ISLANDING is one of the important challenges in power networks in the presence of distributed generation which is considered an undesirable incident due to the possibility of damage to operators, network equipment and consumers. Therefore, it is necessary to quickly detect ISLANDING and make a decision regarding the connection status of local distributed generation units in the network. In this paper, a passive ISLANDING DETECTION method is proposed using ADAPTIVE threshold-based instantaneous frequency droop characteristic. The proposed threshold limit is dynamically changed depending on under studied conditions in order to discriminate the island from load changes, capacitor bank switching, motor start-up and short circuit types. Two medium voltage networks of Cigre and 34-bus IEEE are used to evaluate the proposed method. The simulations are performed in Digsilent software and implementation of the proposed method carried out in MATLAB software. The simulation results indicate that the island is correctly detected with appropriate accuracy in a short period of time from other disturbances in the presence of wind, solar and diesel types of distributed generations.

Integration of Distributed Generations (DGs) in power grids is expected to play an essential role in the infrastructure and market of electrical power systems. Microgrids are small energy systems, capable of balancing captive supply and requesting resources to retain stable service within a specific boundary. Microgrids can operate in grid-connected or ISLANDING modes. Effective ISLANDING DETECTION methods are essential for realizing the optimal operation of microgrids. In this paper, a new passive ISLANDING DETECTION method is presented according to the change rate of DG’s voltage over active power index. This technique has been applied on inverter-based and synchronous-based microgrids. The efficiency of the proposed method is verified through a comprehensive set of simulation studies carried out in Matlab/Simulink.

Due to the increasing need to distributed energy resources in power systems, their problems should be studied. One of the main problem of distributed energy resources is unplanned ISLANDING. The unplanned ISLANDING has some dangers to the power systems and the repairman which are works with the incorrect devices. In this paper, a passive local method is proposed. The proposed method is based on wavelet transform and a new classifier named as wavenet. The wavelet transform is used to extract features from the current waveform of current at the point of common coupling (PCC) point. PCC is assumed as the connection point of distributed generation to the distribution system. The proposed method is implemented on a 15 bus grid in MATLAB/SIMULINK software. The results show the high accuracy of ISLANDING DETECTION of the proposed method. In this paper, one wind turbine is assumed as a distributed resource.

A critical protection requirement for grid connected distributed generators (DG) is antiISLANDING protection. In this paper, a new ISLANDING DETECTION method for any possible network loading is proposed based on utilizing and combining various system parameter indices. In order to secure the DETECTION of ISLANDING, eight intentional disturbances are imposed to the system under study in which two sets of them simulate the ISLANDING condition. The proposed technique uses the ADAPTIVE notch filters for extracting the frequency of oscillation of generator’s output waveform as one of the output parameter indices. An advantage of this technique is that it does not necessitate varying the ISLANDING DETECTION boundaries under various system loading conditions.

This paper investigates a new integrated diagnostic system for ISLANDING DETECTION by means of a neural network approach for distributed generation. ISLANDING is an important concern for grid connected distributed resources due to personnel and equipment safety. Several methods based on passive and active DETECTION scheme have been proposed. While passive schemes have a large non DETECTION zone (NDZ); concern has been raised on active method due to its degrading power quality effect. Reliably detecting this condition is regarded as an ongoing challenges in existing methods are not totally satisfactory. The main emphasis of the proposed scheme is to reduce the NDZ to as close as possible and to keep the output power quality unchanged. In addition, this technique can also overcome the problem of setting the DETECTION thresholds inherent in the existing techniques. In this study, we propose to use a radial basis neural network for ISLANDING DETECTION.The proposed algorithm is compared with the widely used rate of change of frequency relays (ROCOF) and was found to workeffectively in situations where ROCOF fails. This approach utilizes rate of change of frequency at the target distributed generation location and was fed to the radial basis neural network for intelligent ISLANDING DETECTION. Hence a better reliability is provided. This approach utilizes the artificial neural network (ANN) as a machine learning technology for processing and analyzing the large data sets provided from network simulations using MATLAB software. To validate the feasibility of this approach, the method has been validated through several conditions and different loadings, switching operations, and network conditions. Simulation studies showed that the RBNN-based algorithm detects ISLANDING situation more accurately than other ISLANDING DETECTION algorithms.