Abstract:The traveling wave fault location approach for overhead transmission lines is widely applied in actual power grids due to its better fault location accuracy compared with conventional power frequency data-based methods. However, the traveling wave fault location approach is highly related to the frequency distortion phenomenon of traveling wave propagation velocity, which includes two parts: the distortion caused by the reflection and refraction of the transformer substation and the attenuation caused by the distributed resistance of transmission lines. This study aims to improve fault location accuracy, by developing a complete theoretical analysis of the distortion of traveling wave signals and an improved traveling wave fault location approach that considers distortion. Using the PSCAD/EMTDC simulation tool, a typical 500 kV case study model is established to verify the fault location accuracy of the proposed traveling wave fault location approach under the effects of different fault type, fault location, and grounding resistance.

Abstract:The electricity industry has witnessed increasing challenges in power system operation and rapid developments of artificial intelligence technologies in the last decades. In this context, studying the approach of security-constrained unit commitment (SCUC) decision-making with high adaptability and precision is of great importance. This paper proposes an improved data-driven deep learning (DL) approach, following the sample coding and Sequence to Sequence (Seq2Seq) technique. First, an encoding and decoding strategy is utilized for high-dimensional sample matrix dimension compression. A DL SCUC decision model based on a Seq2Seq network with gated recurrent units as neurons is then constructed, and the mapping between load and unit on/off scheme is established through massive data from historical scheduling. Numerical simulation results based on the IEEE 118-bus test system demonstrate the correctness and effectiveness of the proposed approach.

Abstract:Partial discharge (PD) activity is an indicator of insulation deterioration and by extension, the reliability of power lines. Existing data-driven methods, while helpful, treat PD detection as a binary classification problem, thereby failing to provide physical information (e.g., filter PD pulse), and often provide results that contradict physical knowledge. To tackle this challenge, this paper develops a physics-informed temporal convolutional network (PITCN) for PD diagnosis (i.e., PD detection and PD pulse filtering). During training, physical knowledge of the background noise and PD pulse identification is integrated into a learning model. Once the model is trained, the PITCN can automatically detect PD activity from time-series voltage signals with different background noises and filter PD pulses. Experimental results demonstrate that the developed PITCN outper-forms the rest of the data-driven methods implemented, and in particular, the Matthews correlation coefficient of PITCN surpasses the conventional temporal convolutional network by 0.21.

Abstract:Energy storage with virtual inertia and virtual droop control has attracted wide attention due to its improved frequency stability with high penetration of renewable energy sources. However, there are significant spatial differences in frequency response. The location and capacity of energy storage are urgent issues to be resolved to support frequency. This study addresses the minimum investment of hybrid energy storage systems for providing sufficient frequency support, including the power capacity, energy capacity, and location of energy storage. A frequency response model is developed taking into account the network structure and frequency spatial distribution characteristics. In addition, a numerical computation method is provided for determining the frequency dynamic indices and calculating the output power of energy storage. Based on a simplified frequency response model, an optimal hybrid energy storage configuration method is proposed to optimize the control parameters, location, and capacity to satisfy the frequency dynamic constraints. This configuration method can exploit the potential of energy storage with different rates in different frequency support stages. To address the nonconvex drawback of this configuration, a numerical calculation method is provided based on the explicit gradient of the frequency and energy storage indices to enhance the computational efficiency. Simulations of a two-area system and the south-east Australian system verify the effectiveness of the proposed hybrid energy storage configuration method.

Abstract:Energy storage systems support electrical grid stability by enabling strategies to tackle issues, such as power fluctuations, low inertia, and insufficient damping. The present study proposes a battery energy storage system based on a modular multilevel converter with multiplexed submodule arms (M-MMC-BESS) to reduce the number of switching devices while embedding DC short-circuit fault ride-through capability. Compared to the conventional two-stage half-bridge topology, the M-MMC-BESS retains the same number of switching devices but allows uninterrupted operation under DC short-circuit faults. In addition, compared to the two-stage full-bridge topology, the proposed topology reduces the number of switching devices by one-third. The control of the M-MMC-BESS is thoroughly investigated under both normal and DC short-circuit operating conditions. Simulation and experimental results are used to demonstrate the effectiveness of the proposed system and control approach.

Abstract:The ability to accurately classify fault type within traveling wave data is crucial for real-time online fault location and protection using traveling wave technology. However, the current common practice in the power industry relies on manual data screening followed by offline processing, leading to several limitations such as poor timeliness, low accuracy, and high skill requirements for operators. These drawbacks restrict the application of traveling wave acquisition devices. To address these issues, this paper proposes a fault identification method for measuring the traveling wave of transmission lines based on the CSCRFAM-Transformer. Firstly, CSCRFAM is used to encode the temporal and spatial information of the measured traveling wave data. Next, pixel-level features are further aggregated through dimensional interaction. Then, an adaptive encoding hierarchy Transformer adjustment mechanism is employed to extract multi-level differentiated traveling wave high-frequency information from the aggregated features to complete fault identification. This method combines the dimensional interaction of the EMA mechanism and the self-attention mechanism of the Transformer's sensitivity to the spatiotemporal characteristics of traveling waves. The proposed method is trained and tested using a massive dataset of 396 672 measured samples from 110 kV to 220 kV transmission lines in Yunnan Power Grid. The method is used to identify, classify, test, and compare four distinct types of traveling wave data. The obtained results show that the method reduces the number of model parameters and improves the identification accuracy. The mAUC, Accuracy, Precision, and F1 values of the algorithm reach 0.969, 0.969, 0.965, and 0.957, respectively, indicating better detection accuracy and identification efficiency.

Abstract:In an offshore wind farm connected with a high-voltage direct current (HVDC) transmission system based on modular multilevel converter (MMC), a symmetric fault on the outgoing line at the sending end (SFOLSE) exhibits complex controlled characteristics in the fault current, which can undermine the reliability of relay protection. Detailed analysis of the control interaction between the wind farm and the MMC sending station (MMCSS) is conducted to ascertain the fault current characteristics. Considering the constraints imposed by the existence of a stable operating point (SOP) during SFOLSE, the phase angle difference distribution law for short-circuit currents sourced from both the wind farm and MMCSS is analyzed. Furthermore, the influence of control interaction on the reliability of distance protection is discussed. The results show that the additional impedance exhibits specific distribution characteristics under the influence of control interaction. In addition, the setting ratio of the dq-axis current reference for wind farm distance protection is analyzed, and the impact of wind farm control on the adaptability of distance protection under the constraints of the grid-connected guideline is evaluated. The main risk scenarios of misoperation are clarified, and the correctness of the analytical results is validated through PSCAD time-domain simulations.

Abstract:Online temperature monitoring of IGBTs is a crucial means to enhance the reliability of high-power converters. In the existing thermal model methods, the junction temperature is derived through the device power loss, which is difficult to obtain accurately in real time. This paper proposes a power loss observer to estimate the real-time power loss accurately. Unlike conventional methods, the proposed method only needs measure the heatsink temperature. Moreover, the proposed technique is robust to disturbances such as wind speed fluctuations, solder aging, and temperature dependence of thermal parameters, which helps to improve the temperature estimation accuracy throughout the full life cycle of IGBT modules. This paper analyzes the proposed method's mathematical principle, algorithm, and implementation steps, while the loss observer is validated by simulation and experiment.

Abstract:Fault diagnosis of switching devices is crucial for improving the reliability of power converters. In this paper, a current-slope-based fault diagnosis scheme is proposed for power switches in asymmetric half-bridge converters. First, a new current sensor installation scheme is proposed to obtain more current information for fault diagnosis while using fewer power devices and current sensors. In addition, a simple algebraic expression for the phase current under normal conditions is derived. Second, the principles of the fault diagnosis scheme are presented, enabling effective fault diagnosis of switches by digitizing the current slope. Finally, experiments are carried out on a four-phase switched reluctance motor system to verify the effectiveness of the proposed fault diagnosis scheme.

Abstract:With the rapid development of high penetration of offshore wind power, it becomes a challenge to achieve effective and economical operation and maintenance (O&M) scheduling for offshore wind turbines. Moreover, uncertain factors such as climate change bring difficulties to decision-making for complex and frequent maintenance tasks. To overcome these challenges, this paper proposes a practical O&M path planning model for offshore wind farms in Zhanjiang, China. Multi-types of practical constraints and operation costs are taken into account to construct the O&M optimization problem. Furthermore, based on the practical O&M process, the proposed optimization problem can be divided into a master problem and a subproblem. A strengthened elitist genetic algorithm is introduced to solve the above bi-layer problem separately. Finally, multi-case study of the typical offshore wind farm in Guangdong, China verifies that the proposed O&M strategy can ensure lower economic costs and provide significant guidance for the operation of offshore wind farms.

Abstract:Accurate generator information is crucial for the efficient control and operation of a power system. This study proposes a hierarchical data-driven approach for dynamic state estimation (DSE) of generators using cellular computational networks (CCNs) structure. The proposed method initially divides the problem of dynamic state estimation into multiple layers through hierarchical architecture. In the prediction layer, CCNs are employed to reduce the system scale by considering only relevant generators. In the correction layer, a novel adaptive filter is utilized to increase data abundance. Simulation results demonstrate that the proposed hierarchical data-driven method can accurately estimate states using PMU data alone while maintaining high computational efficiency. Additionally, it offers easy scalability and strong robustness against uncertainties. The proposed method has potential applications in online dynamic state estimation and real-time security monitoring.