Non-islanding Situation Research Articles

A hybrid IDM using wavelet transform for a synchronous generator-based RES with zero non-detection zone

<abstract> <p>Increased use of renewable energy sources in distribution grids has led to the growing concern over unintentional islanding. Among the available islanding detection methods, the passive methods have larger non-detection zones and are susceptible to system faults. However, they have no effect on power quality like active methods. We present a hybrid method for the islanding detection of synchronous generator-based renewable energy sources. Here, the larger non-detection zone issue of the passive islanding detection method was reduced by combining it with a wavelet transform. The rate of change of a voltage unbalanced factor was computed from the sequence components of the renewable energy source terminal voltage. Then, the factor was analyzed using a wavelet transform, and a threshold for the factor was found. The robustness of the method was tested for several islanding as well as non-islanding situations. The efficacy of the method was also tested in multi-source test systems. From the analysis, it was observed that the proposed method was fast, reliable, and had a zero non-detection zone.</p> </abstract>

Passive Island Detection Method Based on Positive Sequence Components for Grid-Connected Solar–Wind Hybrid Distributed Generation System

This paper presents the development and practical implementation of a new passive island detection method for grid-connected hybrid distributed generating systems. The method employs positive sequence voltage and current components at the point of common coupling. The product of the difference in positive sequence voltage and the difference in positive sequence current is used to produce the Islanding Detection Index (IDI). Islanding is identified when the IDI magnitude surpasses a predefined threshold over a predefined delay time. Even in the case of a zero power mismatch condition, the proposed method can identify the islanding condition in under 70 msec. Its effectiveness is validated under various operating circumstances. It provides excellent stability against various non-islanding situations, avoiding unwanted tripping. The suggested method also has the advantages of being simple and inexpensive to implement, having no adverse effects on output power quality, not requiring a classifier, not being system-dependent, and working regardless of the quantity and type of distributed generation connected to the utility grid. The obtained detection timings were compared to those previously reported in the literature, confirming the proposed approach’s superiority. The suggested work utilizes the OPAL-RT real-time hardware-in-loop environment and MATLAB/Simulink.

Angular symmetrical components-based anti-islanding method for solar photovoltaic-integrated microgrid

ABSTRACT This article examines how an innovative Angular Symmetrical Components of Voltages be applied to islanding detection and voltage unbalance factor estimation at a photovoltaic inverter-based distribution unit. Positive and Negative Sequence Components are converted into a polar form such as Line Aggregate RMS (LARMS) Voltage and Tangent Angle of Unbalance (TAU) which are determined from two line voltage signals. Three voltage relays are replaced with a single relay which compares the LARMS voltage and TAU with threshold limits to identify the condition of balance. ASCOV relay does not generate the trip signal in a non-islanding situation such as linear load switching and nonlinear load switching. The Non-Detection Zone (NDZ) of this ASCOV relay is very low compared to other relays. The proposed anti-islanding method is tested in a SPV powered microgrid using MATLAB/SIMULINK and the performance was studied under islanding and non-islanding events. Consequently, the simulation outcome indicates that the proposed technique effectively identifies the islanding condition with less computation time as 1 s and the performance of the proposed relay is compared with existing relays. The voltage unbalance factor determined by the proposed method is very close to the actual value in all types of unbalance conditions.

An Effective Islanding Detection Method With Wavelet-Based Nuisance Tripping Suppressing

The reactive power control (RPC) technique is a hopeful solution for the islanding issue in distributed generation (DG). Existing RPC approaches have downsides of inhibiting the required load reactive power or injecting/drawing undesirable excessive reactive power into the utility during normal operation. To overcome these limitations, the positive sequence voltage of point common coupling (PCC) is included in the reference of reactive power, which helps the frequency to deviate rapidly outside its thresholds. Further, power system transients which considered normal events can drift the frequency and result in a false islanding detection. In this article, different operating conditions such as load switching, voltage variations, and faults are considered. To address this issue, the suggested strategy includes a second decision-making criterion based on the discrete wavelet transform of the subtraction of sensed and nominal positive sequences of the PCC voltage. Simulation and experimental findings show that the suggested islanding detection method detects islanding rapidly and with zero nondetection zone. Furthermore, the DG and load reactive powers are closely matched under normal operation, and nonislanding situations are successfully prevented.

Performance analysis of power system parameters for islanding detection using mathematical morphology

Islanding detection is imperative for the protection of distribution system supported by distributed generation. The accuracy of islanding detection technique mainly depends on the selection of suitable parameter. In this work, the 6 different power system parameters are classified based on sensitivity and performance ability. This is the basic step in the selection of suitable parameter for islanding detection technique. The behavior of different power system parameters is examined using mathematical morphology by considering all possible islanding and non-islanding situations. It has been found, from data interpretation, that dilated reactive power parameter using inclined line structuring element indicates the best competence, among the other parameters, to detect the minute disturbances in power supply. Further, the selection of dilated reactive power parameter is justified mathematically to validate its significance. Moreover, islanding detection technique is proposed consisting of dilated reactive power parameter to distinguish the islanding from other events of similar signatures.

Auto‐correlation‐based Islanding detection technique verified through hardware‐in‐loop testing

This study describes a novel Islanding detection technique based on a discrimination factor (DF) which is derived from an auto-correlation factor (ACF) of the voltage signals acquired from the terminal of the distributed generator. The value of DF is calculated from the most affected lags of the ACF during various Islanding and non-Islanding conditions. Further, the validation of the presented scheme has been carried out by developing a hardware-in-loop laboratory setup. In this setup, a power distribution network has been modelled on a real-time digital simulator (RTDS/RSCAD) which is physically connected to the digital signal processor controller. Various non-Islanding and Islanding test scenarios have been generated. The proposed ACF-based technique is able to differentiate Islanding state with non-Islanding states even for perfect power equilibrium situation. The proposed approach can determine the Islanding state in a period of three cycles from the inception of Islanding. Finally, a relative assessment of the above algorithm with other existing methods shows its supremacy in terms of better stability during critical non-Islanding situations and lower non-detection zone in case of Islanding states.

Islanding Detection of Distributed Generation Based on Parallel Inductive Impedance Switching

This paper deals with a new islanding detection method based on parallel inductive impedance (PII) switching at distributed generation (DG) connection point, and monitoring the rate of change of voltage $(dv\!/\!dt)$ at DG output. In the proposed approach, switching the PII causes variation in the $dv\!/\!dt$ . This variation is very small in the case that DG operates in parallel with the main grid, whereas the changes will be very large during the occurrence of islanding. Therefore, the $dv\!/\!dt$ is employed to identify both the islanding and nonislanding situations; however, to better analyze the $dv\!/\!dt$ changes, a fast Fourier transform (FFT) is employed to process the variation of the $dv\!/\!dt$ . To select the type of inserted impedance, the effect of switching various impedance types on the $\text{FFT}$ $(dv\!/\!dt)$ is thoroughly analyzed. It is demonstrated that the purely inductive impedance has better performance than other impedance types. The performance of the proposed method is examined through comprehensive simulation studies in MATLAB. The simulation results indicate that the proposed method retains its efficiency for both inverters- and synchronous-based DGs. Moreover, the proposed method not only has a substantial influence on the reduction of disturbances due to the small amount of PII but also eliminates the nondetection zone entirely in comparison with other available methods.

A novel islanding detection technique using modified Slantlet transform in multi-distributed generation

In this paper, a new hybrid islanding detection scheme based on a combination of a modified Slantlet Transform (MSLT) and machine learning is applied to a passive time frequency islanding detection of multiple distributed generation units. A Harmony Search Algorithm (HSA) is used to optimally specify suitable scales of Slantlet transform and Slantlet decomposition levels for accurate islanding classification. Slantlet transform is utilized to derive the features in the required detection parameters measured from islanding and non-islanding events using identified Slantlet scales. In order to automate classification process, machine learning classifiers are utilized to detect islanding and non-islanding conditions with an objective of increasing the detection rate and avoiding nuisance distributed generation tripping during non-islanding situations. Islanding and non-islanding events are simulated for a multi-distributed generations system and used to assess the performance of the proposed anti-islanding protection method. The numerical results showing the efficiency of the proposed islanding detection technique are explained and conclusions are drawn.

An Integrated Wide-Area Protection Scheme for Active Distribution Networks Based on Fault Components Principle

An integrated wide-area protection scheme for active distribution networks (ADNs) based on a fault components principle is proposed in this paper. The structure of a three-level hierarchically coordinated system that enables the proposed protection strategy is introduced. To manage communication network failures, a backup protection strategy is also proposed. In addition, the proposed scheme can overcome the problems of complex operating conditions, such as fault conditions, in islanding and non-islanding situations, fault resistance, high-impedance faults and topology change. PSCAD/EMTDC is used in simulation analysis of protection schemes for an ADN model, and simulation results have verified the correctness and effectiveness of the protection scheme.

Kriging Empirical Mode Decomposition via support vector machine learning technique for autonomous operation diagnosing of CHP in microgrid

Combined Heat and Power (CHP) is the one of new energy resources which has been added to power system in recent years. High efficiency, Loss reducing of power system and etc, are the main advantages of CHP as same as other distributed generations. But, unwanted islanding is one of the main problems for this generation. This article presents a novel technique for CHP unit islanding detection using Kriging Empirical Mode Decomposition (KEMD) and Support Vector Machine (SVM) pattern learning technique. In this technique the variation of Intrinsic Mode Functions (IMF) of local signals in two-dimensional mode is utilized as input data of relay. An optimal signal selection model is applied to the proposed relay in order to Non-Detection Zone (NDZ) and fails detection reducing. The best signal selection is introduces based on mean square value between islanding and non-islanding conditions. Also, by considering Optimal SVM model for the proposed relay as a pattern recognizing and weighing it using shark smell optimization, this technique has overcome the threshold selection problem. This relay is applied to CHP system in a microgrid system contains various types of DGs. Many islanding and non-islanding situation in various operation conditions in the studied microgrid are simulated. The results of simulation results are show that the proposed relay is suitable for microgrid application. Negligible NDZ, high detection time, zero fail detection and low cost of this relay are the main advantages of the proposed technique.

Standalone condition diagnosing of fuel cell in microgrid composed of wind turbine/fuel cell/combined heat & power using Variational Mode Decomposition analysis model

With increasing load in the power system, several new energy resources such as Fuel Cell (FC) have been added into the systems which increased the systems complexity and uncertainty. Unwanted islanding is one of the main problems for this generation. This article presents a novel technique for fuel cell system islanding detection using Variational Mode Decomposition and Radial Bases Function pattern learning technique. In this technique the state changes of Intrinsic Mode Functions energy of THD signals in two-dimensional mode is utilized as input data of relay. An optimal signal selection model is applied to the proposed relay in order to Non-Detection Zone and fails detection reducing. The best signal selection is introduces based on mean square value between islanding and non-islanding conditions. Also, by considering Optimal Radial Bases Function model for the proposed relay as a pattern recognizing and weighing it using shark smell optimization, this technique has overcome the threshold selection problem. This relay is applied to FC system in a microgrid system contains various types of DGs. Many islanding and non-islanding situation in various operation conditions in the studied microgrid are simulated. The results of simulation results are show that the proposed relay is suitable for microgrid application. Negligible NDZ, high detection time, zero fail detection and low cost of this relay are the main advantages of the proposed technique.

State variable technique islanding detection using time-frequency energy analysis for DFIG wind turbine in microgrid system

This paper introduces a new combined technique for wind turbine islanding detection using the trajectory of state variables and wavelet transform in microgrid system. The proposed relay is utilized of energy variation state of time-frequency transform coefficients of local signals in two-dimensional space. In order to improve of the proposed relay performance, a signal selection method based on the correlation concept between islanding and non-islanding signals. From of all patterns, the best of them with high correlation in islanding status is selected for the proposed relay learning. A neuro-fuzzy machine is used as learning of selected patterns to avoid threshold selection. The proposed technique is utilized to wind turbine islanding detection in a microgrid system contains various types of distributed generation including wind turbine, Combined Heat and Power (CHP) and photovoltaic system. Many islanding and non-islanding situation including motor starting, various load and capacitor switching in various operation conditions in the studied microgrid are simulated. Millstone characteristics of the proposed technique are included passive-based technique, negligible None Detection Zone (NDZ), suitable for microgrid application, performance increment in detection and fast detection time. Operation results of the proposed relay in various conditions confirm the performance of the proposed detection relay.

Islanding Detection Technique based on Karl Pearson’s Coefficient of Correlation for Distribution Network with High Penetration of Distributed Generations

Abstract This article proposes a new Karl Pearson’s coefficient of correlation (‘r’) based islanding detection scheme which utilizes negative sequence voltage and voltage unbalance signal. In order to check the legitimacy of the proposed technique, large numbers of non-islanding and islanding events are generated by modeling IEEE 34-bus network. This scheme detects islanding situation even under 0 % power mis-match condition and thus, reduces the non detection zone (NDZ). Furthermore, the proposed technique detects islanding situation quickly as well as remains stable during the critical non-islanding situation like faults on adjoining feeder having varying location and fault resistance. Moreover, the proposed technique remains immune to variation in external system condition and fundamental frequency. The performance of the proposed technique is comprehensively compared with the schemes currently reported in the literature. This comparison apparently demonstrates the benefits of the proposed technique over existing schemes.

Placement of Synchronized Measurements for Power System Observability during Cascaded Outages

AbstractCascaded outages often result in power system islanding followed by a blackout and therefore considered as a severe disturbance. Maintaining the observability of each island may help in taking proper control actions to preserve the stability of individual islands thus, averting system collapse. With this intent, a strategy for placement of synchronized measurements, which can be obtained from phasor measurement units (PMU), has been proposed in this paper to keep the system observable during cascaded outages also. Since, all the cascaded failures may not lead to islanding situations, therefore, failures leading to islanding as well as non-islanding situations have been considered. A topology based algorithm has been developed to identify the islanding/non-islanding condition created by a particular cascaded event. Additional contingencies such as single line loss and single PMU failure have also been considered after the occurrence of cascaded events. The proposed method is further extended to incorporate the measurement redundancy, which is desirable for a reliable state estimation. The proposed scheme is tested on IEEE 14-bus, IEEE 30-bus and a practical Indian 246-bus networks. The numerical results ensure the observability of the power system under system intact as well as during cascaded islanding and non-islanding disturbances.

Current-based Islanding Detection Scheme in Presence of Distributed Generations

In this article, a new islanding detection technique based on dq0 transformation is presented for the power system network containing distributed generators. The proposed technique is based on the peak value of current (Is) that is obtained by dq0 transformation of current signals. Simulations of various islanding conditions have been carried out using PSCAD/EMTDC software package (Manitoba HVDC Research Centre, Winnipeg, MB, Canada) at different values of active and reactive power mismatches. Moreover, various non-islanding events have also been taken into account. The simulation results clearly point out that the proposed scheme completely eliminates the non-detection zone and rapidly detects islanding. At the same time, it also avoids nuisance tripping during various non-islanding events as well as for the system with multiple distributed generators. Comparative evaluation of the proposed scheme is also carried out with the existing scheme, and it has been observed that the proposed scheme remains stable during a non-islanding situation in which the existing scheme fails.

Relay logic for islanding detection in active distribution systems

This study presents a passive model to detect islanding conditions of synchronous distributed generation resources in a distribution network or a microgrid. The proposed approach uses the classification and regression tree algorithm for distinguishing between islanding and non-islanding situations. It utilises the rate of change of frequency (ROCOF) and harmonic content of the equivalent reactance seen at the location of distributed generation as input features for decision tree construction. Indeed the thresholds of the proposed input features are extracted by the decision tree algorithm. The output if-then rules of the decision tree algorithm are then utilised to make a new relay logic for islanding detection. The proposed scheme successfully classifies the input data with approximately zero non-detection zone and prevent false operation of traditional scheme. The performance of the proposed protective algorithm is compared with the traditional ROCOF relay, artificial neural network and support vector machine techniques. The proposed method is implemented in a typical microgrid, which is connected to the transmission network of the dynamic WSCC three-machine, 9bus test system with considering electric faults at upstream transmission and sub-transmission networks.

A Universal Islanding Detection Technique for Distributed Generation Using Pattern Recognition

Anti-islanding protection methods, proposed in the literature, are distributed generation (DG) type dependent or in other words work efficiently for a specific DG type (synchronous or inverter based). In this paper, we investigate the possibility of developing an efficient universal islanding detection method that can be applied to both inverter and synchronous-based DG. The proposed method relies on extracting a group of features, from measured data simulated for both types of DGs, from which the best features are selected for islanding detection. A random forest (RF) classification technique is used to detect islanding and non-islanding situations with an objective of minimizing the non-detection zone as well as avoiding nuisance DG tripping during non-islanding conditions. Islanding and non-islanding cases were generated for the IEEE 34-bus system and used to train and test the proposed technique. In the paper, k-fold cross-validation was used in order to test the accuracy of the proposed algorithm for detecting islanding. The results show that the proposed methodology has zero non-detection zone, high accuracy, and fast response when applied to both types of DGs independently of the size of the island. Among the various classification approaches investigated, the RF technique proved to be the most efficient approach for the proposed islanding detection method.

Data‐mining‐based intelligent anti‐islanding protection relay for distributed generations

A data-mining-based intelligent anti-islanding detection scheme for distributed generation (DG) protection has been presented. The process starts at deriving highly involved features using discrete Fourier transform-based pre-processor at the DG end. The features derived include both positive and negative sequence quantities and related features. Once the features are retrieved, the decision tree is trained to build a data-mining model for identifying the islanding events from non-islanding situations, including disturbances close to islanding conditions. The proposed anti-islanding relay is extensively tested on simulation model and provides encouraging results considering wide variations in operating conditions. Further, the validation is extended on real-time digital simulator module to test the efficacy of the proposed anti-islanding relay. The test results indicate that the proposed anti-islanding relay can reliably detect islanding conditions while meeting the speed criteria.