Relay Protection Research Articles

Performance Evaluation of Overcurrent Relay and Ground Fault Relay Coordination in Distribution Feeder

Reliable and sustainable electrical distribution systems require effective protection measures against internal and external disturbances, especially short-circuit disturbances, to avoid damage to the equipment. Overcurrent relays (OCR) and ground fault relays (GFR) play an important role in the safety of the distribution network. To improve performance, evaluation of relay coordination is essential. During the short circuit, the nearest relay to the fault point is activated first and the backup relay is activated if the fault current exceeds the adjustment of the main relay. Coordination ensures that timely signaling is sent to interrupt the circuit. Inspection of the OCR relay revealed that it did not comply with the IEC 60255 standard and consequently adjusted the classification time to 0.402 seconds. Similarly, the results of the GFR relay inspection did not meet the standards and required an adjustment of the evaluation time to 0.492 seconds. Resolving these deviations is crucial to maintaining the reliability and compliance of the distribution network with industry standards, ensuring that the protection relays respond quickly and effectively to disturbances, thereby preventing possible damage to equipment

Optimizing DOCRs coordination when synergizing energy management with V2G using innovative optimization algorithms

This manuscript proposes an optimal coordination approach for directional overcurrent relays (DOCRs) within electric vehicle charging stations (EVCS), considering energy management and Vehicle-to-Grid (V2G) integration. The study focuses on maximizing efficiency and grid interaction in charging stations by addressing the challenge of DOCRs coordination. Three innovative optimization algorithms, namely the Hunger Games Search (HGS), Hunter Prey Optimization (HPO), and Sparrow Search algorithm (SSA), are employed to determine the optimal coordination for the DOCRs and get the time dial setting (TDS) and pickup current (IP) targeting minimum operating time for all DOCRs, considering factors such as load balancing, fault detection, and power quality in three different scenarios. EVSC has two types of charges DC and AC with two modes: V2G mode when EV is discharged into the grid, and G2V mode when EV is charged from the grid. Fast chargers are used to proceed as it can work in two modes V2G and G2V. Through extensive simulations and performance evaluations, the results demonstrate the effectiveness of the proposed approach. The three scenarios are simulated by using ETAP and MATLAB software according to fault location and to achieve voltage regulation and sufficient power for loads to achieved continuity of service for up to 1960.7 kW loads that kept fed. The proposed approach achieved the target of protecting the EVCS in minimum operating time of relays using the three algorithms and SSA introduces the best result more than other algorithms for the relay operating time with reduction up to 21 %.

Design of adaptive protection coordination scheme using SVM for an AC microgrid

Microgrids have gathered a significant amount of attention within the past decade and becoming an essential asset in the energy industry. The ability to integrate sustainable renewable energy generation into the distribution network is one of the main reasons for microgrids' popularity. Besides technical advantages, renewable energy sources integration with the grid poses many technical challenges. Due to randomness in generation and different modes of operation, integrating renewable energy sources into power systems will be challenging. Reliable operation of renewable energy sources integrated microgrids even when faults occur, requires an adaptive protection scheme and relay coordination. In order to tackle those issues, this paper developed an adaptive protection coordination scheme using numerical overcurrent relays and support vector machines with a particle swarm optimization approach. Renewable energy sources based on microgrids use power electronics systems that require relays to operate in milliseconds, and the power system will be safe. particle swarm optimization's program execution time exceeds the primary relays. The machine learning algorithm is faster in computation time. This article proposes a method, particle swarm optimization, to estimate the optimum relay operating time. The machine learning model having a support vector machine is proposed to estimate the relay operating time. The proposed research is developed and tested on an IEEE12BUS and IEEE35BUS system. The support vector machine -based on adaptive protection coordination schemes estimates the operating time of directional overcurrent relays, with a lesser program execution time and a low percentage error rate. The derived result defined that the proposed approach is well-suitable for real-time applications of an AC microgrid system.

Integration of Artificial Intelligence for Enhanced Coordination of DOCR Protection in Distributed Generation Systems

Distributed generation (DG) is an approach that involves adding decentralized power generation within a distribution network. Distributed generation systems can reduce transmission losses, increase the reliability of energy supply, minimize carbon emissions, and enable the active participation of consumers in energy production. However, with the increase in distributed generation, electric power systems face new challenges in maintaining operational reliability and safety. Disruptions such as short circuits or overcurrent can occur in the system, and appropriate protective responses are required to protect the power grid from more significant damage. The addition of DG also causes the short circuit current to vary and results in system protection coordination having to be redone. Carrying out coordination will take a long time. This research uses modeling and simulation of a distributed generation system with various operating conditions and works adaptively according to changes in the system due to the addition of DG. The results obtained from the simulation are used in neural network training to study the relationship patterns between directional overcurrent relays (DOCR) parameters and system operating conditions. The backpropagation algorithm is used in the Artificial Neural Network (ANN) training process. The training process utilizes the maximum Short Circuit Current (ISC) input obtained through generation, fault location, and fault type. Time Dial Setting (TDS) and Ipickup values are used as ANN training targets. After testing, the results obtained are in accordance with the target data. The efficacy of this method is further demonstrated through ETAP simulations, which confirm that ANN is a suitable approach for modeling adaptive and optimal relay coordination systems.

A Novel Application of Fractional Order Derivative Moth Flame Optimization Algorithm for Solving the Problem of Optimal Coordination of Directional Overcurrent Relays

The proper coordination of directional overcurrent relays (DOCRs) is crucial in electrical power systems. The coordination of DOCRs in a multi-loop power system is expressed as an optimization problem. The aim of this study focuses on improving the protection system’s performance by minimizing the total operating time of DOCRs via effective coordination with main and backup DOCRs while keeping the coordination constraints within allowable limits. The coordination problem of DOCRs is solved by developing a new application strategy called Fractional Order Derivative Moth Flame Optimizer (FODMFO). This approach involves incorporating the ideas of fractional calculus (FC) into the mathematical model of the conventional moth flame algorithm to improve the characteristics of the optimizer. The FODMFO approach is then tested on the coordination problem of DOCRs in standard power systems, specifically the IEEE 3, 8, and 15 bus systems as well as in 11 benchmark functions including uni- and multimodal functions. The results obtained from the proposed method, as well as its comparison with other recently developed algorithms, demonstrate that the combination of FOD and MFO improves the overall efficiency of the optimizer by utilizing the individual strengths of these tools and identifying the globally optimal solution and minimize the total operating time of DOCRs up to an optimal value. The reliability, strength, and dependability of FODMFO are supported by a thorough statistics study using the box-plot, histograms, empirical cumulative distribution function demonstrations, and the minimal fitness evolution seen in each distinct simulation. Based on these data, it is evident that FODMFO outperforms other modern nature-inspired and conventional algorithms.

A new virtual consensus‐based wide area differential protection

AbstractThis paper introduces a virtual consensus‐based wide area differential protection method through cooperative control concepts and graph theory. Using multi‐agent systems eliminates the need for a direct telecommunication connection between the protective relays to implement the proposed differential protection. In addition, applying telecommunication subgraphs facilitates the establishment of numerous differential protection areas. Collaboration between protected areas is facilitated by common agents, establishing a wide‐area cooperative protection network. The capability of the network operator to define various protection areas and the collaboration between these areas ensure the versatility of the proposed method for various protection purposes. The present paper primarily represents the application of the proposed protection system as a wide‐area supervisor protection for distance relays. Performance evaluations on an IEEE 39‐bus test system illustrate the method's effectiveness in various scenarios. The results show enhanced power system stability and resilience, particularly when traditional methods struggle to detect power swings with high impedance variation rates.

Advancing Distance Relaying Reliability: S Transform Solution for Renewable Energy-Infused Smart Grid

Distance relaying is a crucial component in safeguarding transmission lines globally. However, the presence of Sub-Synchronous Resonance (SSR) introduces challenges, as oscillations in voltage and current magnitudes adversely affect the performance of conventional distance relays. Detecting faults accurately during these oscillations poses a significant challenge for traditional relaying mechanisms. This paper addresses the issue of SSR-induced maloperations in distance relays, particularly in the context of the increasing penetration of renewable energy resources in smart grids. The integration of wind power into the grid generates sub-synchronous oscillations, leading to protection issues. In response, a novel solution leveraging the S transform, a modern signal processing technique, is proposed to enhance the robustness of distance relaying under such challenging conditions. The developed algorithm is implemented in MATLAB and rigorously tested on the First Benchmark Model of sub-synchronous resonance. Simulation of the model is conducted in the PSCAD simulation software, and the results are thoroughly validated in MATLAB. The study demonstrates the efficacy of the S transform-based approach in accurately identifying faults and mitigating maloperations caused by SSR, thereby improving the reliability of distance relaying in smart grids with high renewable energy integration.

Intelligent Monitoring of Protection Devices in Power system with Enhanced Faster R-CNN

Relay protection devices are necessary to guard the power system safety and stability. With the significant number of substations and relay protection devices, the maintenance workload has become difficult due to limited manpower, posing hidden dangers to the reliable operation of protection devices. In view of this, this article proposes an enhanced Faster R-CNN algorithm to diagnose the relay protection devices based on image monitoring. The proposed algorithm uses RestNet50 as the main tool to recognize features from input images to generate feature maps. Meanwhile, to improve the image detection accuracy, the Non-Maximum Sup-pression (NMS) algorithm in regional suggestion network (RPN) is optimized from solid thresh-old to soft threshold. By combining the images captured by the surveillance camera, intelligent inspections are conducted on the status of the pressing plate, fiber bending, external object retention, and other items in the relay protection device. A great number of samples are used to train networks for different tasks. The results indicate that the enhanced Faster R-CNN has a higher image recognition rate, and the recognition accuracy of each inspection item for the protection device is higher than 95%.

Distribution Transformer Protection and Automatic System Using PLC

Protection of transformers is a very challenging problem in power system relaying. Since it is very important to minimize the overcurrent and duration of unwanted outages, this is a high demand imposed on power transformer protective relays. Various relaying principles have been proposed and used to protect transformers against different types of faults. Relays that use over current, over flux and overheating principles protect the transformers against overloads and externally applied conditions. Differential relays protect the transformers against internal fault. In this research, software and hardware of microcontroller-based relay system has been explained and designed. The design implementation and testing of the system are also presented. Today the world is facing the most critical problem of not getting the sufficient power. In many countries including India, people are not getting their primary need of electricity because people are consuming more power than the given limit of the sanctioned load given by MSCBE. Instead, we can use available power in such a way that the specific user will only use the power which is allocated to the user according to the limit of sanctioned load provided by MSCBE. A properly installed and configured monitoring system is a valuable advantage to almost any type of energy consumer. Energy consumers have a wide variety of considerations and concerns where energy usage is involved. Key Words: PLC

Signal Envelope-Based Adaptive Power Swing Blocking

This research paper proposes detection of faults during power swings by using a peak envelope-based method to prevent incorrect distance relay operation. Distance relays should not operate during power swings except to remove a correctly detected fault event. In this study, distinction between faults and power swings is achieved by correctly observing fault occurrence based on dynamic thresholds generated by peak envelopes. The method utilizes a DC offset current in determining the fault inception and fifth harmonic voltage to assess the fault clearance. If the current surpasses the calculated threshold due to fault existence, the relay is allowed to operate. Re-blocking the relay after fault clearance is achieved in the case of a higher value of the voltage than the threshold value. Current and voltage data obtained from two different test systems simulated in PSCAD for various scenarios have been used in the proposed algorithm developed in MATLAB. In order to show the effectiveness of the proposed method, the results on the second test system have also been compared to the results of selected methods in the literature. It has been perceived that the developed method has a high accuracy and provides an appropriate time range for relay and breaker operation.

Geometrical method for a fast practical static stability region evaluation of a smart microgrid

This paper introduces an innovative approach to the offline Static Security Assessment (SSA) of smart MicroGrids (MG). By defining a specific region in the operating hyperspace, the stability of a MG is evaluated in a practical way, through the study of violations of voltage limits settled by protection relays. This method determines whether operating points, generated by Economic Dispatch algorithms, are feasible or not. Unlike traditional methods, it doesn't require continuous training on large datasets if the system topology doesn’t change. The method proactively explores the operating region offline, enabling quick assessment of operational security. By analyzing the feasibility region in the hyperspace from a geometrical perspective, it provides rapid insights of the system security status. This eliminates the need for computationally expensive trajectory studies involving complex equations and online iterative procedures, as applied in energy function approaches and conventional methods like the continuation load flow. The methodology is tested by simulation on a case study reproducing a subset of the experimental MG installed at the PrInCE Laboratory of the Polytechnic University of Bari.

Comparative assessment of fault detection methods in transmission line during power swing: analyzing single and hybrid networks for the southern Kerala grid

The blocking function of power oscillation is a highly effective feature found in distance relays. Its purpose is to prevent unintentional tripping of transmission lines. It is crucial to accurately differentiate between faults and power oscillations to minimise the occurrence of widespread power outages and monetary losses. Therefore, the detection and classification of faults arising from power swings are critical challenges in ensuring the smooth operation and overall health of transmission lines, particularly in extra-high-voltage (EHV) and ultra-high-voltage (UHV) power systems. Modern methods rely on machine learning methods which are in their infancy. The paper proposes to identify various fault conditions in transmission lines during power swing by using a hybrid Neural Network architecture. In this research, different designs such as 2-D convolution neural network (2D-CNN), 1-D convolution neural network (1D-CNN), long short-term memory (LSTM), and CNN-LSTM hybrid networks are investigated to understand the effect of various deep learning approaches. Along with the southern Kerala grid, the standard 9-bus system is selected as a test case to examine various fault circumstances of the system during power swing. Multiple tests were conducted to determine the optimal deep learning architecture, including model parameters and configurations, for accurate fault detection. For the 9 bus system, the 1D CNN network performs better with an accuracy of 98.70%, and for the Kerala grid, both networks are competitive, but the CNN-LSTM Hybrid method slightly outperforms the 1D CNN with an accuracy of 92.40%.

Machine Learning Classifier for Supporting Generator’s Impedance-Based Relay Protection Functions

Transient stability of the electric power system still heavily rests on a timely and correct operation of the relay protection of individual power generators. Power swings and generator pole slips, following network short-circuit events, can initiate false relay activations, with negative repercussions for the overall system stability. This paper will examine the generator’s underimpedance (21G) and out-of-step (78) protection functions and will propose a machine learning based classifier for supporting and reinforcing their decision-making logic. The classifier, based on a support vector machine, will aid in blocking the underimpedance protection during stable generator swings. It will also enable faster tripping of the out-of-step protection for unstable generator swings. Both protection functions will feature polygonal protection characteristics. Their implementation will be based on European practice and IEC standards. Classifier will be trained and tested on the data derived from simulations of the IEEE New England 10-generator benchmark power system.

Strategy for evaluating the status of relay protection equipment for the new generation of intelligent substations

With the rapid development of power grid, the structure and technology of the secondary system in substations are also constantly innovating. The new generation of intelligent substations has achieved online monitoring functions for secondary equipment, making some state variables of relay protection equipment become observable indicators. Based on this, this paper proposes a novel relay protection equipment status evaluation strategy. Firstly, considering the fuzziness and uncertainty of the boundary division of relay protection evaluation levels, a relay protection risk assessment method based on normal cloud model has been proposed. Then, due to the particularity of historical statistical data, a weight calculation method combining analytical hierarchy process (AHP) and entropy weight method is adopted to eliminate subjective factors in the weight calculation process. Meanwhile, the equipment operation risk level was determined by calculating the certainty of the corresponding evaluation level for each indicator. Finally, in case study, the proposed method is used to evaluate the status of relay protection equipment, and the feasibility and accuracy of this method are verified through the analysis of the results.

A Novel Approach for Diagnosing Transformer Internal Defects and Inrush Current Based on 1DCNN and LSTM Deep Learning

In power systems, power transformer (Pt) protection plays a vital role in ensuring that customers have a reliable power supply. Correctly recognizing inrush currents from internal defects and preventing differential relay malfunctions are two of biggest challenges facing the differential protection of power transformers. Although previous approaches suggested to overcome these issues have promising outcomes, increasing the accuracy and reducing the execution time and complexity of transformer differential relays are still interesting topics for researchers. Accordingly, a new fault diagnostic method based on wavelet transform (WT) and deep learning is introduced in paper. In the proposed approach, Discrete WT is used to extract the features of differential currents, and combined one-dimensional convolutional neural networks and long short-term memory (1DCNN-LSTM)) is applied for classify internal faults from other abnormal events. High accuracy, no need for any thresholds or transformer parameters and fast fault detection are the main advantages of the proposed approach. The simulation results for a 132/11 kV, 63 MVA power transformer approved the proposed method for its ability to accurately differentiate between inrush currents and internal defects after 5 ms, as well as its accuracy for abnormal event classification of about 99.4%.

A high speed and sensitive protection for distribution network with DGs using ferrite rings as line boundary

AbstractThere exist problems of hard coordination and threshold setting as well as low sensitivity in overcurrent relay due to the bidirectional and small fault current when distributed generators are integrated into the distribution network. This paper introduces ferrite rings (FRs) to act as boundaries of distribution feeders, and a peak time of voltage traveling wave‐based protection is proposed. The characteristics of FRs are analyzed and their feasibility as line boundary is validated. The fault characteristics of internal and external faults are analyzed when FRs are installed at both ends of the protected feeder. The results show that the peak time of the voltage travelling wave of the internal fault is far smaller than that of the external faults. According to the difference, the fault detection method and the protection scheme are proposed. Simulations in PSCAD and experiment validate that the proposed protection principle can reliably distinguish between internal and external faults. Besides, the proposed protection scheme has higher sensitivity compared to ROCOV‐based protection.

Assessment of Adaptive Coordinated Control of Overcurrent Relay Using Novel Multi-Trial Vector-Based Differential Evolution Algorithm

In this work, we use a unique multi-trial vector-based differentiation algorithm to examine the dynamically coordinated management of over current relays (OCRs) in substations. OCRs are significant for substation protection, and their coordinated control is essential for different sections experiencing varying current levels. Numerous optimization and AI techniques have been explored by researchers to enhance OCR coordinated control performance. This study introduces adaptive control for automatic coordination time adjustment based on forecasting, particularly focusing on integrating wind energy systems with and without reactors in substations. Accurate forecasting is vital for effective adaptive OCR control; hence, a novel multi-trial vector-based evolutionary (MTDE) algorithm is proposed for wind energy forecasting. MATLAB is used for the analysis, comparing the performance of artificial neural network (ANN)-based wind energy forecasting. The proposed method employs ANN for wind energy forecasting in a 110/22KV substation, enhancing OCR coordinated control based on wind energy integration. The ANN is trained using Mean Square Error (MSE), and its weights are tuned using MTDE for improved forecasting accuracy. Simulation results show that the MTDE-tuned ANN significantly enhances wind energy forecasting accuracy compared to conventional ANN, enabling superior adaptive coordinated OCR control, and thereby improving substation protection under fault conditions.

Electrical Test and Relay Protection Analysis of Power Transformer

In this paper, the electrical test and relay protection of power transformer are deeply analyzed. Firstly, the purpose, classification and common methods of electrical test are expounded, and the processing methods of test results are discussed. Then, the basic principle, device classification and configuration method of relay protection are discussed. Furthermore, the relationship between electrical test and relay protection is analyzed, and their application and significance in power transformer protection are discussed. This paper aims to provide theoretical support for improving the operation safety and reliability of power transformers.

Mathematical Model of a Nonlinear Power Transformer for Needs of Relay Protection

In this work, a mathematical model of a three-phase nonlinear transformer is suggested. The model enables simulating the transformer operation with allowance for its nonlinearity and covers needs of the relay protection. Our model has been developed on the basis of a mathematical model with phase coordinates, where differential equations are composed by the Kirchhoff’s phase-voltage law. Based on this model, we first compose a mathematical model for simulating steady-state operation modes of a transformer, taking into account the asymmetry and nonlinearity of its ferromagnetic core. In this model, the initial values of inductances and mutual inductances of loops are determined from the main phase inductance calculated by the experimentally found no-load current, and their current values are determined from the currents in windings and the magnetic fluxes in legs of the transformer core. The magnetic fluxes are calculated by the nodal-pair method. This improved mathematical model is verified through a comparison between the calculated harmonic components of the phase currents and the experimental results. The harmonic components are calculated with the use of Fourier expansion of the calculated phase currents. Their experimental values are determined with a spectrum analyzer. The calculated and experimental harmonic components of the currents of phase A during no-load and rated-load operation of the transformer are tabulated. The comparison of these results shows that the mathematical model of a three-phase transformer we suggest makes it possible to simulate currents in transformer windings under steady-state operation modes with accuracy acceptable for relay protection.

A System Integrity Protection Scheme for Supervising Higher Operating Zones of Distance Relay Under Large Wind Power Integration

A System Integrity Protection Scheme for Supervising Higher Operating Zones of Distance Relay Under Large Wind Power Integration