads to increased costs in the carbon market transactions in later stages. Therefore, it is beneficial for steel companies to conduct cost analyses of their participation in multivariate market transactions in the long run and adapt to market changes in advance and formulate rational market trading strategies.

Optimal planning for the park integrated energy system (PIES) is essential for energy efficiency improvement and carbon neutrality. A reasonable evaluation method is the key to guide PIES planning. However, indicators for the PIES planning schemes are various with high penetration of renewable energy, large carbon emissions and multiple energy forms coupling, which brings challenges to find out the benchmark planning scheme for PIES development. Herein, we extend a competitive evaluation method that considers the aspects of energy, economy, environment, reliability and greening development level for PIES with different energy planning trends. We formulate a multi-stage planning model for PIES dynamic development aiming at investment and operation cost minimization. We set a group of comparable PIESs with different energy planning trends to evaluate and determine the benchmark PIES to motivate the others. The competitiveness of our evaluation method is reflected in that the benchmark is determined by the competition of different PIESs and it may change through multi-stage planning. A practical PIES with eight functional areas is adopted for competitive evaluation in multi-criteria over multi-stage planning optimization, and their pros and cons are evaluated and compared. By comparing the evaluation efficiency, the benchmark of PIES planning can be dynamically adjusted.

With the advent of large-scale electronic transportation, the construction of electric vehicle charging stations (EVCSs) has increased. The stochastic characteristic of the charging power of EVCSs leads to a risk of destabilization of the DC distribution network when there is a high degree of power electronification. Current deterministic stability analysis methods are too complicated to allow for brief descriptions of the effect of probabilistic characteristics of EVCSs on stability. This paper develops a probabilistic small-signal stability analysis method. Firstly, the probabilistic information of the system is obtained by combining the s-domain nodal impedance matrix based on the point estimation method. Then, the probability function of stability is fitted using the Cornish–Fisher expansion method. Finally, a comparison experiment using Monte Carlo simulation demonstrates that this method performs well in balancing accuracy and computational efficiency. The effects of line parameters and system control parameters on stability are investigated in the framework of probabilistic stability. This will provide a probabilistic perspective on the design of more complex power systems in the future.

The growing complexity and need for electricity in contemporary grids have resulted in an increased dependence on Distribution Automation Technology (DAT) to improve the effectiveness and reliability of distribution networks. Automation technologies, like smart sensors and fault detection systems, are critical for enhancing operational efficiency and lowering power outages in distribution networks. This study investigates the influence of distribution automation on the dependability of electricity networks, concentrating on important functional metrics and their relationship with network efficiency. Objectives: The main objective of this research is to examine the factors that influence the reliability of distribution networks, with a focus on distribution automation technology. This study uses a variety of efficiency indicators, like automation coverage, fault detection time, and consumer complaints, to discover the primary factors of network reliability. This paper introduced the Reliability-Optimized Meta-Learning Ensemble (ROME) algorithm, which seeks to predict the reliability category of various areas using these indicators. Methodology: This study utilizes the Distribution Network Reliability Dataset, which includes several areas with a variety of characteristics such as network age, automation coverage, smart sensor installation, power outages, fault detection time, and other operational metrics. The ROME algorithm is used, which integrates numerous base models (SVM, Random Forest, MLP) and a meta-learner (Gradient Boosting) to predict each region’s Reliability Category (High, Medium, Low). The dataset is thoroughly preprocessed, which includes mean and mode imputation, label encoding, standardization, and SMOTE balancing. Recursive Feature Elimination (RFE) is used for feature selection. Results: The findings show a strong correlation between automation coverage, fault detection time, and reliability category. When compared to traditional classification techniques, the ROME algorithm surpassed SVM, RF, MLP, and GB models with 94.7% accuracy, 0.18 Log-Loss, 91.2% Jaccard Index, 0.08% fall-out, and 95.3% specificity. Conclusion: This research emphasizes the value of distribution automation in improving network reliability. Utilities and grid operators can use the ROME algorithm to better predict and enhance network reliability. The results highlight the requirement for targeted investments in automation technologies, particularly in regions with lower reliability scores, to guarantee sustainable and effective electricity distribution.

The increasing integration of renewable energy-based distributed generation (DG) in modern distribution networks is essential for reducing reliance on fossil fuels. However, the unpredictability and intermittency of renewable sources such as wind and photovoltaic (PV) systems introduce significant challenges for distribution network planning. To address these challenges, this paper proposes a Q-learning-based Distributionally Robust Optimization (DRO) model for expansion planning of distribution networks and generation units. The proposed model incorporates energy storage systems (ESSs), renewable DG, substations, and distribution lines while considering uncertainties such as renewable generation variability, load fluctuations, and system contingencies. Through a dynamic decision-making process using Q-learning, the model adapts to changing network conditions to minimize the total system cost while maintaining reliability. The Latin Hypercube Sampling (LHS) method is employed to generate multi-scenario data, and piecewise linearization is used to reduce the computational complexity of the AC power flow equations. Numerical results demonstrate that the model significantly improves system reliability and economic efficiency under multiple uncertainty scenarios. The results also highlight the crucial role of the ESS in mitigating the variability of renewable energy and reducing the expected energy not supplied (EENS).

In recent years, with the steady growth of load demand in distribution networks, the fluctuation and uncertainty of power loads have significantly increased. Meanwhile, the rising penetration of photovoltaic generation has further exacerbated the challenges of power system accommodation capability. To enhance photovoltaic accommodation capability and realize the secure and economic operation of distribution networks, a multi-time scale hierarchical coordinated optimization operation strategy for distribution networks with aggregated distributed energy storage has been proposed. First, the regulation requirements of aggregated distributed energy storage are analyzed, and a distributed energy storage aggregation model is established based on an inner approximate Minkowski Sum. Subsequently, a multi-time scale optimization operation model considering source-load uncertainties for day-ahead, intra-day, and real-time stages is developed based on a stepped carbon emission cost model. Then, a power allocation method within the aggregated distributed energy storage based on the water-filling algorithm is presented. Finally, a practical distribution network in a demonstration county in China is used as a case study to validate the proposed method. The results demonstrate that the proposed strategy effectively reduces system operation costs while improving photovoltaic accommodation capacity and enhancing the reliability of system operation.

With the development of electric vehicles (EVs), EV on-board power chargers are expected to pursue higher power density. Compared with active power devices, passive magnetics seem to be the main restriction for achieving higher efficiency and power density. The CLLC resonant converter is a common bidirectional isolation circuit topology used for the rear-stage DC/DC converter of the on-board charger, which requires multiple magnetic components. To reduce the number and volume of magnetic components and improve the power density of the CLLC converter, the two resonant inductors can be magnetically integrated with the transformer. This paper proposes a novel magnetic integration method that achieves asymmetric controllable leakage inductance through independent magnetic legs. Detailed integration methods and parameter design processes are presented. A 2000-W, 400-V to 300-V CLLC dc–dc converter is demonstrated to verify the effectiveness and feasibility of the proposed methods.

The development of the elastic balance area within the distribution network places greater demands on the interaction between sources and loads, which impacts the stability of the power system. While achieving symmetry in active power is essential for stable operation, it is challenging to attain perfection due to various disruptions that can exacerbate frequency and voltage instability. Additionally, due to the inherent resonance characteristics of LCL filters and the time-varying nature of weak grid line impedance, grid-connected inverters may interact with the grid, potentially leading to oscillation issues. A grid-forming inverter control method that incorporates resonance suppression is proposed to address these challenges. First, a control model for the grid-forming inverter based on the Virtual Synchronous Generator (VSG) is established, enabling the system to exhibit inertia and damping characteristics. Considering the interaction between the VSG grid-connected system and the weak grid, sequence impedance models of the VSG system, which feature voltage and current double loops within the αβ coordinate system, are developed using harmonic linearization techniques. By combining the impedance analysis method, the stability of the system under weak grid conditions is evaluated using the Nyquist criterion. The validity of the analysis is confirmed through simulations. Finally, in order to ensure the effectiveness and correctness of the simulation, an experimental prototype of an NPC three-level LCL grid-forming inverter is built, and the experimental results have verified that the system has good elastic support capability and resonance suppression capability in the elastic region.

During extreme meteorological events, the output of wind farms may undergo noticeable fluctuations within short timeframes, leading to wind power ramp events (WPREs), which can result in severe disruptions to the power system, potentially causing widespread outages. However, limited studies have dedicated to addressing the adverse impacts of WPREs on the power system. Moreover, few articles consider the influence of power correlations among multiple wind farms on WPREs. This paper proposes an improved multi‐parameter segmentation algorithm for detecting WPREs based on the generation of wind farm output scenarios with different correlation coefficients. And a bi‐level optimization model is developed for the allocation of electrochemical energy storage and thermal power units to address offshore WPREs, considering the correlations among wind farms. The effectiveness of the proposed approach is validated through case study based on the modified IEEE RTS‐24 node system. Results demonstrate that stronger correlation among wind farms lead to fewer WPREs and lower requirements for thermal power units and electrochemical energy storage capacity, thereby reducing both investment and operational costs.