Self Adaptable Coordination Overcurrent Digital Protection Relay For Power Distribution Network

Dengan terintegrasinya berbagai jenis sumber energi listrik dengan menggunakan distributed generation (DG) dalam satu sistem jaringan distribusi akan menimbulkan perubahan seting pada tiap perangkat rele, dimana mengikuti perubahan status dari DG yang mempunyai sifat intermitten. Pada penelitian ini telah dilakukan terobosan inovasi baru berupa pengembangan rele digital yang mempunyai fitur user-defined characteristic curve dengan cara memodelkan kurva karakteristik dengan menerapkan ujicoba algoritma polynomial Lagrange hingga algoritma artificial intelligence. Keberhasilan dalam memodelkan kurva karakteristik rele digital membawa dampak yang sangat luas, antara lain kemampuan perencanaan kurva non-standar, multi curve dalam satu rele serta kemampuan pemilihan kurva yang bersifat adaptif mengikuti kondisi atau status dari DG yang terdapat dalam jaringan distribusi. Inovasi terobosan baru selain pemodelan kurva adalah optimisasi parameter dari kurva karakteristik rele yakni plug seting (PS) dan time multiplier seting (TMS), dua parameter penting tersebut harus dicari nilai optimumnya agar dihasilkan koordinasi antar rele dalam satu jaringan distribusi yang maksimal dimana perangkat sumber energi listrik dan beban terlindungi dengan baik dengan bantuan algoritma optimisasi firefly yang dikembangkan, yakni modified firefly algorithm (MFA) serta adaptive modified firefly algorithm (AMFA). Ketercapaian pemodelan kurva karakteristik userdefined dengan menggunakan artificial neural network (ANN) mencapai nilai MSE=0,00010, sedang nilai optimisasi yang tertinggi mencapai 42,22% dengan menggunakan AMFA. Dengan keberhasilan ini maka kebutuhan akan adaptive digital overcurrent relay yang mampu beroperasi pada active distribution network telah terjawab dan siap untuk dikembangkan pada tataran implementasi. ================================================================================== With the integration of various types of electrical energy sources by using distributed generation (DG) in a distribution network system will cause changes in settings in each relay device, which follows the change in status of DG that has intermittent properties. In this research, innovation of digital relay has been developed with user-defined curve capability by modeling characteristic curves using Lagrange polynomial interpolation algorithm and artificial intelligence algorithm. Success in modeling digital relay characteristic curves has a wide impact, such as the ability of non-standard curve planning, multi curve in one relay and adaptive curve selection capability following the condition or status of DG in the distribution network. New breakthrough innovations besides curve modeling are parameter optimization of relay characteristic curves ie plug setting (PS) and time multiplier setting (TMS), two important parameters must be obtained optimum value to generate coordination between relays in a distribution network as much as possible, where the device The source of electrical energy and load is well protected, with the help of modification firefly optimization algorithm, called modified firefly algorithm (MFA) and adaptive modified firefly algorithm (AMFA). The user-defined curve modeling results using artificial neural network (ANN) get MSE 0.00010, while the highest optimization value reached 42.22% using AMFA. With the success of this research, the need for adaptive digital overcurrent relay capable of operating on active distribution network has been answered and ready to be developed at the implementation level

Recent Developments and Challenges on AC Microgrids Fault Detection and Protection Systems–A Review

The protection of AC microgrids (MGs) is an issue of paramount importance to ensure their reliable and safe operation. Designing reliable protection mechanism, however, is not a trivial task, as many practical issues need to be considered. The operation mode of MGs, which can be grid-connected or islanded, employed control strategy and practical limitations of the power electronic converters that are utilized to interface renewable energy sources and the grid, are some of the practical constraints that make fault detection, classification, and coordination in MGs different from legacy grid protection. This article aims to present the state-of-the-art of the latest research and developments, including the challenges and issues in the field of AC MG protection. A broad overview of the available fault detection, fault classification, and fault location techniques for AC MG protection and coordination are presented. Moreover, the available methods are classified, and their advantages and disadvantages are discussed

Modeling Electromechanical Overcurrent Relays Using Singular Value Decomposition

This paper presents a practical and effective novel approach to curve fit electromechanical (EM) overcurrent (OC) relay characteristics. Based on singular value decomposition (SVD), the curves are fitted with equation in state space under modal coordinates. The relationships between transfer function and Markov parameters are adopted in this research to represent the characteristic curves of EM OC relays. This study applies the proposed method to two EM OC relays: the GE IAC51 relay with moderately inverse-time characteristic and the ABB CO-8 relay with inverse-time characteristic. The maximum absolute values of errors of hundreds of sample points taken from four time dial settings (TDS) for each relay between the actual characteristic curves and the corresponding values from the curve-fitting equations are within the range of 10 milliseconds. Finally, this study compares the SVD with the adaptive network and fuzzy inference system (ANFIS) to demonstrate its accuracy and identification robustness

Desain Kurva Konvensional Menggunakan ANFIS pada Digital Protection Relay

Pada umumnya konstruksi rele dapat dibedakan menjadi 3 jenis yaitu elektromekanis, statis, dan digital. Rele proteksi yang berbasiskan system digital mempunyai beberapa keunggulan dibanding rele tipe elektro mekanis dan statis. Keunggulan relay proteksi digital yaitu respon yang cepat terhadap gangguan, keakuratan dalam perhitungan, fleksibel serta dapat berkoordinasi dengan baik. Rele digital proteksi dapat dimanfaatkan menjadi rele arus lebih (over current relay). Rele arus lebih dengan karakteristik kurva konvensional yaitu normal inverse, very inverse, extremely inverse, dan long time inverse sangat bermanfaat untuk mengamankan overload/bebanlebih. Karena bekerja dengan waktu tunda yang tergantung dari besarnya arus secara terbalik (inverse time), makin besar arus makin kecil waktu pemutusan. Dalam mensetting sebuah rele diperlukan arus dan waktu pemutusan. Dari data arus dan waktu pemutusan tersebut dapat terbentuk sebuah kurva inverse setting rele. Keakuratan rele dalam melakukan pemutusan sangat penting dilakukan dalam menanggapi terjadinya gangguan. Dalam tugas akhir ini akan dilakukan studi mengenai cara mendapatkan desain kurva konvensional yang akurat menggunakan metode ANFIS. Desain kurva ini digunakan pada digital relay protection. Desain kurva konvensional akan dibuat software dan prototipe digital relaynya untuk memvalidasi keakuratannya. ========================================================================================================== In general construction relay can be divided into three types namely electromechanical, static, and digital. Relay protection based digital system has several advantages compared to the type of electromechanical relays and static relays. Superiority of digital protection relay is a fast response to disturbances, the accuracy of the calculation, flexible and able to coordinate well. Digital relay protection can be utilized as an overcurrent relays. Overcurrent relays to the characteristics of the conventional curve is normal inverse, very inverse, extremely inverse, and inverse long time is very useful for securing overload. Because it works with time delay depending on the magnitude of current in reverse (inverse time), the greater the flow of the smaller time of break. In setting up a relay required flow and time of break. From the data flow and the disconnection time can form an inverse curve setting relay. The accuracy of the relay is very important terminate done in response to the disruption. In this final task will be carried out a study on how to get accurate conventional curve design using ANFIS method. This curve is used in the design of digital relay protection. Conventional curve design will be made software and digital prototypes to validate the accuracy of its relay

Optimal overcurrent relay coordination in wind farm using genetic algorithm

Wind farms are ones of the most indispensable types of sustainable energies which are progressively engaged in smart grids with tenacity of electrical power generation predominantly as a distribution generation system. Thus, rigorous protection of wind power plants is an immensely momentous aspect in electrical power protection engineering which must be contemplated thoroughly during designing the wind plants to afford a proper protection for power components in case of fault occurrence. The most commodious protection apparatus are overcurrent relays (OCRs) which are responsible for protecting power systems from impending faults. In order to employ a prosperous and proper protection for wind farms, these relays must be set precisely and well-coordinated with each other to clear the faults at the system in the shortest possible time. These relays are set and coordinated with each other by applying IEEE or IEC standards methods, however, their operation times are relatively long and the coordination between these relays are not optimal. The other common problem in these power systems is when a fault occurs in a plant, several OCRs operate instead of a designated relay to that particular fault location. This, if undesirable can result in unnecessary power loss and disconnection of healthy feeders out of the plant which is extremely dire. It is necessary to address the problems related inefficient coordination of OCRs. Many suggestions have been made and approaches implemented, however one of the most prominent methods is the use of Genetic Algorithm (GA) to improve the function and coordination of OCRs. GA optimization technique was implemented in this project due to its ample advantages over other AI techniques including proving high accuracy, fast response and most importantly obtaining optimal solutions for nonlinear characteristics of OCRs. In addressing the mentioned problems, the main objective of this research is to improve the protection of wind farms by optimizing the relay settings, reducing their operation time, Time Setting Multiplier (TSM) of each relay, improving the coordination between relays after implementation of IEC 60255-151:2009 standard. The most recent and successful OF for GA technique has been used, unique parameters for GA was selected for this research to significantly improve the protection for wind farms that is highly better compared to any research accomplished before for the purpose of wind farm protection. GA was used to obtain improved values for each relay settings based on their coordination criteria. Each relay operation time and TSM are optimized which would contribute to provide a better protection for wind farm. Thus, the objective of this work which is improving the protection of wind farms by optimizing the relay settings, reducing their operation time, Time Setting Multiplier (TSM) of each relay, improving the coordination between relays, have been successfully fulfilled and solved the problems associated with wind farm relay protection system settings. The new approach has shown significant improvement in operation of OCRs at the wind farm, have drastically reduced the accumulative operation time of the relays by 26.8735% (3.7623 seconds)

A Review on Application of Artificial Intelligence Techniques in Microgrids

A microgrid can be formed by the integration of different components such as loads, renewable/conventional units, and energy storage systems in a local area. Microgrids with the advantages of being flexible, environmentally friendly, and self-sufficient can improve the power system performance metrics such as resiliency and reliability. However, design and implementation of microgrids are always faced with different challenges considering the uncertainties associated with loads and renewable energy resources (RERs), sudden load variations, energy management of several energy resources, etc. Therefore, it is required to employ such rapid and accurate methods, as artificial intelligence (AI) techniques, to address these challenges and improve the MG's efficiency, stability, security, and reliability. Utilization of AI helps to develop systems as intelligent as humans to learn, decide, and solve problems. This paper presents a review on different applications of AI-based techniques in microgrids such as energy management, load and generation forecasting, protection, power electronics control, and cyber security. Different AI tasks such as regression and classification in microgrids are discussed using methods including machine learning, artificial neural networks, fuzzy logic, support vector machines, etc. The advantages, limitation, and future trends of AI applications in microgrids are discussed.©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

A resilience-oriented bidirectional anfis framework for networked microgrid management

This study implemented a bidirectional artificial neuro-fuzzy inference system (ANFIS) to solve the problem of system resilience in synchronized and islanded grid mode/operation (during normal operation and in the event of a catastrophic disaster, respectively). Included in this setup are photovoltaics, wind turbines, batteries, and smart load management. Solar panels, wind turbines, and battery-charging supercapacitors are just a few of the sustainable energy sources ANFIS coordinates. The first step in the process was the development of a mode-specific control algorithm to address the system’s current behavior. Relative ANFIS will take over to greatly boost resilience during times of crisis, power savings, and routine operations. A bidirectional converter connects the battery in order to keep the DC link stable and allow energy displacement due to changes in generation and consumption. When combined with the ANFIS algorithm, PV can be used to meet precise power needs. This means it can safeguard the battery from extreme conditions such as overcharging or discharging. The wind system is optimized for an island environment and will perform as designed. The efficiency of the system and the life of the batteries both improve. Improvements to the inverter’s functionality can be attributed to the use of synchronous reference frame transformation for control. Based on the available solar power, wind power, and system state of charge (SOC), the anticipated fuzzy rule-based ANFIS will take over. Furthermore, the synchronized grid was compared to ANFIS. The study uses MATLAB/Simulink to demonstrate the robustness of the system under test

Using Probabilistic Neural Network for Classification High Impedance Faults on Power Distribution Feeders

An intelligent approach probabilistic Neural Network (PNN) combined with advanced signalprocessing techniques such as Discrete Wavelet Transform (DWT) is presented for detection High impedance faults (HIFs) on power distribution networks. HIFs detection is usually very difficult using the common over current devices, both frequency and time data are needed to get the exact information to classify and detect no fault from HIF. In this proposed method, DWT is used to extract features of the no fault and HIF signals. The features extracted using DWT which comprises the energy, standard deviation, mean, root mean square and mean of energy of detail and approximate coefficients of the voltage, current and power signals are utilized to train and test the PNN for a precise classification of no fault from HIFs. The proposed method shows that it is more convenient for HIF detection in distribution systems with ample varying in operating cases

Power System Simulation, Control and Optimization

This Special Issue “Power System Simulation, Control and Optimization” offers valuable insights into the most recent research developments in these topics. The analysis, operation, and control of power systems are increasingly complex tasks that require advanced simulation models to analyze and control the effects of transformations concerning electricity grids today: Massive integration of renewable energies, progressive implementation of electric vehicles, development of intelligent networks, and progressive evolution of the applications of artificial intelligence