Conventional Protection Research Articles

Singular Value Decomposition Based Pilot Protection for Transmission Lines With Converters on Both Ends

The modular multilevel converter-based high voltage direct current transmission (MMC-HVDC) technology has become an effective integrating method for large-capacity offshore wind farms. However, if AC side faults occur between the MMC and the wind farm, conventional current differential protection methods based on phasor measurements might not detect the large fault current at the wind farm and the MMC, resulting in poor protection performance. In the worst case, MMC blocking may occur during faults near the MMC station, causing relay refusal. The fault currents provided by both converters are analyzed in detail. Due to the fault-ride-through control objectives, it is proved that the singularity of fault current between wind farms and MMC is different. Therefore, a novel singular value decomposition (SVD) based pilot protection is proposed to solve this problem. A Hankel matrix is established for the short-circuit current at each end of the AC line. The Hankel matrix rapidly detects the singularity of the signal and its derivative, and the SVD method uses a short time window to extract the waveform features. This approach enables the detection of the fault features before MMC blocking in the worst-case fault scenario and prevents the incorrect fault identification caused by the minor amplitude of fault current provided by the converters. A detailed model of an offshore wind farm connected to the MMC-HVDC system is established in the real-time digital simulator (RTDS). A protection device compliant with industry standards is used to implement the SVD-based protection algorithm. Hardware-in-loop dynamic experimental tests are conducted to verify the performance of the proposed protection method. The experimental results demonstrate that the SVD-based protection method has excellent performance during various fault scenarios.

Fault Detection and Classification in Transmission Lines Connected to Inverter-Based Generators Using Machine Learning

Integrating inverter-based generators in power systems introduces several challenges to conventional protection relays. The fault characteristics of these generators depend on the inverters’ control strategy, which matters in the detection and classification of the fault. This paper presents a comprehensive machine-learning-based approach for detecting and classifying faults in transmission lines connected to inverter-based generators. A two-layer classification approach was considered: fault detection and fault type classification. The faults were comprised of different types at several line locations and variable fault impedance. The features from instantaneous three-phase current and voltages and calculated swing-center voltage (SCV) were extracted in time, frequency, and time–frequency domains. A photovoltaic (PV) and a Doubly-Fed Induction Generator (DFIG) wind farm plant were the considered renewable resources. The unbalanced data problem was investigated and mitigated using the synthetic minority class oversampling technique (SMOTE). The hyperparameters of the evaluated classifiers, namely decision trees (DT), Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), and Ensemble trees, were optimized using the Bayesian optimization algorithm. The extracted features were reduced using several methods. The classification performance was evaluated in terms of the accuracy, specificity, sensitivity, and precision metrics. The results show that the data balancing improved the specificity of DT, SVM, and k-NN classifiers (DT: from 99.86% for unbalanced data to 100% for balanced data; SVM: from 99.28% for unbalanced data to 99.93% for balanced data; k-NN: from 99.64% for unbalanced data to 99.74% for balanced data). The forward feature selection combined with the Bag ensemble classifier achieved 100% accuracy, sensitivity, specificity, and precision for fault detection (binary classification), while the Adaboost ensemble classifier had the highest accuracy (99.4%), compared to the other classifiers when using the complete set of features. The classification models with the highest performance were further tested using a new dataset test case. They showed high detection and classification capabilities. The proposed approach was compared with the previous methodologies from the literature.

A novel graph search and machine learning method to detect and locate high impedance fault zone in distribution system

AbstractHigh impedance fault (HIF) is difficult to detect by conventional overcurrent protection relays due to the lower fault current values, which are normally lower than the normal current. A fast and reliable algorithm is required to detect this type of fault. This paper proposes a novel method for detecting the location of HIF fault zone in a distribution system by using a novel graph theory‐based zone detection technique along with a Random Search Multilevel Support Vector Machine (RSMSVM) algorithm to classify the faulted zone. Due to shift in‐variance property of “Dual Tree Complex Wavelet Transform (DTCWT),” which has been used, in this paper, to decompose the voltage/current waveform to collect the signature of the signals and feed to the optimized RSMSVM model for classifying fault zone. The proposed method is evaluated on the IEEE 33‐bus system and also IEEE 39 bus test system under normal and noisy conditions. The proposed method is also evaluated for distribution network with the integration of distributed generation.

Hybrid deep learning – picture fuzzy set model for monitoring human behaviour in forest protection

In conventional monitoring forest protection, detection methods use optical sensors or RGB cameras combine features including smokes, fires and human-destroyed forests at national forests. This paper has presented a new approach using Deep learning integrated with Picture Fuzzy Set for the surveillance monitoring system to be activated to confirm human behaviour in real-time in forest protection. Picture Fuzzy Graph (PFG) are applied to solve many complex problems in the real-world problems. The paper has presented a novel approach using deep learning with knowledge graphs to find a human profile including the detection of humans in large data. In the proposed model, digital human profiles are collected from conventional databases combination with social networks in real-time, and a knowledge graph is created to represent complex-relational user attributes of human profile in large datasets. PFG is applied to quantify the degree centrality of nodes. To confirm the proposed model, the proposed model has been tested with data sets through case studies of a forest. Experimental results show that the proposed model has been validated on real world datasets to demonstrate this method’s effectiveness.

The usage of beneficial insects as a biological control measure in large-scale farming - a case study review on Trichogramma spp.

Large scale crops like maize, soybean, wheat and rice have changed the ecosystems worldwide, causing a major impact on global agricultural diversity. Intensive farming includes wide range of synthetic substances which are very often applied irrationally and excessively. Given the prevalence of large-scale farming in world agriculture, it is necessary to begin the transition from conventional crop protection to integrated pest management (IPM) in these agroecosystems. One of the most important components of IPM are biological control measures with augmentative release of commercially available species of the genus Trichogramma Westwood, 1833 (Hymentoptera: Trichogrammatidae) as potentially successful and environmentally friendly methods. Besides Trichogramma, many other beneficial organisms are constantly being tested as potential biocontrol agents such as Chrysopa spp. (Neuroptera: Chrysopidae) and Orius spp. (Hemiptera: Anthocoridae). Minimizing the use of chemicals and replacing them with biological plant protection is fully in line with the agriculture development strategy and confirmed to be achievable in practice. It is especially important to apply such tactical decisions in the production of large-scale crops, which, at the same time, represent the biggest polluters of the environment as well.

Artificial Intelligence-Based Protection for Smart Grids

Lately, adequate protection strategies need to be developed when Microgrids (MGs) are connected to smart grids to prevent undesirable tripping. Conventional relay settings need to be adapted to changes in Distributed Generator (DG) penetrations or grid reconfigurations, which is a complicated task that can be solved efficiently using Artificial Intelligence (AI)-based protection. This paper compares and validates the difference between conventional protection (overcurrent and differential) strategies and a new strategy based on Artificial Neural Networks (ANNs), which have been shown as adequate protection, especially with reconfigurable smart grids. In addition, the limitations of the conventional protections are discussed. The AI protection is employed through the communication between all Protective Devices (PDs) in the grid, and a backup strategy that employs the communication among the PDs in the same line. This paper goes a step further to validate the protection strategies based on simulations using the MATLABTM platform and experimental results using a scaled grid. The AI-based protection method gave the best solution as it can be adapted for different grids with high accuracy and faster response than conventional protection, and without the need to change the protection settings. The scaled grid was designed for the smart grid to advocate the behavior of the protection strategies experimentally for both conventional and AI-based protections.

Transient Wavelet Energy-Based Protection Scheme for Inverter-Dominated Microgrid

When faults occur in the microgrids, high frequency transients will be superimposed on the system currents and voltages. The magnitude of those transients will attenuate as it encounters the discontinuity points in the network such as busbars, or any other impedance discontinuity points. This phenomenon can also be quantified by wavelet energy, which provides a useful tool to detect faults and locate the faulted feeder in the microgrid. In this paper, a novel protection scheme based on the transient wavelet energy of the superimposed current extracted by the Maximal Overlap Discrete Wavelet Transform (MODWT) algorithm is developed to detect faults and locate the faulted feeder in microgrids. Compared with existing protection schemes, the proposed protection scheme has the advantage of being largely immune to the changes in system fault level, fault types and positions, microgrid operating status and the control strategies deployed on the inverters, while presenting much lower requirement on the sampling frequency (10 kHz) compared with travelling wave-based methods. Unlike the conventional differential protection, the proposed scheme does not require synchronized measurement or high bandwidth communication channels, and thus, it can be considered as an economical and promising solution for microgrids.

RASCv2: Enabling Remote Access to Side-Channels for Mission Critical and IoT Systems

The Internet of Things (IoT) and smart devices are currently being deployed in systems such as autonomous vehicles and medical monitoring devices. The introduction of IoT devices into these systems enables network connectivity for data transfer, cloud support, and more, but can also lead to malware injection. Since many IoT devices operate in remote environments, it is also difficult to protect them from physical tampering. Conventional protection approaches rely on software. However, these can be circumvented by the moving target nature of malware or through hardware attacks. Alternatively, insertion of the internal monitoring circuits into IoT chips requires a design trade-off, balancing the requirements of the monitoring circuit and the main circuit. A very promising approach to detecting anomalous behavior in the IoT and other embedded systems is side-channel analysis. To date, however, this can be performed only before deployment due to the cost and size of side-channel setups (e.g., and oscilloscopes, probes) or by internal performance counters. Here, we introduce an external monitoring printed circuit board (PCB) named RASC to provide r emote a ccess to s ide- c hannels. RASC reduces the complete side-channel analysis system into two small PCBs (2 \( \times \) 2 cm), providing the ability to monitor power and electromagnetic (EM) traces of the target device. Additionally, RASC can transmit data and/or alerts of anomalous activities detected to a remote host through Bluetooth. To demonstrate RASCs capabilities, we extract keys from encryption modules such as AES implemented on Arduino and FPGA boards. To illustrate RASC’s defensive capabilities, we also use it to perform malware detection. RASC’s success in power analysis is comparable to an oscilloscope/probe setup but is lightweight and two orders of magnitude cheaper.

Amidation of α-Amino Acids Using Dichloro(methyl)(3,3,3-trifluoropropyl)silane and Imidazole without Conventional Protection and Deprotection of α-Amino Group

AbstractAmidation of amino acids using dichloro(methyl)(3,3,3-trifluoropropyl)silane (MTFPSCl2) and imidazole is described. MTFPSCl2 activates the carboxy group and protects the α-amino group of amino acids. The amidation proceeded with 19 amino acids and 19 amines, including α-branched amines and anilines; the corresponding amino acid amides were synthesized in good-to-high yields (up to 96%) with low-to-no racemization.

Protection against failure of machine-learning-based QoT prediction

Machine learning (ML)-based methods are being widely explored to predict the quality of transmission (QoT) of a lightpath. They are expected to reduce the signal-to-noise ratio margin reserved for the lightpath, thus improving the spectrum efficiency of an optical network. However, many studies on this prediction are often based on synthetic datasets or datasets obtained from laboratories. As such, these datasets may not accurately represent the entire state space of a practical optical network, which is exposed in harsh environments. There are risks of failure when using these ML-based QoT prediction models. Thus, it is necessary to develop a mechanism that can guarantee the reliability of lightpath service even if the prediction model fails. For this scenario, we propose to employ the conventional network protection techniques that are usually implemented to protect against network node/link failures to also protect against the failure of QoT prediction. Based on the two representative protection techniques, i.e., 1 + 1 dedicated path protection and shared backup path protection (SBPP), the performance of the proposed protection mechanism is evaluated by reserving different margins for the working and protection lightpaths. For 1 + 1 path protection, we find that the proposed mechanism can achieve a zero design margin (D-margin) for a working lightpath, thereby significantly improving network spectrum efficiency, while not sacrificing the availability of lightpath services. For SBPP, we find that an optimal D-margin should be identified to balance the spectrum efficiency and service availability; the proposed mechanism can save up to 0.5-dB D-margin for a working lightpath, while guaranteeing service availability.

A d-axis based current differential protection scheme for an active distribution network

The emergence of distributed generators has changed the operational mode and fault characteristics of the distribution network, in a way which can severely influence protection. This paper proposes a d-axis-based current differential protection scheme. The d-axis current characteristics of inverter-interfaced distributed generators and synchronous generators are analyzed. The differential protection criterion using sampling values of the d-axis current component is then constructed. Compared to conventional phase-based current differential protection, the proposed protection reduces the number of required communication channels, and is suitable for distribution networks with inverter-interfaced distributed generators with complex fault characteristics. Finally, a 10 kV active distribution network model is built in the PSCAD platform and protection prototypes are developed in RTDS. Superior sensitivity and fast speed are verified by simulation and RTDS-based tests.

Centralized Multiple Back-Up Protection Scheme with Sharing Data between Adjacent Substations Based on IEC 61850

A centralized multiple back-up protection scheme with sharing data between adjacent substations based on IEC 61850 is suggested in this paper to overcome the inherent defects of a conventional back-up protection scheme such as uncertainty of a remote end condition, especially for a distance relay, long operating time delay, and IED/CT fails. A current differential protection element, a distance-based directional protection element, local back-up, and remote back-up protection elements are applied for the multiple back-up protection scheme suggested in this paper. The phasor voltage/current data of the IMU are transferred to a CPCU. In this paper, the IMU has two functions, which are local protection IED and merging unit, as well. The CPCU performs the back-up protection functions by using the phasor voltage/current data collected from other CPCUs located at the adjacent substations. IMU/CT fails are also considered. To verify the back-up protection scheme proposed, a testbed was composed of IMUs (implemented with ADS8688 and TMDSIDK572X), grand-master-clocks (LANTIME M1000-IMS), network switches (Syn 1588 Gbit switch), RTDS, and CPCUs. A target power system was modelled with RSCAD. The results of this research can be expected to provide a practical guide to constructing the future centralized protection for a digital substation.

Rapid Determination of RNA Modifications in Consensus Motifs by Nuclease Protection with Ion-Tagged Oligonucleotide Probes and Matrix-Assisted Laser Desorption Ionization Mass Spectrometry.

The reversible and substoichiometric modification of RNA has recently emerged as an additional layer of translational regulation in normal biological function and disease. Modifications are often enzymatically deposited in and removed from short (~5 nt) consensus motif sequences to carefully control the translational output of the cell. Although characterization of modification occupancy at consensus motifs can be accomplished using RNA sequencing methods, these approaches are generally time-consuming and do not directly detect post-transcriptional modifications. Here, we present a nuclease protection assay coupled with matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) to rapidly characterize modifications in consensus motifs, such as GGACU, which frequently harbor N6-methyladenosine (m6A). While conventional nuclease protection methods rely on long (~30 nt) oligonucleotide probes that preclude the global assessment of consensus motif modification stoichiometry, we investigated a series of ion-tagged oligonucleotide (ITO) probes and found that a benzylimidazolium-functionalized ITO (ABzIM-ITO) conferred significantly improved nuclease resistance for GGACU targets. After optimizing the conditions of the nuclease protection assay, we applied the ITO and MALDI-MS-based method for determining the stoichiometry of GG(m6A)CU and GGACU in RNA mixtures. Overall, the ITO-based nuclease protection and MALDI-MS method constitutes a rapid and promising approach for determining modification stoichiometries of consensus motifs.

Microgrid Fault Detection and Classification Based on the Boosting Ensemble Method With the Hilbert-Huang Transform

In this paper, a sequential ensemble of intelligence-based methods is used for fault detection and classification in microgrids. The proposed scheme is recommended because of the impracticality of conventional fault detection and protection due to microgrid dynamic behavior and dependency of traditional methods on faults current level or impedance. These methods use the collective decision of learners to increase their accuracy. This is done by distributing the knowledge among the classifiers. The proposed ensemble method is called Brownboost. Its main advantage over its counterparts is that it uses a nonconvex optimization method. This makes it robust to overfitting and applicable and practical for a real-world noisy or misclassified data. In addition, a signal processing method called the Hilbert-Huang transform was chosen for feature extraction from signals transient behavior to reduce the noise sensitivity. In this method, current difference of the both ends of line is selected to decompose various types of adaptive basis signal processing method. The results are validated in IEC test microgrid with various noise penetration level and synchronization delays. Compared to traditional strong classifiers, this method is easy to tune and program and is robust to overfitting. Moreover, it can be justified for real-world imperfect data.

Service Chain Provisioning With Sub-Chain-Enabled Coordinated Protection to Satisfy Availability Requirements

This paper proposes a sub-chain-enabled coordinated protection model for the availability-guaranteed service function chain (SFC) provisioning, which considers the availability of each component to constitute an SFC, including links and VNFs. Unlike conventional protection models providing certain protection for the whole chain, the proposed model configures sub-chains for each SFC and provides proper protection for each sub-chain to achieve the required availability in a cost efficient way. We formulate the proposed model as an optimization problem to minimize the deployment cost. A game approach is presented to tackle the problem. The numerical results show that the proposed model outperforms the conventional ones in terms of deployment cost; the game approach has scalability of tackling the proposed model as the problem size increases.

A multi-scale dual-stage model for PV array fault detection, classification, and monitoring technique

The output generated by photovoltaic arrays is influenced mainly by the irradiance, which has non-uniform distribution in a day. This has resulted in the current-limiting nature and nonlinear output characteristics, and conventional protection devices cannot detect and clean faults appropriately. This paper proposes a low-cost model for a multi-scale dual-stage photovoltaic fault detection, classification, and monitoring technique developed through MATLAB/Simulink. The main contribution of this paper is that it can detect multiple common faults, be applied on multi-scale photovoltaic arrays regardless of environmental conditions, and be beneficial for photovoltaic system maintenance work. The experimental results show that the developed algorithm using supervised learning algorithms mutual with k-fold cross-validation has produced good performances in identifying six common faults of photovoltaic arrays, achieved 100% accuracy in fault detection, and achieved good accuracy in fault classification. Challenges and suggestions for future research direction are also suggested in this paper. Overall, this study shall provide researchers and policymakers with a valuable reference for developing photovoltaic system fault detection and monitoring techniques for better feasibility, safety, and energy sustainability.

A Memory Based Current Algorithm for Pole-to-Pole Fault Detection in Microgrids

Abstract: The world’s growing attention to sustainable energy and development can be causal to the recently observed disrupt in the existing global power system networks. Moreover, an additional incentive towards this change are the challenges associated with the traditional power grid, including its rigid structure, aging architecture, and ecologically profligate nature. Modern power systems have observed a rapidly growing trend of decentralized energy generation in the recent past. A prominent structure incorporating decentralized energy generation and renewable energy are microgrids. While microgrids promote on-site generation and distributed energy resources (DERs), their unique characteristics of bidirectional flow of power, renewable generational intermittency, and varying levels of current causes challenges uncommon to the traditional grid. One of the vital challenges associated with microgrids is the protection of microgrids against faults and disturbances that can cause impairment to life and property. Conventional protection algorithms are ineffective in protecting the system from faults due to the unconventional topology of the microgrid. This paper attempts to contribute to work in the sector related to the protection of microgrids. This paper presents the pole-to-pole fault analysis of a hybrid photovoltaic system and presents a current based algorithm for the detection of DC pole-to-pole faults in the system under study. The protection algorithm is further verified on a hybrid Photovoltaic-Wind-Battery microgrid. Keywords: DC Faults, Hybrid Microgrids, Power Systems, Protection Algorithm, DERs

Back-Up Protection Scheme for Series-Compensated Transmission Line Connected to Wind Farm

Series compensation poses serious challenges to the protection schemes of transmission lines (TLs). Moreover, with the penetration of distributed generation (DG), the conventional protection schemes fail to detect the fault and identify the faulty phase as the backfeed currents from the DG units mask the faulty phase. This work presents an algorithm for the protection of series-compensated TLs (SCTL) integrated with wind farms. The algorithm uses the phase difference between the positive sequence currents at the two ends of the TL to discriminate internal and external faults. The faulty phase for internal fault is identified from the difference in complex power at the two ends of the TL. Extensive simulations with several fault and system parameters assess the reliability and applicability of this technique.

Transient power polarity based fault detection in DC microgrid with localized backup scheme

Increasing energy demand and the proliferation of renewable energy sources inflicts new challenges to the conventional protection and control methodologies. Hence, a high speed, precise and reliable protection scheme is required for modern power system. In this paper, a novel protection scheme based on the characteristics of transient power is proposed for DC microgrid in grid-connected and islanded mode of operation. The proposed scheme incorporates primary as well as localized backup protection scheme. The primary protection utilizes the polarity of transient power determined at both ends of the cable under different fault conditions to discriminate between internal and external faults. The primary protection scheme doesn’t require high speed communication and synchronous data. However, in case of a communication link failure, primary protection may also fail to detect a fault; hence a localized backup protection scheme based on the change in average transient power observed at either end of the cable under different operating conditions is used to isolate the faulty section. The effectiveness of the proposed scheme is validated by simulating different fault conditions such as internal and external faults along with system transients such as AC side faults, noisy environment, load variation, DG outage and generation uncertainties due to change in solar irradiance and wind speed. Further, the proposed scheme is also validated on a scaled-down hardware setup in the laboratory, which illustrates the effectiveness and robustness of the proposed protection scheme.

A novel fault detection and classification scheme for DC microgrid based on transient reactor voltage with localized back-up scheme

Integration of renewable energy sources in distribution system inflicts new challenges to the conventional protection scheme by substantially changing the amplitude and direction of fault current. In this paper, a novel protection scheme for DC microgrid based on the characteristics of reactor voltage during transient operation in both grid-connected and islanded mode of operation is proposed. The proposed protection scheme incorporates both primary as well as localized back-up protection scheme. In order to detect and classify faults, primary protection scheme utilizes the polarity of the transient reactor voltage at both ends of the cable under different operating conditions. As the primary protection uses the polarity of reactor voltage at both ends, thus the proposed scheme doesn’t require high speed communication and synchronous data. In case of communication failure, localized back-up protection scheme based on the change in reactor voltage at one end of the cable is used to identify the faulty section. The efficacy of the proposed scheme is verified against various internal and external faults along with system transients such as AC side faults, load variation, DG outage, noisy environment and generation uncertainties due to change in solar irradiance and wind speed. Finally, a scaled down hardware setup is used to validate the proposed protection scheme, which further illustrates the feasibility and reliability of the proposed protection scheme.