Search Results (4)

This paper designs a multi-variable hybrid islanding-detection method (HIDM) using signal-processing techniques. The signals of current captured on a test system where the renewable energy (RE) penetration level is between 50% and 100% are processed by the application of the Stockwell transform (ST) to compute the Stockwell islanding-detection factor (SIDF) and the co-variance islanding-detection factor (CIDF). The signals of current are processed by the application of the Hilbert transform (HT), and the Hilbert islanding-detection factor (HIDF) is computed. The signals of current are also processed by the application of the Alienation Coefficient (ALC), and the Alienation Islanding Detection Factor (AIDF) is computed. A hybrid islanding-detection indicator (HIDI) is derived by multiplying the SIDF, CIDF, AIDF, and an islanding weight factor (IWF) element by element. Two thresholds, designated as the hybrid islanding-detection indicator threshold (HIDIT) and the hybrid islanding-detection indicator fault threshold (HIDIFT), are selected to detect events of islanding and also to discriminate such events from fault events and operational events. The HIDM is effectively tested using an IEEE-13 bus power network, where solar generation plants (SGPs) and wind generation plants (WGPs) are integrated. The HIDM effectively identified and discriminated against events such as islanding, faults, and operational. The HIDM is also effective at identifying islanding events on a real-time distribution feeder. The HIDM is also effective at detecting islanding events in the scenario of a 20 dB signal-to-noise ratio (SNR). It is established that the HIDM has a small non-detection zone (NDZ). The effectiveness of the HIDM is better relative to the islanding-detection method (IDM) supported by the discrete wavelet transform (DWT), an IDM using a hybridization of the slantlet transform, and the Ridgelet probabilistic neural network (RPNN). An IDM using wavelet transform multi-resolution (WT-MRA)-based image data and an IDM based on the use of a deep neural network (DNN) were used. The study was performed using the MATLAB software (2017a) and validated in real-time using the data collected from a practical distribution power system network. Full article

In the arena of modern electrical power distribution systems, distributed generators (DGs) are emerging as a manifestation of electric power personalization. Even though DGs have various advantages, unintentional islanding phenomena caused by DGs during abnormal grid operations can damage equipment connected to the grid. Therefore, islanding detection mechanisms are essential for DGs in grid-connected mode to disconnect the DG from the grid in case of grid abnormalities by obeying to specific grid codes. In this regard, a novel approach to develop a secure hybrid islanding detection method (IDM) is presented in this paper. The proposed hybrid IDM is developed by combining two passive IDMs known as rate of change of active power and rate of change of reactive power with an active IDM called load connecting strategy. An 11 kV Malaysian distribution system integrated with three types of DGs, namely synchronous generator, photovoltaic, and biomass, has been chosen as a testbed for the verification of the proposed hybrid IDM. Seven different case studies have been conducted in the PSCAD/EMTDC platform to validate the performance of the proposed IDM for islanding and non-islanding events. The simulation results confirm that the proposed IDM can detect islanding within 0.09 s, which is within 2 s complying with IEEE and IEC standards. Further, a comparative study based on the detection time and non-detection zone has been carried out, which has confirmed that the proposed IDM demonstrates better performance compared to the previously developed hybrid IDMs. Full article

This paper proposes a new hybrid islanding detection method for grid-connected photovoltaic system (GCPVS)-based microgrid. In the presented technique, the suspicious islanding event is initially recognized whilst the absolute deviation of the point of common coupling (PCC) voltage surpasses a threshold. After an intentional delay, a transient disturbance is injected into the voltage source inverter’s d-axis reference current to decline the active power output. As a result, the PCC voltage reduces in islanding operating mode whilst its variation is negligible in the grid presence. Therefore, the simultaneous drop of PCC voltage and active power output is used as an islanding detection criterion. The effectiveness of the proposed algorithm is investigated for various islanding and non-islanding scenarios for a practical distribution network with three GCPVSs. The simulation results in MATLAB/Simulink show successful islanding detection with a small non-detection zone within 300 ms without false tripping during non-islanding incidents. In addition to the precise and fast islanding classification, the presented scheme is realized inexpensively; its thresholds are determined self-standing, and its output power quality degradation is eminently small. Moreover, the active power output is restored to the nominal set after islanding recognition, enhancing the chance of GCPVS generation at its highest possible level in the autonomous microgrid. Full article

The emergence of Distributed Generation (DG) in the electric system has brought about the appearance of the islanding phenomenon. In AC networks, there are a lot of Islanding Detection Methods (IDMs) have been studied. However, not too much IDMs in DC networks have been published because of the absence of frequency and reactive power. The hybrid IDM based on injected perturbation signal and rate of change of power output is proposed. This IDM can detect islanding condition not only in the worst case (the power of load and PV are equal) but also in another case (the power of load is greater than the power of PV). It can be applicable to both single and multi-PV operation scenarios. Besides, the effectiveness of the proposed method is verified by simulation in Matlab/Simulink. Full article