Iet Generation Transmission & Distribution最新文献

Nowadays, power system protection is increasingly important because of the growing number of customers and the pressing need for timely fault resolution and relay operations. This paper addresses the non-linear nature of the objective function in the optimal coordination of directional overcurrent relays (DOCRs) by employing a quadratic Taylor series expansion around an operating point, converting the problem into a quadratically constrained quadratic programming problem, ensuring a global optimal solution with increased computational efficiency. Additionally, the quadratic constraints are converted into second-order cone constraints for compatibility with the CPLEX solver. Using the least square method, the operating point values are determined and further fine-tuned using iterations with the DOCR operation times. The IEEE 3-bus, IEEE 8-bus, and IEEE 14-bus test systems are used to test the method, which shows higher improvement rates in reducing DOCR operation times and enhancing cooperation than conventional and metaheuristic methods. The simulation results verify the numerical superiority of the method in optimizing the protection system's efficiency while obtaining rapid and accurate solutions. The proposed method was tested on IEEE 3-bus, 8-bus, and 14-bus systems, optimizing relay operating times to 0.87, 2.96, and 7.05 s, respectively, demonstrating the method's efficiency over conventional approaches.

Transient stability assessment (TSA) plays an important role in ensuring the reliable operation of power systems. With the popularity of phasor measurement units (PMUs), data-driven TSA methods have been widely concerned. However, the performance of TSA model may deteriorate when data loss occurs due to PMU failure. This paper proposes an adaptive assessment method for transient stability of power systems considering PMU data loss. First, considering the importance of temporal features, a collection of PMU clusters is constructed to minimize the failure risk and maintain full observability of the whole buses of the grid. Secondly, a weighted integrated assessment model based on PMU clusters is constructed by using an improved eXplainable Convolutional neural network for Multivariate time series classification (XCM) as a TSA classifier. The model can make full use of time series information to carry out adaptive TSA and maintain the robustness of the assessment performance even when PMU failure occurs. Finally, it is verified in a modified IEEE 39-bus system with wind power and solar power. The effect of the proposed method shows high accuracy and strong anti-noise interference ability in case of data loss.

This paper proposes a distributed cooperative control scheme for multiple energy storage unit (ESU) in DC microgrids to achieve the control objectives of SoC balancing, power sharing, and bus voltage recovery. In the primary control part, the proposed scheme constructs a control function between the SoC values of each ESU and the droop coefficients to dynamically adjust the droop coefficients. Through a communication network, information is exchanged with neighbouring ESUs to achieve SoC convergence. In the secondary control part, by exchanging power information with neighbouring ESUs, precise power distribution is achieved. Additionally, the proposed scheme maintains bus voltage stability. Finally, a DC microgrid simulation model and experimental platform were developed, demonstrating the feasibility and plug-and-play capability of the proposed control strategy through both simulation and experimental case tests.

This paper proposes training improvement methods of artificial neural networks (ANN) trajectory predictors. First, a dynamic power system time-series trajectory is split into several different segments to simplify the original ANN training problem. Moreover, the time-derivative of the trajectory is included to obtain an augmented loss function. Compared to previous studies which mainly focused on increasing the prediction accuracy, the aim of these novel techniques is to reduce the computational burden where the ANN output performance is still acceptable. The effectiveness of the developed methods is validated based on the WSCC three-machine nine-bus and IEEE 39-bus system models. The mean absolute error (MAE) and trajectory prediction results are analysed, in which the numbers of neurons, hidden layers, and training epochs are constrained during the ANN training process. Rotor-angle difference between generators and the system frequency are investigated as the dynamic trajectories of the power system models. The approaches are revealed to be effective when the ANN architecture and epochs are constrained. The MAE results can be reduced by up to 65% in the power system models depending on the ANN hyperparameters and training epochs. The ANN training results can better reflect the original trajectory as well.

Compared to offline measurement (interrupting the applied voltage before measurement), online measurement of surface charge on gas-insulated transmission line (GIL) insulators effectively avoids the impact of spontaneous dissipation of charges. Thus, the accuracy of results can be improved. In this article, a measurement model of capacitive electrostatic probes for surface charges on ±320 kV GIL tri-post insulator is established. The geometry of probes is optimized. The feasibility of online measurement of surface charges is analyzed. Results indicate that the surface leakage distance of induced charges is positively related to the length. Furthermore, as the diameter of probes increases, the maximum electric field on the sensitive electrode surface rises, while the radius of the sensitive electrode has the opposite effect. Hence, the diameter of the probe is 6 mm. The length and radius of sensitive electrodes are 50 mm and 1.2 mm, respectively. For online measurement of surface charges on ±320 kV tri-post insulators, the induced potential distribution of the probe is consistent with surface charges. Therefore, the capacitive electrostatic probe in the embedded electrode is appropriate for the online measurement of surface charges on tri-post insulators. It provides a reference for avoiding flashover triggered by surface charge accumulation on DC GIL insulators.

The development of the carbon market is a strategic approach to promoting carbon emission restrictions and the growth of renewable energy. As the development of new hybrid power generation systems (HPGS) integrating wind, solar, and energy storage progresses, a significant challenge arises: how to incorporate the electricity-carbon market mechanism into the planning of power system capacity. To address this challenge, this article proposes a coupled electricity-carbon market and wind-solar-storage complementary hybrid power generation system model, aiming to maximize energy complementarity benefits and economic efficiency. The model employs a bi-level optimization method based on the Improved Coati Optimization Algorithm (ICOA) to optimize the system's capacity planning. Simulations reveal that under the coupled electricity-carbon market scenario, renewable energy capacity increases by 23% over a 5-year planning period. Additionally, in this scenario, the total cost is 0.042% lower compared to the scenario without coupling. Under the constraint of a 30% renewable energy penetration rate, the capacity development of wind, solar, and storage surpasses thermal power, while demonstrating favourable total cost performance and the comprehensive complementarity index for HPGS. This model offers decision-making support for optimizing energy resource allocation and improving system reliability and economic viability.

Power systems face significant challenges in maintaining power balance because of the stochasticity of sources and loads. This unpredictability makes it difficult to characterize the typical demand for grid power balance regulation, which results in a lack of clear objectives for evaluating the contributions of demand-side users to power balance. To bridge these gaps, in this paper, a new approach is proposed on the basis of the inherent difficulties in grid power balance regulation. First, a method for portraying the change in power balance regulation demand is presented, which emphasizes the trend characteristics of time-varying regulation demand through the weighting of trend segments. Second, demand periods and curves are calculated on the basis of a regulation capacity of 5% of the maximum load, and their distribution during the monthly cycle is analysed. Finally, to elucidate the contribution of random samples to typical demand features, a sample weighting method utilizing clustering categories is proposed and a distance-minimum optimization model is constructed to estimate typical features by leveraging the “group effect” concept. Actual power data from a region in China are selected for verification, confirming that the proposed typical value calculation method is more representative of random sampling situations. Moreover, considering the power grid's time-varying nature, the evaluation of the contribution of demand-side users to the power grid is improved.

As the application of renewable energy sources continues to increase, the virtual synchronous generator (VSG) has been proposed and received widespread attention. In China, new energy stations are typically situated at the transmission end of LCC-HVDC lines. A failure in DC commutation can lead to overvoltage at the DC transmission end, thus posing a significant risk of disconnection from the grid due to the occurrence of overvoltage. Compared to when low-voltage occur, it was discovered that, during overvoltage, VSGs face difficulties in rapidly attaining the preset power and transient instability. These issues can significantly impact the voltage support capability of the VSG. To address these concerns, an enhanced control strategy has been proposed. The control strategy sets reasonable power reference values, leveraging the advantage of fast voltage response in VSG by matching its voltage with virtual impedance. This ensures that the power rapidly reaches the designated reference value while reducing fluctuations in power angle. The transient power-angle stability is validated using the energy function method. This approach enhances the reactive power absorbed by VSG, thereby mitigating transient overvoltage. Finally, simulation results from PSCAD/EMTDC validate the rationale and effectiveness of the control strategy.

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 article 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 article 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.

The current bolt detection for overhead work robots used for transmission lines faces the problems of lightweight algorithms and high accuracy of target detection. 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 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. The mAP accuracy is improved in this paper by 9.9%, while the number of model parameters and the model size is reduced by 0.973 × 10⁶ and 1.7  MB, respectively. The improved algorithm improves the accuracy of the bolt detection while reducing the computation complexity to achieve more lightweight model.