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This study introduces a predictive auto‐reclosure scheme based on faster‐than‐real‐time (FTRT) analysis for synchronous generator type of distributed generation (DG) units to decrease DG oscillations after successful reclosure and also enhance transient stability margin of the unit after unsuccessful reclosure. The FTRT analysis, as a new concept for the protective relaying, represents a powerful predictive auto‐reclosure scheme which is based on a state‐space model of the DG unit. After a fault scenario and the first operation of the associated breaker, the FTRT analysis predicts energy level of the DG unit and determines an optimum dead‐time corresponding to the minimum energy level of the DG unit for reclosure that can provide an appropriate margin for transient stability. The effectiveness and accuracy of the proposed method are verified based on a test system. The results confirm that the proposed approach can effectively enhance transient stability for unsuccessful reclosure and reduce the corresponding oscillations after reclosure. [ABSTRACT FROM AUTHOR]

The excitation system of a synchronous generator has a major role in proper operation and control of the generator. Loss of excitation (LOE) may cause harmful effects on the both generator and power system. This paper presents a new approach to detect LOE based on calculating the synchronous generator internal voltage. This parameter is considerably sensitive to LOE and can be used as a straightforward index to discriminate LOE events from system disturbances more quickly and robustly than the conventional indices. A novel and practical technique with minimum computational burden is presented to calculate the generator internal voltage. To evaluate the proposed algorithm, LOE events in a multimachine test system are investigated. In addition, the proposed approach is experimentally evaluated on a 10 kVA synchronous generator. Performed studies demonstrate that the proposed strategy not only noticeably decreases the LOE relay operation time, but also desensitizes its maloperation to system disturbances. [ABSTRACT FROM PUBLISHER]

Loss of excitation (LOE) event threatens both the generator and power system stability. Numerous approaches have been proposed for LOE detection, most of which use the impedance trajectory. Such methods may maloperate under system disturbances, especially power swings. This study presents a new approach for LOE detection by estimating the field flux linkage participation (FFLP) in the stator flux linkage. Intrinsically, this newly defined index mainly depends on the generator excitation system and thus highly reduces when an LOE takes place. The FFLP parameter can easily be calculated using stator voltage and current signals. All kinds of LOE events cannot be studied using most available simulation software. To cover all of them, utilising state variables, a generator model connected to a power system is implemented. Performed simulation studies demonstrate that the proposed strategy not only considerably decreases the LOE relay operation time, but also improves the LOE relay security against system disturbances. In addition, the proposed approach is experimentally evaluated on a 10 kVA synchronous generator. [ABSTRACT FROM AUTHOR]

Concerned with deploying zero-emission energy sources, reducing energy wasted through transmission lines, and managing power supply and demand, monitoring and controlling microgrids have found utter importance. Accordingly, this paper aims to investigate the efficacy of state estimation fusion for a synchronous generator as well as an induction motor in order to ameliorate system monitoring. A third-order nonlinear state-space model, that operates based on actual input data taken from the Smart Microgrid Laboratory, is assumed for each of the electrical machines. The model parameters are set according to the parameters of the electrical machines. A fusion structure based on the internet of things communication network, which is modified to increase uncertainty, is presented for fusing the state estimates. The data fusion topology is distributed and relies on two data fusion models. The first model is a set of state estimators, referred to as data input-feature output model. The second one fuses the estimators' outputs based on supervised machine learning methods, referred to as feature input-feature output model. The simulation results in MATLAB and Python show the efficiency of linear regression methods compared with other leveraged methods for data fusion. By comparing the results obtained from both simple and complex estimation filters, it can be deduced that combining simple filters, extended Kalman filter in this case, with simple data fusion methods, linear regression in this case, can produce much more accurate results in a short period of time. Besides, this study shows that the averaging operators are unsuitable for estimation fusion by referring to their convexity condition. • Regression methods can outperform other methods for the purpose of data fusion. • Simple filters combined with simple fusion methods boost state estimation accuracy. • The averaging operators are unsuitable for estimation fusion due to performing convex fusion. [ABSTRACT FROM AUTHOR]