Detection Of Disturbances Research Articles

Detection of Forest Disturbances with Different Intensities Using Landsat Time Series Based on Adaptive Exponentially Weighted Moving Average Charts

Forest disturbance detection is important for revealing ecological changes. Long-time series remote sensing analysis methods have emerged as the primary approach for detecting large-scale forest disturbances. Many of the existing change detection algorithms focus primarily on identifying high-intensity forest disturbances, such as harvesting and fires, with only a limited capacity to detect both high-intensity and low-intensity forest disturbances. This study proposes an online continuous change detection algorithm for the detection of multi-intensity forest disturbances such as forest harvest, fire, selective harvest, and insects. To initiate the proposed algorithm, the time series of the Normalized Difference Vegetation Index (NDVI) is fitted into a harmonic regression model, which is then followed by the computation of residuals. Next, the residual time series is entered into the adaptive exponentially weighted moving average (AEWMA) chart. This chart adaptively adjusts the smoothing coefficients to identify both high-intensity and low-intensity disturbances. When the chart value consistently deviates from the control limit, the forest pixel is classified as disturbed. With an overall spatial accuracy of 85.2%, including 86.1% producer’s accuracy and 84% user’s accuracy, along with a temporal accuracy of 96.7%, the algorithm enables precise and timely detection of forest disturbances with multiple intensities. This method provides a robust solution for detecting multi-intensity disturbances in forested regions.

Automatic Detection of Magnetic Disturbances in Magnetic Inertial Measurement Unit Sensors Based on Recurrent Neural Networks.

This paper proposes a new methodology for the automatic detection of magnetic disturbances from magnetic inertial measurement unit (MIMU) sensors based on deep learning. The proposed approach considers magnetometer data as input to a long short-term memory (LSTM) neural network and obtains a labeled time series output with the posterior probabilities of magnetic disturbance. We trained our algorithm on a data set that reproduces a wide range of magnetic perturbations and MIMU motions in a repeatable and reproducible way. The model was trained and tested using 15 folds, which considered independence in sensor, disturbance direction, and signal type. On average, the network can adequately detect the disturbances in 98% of the cases, which represents a significant improvement over current threshold-based detection algorithms.

The risk of sleep‐related cognitive impairment increases in middle‐age to older adulthood

AbstractBackgroundThe association between sleep quality and cognition is widely established but the role of aging in this relationship has been unclear due to inconsistent findings with heterogeneous methods and a lack of studies in wide age ranges. The current study examined how age impacts the sleep‐cognition relationship using a large‐scale dataset with adults from young adulthood to older adulthood. Furthermore, we tested non‐linear effect of age to investigate the presence of age ranges in which the association between sleep and cognition is modified.MethodSample included 711 individuals (ages 36.00‐89.83, 59.66 ± 14.91, 55.7% female) from the Human Connectome Project‐Aging (HCP‐A). Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI), and cognitive outcome measures included the NIH Toolbox fluid cognition (Dimensional change Card Sort Test, List Sorting Working Memory Test, Picture Sequence Memory Test, Pattern comparison Processing Speed Test, Flanker Inhibitory Control and Attention Test) and crystallized cognition composite scores (Oral Reading Recognition Test and Picture Vocabulary Test), the Trail Making Test (TMT) A and B, and the Rey Auditory Verbal Learning Test (RAVLT). The association between sleep quality and cognitive performance was measured using linear regression models, with sex, race, use of sleep medication, and years of education as covariates. Age moderation was tested via the interaction between sleep and age on cognition, with age as linear and non‐linear (quadratic) terms.ResultThere was a significant interaction term between the PSQI and non‐linear age term (age2) on TMT‐B (p = 0.02) and NIH Toolbox crystallized cognition (p = 0.02), indicating that the association between poor sleep quality and cognitive performance was moderated by age. Further analyses indicated that there were “critical age ranges” in which the sleep‐cognition association was more salient (ages 50‐75 for TMT‐B and 66‐70 for crystallized cognition).ConclusionThe association between sleep quality and cognitive performance may be modified by age. Individuals of the middle‐age to early older adulthood may be more vulnerable to sleep‐related cognitive impairment, indicating the clinical importance of detection and treatment of sleep disturbance in this particular age group.

Power Quality Disturbance Detection Based on Partitioning-Improved Empirical Wavelet Transform

Power quality is an important indicator to measure the quality of power supply in power grids. With the large evasion accession of new energy sources, its randomness and volatility cause frequent transient power quality problems in power grids. With the large evasion accession of new energy sources, its randomness and volatility cause frequent transient power quality problems in power grids. Aiming at the detection of transient power quality disturbance, this paper proposes the empirical wavelet algorithm which improves the adaptive division. This paper improves the spectrum processing, frequency point selection and frequency band division to improve the adaptive division ability of the algorithm under complex perturbation signals. Combined with the Hilbert transform, a perturbation detection algorithm is proposed based on an improved empirical wavelet algorithm to extract the transient characteristics of the perturbation signal. Through software simulation, this method has good adaptive ability in the face of composite power mass transient disturbance and can accurately extract the transient characteristics of the signal.

Not-so-random forests: Comparing voting and decision tree ensembles for characterizing partial harvest events

Ensemble-based change detection can improve map accuracies by combining information from multiple datasets. There is a growing literature investigating ensemble inputs and applications for forest disturbance detection and mapping. However, few studies have evaluated ensemble methods other than Random Forest classifiers, which rely on uninterpretable “black box” algorithms with hundreds of parameters. Additionally, most ensemble-based disturbance maps do not utilize independently and systematically collected field-based forest inventory measurements. Here, we compared three approaches for combining change detection results generated from multi-spectral Landsat time series with forest inventory measurements to map forest harvest events at an annual time step. We found that seven-parameter degenerate decision tree ensembles performed at least as well as 500-tree Random Forest ensembles trained and tested on the same LandTrendr segmentation results and both supervised decision tree methods consistently outperformed the top-performing voting approach (majority). Comparisons with an existing national forest disturbance dataset indicated notable improvements in accuracy that demonstrate the value of developing locally calibrated, process-specific disturbance datasets like the harvest event maps developed in this study. Furthermore, by using multi-date forest inventory measurements, we are able to establish a lower bound of 30% basal area removal on detectable harvests, providing biophysical context for our harvest event maps. Our results suggest that simple interpretable decision trees applied to multi-spectral temporal segmentation outputs can be as effective as more complex machine learning approaches for characterizing forest harvest events ranging from partial clearing to clear cuts, with important implications for locally accurate mapping of forest harvests and other types of disturbances.

Power Quality Disturbances Classification Through Optimal Feature Selective Mechanism

In the Detection and classification of Power Quality Disturbances, feature selection is very important because the more number of features causes more complexity and less number of feature impacts the accuracy. Hence, this paper proposed a new feature selection mechanism called as Flexible Mutual Information based Feature Selection (FMIFS) to represent the Power Quality Disturbances with an effective set of features. FMIFS computes the Mutual Redundancy between PQDs and removes redundant features between them. Once the optimal features are obtained for each PQD, then they are fed to Multi-class Support Vector Machine (MC-SVM) for classification. MC-SVM classifies each PQD based on the feature trained to the system. At experimental analysis, we applied our method on totally 11 types of PQDs for classification and the performance is measured through recall, precision, F1-score, and False Alarm Rate (FAR).

“The Rise of Tech and the Question of Power” A Review on Detection and Classification of Multiple Power Quality Disturbances

“The Rise of Tech and the Question of Power” A Review on Detection and Classification of Multiple Power Quality Disturbances

Power grid monitoring based on Machine Learning and Deep Learning techniques

<p><strong>Background:</strong> In this work, some application examples of machine learning and deep learning techniques in the monitoring of electricity distribution services and infrastructures are proposed. Three different fields of application are considered to highlight the use of techniques based on neural networks: detection and classification of power quality disturbances, monitoring of underground cables in medium voltage lines and diagnosis of joints in high voltage overhead power lines. <strong>Methods:</strong> In the field of power grid monitoring, this work proposes a classification method based on a complex valued neural network to assess working conditions of junction regions in high-voltage overhead lines and insulating materials in medium voltage underground networks. The purpose of this method is to prevent the rupture of joint structures and the abnormal degradation of underground cables via frequency response measurements. This approach allows the direct processing of complex measurements and to reduce the computational effort compared to other methods available in the literature. <strong>Results:</strong> The results obtained in the monitoring of underground cables and joints of high voltage lines guarantee an overall classification rate higher than 90%. In the field of power quality, several deep learning and machine learning methods are proposed to detect the most common voltage disturbances. <strong>Conclusions:</strong> In this paper, an innovative use of widespread algorithms such as convolutional neural networks is proposed with excellent results. Furthermore, the use of a complex-valued neural network in electrical infrastructure monitoring is presented, introducing a minimally invasive classification method that could be instrumental in the transition from corrective to predictive maintenance in the near future.</p>

Towards tree-based systems disturbance monitoring of tropical mosaic landscape using a time series ensemble learning approach

Tree clearing and degradation inside and outside forest ecosystems in Africa are important contributors to global carbon budget and emissions. Part of the uncertainties in emission estimates, among other things, is related to the non-inclusion of disturbances across all tree-based systems and limitations in the capacity of existing methodologies to detect subtle changes or degradation. Here, we present a tree-based system (TBS) disturbance monitoring approach in a mosaic landscape in East Africa. For this purpose, high- and low-intensity disturbance detection time series algorithms, namely Breaks for Additive Season and Trend monitor (BFASTmonitor), Continuous Degradation Detection (CODED), and Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr), were used. These algorithms were tested in three approaches based on reference canopy cover change data from repeated airborne laser scanning. Data processing was performed using Collection 2 Landsat (5, 7, and 8) time series in Google Earth Engine. The approaches were based on testing the accuracy of 1) the algorithms' magnitude of change outputs using multispectral information (i.e., several vegetation indices and bands); 2) multispectral ensemble learning approach applied at each of the three algorithms; and 3) combining the multiple algorithms in an ensemble learning approach. Our results showed an improvement in the accuracy of detecting a TBS disturbance from approach 1 to 3. The best result (44% omission error and 31% commission error against reference airborne laser scanning data) was obtained when BFASTmonitor and CODED were combined in an ensemble learning approach. The shortwave infrared bands were more important for detecting TBS disturbance than green, red, and near-infrared bands. The application of the best model in TBS disturbance monitoring in the region revealed that disturbances were more dominant outside forest ecosystems than inside. Improved disturbance monitoring in TBS can contribute to more accurate emission estimates in Africa and elsewhere in the tropics.

Deep learning and automatic reference label harvesting for Sentinel-1 SAR-based rapid tropical dry forest disturbance mapping

The advent of temporally dense radar data such as the Sentinel-1 SAR have opened the door for rapid forest disturbance detection in the humid tropics. Tropical dry forest disturbance detection, however, were challenged by seasonality and more open canopy characteristics. In this manuscript, we proposed a Sentinel-1 SAR and deep learning based rapid forest disturbance detection approach for tropical dry forests. We demonstrated a weakly supervised method for reference label harvesting based on medium resolution globally available forest and forest disturbance maps. We trained a deep neural network model to derive forest and forest disturbance probabilities from Sentinel-1 images in the first step. We then implemented a probabilistic disturbance detection and refinement method to map forest disturbances in near real-time in two test regions in Paraguay and Mozambique. We mapped new forest disturbances in an emulated near real-time scenario for 2020 and 2021 and evaluated the spatial accuracy of the disturbance alerts by generating area adjusted precision, recall and F-1 score. We also evaluated the improvement in timeliness of disturbance detection by estimating mean time difference of disturbance events detection with that of Landsat-based GLAD alerts. The generated alerts in Paraguay and Mozambique achieved a precision, recall and F-1 score of 0.99, 0.61, 0.75 and 0.97, 0.51, 0.66, respectively. The proposed method detected disturbances with a mean of 21 days (± 18 days) earlier in Paraguay and 18 days (± 18 days) earlier in Mozambique than the Landsat-based GLAD alerts. These results demonstrated the efficacy of the proposed method and its viability to be used in an operational setting to generate large area rapid near real-time disturbance alerts in the dry tropics.

Utilization of Stockwell Transform and Random Forest Algorithm for Efficient Detection and Classification of Power Quality Disturbances

Power quality disturbances (PQDs) can lead to significant operational and financial losses in power systems. Accurate detection and classification of PQDs are essential for maintaining power quality and preventing power system failures. This research article introduces an innovative approach for the precise detection and classification of single- and multiple-state power quality disturbances (PQDs) using the Stockwell transform (ST) and a random forest classifier. To create realistic PQD signals, seventeen distinct classes are generated in accordance with IEEE Standard 1159, employing mathematical equations implemented in MATLAB software. The ST is employed to extract relevant features from the PQD signals, which are subsequently utilized as input for the random forest classifier. The classifier employs bootstrapping sampling to generate multiple training sets from the original dataset. Each training set is used to construct a decision tree by recursively partitioning the data based on significant features. To mitigate overfitting and enhance robustness, a random subset of features is selected at each node of the decision tree, thereby reducing tree correlation. The performance of the random forest classifier is compared with other widely utilized machine learning classifiers. The results exhibit the efficacy of the proposed approach in accurately detecting and classifying PQ events, highlighting its superiority over alternative methods.

Activity in Group-Housed Home Cages of Mice as a Novel Preclinical Biomarker in Oncology Studies.

Improving experimental conditions in preclinical animal research is a major challenge, both scientifically and ethically. Automated digital ventilated cages (DVC®) offer the advantage of continuous monitoring of animal activity in their home-cage. The potential utility of this technology remains understudied and deserves investigation in the field of oncology. Using the DVC® platform, we sought to determine if the continuous assessment of locomotor activity of mice in their home cages can serve as useful digital readout in the monitoring of animals treated with the reference oncology compounds cisplatin and cyclophosphamide. SCID mice of 14 weeks of age were housed in DVC® cages in groups of four and followed with standard and digital examination before and after treatment over a 17-day total period. DVC® detected statistically significant effects of cisplatin on the activity of mice in the short and long term, as well as trends for cyclophosphamide. The activity differences between the vehicle- and chemotherapy-treated groups were especially marked during the nighttime, a period when animals are most active and staff are generally not available for regular checks. Standard clinical parameters, such as body weight change and clinical assessment during the day, provided additional and complementary information. The DVC® technology enabled the home cage monitoring of mice and non-invasive detection of animal activity disturbances. It can easily be integrated into a multimodal monitoring approach to better capture the different effects of oncology drugs on anti-tumor efficacy, toxicity, and safety and improve translation to clinical studies.

The value of some indices of heart rate variability in the diagnosis of fetal growth retardation

Fetal growth retardation (FGR) is one of the most serious problems in current perinatology. This pathology leads not only to the deterioration of perinatal outcomes but also significantly increases morbidity during adult life. FGR is known to be featured by its delayed “neurological maturation”. This is manifested in a decrease in autonomic nervous regulation according to the fetal heart rate variability (HRV) scores and indices. The objective: to study HRV in fetuses with normal biometric indices compared to similar parameters of growth retarded fetuses. Materials and methods. The 47 pregnant women who had fetuses with normal biometric indices (I group) and 33 patients with fetal growth retardation (II group) were included in the study. The investigation of the fetal HRV was performed by processing the R–R intervals obtained via non-invasive electrocardiogram (ECG) and involved the following indices: STV (short term variations), LTV (long term variations), AC/DC (acceleration capacity/deceleration capacity), TP (total power), SI (stress index), SDNN (standard deviation of normal to normal intervals), EnRE (entropy of the time series), D2 (correlation dimension of the time series), Z (temporary irreversibility) і FL (measure of fuzzy logic, integrated measure of HRV). Results. The obtained data indicated that the indices of fetal HRV were reduced in the patients with FGR. These indices have utility in screening programs for the detection of fetal intrauterine nutrition and growth disturbances. The results of the study allow us to believe that the ability to accelerate heart rate patterns is partly preserved even during FGR. This may indicate a very ancient phylogenetic nature of the reaction of the heart system of the fetus to its motile activity. However, decelerations according to the DC were typical for FGR. A non-invasive fetal ECG could become a useful supplementary method in the current methods’ spectrum used in perinatal medicine. Conclusions. Therefore, the development of algorithms for the diagnosis of FGR requires the inclusion of the following HRV indices: STV, LTV, DC, TP, SI, SDNN, D2, Z, and FL. These biophysical markers of FGR can be used as a screening program in conditions of lack of access to high-quality ultrasound diagnostics or be a valuable addition to existing perinatal strategies.

Trawling for evidence: An ecosystem-based multi-method trawling impact assessment

Robust methods for assessing fisheries impacts are required to support evidence-based management and contribute to the assessment of protected areas. Under marine policies, such as the Marine Strategy Framework Directive, effective management requires an understanding of impacts at the fisheries scale and needs to account for the responses of various benthic components. This study investigated the impacts of trawling for Nephrops and whitefish along the Northeast England coast, comparing, and assessing benthic communities and habitats at muddy sediment sites across a gradient of fishing pressure. An ecosystem-based sampling approach assessed macrofauna, meiofauna, and sediment characteristics by combining traditional and innovative methods, conducting a balanced assessment of the north-east England trawl fishery. Combining multiple methods effectively highlighted contrasting trends between infaunal size classes. Significant negative impacts of trawling pressure were observed on macrofauna and meiofauna richness, diversity, and community composition, and sediment compaction and oxygenation. Key metrics were comparatively assessed for their suitability for the detection of benthic disturbance, with recommendations discussed in relation to future monitoring.

Advances in remote sensing for sustainable forest management: monitoring and protecting natural resources

Remote sensing has emerged as a powerful tool for the monitoring and management of forests, contributing to the sustainable utilization and protection of natural resources. This paper presents a review of recent advances in remote sensing techniques and technologies for forest management, highlighting their role in monitoring and protecting forests. The integration of remote sensing with other geospatial methods enhances the accuracy and efficiency of data acquisition, aiding in the assessment of forest cover, biomass estimation, disturbance detection, and biodiversity monitoring. Furthermore, the potential of remote sensing for supporting decision-making processes in sustainable forest management is explored, emphasizing its versatility, cost-effectiveness, and ability to provide invaluable insights at both local and global scales. The paper concludes by discussing current challenges and future opportunities in remote sensing applications for sustainable forest management.

Hardware Realization of an Innovative Disturbance Detection Algorithm for Control Strategy of Solid-State Transfer Switch

An accurate and fast Solid-State Transfer Switch (SSTS) has the potential to automate conventional utility network. Presently, It consumes most of the transfer time in detecting the condition for initiating the transfer process. Recent developments in the power semiconductor devices technology have reduced the switching time to the tune of nanoseconds. Hence, time consumed by the disturbance detection is significant in SSTS. IEEE Std. 1100-2005 and IEEE Std. 446-1995 requires fast load transfer for SSTS. Realization of an innovative machine learning technique is implemented on three AVR microcontrollers for each of three phases. The microcontroller executes segmentation of acquired signal and calculates derivative of each segment. From each pre-processed 10-sample segment, mean absolute deviation (MAD) and energy (E) features are extracted. On the basis of feature values, SVM classifier with the linear kernel detects disturbance. Logical OR gate generates transfer enable signal to initiate the transfer process. Results of case studies appreciate the ability of proposed hardware to detect common disturbances in power system under different operating conditions. The hardware provides a feasible solution for solid state transfer switch with required accuracy and speed.

Simulation and FPGA Implementation of Transient Power Quality Disturbance Detection

With the access to new energy in the smart grids and the massive use of nonlinear and unbalanced large loads, the problem of power quality becomes more and more serious. At present, the transient power quality disturbance problem has been paid more attention to. The research on transient power quality is relatively late, and various detection methods and technologies are not mature. It is necessary to use more efficient methods to detect and identify transient power quality to improve the safety of electricity use. This paper proposes to detect transient power quality disturbance by wavelet transform. In this paper, the original signal modelling of five kinds of power quality problems such as voltage sag and voltage swell. The 9/7 wavelet lifting coefficient is optimized in this paper and is carried out on the MATLAB software platform to improve the processing speed of the algorithm and realize parallel processing. The simulation results show that the improved lifting wavelet processing can effectively detect the transient power quality disturbance.

Islanding detection and classification of non-islanding disturbance in multi-distributed generation power system using deep neural networks

This paper proposes a statistical feature-based deep neural network (S-DNN) islanding detection technique and classification of non-islanding disturbances for hybrid systems with both synchronous and inverter-based distributed generators. The proposed technique extracts five statistical features from the point of common coupling (PCC)'s three-phase voltage. Several islanding and non-islanding scenarios for the test system were developed in the MATLAB environment. The S-DNN model is employed to effectively detect islanding and classify non-islanding disturbances using extracted features. In the detection phase of this study, the model is designed to identify events that occur in both islanded and non-islanding modes, providing the capability to accurately detect islanding conditions. In the classification phase, the model focuses specifically on non-islanding disturbances, such as line faults, load switching, and capacitor switching disturbances, enabling precise categorization of these distinct types of events. By leveraging the S-DNN model's capabilities, this study aims to enhance both islanding detection and non-islanding disturbance classification. By introducing white Gaussian noise with varying signal-to-noise ratios (30 dB and 40 dB) to the data, we aimed to evaluate the method's ability to handle and mitigate the effects of noisy and uncertain input signals. The inclusion of noise was intended to simulate realistic conditions where measurement inaccuracies or disturbances can be present. The decision tree (DT), multilayer perceptron (MLP), and support vector machine (SVM) are put up against the proposed technique, and it performs better than them in terms of accuracy, precision, recall, and F1 score.

ReCuSum: A polyvalent method to monitor tropical forest disturbances

Change detection methods based on Earth Observations are increasingly used to monitor rainforest in the intertropical band. Until recently, deforestation monitoring was mainly based on remotely sensed optical images which often face limitations in humid tropical areas due to frequent cloud coverage. This leads to late detections of disturbance events. Since the launch of Sentinel-1 acquiring images with a revisit time of 12 days and a spatial resolution of 5 × 20 m in Brazil, Synthetic Aperture Radar (SAR) images have been increasingly used to monitor deforestation. In this study, we propose a multitemporal version of the change detection method we previously applied to timeseries of Sentinel-1 SAR images, to monitor deforestation/degradation in the Congo rainforest. Our approach is based on a Cumulative Sum (CuSum) method combined with a spatial recombination of Critical Thresholds (CuSum cross-Tc). The newly developed multitemporal CuSum method (ReCuSum) was applied to a time-series of 82 dual polarization (VH, VV) Ground Range Detected (GRD) Sentinel-1 images acquired in the Para State, in the Brazilian Amazonia, from 29/09/2016 to 01/07/2019. The ReCuSum method consists of iteratively applying the CuSum cross-Tc to monitor multiple changes in a time-series by splitting the time-series at each date of detected change and by independently iterating over the time periods resulting from the splits. The number of changes in the time-series was then analysed according to the vegetation type (Forest, non-forest vegetation) determined by visual inspection of optical Sentinel-2 image and PlanetScope monthly mosaic. This showed a difference between non-forest vegetation and forested areas. A threshold based on the number of changes (Tnbc) was then developed to differentiate forest from non-forest disturbances. The ability to monitor non-forest vegetation was analysed: the CuSum cross-Tc detected up to 90% of the total non-forest vegetation area over the study region in the past period. After removing past disturbances and past non-forest vegetation, then removing the pixels covered with non-forest vegetations based on Tnbc, the application of the ReCuSum led to a precision of 81%, a recall of 68%, a kappa coefficient of 0.72 and a F1- score of 0.74 in forest disturbance monitoring. According to these results, ReCuSum applied to Sentinel-1 time-series of images can be used for efficient forest disturbance monitoring and for generating a forest / non-forest map after the application of newly developed post-processing steps. Sentinel-1 imagery can be used for both Forest / Non-forest mapping and for forest disturbance detection. ReCuSum was released as an open-source GIT project available at: https://forgemia.inra.fr/bertrand.ygorra/cusum-deforestation_monitoring.

A data driven based adaptive unit protection for transmission line integrated with large penetrated wind farms

The dynamic fault current characteristics, transient behaviour, and changing output power of large-scale doubly fed induction generator-based wind farms (WFs) have severe influence on transmission line conventional current differential schemes. Under normal conditions, the current difference between two ends of transmission line grows as its length increases. The capacitive charging current then might equal load current if power output of WF is low. In the presence of WFs, Traditional differential protection techniques are experiencing challenges. In order to ascertain the possible impacts of various scenarios with substantial offshore wind farms connected to transmission lines on differential relay characteristics, a data-driven approach is proposed in this paper. The STMPM based signal reconstruction method is used to compute the per-phase differential current and then their first and second-order derivatives are estimated to find the net change in the current magnitude. Finally, the Euclidean distance between these first and second derivative components are calculated and employed as a disturbance detection index (DDI). The conducted PSCAD/EMTDC simulation studies demonstrate satisfactory operation of proposed startergy for a wide range of disturbances, including external and internal faults, fault resistance, variable fault location, change in WF penetration, and various kinds of non-fault switching events.