Coordinate correction algorithm for WLAN indoor positioning

In the fingerprint matching-based wireless local area network(WLAN) indoor positioning system,Kalman filter(KF) is usually applied after fingerprint matching algorithms to make positioning results more accurate and consecutive.But this method,like most methods in WLAN indoor positioning field,fails to consider and make use of users' moving speed information.In order to make the positioning results more accurate through using the users' moving speed information,a coordinate correction algorithm(CCA) is proposed in this paper.It predicts a reasonable range for positioning coordinates by using the moving speed information.If the real positioning coordinates are not in the predicted range,it means that the positioning coordinates are not reasonable to a moving user in indoor environment,so the proposed CCA is used to correct this kind of positioning coordinates.The simulation results prove that the positioning results by the CCA are more accurate than those calculated by the KF and the CCA is effective to improve the positioning performance.

Intelligent Assessment of Active and ReactivePower Flow with Satisfying Accuracy forN-k_(1)-k_(2) Cascading Outages

Addressed to the N-k_(1)-k_(2) cascading outages,it is computationally burdensome for the reliable calculation of active and reactive power flows.This paper builds a comprehensive framework with three algorithms,including the distribution factor(DF),the Newton-Raphson(NR),and the first iteration of NR algorithm(termed as 1J).Classifiers are designed to determine whether the NR algorithm should be employed for accuracy.Classifier features are extracted upon the analytical error of 1J.As reactive power is partially considered in the 1J but neglected in the DF algorithm,the deviation between the solutions is taken as one crucial feature.The support vector machine(SVM)is then utilized for classifier training.As the deep integration of the causal inference and the statistical paradigm,this framework calculates active and reactive power flows rapidly,reliably,and robustly.The effectiveness and robustness are fully validated in three typical IEEE systems.

A novel coordinated control for NZEB clusters to minimize their connected grid overvoltage risks

The increasing applications of net-zero energy buildings (NZEBs) will lead to more frequent and larger energy interactions with the connected power grid, thereby being able to result in severe grid overvoltage risks. Control optimization has been proven effective to reduce such risks. Existing controls have oversimplified the overvoltage quantification by simply using the aggregated power exchanges to represent the connected grid overvoltages. Ignoring the complex voltage influences among the grid nodes, such oversimplification can easily result in low-accuracy impact evaluations of the NZEB-grid energy interactions, thereby causing non-optimal/unsatisfying overvoltage mitigations. Therefore, this study proposes a novel coordinated control method in which a power-distribution-network model has been adopted for more accurate overvoltage quantification. Meanwhile, the battery operations of individual NZEBs are iteratively coordinated using a sequential optimization approach for achieving the global optimum with substantially reduced computation complexity. For verifications, the proposed coordinated control has been systematically compared with an uncoordinated control and a conventional coordinated control in grid overvoltage minimization. The study results show that the overvoltage improvements can reach 23.5% and 12.3% compared with the uncoordinated control and the conventional coordinated control, respectively. The reasons behind the improvements have also been analyzed in detail. The proposed coordinated control can be used in practice to improve NZEB-clusters’ grid friendliness.