Abstract:

The detection of weak acoustic waves excited by partial discharge (PD) in oil-paper insulation is considered as a critical challenge in transformer condition monitoring, while existing high-sensitivity acoustic sensing methods are constrained by the low signal-to-noise ratio (SNR) of the acoustic waves. In this paper, an acoustic enhancement detection method based on gradient acoustic metamaterial (GAM) is proposed, achieving compression and amplification of broadband acoustic waves and providing a solution for improving the SNR of weak PD acoustic signals. The GAM is constructed using 30 circular plate units with diameters increasing according to a quadratic function. The thickness of each unit is 1 mm, the gap distance is 1 mm, the minimum diameter is 5 mm, and the maximum diameter is 47.05 mm, with the overall structure's maximum size being less than 6 cm. Numerical calculations of the acoustic pressure amplification effect of the GAM reveal that although the gain factor is reduced by the limited acoustic impedance difference between the GAM and the insulating oil, the amplification bandwidth is broadened, enabling the enhancement of broadband PD acoustic signals. By integrating the acoustic sensor with the end unit of the GAM, narrow-gap acoustic field detection and structural integrity of the GAM are simultaneously achieved, and the enhancement effect of the GAM in insulating oil is demonstrated experimentally. A gain effect is exhibited by the GAM in the frequency range of 45 kHz to 200 kHz, with a peak gain factor exceeding 3. Finally, the results of needle-tip PD show that the detected acoustic wave peak is directly enhanced by the GAM, and the frequency content within the GAM amplification band is significantly improved, thus achieving enhanced detection of PD acoustic waves. This study represents acoustic metamaterials to enhance acoustic detection, which is considered highly significant for the early detection of PD.

Abstract:

Gas insulated transmission lines (GIL) operate under high voltage and high current conditions, and their temperature rise characteristics are influenced by the interaction of multiple physical fields. However, traditional GIL temperature rise analysis methods only consider the current magnitude and idealized simplified models, making their results insufficient to effectively determine whether GIL has abnormal temperature rise responses. To address this issue, this paper presents a study on the temperature rise characteristics of key GIL components based on a multi-physics coupling strategy. Firstly, a magnetic-thermal-fluid coupling theory is established, and the finite element and physical models of GIL are constructed, taking into account the effect of contact resistance at the contacts. Secondly, a steady-state temperature rise analysis under power frequency conditions is performed to reveal the temperature rise response patterns of key GIL components. Finally, the effectiveness of the models and temperature rise response conclusions is validated through live testing experiments. Based on the aforementioned foundation, this paper proposes a 220 kV GIL response correlation model, which relates the conductor temperature to the average temperature at the top of the enclosure and the operating current. By comparing the model results with experimental data, it is demonstrated that this model has high accuracy and can serve as an empirical calculation method for GIL conductor temperature in practical engineering applications.

Abstract:

The stable operation of the power cable system is profoundly affected by the aging degree of the main insulation material cross-linked polyethylene (XLPE). In this paper, firstly, the accelerated aging experiments are carried out on the XLPE insulation of unused cables. Based on the changes in their macroscopic properties and microscopic structures, it is found that the antioxidant index can be used as an evaluation parameter for the insulation condition of old XLPE cables. Then, this method is applied to the aging assessment of XLPE in cables that have been in operation for 25 and 30 years. The results show that the mechanical, thermal, and electrical properties of XLPE, as well as its molecular and crystalline structure, deteriorate slowly at first and then quickly during the ageing process. The inflection point of change can be observed. The antioxidant index of XLPE decreases monotonically and continuously with the aging time. No inflection point is observed for the antioxidant index. Therefore, the antioxidant index can be used as an effective parameter for assessing the aging degree of old cables. The macroscopic performance and microstructure parameters of the 25-year-old XLPE cable are positioned before the inflection point of the accelerated aging curve of the unused XLPE cable insulation, while those of the 30-year-old XLPE cable are after the inflection point. Changes in these parameters are minimal compared to the results of testing the unused XLPE cable insulation. However, their antioxidant indices differ significantly. The internal antioxidant index of the XLPE insulation in the 25-year-old cable is far from the inflection point. And the 25-year-old cable insulation has not yet entered the stage of rapid performance deterioration. However, the internal antioxidants in the XLPE insulation of the 30-year-old cable have been exhausted. It is in the rapid deterioration stage. Therefore, its operating status requires high attention. Based on the significant findings of this study, standardized aging assessment method of actual operating cables may be established.

Abstract:

Ultra-high frequency (UHF) detection method is highly accurate and has fault localization function. At present, most gas-insulated switchgear (GIS) systems are equipped with online UHF monitoring devices to detect partial discharges. In order to ensure the accuracy of the detection results, UHF sensors need to be verified regularly. UHF sensors used for on-line monitoring are usually installed at the handholes of the GIS and cannot be removed. Measuring laboratory verification indexes (e.g. equivalent height, dynamic range, etc.) of the sensors directly is very difficult. However, it is easier to measure S₁₁ of the sensor to be verified and S₂₁ between it and the neighboring sensors by injecting power signals. Accordingly, this paper presents a simulation study of the GIS partial discharge UHF sensor verification method based on the joint cross-comparison of S₁₁ and S₂₁. Firstly, the equivalent S-parameter network and the change characteristics of S₁₁ and S₂₁ during the verification of GIS UHF sensors are analyzed. Secondly, UHF sensors and degraded sensors caused by typical factors are modelled. Finally, the above sensors are coupled in the handholes of different structural GIS according to the actual situation. The S₁₁ and S₂₁ curves of various types of sensors are simulated and the accuracy of the simulation models are verified by experiments. Analyzing the results of this simulation model, S₁₁ is mainly related to the sensor performance and the handhole; S₂₁ is mainly related to the sensor performance and the GIS structure. Therefore, S₁₁ and S₂₁ can be used as indicators for on-site verification of GIS UHF sensors. Then, the performance of the sensors can be effectively verified by cross-comparing S₁₁ and S₂₁ at corresponding positions across different phases of GIS.

Abstract:

Dissolved gas analysis in transformer oil is regarded as an important indicator for evaluating the operational status of transformers. Accurate prediction of trends in dissolved gases in oil is beneficial for preventing power transformer failures. A Temporal Fusion Transformer (TFT) model, optimized via Optuna hyperparameter tuning, is proposed to address the technical challenge of low prediction efficiency inherent in traditional models that rely on a single variable. Static variables including transformer group, winding phase, and gas type are introduced into the model, and an interpretable multi-head attention mechanism is integrated as well. Synchronous prediction of all dissolved gases in the oil of multiple transformers is thereby achieved, improving the early warning efficiency of substation operation and maintenance systems. An average relative error of only 0.306% is achieved by the proposed model, representing a 66.7% reduction relative to the Transformer baseline model. Higher predictive accuracy is also demonstrated in both short-term and long-term forecasting. In addition, the model's training time is only one quarter that of the Transformer baseline model. This efficiency aligns with the current trend toward simultaneous prediction across multiple device groups in intelligent early-warning platforms. Strong correlations between hydrogen and methane and between carbon dioxide and methane are indicated by the model's multi-head attention mechanism. These correlations are consistent with the gas generation patterns of oil-paper insulation degradation, further demonstrating the model's good interpretability and providing technical support for synchronous prediction in multiple device groups.

Abstract:

Complex laying environments and external electromagnetic interference have significantly impacted the accuracy of cable fault location, particularly in modern power grids with the widespread application of cross-linked polyethylene (XLPE) power cables. In order to solve the low accuracy of existing fault location methods in high-noise environments, an improved fault positioning method based on spread spectrum time domain reflectometry (SSTDR) is proposed. Firstly, by introducing a multi-period signal modeling method using M-sequences, combined with windowed filtering and adaptive wavelet denoising, the reflected signals are optimized. Subsequently, a quadratic cross-correlation weighting method based on the smoothed coherence transform (SCOT) weighting function is employed to process the incident signals and the filtered reflected signals for each period, obtaining the correlation coefficients for each period. The final location information is derived through arithmetic averaging. Finally, Gaussian smoothing filtering is applied to enhance the peak characteristics of the fault location, and the fault position is determined using a peak detection algorithm. Simulation experiments conducted on the MATLAB/Simulink platform demonstrate that under a low signal-to-noise ratio of -20 dB, the average location error for short-circuit, open-circuit faults at different positions, and multi-branch faults is less than 0.062 m, with an average relative error below 0.25%. Compared to traditional methods and existing methods, the proposed method exhibits effective localization under low signal-to-noise ratio conditions, with the relative localization error improving by at least 0.01%. Furthermore, the experimental results validate that the method proposed in this paper has robust noise-resistant fault localization performance.

Abstract:

In power wireless sensor networks (PWSNs), concurrent uplink access by multiple users is constrained by limited spectrum and power resources, while heterogeneous monitoring services exhibit markedly different requirements in terms of reliability and latency. These factors make it challenging for resource scheduling to simultaneously satisfy overall system efficiency and user-perceived quality. In this work, a joint resource allocation mechanism capable of providing differentiated quality-of-service guarantees under heterogeneous service demands is formulated within an uplink orthogonal frequency division multiplexing (OFDM) framework. A quantifiable user-satisfaction function is designed, and the joint optimization of subcarrier and power allocation is modeled as a Markov decision process (MDP). A dueling double deep Q network (D3QN) algorithm is further introduced to dynamically adjust the allocation strategy. In addition, an action-space down-sampling mechanism is proposed to reduce computational complexity and enhance training efficiency. Simulation results demonstrate that the proposed algorithm achieves fast convergence under various node densities and subcarrier configurations, and yields significant improvements in user satisfaction compared with conventional DQN, random allocation, and uniform allocation methods.

Abstract:

To address the issues of insufficient sensitivity and narrow frequency band in traditional acoustic methods for partial discharge detection in power transformers, a high-sensitivity and wide-band Fabry-Pérot etalon (FPE) sensor is developed in this paper. By optimally the designed of etalon, collimator, and laser, the sensor achieves high sensitivity and wide-band response characteristics.The etalon is assembled using an optical adhesive method, ensuring that the parallelism error of the reflective surface is less than 2". In addition, a working point control method based on a solid etalon is designed, which effectively suppresses drift caused by temperature and light source fluctuations. Experimental results show that the FPE sensor responds well to partial discharge acoustic signals, with a sensitivity of 11.88 mV/Pa at 50 kHz and a measurement bandwidth from 10 kHz to 1 MHz. Its frequency response performance is close to that of the reference acoustic sensor Eta250 and superior to the 4939-A-011 acoustic sensor. Meanwhile, its measurement sensitivity is higher than that of both Eta250 and piezoelectric sensors, approximately four times that of the 4939-A-011 acoustic sensor. The development of this sensor provides a new optical acoustic sensing technology for partial discharge detection in power transformers. Owing to its strong anti-electromagnetic interference capability, wide-band detection, and high sensitivity characteristics. It can effectively improve the accuracy of insulation defect location, offering important technical support for enhancing the accuracy and reliability of transformer insulation condition monitoring.

Abstract:

Winding failures are recognized as one of the primary causes of transformer accidents, making effective monitoring of winding conditions crucial. A study on autotransformer (AT) winding faults diagnosis is conducted through the following procedure. Firstly, an experimental platform is established to simulate typical single and combined winding faults in autotransformers, through which frequency responses under various fault conditions are tested. Subsequently, a fast vector matching method is employed to fit transfer functions of winding systems under normal and faulty states, from which zero point distribution diagrams in polar coordinates are derived. Then, the gray level difference statistical (GLDS) features and gray-gradient co-occurrence matrix (GGCM) features are extracted from the zero point distribution diagrams, and the particle swarm optimization (PSO)-random forest (RF) algorithm is combined to realize the classification of faulty windings and fault types. Finally, the proposed method is validated using actual autotransformer fault cases. The results show that the zero point distributions in polar coordinates obtained by fast vector fitting can capture the subtle differences in the original frequency response curves by combining amplitude-frequency and phase-frequency information. Compared with optimization algorithms such as cuckoo search and genetic algorithm, the PSO-RF algorithm maintains an accuracy rate consistently exceeding 93% in identifying winding faults and fault types of autotransformers. The analysis results of the proposed method are consistent with the tank lifting inspection results in real autotransformer fault cases.

Abstract:

Accurate forecasting of photovoltaic (PV) power is essential for power system dispatch and decision-making. To enhance the prediction accuracy of PV power, a data-driven short-term forecasting method based on similar days and a bi-layer correction long short-term memory (LSTM) model is proposed. Firstly, both PV power and related meteorological data are normalized, and key factors influencing PV power are identified using the Pearson correlation coefficient, reducing the dimensions of the training dataset. Nextly, the Fréchet distance algorithm is applied to match similar days with the target prediction day, improving the quality of the training data. Then, based on numerical weather predictions, the initial PV power forecast is obtained through the baseline LSTM using feature learning. A correction LSTM, using a time series approach, predicts the error and adjusts the initial forecast to produce the final prediction. A case study uses real-world data under different weather conditions, i.e., sunny, cloudy, and rainy conditions. It shows that the proposed model consistently delivers accurate short-term PV power predictions for the next 24 hours. The model significantly improves accuracy compared to existing methods.

Abstract:

Existing wind-storage dispatch strategies often overlook the optimization of energy storage utilization and the impact of fluctuations in tie-line power. To address these issues, a two-stage wind-storage dispatch strategy is proposed. In the day-ahead scheduling stage, a multi-objective optimization model is formulated to minimize system operating costs, wind curtailment, and maximize energy storage utilization. The model is solved using a multi-objective particle swarm optimization (MOPSO) algorithm. The model fully accounts for the volatility of renewable energy sources such as wind power and photovoltaic power, and improves energy storage utilization efficiency and dispatch economy by optimizing the charge-discharge schedule of energy storage. In the intra-day scheduling stage, model predictive control (MPC) is employed to dynamically adjust the output of energy storage and dispatchable resources, minimizing scheduling errors and enhancing system stability. Simulation results demonstrate that the proposed strategy significantly improves system performance. Specifically, MPC reduces scheduling errors by 50%, limits exceedance by 57%, improves tie-line stability, increases wind power utilization by 15.6%, boosts energy storage efficiency by 12%, and lowers operating costs by 10.5%. These findings validate that the proposed strategy optimizes energy storage utilization, reduces scheduling errors, and enhances the reliability and economic efficiency of the wind-storage system.

Abstract:

In order to cope with the impact of wind power and photovoltaic uncertainty on the safe and stable operation of the power grid and to make up for the shortcomings of the traditional single-objective optimal power flow model, a transient stability constrained multi-objective optimal power flow (TSCMOOPF) model and a solution method are proposed to take into account the wind and solar uncertainty. Firstly, an ensemble learning method based on artificial neural network (ANN), deep neural network (DNN) and surprisal-driven zoneout long short-term memory (SZLSTM) are adopted to construct a wind and photovoltaic output prediction model to improve the prediction accuracy and robustness. Secondly, considering the economy and stability of the system, a multi-objective function including the minimization of active network loss, the minimization of fuel cost, and the optimization of the voltage stability index is established to construct a TSCMOOPF model. Then, an improved reference vector guided evolutionary algorithm (RVEA) is designed for the solution. Finally, simulation experiments are carried out on the improved IEEE 39-bus system. The results show that the proposed ensemble learning method performs well in wind and photovoltaic output prediction, the multi-objective optimization model ensures transient stability while active network loss and fuel cost are reduced significantly, and the improved RVEA algorithm is better than the traditional multi-objective algorithm in terms of convergence and diversity.

Abstract:

In the context of high penetration of photovoltaics in distribution networks, researching the load regulation potential is crucial for grid safety and refined dispatching. Thus, a data-driven and model-driven evaluation and prediction method for load regulation potential is proposed. Firstly, a multi-dimensional evaluation index system and assessment model for load regulation potential are constructed. A two-step clustering method, including k-means for load feature extraction and self-organizing map (SOM) for regulation potential feature fusion, is introduced. This enables the assessment of the temporal up-regulation and down-regulation potential of various loads. Secondly, an intelligent prediction method that integrates bidirectional long short-term memory (BiLSTM) and dynamic mode decomposition (DMD) is proposed to predict the 15-minute level load regulation potential for the next day. Finally, simulation verification of the proposed strategy is conducted using local load data. The evaluation results confirm the effectiveness of the proposed assessment and prediction methods, demonstrating that the BiLSTM-DMD model can achieve high prediction accuracy.

Abstract:

To address the issue of low transmission efficiency in dual active bridge (DAB) converters during electric vehicle charging and discharging processes, a minimum current stress optimization control strategy combining the differential extremum method with segmented control is proposed. This strategy effectively optimizes current stress and suppresses backflow power under soft-switching constraints, thereby significantly improving transmission efficiency. Firstly, taking forward power transmission as an example, the conditions for achieving zero voltage soft-switching for all switches in two operation modes under extended phase shift (EPS) control are derived, and the mechanism of backflow power generation is analyzed, elucidating how reducing current stress contributes to its suppression. Subsequently, the optimization phase-shift combinations for minimum current stress are derived using the differential extremum method, and a segmented control scheme is implemented based on the soft-switching ranges of different modes. Finally, experimental results demonstrate that when the voltage conversion ratio is greater than 1, the proposed strategy achieves soft-switching for all switches across the full power range, while effectively reducing current stress and suppressing backflow power, leading to a significant improvement in transmission efficiency. However, when the voltage conversion ratio is less than 1, while current stress is still reduced, zero voltage switching cannot be achieved for all switches.

Abstract:

Grid voltage imbalances can significantly affect the performance of power electronic converters. A balanced power fuzzy active disturbance rejection control (BPF-ADRC) method is proposed for a three-phase voltage-source pulse width modulation (PWM) rectifier. The method aims to mitigate the adverse effects of grid voltage unbalances on power quality. The approach integrates dual-current balanced power control with active disturbance rejection control (ADRC) to eliminate harmonic components in the system's power. A fuzzy controller is employed to optimize ADRC parameters in real-time, reducing the number of parameters requiring manual tuning and simplifying the overall design process. This integration enhances the system's disturbance rejection capabilities and improves its adaptability to parameter variations under unbalanced grid conditions. Both simulation and experimental results validate the effectiveness of the proposed method. Compared to traditional ADRC and ADRC with power balancing, the proposed algorithm significantly improves steady-state performance. Notably, the grid-side current total harmonic distortion (THD) is reduced from 17.5% to 1.7%. The results demonstrate that the proposed method effectively mitigates the harmonic effects of grid imbalances and provides superior disturbance rejection capabilities.