For any power system, the reliability of measurement data is essential in operation, management and also in planning. However, it is inevitable that the measurement data are prone to outliers, which may impact the results of data-based applications. In order to improve the data quality, the outliers cleaning method for measurement data in the distribution network is studied in this paper. The method is based on a set of association rules (AR) that are automatically generated form historical measurement data. First, the association rules are mining in conjunction with the density-based spatial clustering of application with noise (DBSCAN), k-means and Apriori technique to detect outliers. Then, for the outliers repairing process after outliers detection, the proposed method uses a distance-based model to calculate the repairing cost of outliers, which describes the similarity between outlier and normal data. Besides, the Mahalanobis distance is employed in the repairing cost function to reduce the errors, which could implement precise outliers cleaning of measurement data in the distribution network. The test results for the simulated datasets with artificial errors verify that the superiority of the proposed outliers cleaning method for outliers detection and repairing.

With an increasing application of Phase Measurement Units in the smart grid, it is becoming inevitable for PMUs to operate in severe conditions, which results in outliers and missing data. However, conventional techniques take excessive time to clean outliers and fill missing data due to lacking support from a big data platform. In this paper, a flexible ensemble algorithm is proposed to implement a precise and scalable data clean by the existing big data platform “Apache Spark.” In the proposed scheme, an ensemble model based on a soft voting approach utilizes principal component analysis in conjunction with the K-means, Gaussian mixture model, and isolation forest technique to detect outliers. The proposed scheme uses a gradient boosting decision tree for each extracted feature of PMUs for the data filling process after detecting outliers. The test results demonstrate that the proposed model achieves high accuracy and recall by comparing simulated and real-world Phase measurement unit data using the local outlier factor algorithm and Density-Based Spatial Clustering of Application with Noise (DBSCAN). The mean absolute error, root mean square error and R²-score criteria are used to validate the proposed method’s data filling results against contemporary techniques such as decision tree and linear regression algorithms.

This paper proposes a determination method of the cascaded number for lumped parameter models (LPMs) of the transmission lines. The LPM is used to simulate long-distance transmission lines, and the cascaded number significantly impacts the simulation results. Currently, there is a lack of a system-level determination method of the cascaded number for LPMs. Based on the theoretical analysis and eigenvalue decomposition of network matrix, this paper discusses the error in resonance characteristics between distributed parameter model and LPMs. Moreover, it is deduced that optimal cascaded numbers of the cascaded π-type and T-type LPMs are the same, and the Г-type LPM has a lowest analog accuracy. The principle that the maximum simulation frequency is less than the first resonance frequency of each segment is presented. According to the principle, optimal cascaded numbers of cascaded π-type, T-type, and Г-type LPMs are obtained. The effectiveness of the proposed determination method is verified by simulation.

The integration of large-scale distributed photovoltaics (DGPVs) and the generation of distributed photovoltaics (PVs) and loads with distinct characteristics in different transformer areas causes voltage problems in distribution networks, significantly compromising operational reliability and economy. To address this challenge, this study proposes the installation of a relocatable static var compensator (RSVC) to enhance the voltage regulation capability in addition to conventional voltage regulation methods. An RSVC can be deployed at critical nodes of distribution lines to provide continuous adjustable reactive power. RSVCs’ relocation capability in response to seasonal shifts in reactive power demand makes them an effective solution for spatiotemporal load disparities across transformer areas. A bi-level planning framework is established by first generating multiple typical scenarios based on load categories and their seasonal characteristics. The lower level achieves optimal operation in multiple scenarios through the coordination of active–reactive power regulation devices. The upper level employs a particle swarm optimization algorithm to determine the optimal siting and sizing of energy storage and the RSVC, iteratively invoking the lower-level model to minimize the total investment and operational costs. Validation was conducted on a modified IEEE 33-node test system. The results demonstrate that the proposed method effectively mitigates voltage violations caused by DGPVs and spatiotemporal load disparities while significantly enhancing the economic efficiency of distribution networks.

Rural integrated energy systems (RIESs) suffer from volatile renewable generation, surging agricultural peak loads, and unevenly distributed forecast errors, which severely undermine scheduling robustness and economic efficiency. Conventional fixed-scenario robust scheduling cannot dynamically adapt to error variations, leading to either excessive conservatism or insufficient operational safety. This paper proposes a forecast-error-driven multi-time-scale hybrid robust-scenario scheduling framework with a three-layer closed-loop architecture: day-ahead multi-scenario robust baseline scheduling, intraday error-driven rolling correction, and real-time dynamic-simulation calibration. Case studies on a practical Yunnan rural system verify that within ±25% forecast error, the proposed method reduces operating costs by 12.8%, increases the renewable energy accommodation rate by 22.6%, lowers the dynamic constraint-violation rate below 0.05%, and maintains the average solution time within 9 s, achieving a superior balance of resilience and economy for complex rural operations.