Low and medium speed magnetic levitation traffic with short power supply distance and complex grounding network structure, prone to power supply rail grounding faults. However, the existing fault location methods do not accurately locate the fault point, which in turn makes it difficult for the protection device to act to cut off the fault. To address the above problems, this paper builds a dynamic simulation model of the low and medium speed magnetic levitation power supply rails to study the distribution characteristics of the fault traveling waves after a ground fault occurs in the power supply rails, and analyses the generation mechanism of the traveling wave spectrum through formula calculation. The first step is to determine whether an earth fault has occurred by analysing the difference in current between the positive and negative busbars. Secondly, the direction of the current difference between stations is compared to locate the faulty section. Finally, the fault distance is calculated from the frequency difference of the fault voltage at the double-ended station. Through simulation, the method is validated to be unaffected by fault location, fault transition resistance, noise interference, and is applicable to short circuit faults caused by lightning strikes, and the ranging error always remains within 20m. The method has strong robustness, can effectively solve the problem of protection misoperation and accurately locate the fault point. It is suitable for low and medium speed magnetic levitation transportation power supply rail ground fault.

The aggregation of the remaining battery capacity of EVs can be used as distributed energy storage to participate in electricity market auxiliary services in the form of V2G, however, their reliability is affected by the uncertainty of EV travel. In this paper, we consider the uncertainty of EV load response and propose a resolution method for quantifying the reliability of EV aggregation capacity in the day-ahead market. The paper first classifies EV users by analyzing historical charging data. Secondly, based on the composite Poisson process, the V2G capacity and reliability model for the aggregator’s participation in the day-ahead auxiliary service bidding are proposed and validated using actual data. Based on this model, aggregators can decide the power of V2G bidding for any time slot on day t+1 according to the reliability requirements of the standby market for the standby capacity contracted before that day. Finally, this paper organically combines PV and EV aggregators in the microgrid and uses genetic algorithms to optimize the simulation of the microgrid voltage operation state under different scenarios in the IEEE 33-node system. The results show the effectiveness of the coordinated optimization scheme of PV and EV clusters for the optimization of microgrid voltage.

Partial discharge (PD) diagnostics test is reliable for estimating insulation health conditions in power system components. During laboratory PD diagnostics, the effect of harmonic components in the voltage waveform generated by variable frequency drives (VFD) fed electric motors (EMs) is often overlooked. However, these harmonic components can cause significant distortion in the applied voltage waveform. As VFD-fed EMs operate at low speeds, the harmonic concentration in the voltage increases. It has been found that PD activity in VFD-fed EM is significantly affected by the addition of harmonic components in the voltage, thus making it necessary to consider their impact when performing true PD diagnostics. This paper investigates the influence of voltage harmonic distortion produced in VFD-fed EM on PD severity and the subsequent insulation degradation. Eight VFD-fed EM are used to study PD behavior under different levels of harmonic pollution. The PD characteristics, such as inception voltage, pulse repetition rate, accumulated apparent charge, average discharge current, discharge power, and quadratic rate, are determined. With the help of probability distribution functions, the chance of PD activity and its severity at various voltage harmonic levels are estimated. The proposed technique can be used to plan maintenance activities that ensure reliability for industrial applications dealing with voltage harmonic distortion.

Large penetration of renewable energy sources into the power grid has increased the complexity of power system operation and greatly reduced the ability of the system to withstand large disturbances. The frequent occurrence of voltage instability problems in the power grid has brought new challenges to the assessment of transient stability analysis of the power system. To achieve fast and accurate assessment of transient voltages, a transfer learning-based transient voltage stability assessment with small samples is proposed, introducing a domain transfer learning approach, embedding a cooperative attention mechanism in the residual network during the feature extraction stage to capture long-range correlations between features, and using adversarial approaches to reduce the differences between samples from different data sets, using the source domain to guide the target domain for network training to improve model's evaluation capability when the number of samples is insufficient, enhance the generalisation performance of the network, and effectively improve the performance of real-time power system transient voltage stability evaluation in the absence of sufficient historical data. Testing on an improved New England 39-node system validates the superiority of this method in transient voltage stability assessment and provides a new approach to practical field transient voltage stability assessment.