Iet Generation Transmission & Distribution最新文献

In this paper, three methods based on harmonic resonance mode analysis are proposed for placing PQMs to monitor all harmonic resonance conditions in a power system. These methods compute the optimal number and sites of PQMs for varied load profiles and network configurations such that all harmonic resonance conditions would be monitored. This paper introduces the harmonic resonance severity index that enables electric network owners to place PQMs in a way to monitor only worst-case harmonic resonance conditions. Also, in this paper, the harmonic resonance observability index is introduced, enabling electrical owners to afford suitable numbers of PQMs based on their budget limitations. The efficiency of the proposed methods has been examined on test systems. The results indicate that using the proposed approach, all harmonic resonance conditions can be captured with an optimal number of PQMs. The proposed approaches also operate with system uncertainties present, which overcomes one of the major weaknesses of the prior approaches. It observes the bus voltage if either this bus or one of the adjacent buses is equipped with at least one PMQ, reduces the number of PQMs, and rearranges their locations to develop a cost-effective strategy, addressing a long-standing challenge.

Anomaly detection in power systems is crucial for ensuring the safety and stability of electrical grids. Traditional methods struggle to extract meaningful features from electricity consumption data due to significant differences in usage patterns across various user types, such as residential and industrial users. Applying a single model for all user categories increases feature complexity and computational demands. Additionally, non-Gaussian outliers caused by equipment and measurement noise can significantly deviate from normal data patterns, making them difficult to filter using standard methods. To address these challenges, this paper proposes a robust, user-type-specific anomaly detection method. After data preprocessing, a correntropy-based K-means clustering method is used to separate users with noisy data. A two-stage detection framework combining fuzzy logic and a convolutional neural network (CNN)-long short-term memory (LSTM) model enhances both detection efficiency and accuracy. The experiments were conducted using open-source datasets, and the results demonstrated that our method achieved an accuracy of 95%, which is approximately 4% higher than the traditional Isolation Forest method. This indicates that our approach effectively balances efficiency and accuracy in anomaly detection, with its generalizability further validated on an additional dataset.

VES is a method of balancing the energy of a power system with other equipment or scheduling strategies, particularly with respect to controllable loads, owing to end-user electrification. This paper summarises the connotations, classifications, and typical modelling applications for VES users. Thereafter, the modelling methods, characteristics, and specific operation cases of five types of VESs are introduced, including electric vehicles, buildings, cold storage, industrial production and hydrogen storage. Furthermore, the energy storage capacity planning, energy scheduling strategy, and power control strategy of a VESS are realised through optimal control strategies. Finally, in conjunction with demand response, the development prospects of VES in modelling and control strategies are discussed to improve the economic and environmental benefits of microgrids.

The optical detection method has attracted much attention in the field of partial discharge (PD) detection due to its strong anti-interference ability. To improve the detection performance of optical PD sensors, a novel optical PD sensor based on the light guide rod (LGR) is proposed. A simulation model is constructed according to the actual dimensions of gas-insulated switchgear (GIS). The optical properties of the LGR are analysed in terms of material, front microstructure, and length. Subsequently, an optical and electrical PD detection system is set up. Four typical PD defects are designed. The detection performance of different detection methods is compared, and the characteristic parameters such as partial discharge inception voltage (PDIV), phase distribution, pulse amplitude, and quantity are analysed. The results show that the PD sensor based on LGR performs better in detecting typical PD signals of GIS and can reflect the characteristics of PD signals. These results provide a reference for the design and application of optical PD sensors for GIS.

A vendor agnostic design framework is proposed for multi-terminal high voltage direct current (HVDC) here. An analytical assessment is then proposed as a first step of this assessment to quantify the domain of operating point. The criterion is indexed by the margin against loss-of-equilibrium for an Multi-Vendor-Multi-Terminal (MVMT)-HVDC network with terminal behaviour of connected converters. Based on a multi-terminal control architecture, a static analytical model is established, including relevant parameters within the direct current network, its topology, and operation. Two types of scalar index are proposed and then normalized to indicate the margin against loss-of-equilibrium and then employed to quantify the domain of operating point. It is proposed that the domain of security shall correspond to a value in closer proximity to ‘1’ for better security. The effectiveness of the indices is verified and analysed with simulations in the environments of both Matlab/Simulink and real-time digital simulator with pseudo-steady-state and detailed electro-magnetic transient modelling, respectively. This approach attempts to represent one MVMT control scheme to support practical specification, testing and demonstration of the first MVMT-HVDC control system as a business case.

Integrated energy systems (IESs) with deep coupling of electric power and natural gas have recently attracted much attention. The issue of increasing uncertainty in both the generation and load sides of an integrated electric-natural gas system (IENGS) is a concerning problem. In this paper, a hybrid robust-stochastic optimization (RSO) framework is introduced to address the day-ahead contingency-constrained unit commitment (CCUC) problem of an IENGS equipped with promising technologies. The objective of the research is to minimize the total operation cost of the CCUC problem while ensuring system security under multiple uncertainties and contingencies. The uncertainties of load and gas demands are generated using the Monte Carlo Simulation (MCS) method, while a robust uncertainty set is applied to handle wind power deviations. Generation scheduling and topology control action are also co-optimized. To solve the proposed CCUC as a nonconvex problem, an augmented nested column-and-constraint generation algorithm is used to enhance the performance of the decomposition procedure. The quantitative results imply that with a negligible increase of 6.88% in operation cost, a huge cost reduction of 38.06% in the long run model can be anticipated. Additionally, the results show the effectiveness of the TLS action in day-ahead operation, reducing the total cost by up to 17.5%.

When a grounding fault occurs in a distribution network with distributed generation (DG), it poses significant challenges for fault localization, including difficulties in extracting fault features, complex changes in network topology, and limited fault samples. A distribution network fault localization method based on a domain-adaptive transfer dynamic graph attention network (TGATv2) is proposed to tackle these challenges. First, a feature selection (FS) module is embedded into the dynamic graph attention network (GATv2), which can automatically select zero-sequence current statistical features related to node faults, thereby reducing noise interference. Subsequently, the network topology is reconstructed to account for both fault conditions and economic efficiency. This enhances the model's generalization ability across various degrees of network topology changes and scenarios. Additionally, a domain-adaptive transfer learning method with an optimized dynamic loss function is employed to reduce the distribution differences between the source domain and target domain data, addressing the issue of fault location with limited samples. Finally, simulations and experimental tests are conducted using different distribution networks. The results demonstrate that compared to TGATv2 without FS embedding, the proposed method improves average accuracy by at least 8% and achieves approximately 3–22% higher accuracy compared to other methods, demonstrating strong robustness.

When a fault occurs in transmission lines, the voltage at the point of common coupling of renewable energy stations can drop drastically. This causes the phase-locked loop (PLL) performance to degrade, reducing the transient voltage support capability of the STATCOM in the station. To address this issue, this paper proposes a novel phase-locked method for STATCOM based on power balance (PB) theory. First, the paper establishes a mathematical model of the traditional STATCOM to identify the causes of PLL performance degradation during transient events. Next, the paper introduces the PB phase-locked strategy: this strategy adjusts the power exchange between the grid and STATCOM, simulating the synchronization process of a synchronous condenser. Finally, the paper uses PSCAD/EMTDC to build simulation models and verify the performance of the proposed scheme. The simulation results show that overvoltage and overcurrent are reduced by 16.5% and 20.1%, respectively, compared to traditional SRF-PLL. The proposed strategy provides sufficient transient voltage support even when the traditional PLL fails.

Large-scale power systems typically require long-distance transmission of electrical energy, and high-voltage direct current (HVDC) technology is a commonly used high-capacity means of connecting power sources to load centres. When a blocking fault occurs in an HVDC transmission system based on line commutated converters (LCC), the sending-end system is prone to transient overvoltage (TOV) risks. This is especially severe in systems with large-scale renewable energy integration, where excessive TOV can lead to widespread disconnection of renewable energy units, seriously threatening the safe and stable operation of the power system. Therefore, predicting the TOV magnitude in renewable energy stations (RES) under DC blocking (DCB) scenarios is of great importance for maintaining system stability and facilitating emergency control decisions. This paper first derives an analytical expression for the TOV magnitude at critical nodes in the system caused by DCB faults. Subsequently, an analytical formula is developed to characterize the relationship between the multiple renewable energy stations short circuit ratio (MRSCR) and the transient voltage rise (TVR) at the point of common coupling (PCC) of RES. Based on this, a physics-informed neural network-based transient overvoltage magnitude prediction (PINN-TOMP) method for RES under DCB scenarios is proposed. The method introduces a regularization term for MRSCR into the loss function to ensure that the PINN model adheres to the physical laws and constraints governing the power system, thereby enhancing the prediction accuracy. Finally, the proposed method was tested on a real regional power system in China, and the results validated its effectiveness.

In this paper, the optimal operation and arbitrage strategies for user-side energy storage systems are studied considering an accurate battery model to capture the charging and discharging features. Meanwhile, the proposed method uses Benders decomposition to help solve the consequential decision-making problems of energy arbitrage in electricity market with dynamic price signal and continuous iteration of charging cycling counting and battery life costs. Empirical analysis and simulation based on different scenarios of Chinese provisional electricity markets demonstrate the variability of such energy arbitrage strategies in realistic applications, which mainly depends on the trade-off between the physical operation features of the energy storage systems and the market pricing scheme. The main findings and results confirm the advantages of involving such user-side energy storage system resources that can participate in the deregulated electricity market, ultimately benefiting the energy end-users at the distribution level, as well as the upper-level market or system operation. It is believed that further study of a more accurate battery model and a more precise battery cycling life cost can also contribute to improving the reasonable market mechanism design.