Islanding Detection

One of the most crucial problems in distribution networks (DNs) is islanding detection. This paper presents a new Gibbs phenomenon-based hybrid method for islanding detection in low voltage (LV) DNs, which is essentially based on a combination of active and passive methods of frequency rate of change at a given moment and measurement of constant total harmonic distortion (THD). The proposed method exploits Gibbs phenomenon occurring at the interpolation of sinusoidal functions. Beside identification of Gibbs phenomenon, root mean square (RMS) of the voltage is tracked in parallel with THDU for firm islanding detection. Finally, to prove the effectiveness of the proposed islanding detection strategy, offline digital time-domain simulation studies have been performed on a test microgrid including voltage-sourced converter (VSC)-based renewable/ distributed energy resources (RERs/DERs) in MATLAB/Simulink environment for different configurations and load conditions and the results have been verified by comparing with previously-reported methods. The results indicate that the proposed method performs fast and precise islanding detection in both low-power and high-power LV microgrids with reasonable impact on power quality (PQ) according to EN 50160 standard. By combining these fields as active and passive techniques, the proposed hybrid method gives non detection zones (NDZs) and detection time (DT) much smaller compared with frequency-and harmonic-based individual islanding detection methods. Index Terms-Distributed energy resources, frequency rate of change, islanding detection, power quality, total harmonic distortion. NOMENCLATURE X(t) New interpolated function, A Signal amplitude having frequency f1, B Signal amplitude having frequency f2, f1 frequency of DG that is 52 Hz, f2 frequency of DN that is 50 Hz RMSU Root mean square value of voltage signal, D. Mlakić is with the Department of Measurement and Network Management in Electrical Energy Systems, Distribution Area, Centar", JP Elektroprivreda HZ HB" d.d,

This paper presents a new islanding detection strategy for low-voltage (LV) inverter-interfaced microgrids based on adaptive neuro-fuzzy inference system (ANFIS). The proposed islanding detection method exploits the pattern recognition capability of ANFIS and its nonlinear mapping of relation between inputs. The ANFIS monitors seven inputs measured at point of common coupling (PCC), namely root-mean square (RMS) of voltage and current (RMSU and RMSI), total harmonic distortion (THD) of voltage and current (THDU and THDI), frequency (f), and also active and reactive powers (P, Q) that are experimentally obtained based on practical measurement in a real-life microgrid. The proposed method is composed of passive monitoring of the mentioned inputs and therefore, does not influence power quality (PQ); but considerably decreases non detection zones (NDZs). In order to cover as much situations as possible, minimizing false tripping and still remaining selective, type and number of samples are introduced. Here, one of the main goals is reducing NDZ by still keeping PQ in order. Based on the sampled frequency and number of samples, we find that the proposed method has less detection time and better accuracy, compared to the reported methods. Simulations performed in MATLAB/Simulink software environment and several tests performed based on different active load conditions and multiple distributed generation (DG)s, prove the effectiveness, authenticity, selectivity, accuracy and precision of the proposed method with allowable impact on PQ according to UL1741 standard.

This paper describes the islanding phenomenon on Grid-Connected Photovoltaic (PV) Arrays for developing new techniques for islanding detection. A novel islanding prevention scheme is presented. It consists in monitoring the dc-link voltage of Voltage Source Converter (VSC). This Anti-Islanding (AI) method is validated by directly comparing the islanding detection time in various scenarios with the detection time offered by the rate of change of frequency (ROCOF) technique.
Index Terms--Anti-islanding; Grid-connected photovoltaic; Islanding detection.

This paper presents the performance of a novel hybrid islanding detection method (IDM) for multi-single-phase photovoltaic (PV) inverters based on the combination of four active methods and three passive methods. Although islanding detection in PV multi-inverter systems has been widely researched, most islanding studies are focused on three-phase inverters, rather than single-phase ones. In this paper, different active and passive methods are used to detect the islanding of four paralleled single-phase PV inverters. By combining those methods synergistically, it reduces their weakness of each method, while combining their advantages. The novelty of the proposed system methodology consists of four paralleled single-phase inverters equipped with four different active methods, named Active Frequency Drift (AFD), Sandia Frequency Shift (SFS), Sliding Mode frequency Shift (SMS), and Sandia Voltage Shift (SVS) triggered by a block composed by three passive methods: voltage frequency protection (VFP), rate of change of frequency (ROCOF), and DC-link. This novel hybrid system is studied under different detailed scenarios, where it shows its performance and characteristics.

In this paper a novel hybrid method for islanding
detection of inverter- based distributed generation is proposed.
This algorithm which is a combination of Slip mode frequencyshift
(SMS) and reactive power versus frequency (Q−f) as active
methods detects the islanding phenomena by forcing the DG lose
its stable operation and drift the frequency out of the allowed
normal range of the frequency relays. A simple passive islanding
detection scheme that relies on frequency relays such as
under/over frequency protection (UFP/OFP) would be sufficient
to detect the moment of islanding. For demonstrating the
performance of the proposed method, it is evaluated under the
IEEE 1547, UL 1741 anti-islanding test configuration and
multiple-DG operation. The studies reported in this paper are
based on time-domain simulations in the MATLAB/Simulink.
The proposed hybrid method detects islanding more efficiently
for loads with high quality factor (Qf); also it operates accurately
in condition of load switching and does not interfere with the
power system operation during normal conditions. Simulation
results prove that the proposed method decreases the time of
islanding detection and has less Non Detection Zone in
comparison to previous methods. In addition, the technique
represents to be robust under multiple-DG operations.

Integration of the distributed generators (DGs) to power system is growing thanks to their technical, economic and environment benefits. The high penetration of DGs however raises a number of technical problems. One of these problems is islanding, which occurs when one or more DGs continue to energize a portion of the power system which has been isolated from the main grid. Timely islanding detection and discrimination between islanding and non-islanding disturbance conditions are very important for the safety of the personnel and the safe operation of equipment. This paper proposes a hybrid islanding detection method for distribution systems containing synchronous distributed generation (SDG) based on two active and reactive power control loops and a signal processing technique. The operation of the control loops accelerates islanding detection at early stages of instability by processing the terminal voltage of SDG. The proposed method is validated by extensive simulations. The results demonstrate that the proposed method is able to timely and precisely detect islanding situations and can clearly distinguish between islanding and non-islanding events, even if active and reactive power between generation and demand are very close. In addition, the proposed method has negligible impacts on power quality and is capable to allow the islanded system to operate in autonomous mode after separation from the main grid.

The paper proposes an unplanned island detection method in a microgrid with micro phasor measurement unit (lPMU). The lPMU extracts certain features, by using multi domain nature of discrete fractional Fourier transform, from measured voltage data and feeds it to a random forest classifier model to make decision of island or fault occurrence. The method was tested on an IEEE 34 node distribution network. The results show that the method is robust, reliable, cost effective and increases situational awareness. The proposed method has 99.8% accuracy, 99.89% security, 98.11% reliability, zero non detection zone and detection time of 49 ms.