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Wavelet transform is proposed in this paper for detection of islanding and fault disturbances distributed generation (DG) based power system. An IEEE 14-bus system with DG penetration is considered for the detection of disturbances under different operating conditions. The power system is a hybrid combination of photovoltaic, and wind energy system connected to different buses with different level of penetration. The voltage signal is retrieved at the point of common coupling (PCC) and processed through wavelet transform to detect the disturbances. Further, energy and standard deviation (STD) as performance indices are evaluated and compared with a suitable threshold in order to analyze a disturbance condition. Again, a comparative analysis between the existing and proposed detection is studied to prove the better performance of wavelet transform.
Uninterruptible power supply is the main motive of power utility companies that motivate them for identifying and locating the different types of faults as quickly as possible to protect the power system prevent complete power black outs using intell
A significant portion of the literature on fault localization assumes (more or less explicitly) that there are sufficient reliable measurements to guarantee that the system is observable. While several heuristics exist to break the observability barr
In this work, we performed a thorough comparative analysis on a radio frequency (RF) based drone detection and identification system (DDI) under wireless interference, such as WiFi and Bluetooth, by using machine learning algorithms, and a pre-traine
For additive actuator and sensor faults, we propose a systematic method to design a state-space fault estimation filter directly from Markov parameters identified from fault-free data. We address this problem by parameterizing a system-inversion-base
Submission withdrawn because the authors erroneously submitted a revised version as a new submission, see nlin.CD/0002028.