Gaussian PMU measurement error has been assumed for many power system applications, such as state estimation, oscillatory modes monitoring, voltage stability analysis, to cite a few. This letter proposes a simple yet effective approach to assess this assumption by using the stability property of a probability distribution and the concept of redundant measurement. Extensive results using field PMU data from WECC system reveal that the Gaussian assumption is questionable.

With the ever increasing penetration of renewable energy, smart loads, energy storage, and new market behavior, today's power grid becomes more dynamic and stochastic, which may invalidate traditional study assumptions and pose great operational challenges. Thus, it is of critical importance to maintain good-quality models for secure and economic planning and real-time operation. Following the 1996 Western Systems Coordinating Council system blackout, North American Electric Reliability Corporation (NERC) and Western Electricity Coordinating Council (WECC) in North America enforced a number of policies and standards to guide the power industry to periodically validate power grid models and calibrate poor parameters with the goal of building sufficient confidence in model quality. The PMU-based approach using online measurements without interfering with the operation of generators provides a low-cost alternative to meet NERC standards. This paper presents an innovative procedure and tool suites to validate and calibrate models based on a trajectory sensitivity analysis method and an advanced ensemble Kalman filter algorithm. The developed prototype demonstrates excellent performance in identifying and calibrating bad parameters of a realistic hydro power plant against multiple system events.

Accurate pin-pointing of faults on transmission lines minimizes outage time, labor, and costs. This letter proposes a simple yet effective ensemble Kalman filter (EnKF) approach to accurately locating the faults on transmission lines within half-cycle time. The approach is easy to implement and does not require foreknowledge of either the fault type or an approximate guess of the fault location. Extensive results validate the effectiveness and accuracy of the EnKF-based fault location approach.

Dynamic simulation for transient stability assessment is one of the most important, but intensive, computational tasks for power system planning and operation. Several commercial software tools provide functionality for performing multiple dynamic simulations such as those in contingency analysis simultaneously on parallel computers. Nevertheless, a single dynamic simulation is still a time consuming process performed sequentially on one single computing core as the tools were originally designed. Modern high performance computing (HPC) holds the promise to accelerate a single dynamic simulation by parallelizing its kernel algorithms without compromising computational accuracy. Parallelizing a single dynamic simulation is a much more challenging problem than the contingency-type parallel computing. It requires a good match between simulation algorithms and computing hardware. This paper provides guidance for such a match so as to design and implement parallel dynamic simulation to maximize the utilization of computing hardware and the performance of the simulation. The guidance is derived through comparative implementation of four parallel dynamic simulation schemes in two state-of-the-art HPC environments: 1) message passing interface and 2) open multi-processing. The scalability and speedup performance of parallelized dynamic simulation are thoroughly studied to determine the impact of simulation algorithms and computing hardware configurations. Several testing cases are presented to illustrate the derived guidance.

The fast adoption of electric vehicles (EVs) has resulted in a growing concern for the planning and operation of the distribution power system. Thus, it is crucial to comprehensively assess the influence of EV charging load on the distribution system. To mitigate the operational risks and optimize control measures of the power network, a two-stage time series decomposition and BiLSTM deep learning architecture is proposed for accurately predicting EV charging load series. In the first stage, a Variational Mode Decomposition (VMD) evaluation method is introduced for assessing the decomposition loss, and the Lion Swarm Optimization (LSO) is employed to determine the optimal combination of decomposition parameters, thereby reducing the empirical parameter randomness and minimizing the signal loss during the decomposition process. Then, Bidirectional Short and Long-term Memory Network model (BiLSTM) is established for forecasting each sub-sequence obtained through the decomposition process. In the second stage, a DeepBiLSTM model is built to realize the accurate prediction of residual series from the first stage. The performance of the proposed architecture was tested using real-world charging load data collected from EV stations located in Southern China. It demonstrates that our proposed forecasting method can outperform traditional algorithms in terms of MAPE and RMSE metrics for 1h-ahead, 2h-ahead, 4h-ahead,and 24h-ahead EV load prediction.

This letter proposes a novel Gaussian mixture model-based ensemble Kalman filter (GMM-EnKF) approach to the accurate calibration of the parameters of machine dynamic models. This approach aims to overcome some practical challenges affecting parameter calibration accuracy. Results show the proposed approach can provide precise calibrated parameters even when the machine operates under unbalanced network conditions with non-Gaussian measurement noises.