Anti-islanding

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This paper proposes a robust hybrid anti-islanding method for Inverter-based Distributed Generation (DG). This method is based on Active and passive detection techniques. Active anti islanding technique generates disturbances at the output of the distributed generation by Positive feedback and Continuous feedback signal injection which is based on DQ implementation. On the other hand passive anti islanding technique detects islanding by measuring voltage, frequency, active and reactive power and total harmonic distortion (THD). This technique uses resistive, capacitive and inductive connected in parallel RLC load and compares the performance of the technique with and without the controller. This method of control is shown to be robust, able to detect islanding under different types of loads, and fast in response. Possible islanding conditions are simulated and verified with full analysis.

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5685901

Protection coordination as well as anti-island protection play significant role in the process of biomass power plant connection on the distribution network. Distribution generation island operation in Croatia is unacceptable according to the existing National grid code Paper presents a protection coordination of all passive protections used in the real biomass power plant and connected distriubution network feeder. Short-circuits three phase, two phase and single line to ground faults and generator islanding simulations have been performed and simulated in the time domain at the different network locations using DIgSILENT Power Factory software. The time-current plots coordination of protective devices are made using Smart PDC module in Easy Power Protector software tool.

In this paper hybrid anti-islanding algorithm for utility interconnection of distributed generation is proposed. This algorithm is based on two detection schemes; namely active and passive. Active detection algorithm generates disturbances at the output by Positive feedback and Continuous feedback signal injection which is based on DQ implementation. On the other hand passive detection algorithm detects islanding by measuring voltage, frequency, active and reactive power. Using total harmonic distortion (THD) as an additional parameter is proposed in this paper. Passive detection algorithm uses several types of loads such as resistive, capacitive and inductive connected in parallel and compares the performance of the algorithm in each case.

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5412095&tag=1

This paper proposes three phase grid connected anti-islanding controller based on distributed generation interconnection. This method is a combination of two detection methods namely active and passive. Firstly, Active islanding detection generates disturbances at the output of the distributed generation by use of Positive feedback and Continuous feedback signal injection which is based on DQ implementation. Secondly, passive islanding detection method is based on measuring the system parameters such as voltage, frequency, active and reactive power and total harmonic distortion (THD). This method use three phase RLC load and compares the performance of the technique with and without the anti islanding controller. Islanding conditions are simulated and verified with full analysis simulation and experimental result.

ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5697673

The Anti Islanding topics and idea with BCJ Controls. We discuss it in this PDF for you to know what is anti Islanding and what is the importance of applying it to the power system. Visit us for more details: http://bcjcontrols.com.au/.

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—Small-scale distributed energy systems possess the potential to meet local demand of power as well as feed the excess power to the grid. In such grid-tied systems, it is necessary to ensure the reliable tripping of grid-interfaced inverters whenever the grid is unintentionally disconnected. As such, anti-islanding protection schemes are crucial for grid-interfaced Distributed Generation (DG) systems which assure the safety of critical loads, devices and personnel. This paper evaluates the implementation of various FPGA based passive anti-islanding protection schemes and their comparative performance. The synchronization of the DG inverter with the grid is ensured with a Moving Average Filter based Phase Locked Loop (MAF-PLL). The filter also aids for providing an input to the Over/Under Frequency and the Rate of Change of Frequency (ROCOF) protection techniques that has been employed for protection against events of islanding in the DG system. The dc-link capacitor voltage is a reflection of the active power exchange at the Point of Common Coupling (PCC) and hence any mismatch at the PCC causes the dc-link voltage to fluctuate. An over and under voltage protection scheme at the PCC has also been used. The schemes are tested in simulation as well as hardware co-simulation environment to validate their functioning. Index Terms—Field Programmable Gate Array (FPGA),Photovotaic (PV).

This paper presents a passive islanding detection method for inverter-based distributed generation based on empirical mode decomposition (EMD) technique. The voltage of point of common coupling (PCC) is measured and its intrinsic mode functions (IMFs) are obtained using EMD. The first IMF component of PCC per unit voltage is the parameter used for islanding detection. Performance of the proposed method is evaluated for single-DG and multi-DG cases. Simulation results performed in MATLAB/SIMULINK environment show that the islanding can be detected in less than two cycles, even for zero power mismatch.Moreover, the proposed method functions properly for various configurations of multi-DG systems, DGs switching events, various loadings of DGs and different DG interface controls.

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.

Many techniques used and still in usage for solving the problem of islanding detection are intrinsically passive, active, or hybrid of both. Each one of them has its own benefits and drawbacks. In this paper, we propose a method, which takes the advantage of a machine learning (ML)-based algorithm, namely, support vector machine (SVM), in order to produce the results more efficiently. The results of the simulations based on the model and experimentally measured parameters of a real-life practical photovoltaic (PV) plant give much better output than the traditional reported methods. During the tests and simulations, an additional problem, namely, grid fault, emerged, posing new challenges for the proposed method. Occurrences of islanding and grid fault are grouped together with the same kernel dimension and no custom hyperplane bordering. Discrimination between islanding and grid fault events is an essential dilemma, which is handled by the proposed SVM-based algorithm to achieve more precision in islanding detection and simultaneously detect the grid faults authentically. Nondetection zones (NDZs) and detection time (DT) are tested using two dimensions, namely, the generated active energy from PV plant (0%-110% of P n) and distribution network voltage levels (±10% of U n). Simulations based on the model and parameters of a real-life practical PV power plant are performed in MATLAB/Simulink environment, and several tests are executed for several scenarios. Finally, comparisons with previously reported techniques prove the effectiveness, authenticity, selectivity, accuracy, and precision of the proposed islanding and grid fault detection strategy with allowable impact on power quality according to UL1741 and its superiority over other methods.

The question of the islanding operation of distributed generations (DGs) and that of the current inadmissible networking policy of islanding operations sets a demanding task for an efficient islanding detection, preferably from a single place, using either passive or active islanding-detection method. Islanding detection shall be faster than the fast automatic reclosing of the connected transmission line connected to the DG unit. This paper deals with the detection method of islanded operation mode for the large power synchronous generator-based DGs in bio-mass power plants. The proposed method has been applied to a practical 10-MW bio-mass power plant and experimentally tested to verify the simulation results. The rate of change of reactive power (dQ/dt >) criterion is derived as a yardstick for islanding detection. The practical power plant model has been implemented to be simulated in DIgSILENT PowerFactory and MATLAB/Simulink software environments. Offline digital time-domain simulations and experimental tests on a practical bio-mass power plant reveals that the proposed method efficiently, and authentically detects islanding phenomena with high accuracy, low detection time, very small number of non-detected zones and, with almost zero power balance (ΔP = 0).

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 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 of the voltage is tracked in parallel with $THD_{{U}}$ 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-based renewable/distributed energy resources 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 according to EN 50160 standard. By combining these fields as active and passive techniques, the proposed hybrid method gives smaller non-detection zones and shorter detection times when compared to frequency- and harmonic-based individual islanding detection methods.

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 ...

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. S imulations performed in MATLAB/S imulink 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. Index Terms-Adaptive neuro-fuzzy inference system; distributed generation; inverter-interfaced microgrid; islanding detection, power quality; total harmonic distortion. I. INTRODUCTION A.