scholarly journals An Adaptive and Scalable Protection Coordination System of Overcurrent Relays in Distributed-Generator-Integrated Distribution Networks

2021 ◽  
Vol 11 (18) ◽  
pp. 8454
Author(s):  
Duong Minh Bui ◽  
Phuc Duy Le ◽  
Thanh Phuong Nguyen ◽  
Hung Nguyen
Keyword(s):  
Search Algorithm ◽  
Optimization Algorithms ◽  
Distribution Network ◽  
Gravitational Search Algorithm ◽  
Distribution Networks ◽  
Fault Current ◽  
Test Bed ◽  
Dial Setting ◽  
Definite Time ◽  
Overcurrent Relays

Integration of distributed generators (DGs) into a distribution network (DN) can cause coordination challenges of overcurrent relays (OCRs) because of different fault-current contributions of DGs as well as the directional change in fault currents. Therefore, the OCRs should be properly coordinated to maintain their adaptability and scalability to protect the DG-integrated distribution network. In this study, an adaptive and scalable protection coordination (ASPC) approach has been developed for the OCRs in a DG-contained distribution network based on two implementation stages. At the first stage, the reliability improvement of fault-current calculation results is performed by determining the min-max confidence interval of fault current for each different fault type, which is the basis for properly selecting tripping and pick-up thresholds of definite-time and inverse-time OC functions in the same OCR. At the second stage, optimization algorithms are used for calculating protection-curve coefficients and Time-Dial Setting (TDS) multiplier for the inverse-time OC functions in the OCR. A real 22 kV DG-integrated distribution network which is simulated by ETAP software is considered a reliable test-bed to validate the proposed ASPC system of OCRs in the multiple-DG-contained distribution network. In addition, the coordination results of OCRs can be obtained by three common optimization algorithms, Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Genetic Algorithm (GA). These relay coordination results have shown an effective protection combination of the definite-time OC functions (50P and 50G) and the inverse-time OC functions (51P and 51G) in the same OCR to get the adaptable and scalable DN protection system.

scholarly journals Security-Constrained Optimal Protection Coordination for Dual-Setting Digital Directional Overcurrent Relays in the Distribution Network Including Non-Renewable/Renewable Synchronous Distributed Generation

2021 ◽  
Author(s):  
Sasan Pirouzi ◽  
Hosein Hasan Shahi ◽  
Mohammad Reza Akbai Zadeh ◽  
Amirreza Naderipour ◽  
Zulkurnain Abdul-Malek
Keyword(s):  
Distributed Generation ◽  
Distribution Network ◽  
Optimal Solution ◽  
Operation Time ◽  
Distribution Networks ◽  
Time Interval ◽  
Dial Setting ◽  
Overcurrent Relays ◽  
Overcurrent Relay ◽  
Optimal Protection

Abstract In this paper, the security-constrained optimal protection coordination (SCOPC) is introduced for dual setting digital directional overcurrent relay (DDOCR) in distribution network, which including renewable and non-renewable synchronous distributed generation (SDG). The SCOPC minimizes the total operation time of DDOCRs in primary and backup protection operating to achieve a fast protection coordination. Also, to improve the flexibility in DDOCRs setting, the allowable limits of A and B coefficients, pickup current (PC) and time dial setting (TDS) in both reverse and forward directions are considered as constraints. Another constraint is the Coordination Time interval (CTI). To consideration of the mentioned scheme security, the SCOPC mechanism considered the unavailability of DDOCRs due to their failure, so the stochastic method is used to modelling of this parameter. To calculate the fault current, network variables are proportional to the daily stochastic operation results of distribution network. Moreover, the proposed problem is implemented on the standard distribution networks, and then the optimal solution is obtained with hybrid algorithm of grey wolf optimization (GWO) and training and learning optimization (TLBO). The numerical results illustrate that the proposed algorithm is able to achieve a reliable and fast protection coordination that has a low standard deviation.

scholarly journals Study on a protection coordination approach for over-current relays in distribution generator-integrated distribution networks

2021 ◽  
Vol 4 (2) ◽  
pp. first
Author(s):  
Phúc Duy Lê ◽  
Hoan Thanh Nguyễn ◽  
Phúc Công Huỳnh ◽  
Dương Minh Bùi ◽  
Minh Ngọc Đoàn ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Distribution Networks ◽  
Time Interval ◽  
Distributed Generator ◽  
Protection Relay ◽  
Dial Setting ◽  
Gravitational Search

Sự hiện diện của nguồn phân tán DG (Distributed Generator) đã gây ra những thách thức đến việc duy trì độ tin cậy của những OCPR quá dòng OCPR (Over-current Protection Relay) khi hoạt động để bảo vệ lưới điện phân phối (LĐPP). Trong quá trình vận hành để đảm bảo cung cấp điện cho LĐPP, những đặc tính vận hành của nguồn DG đã làm thay đổi đáng kể giá trị dòng điện sự cố và đây là nguyên nhân chính dẫn đến những hiện tượng OCPR hoạt động không tin cậy, chẳng hạn như mất tính chọn lọc, giảm độ nhạy, hoạt động vượt cấp hoặc hoạt động đồng thời. Do đó, việc điều phối những OCPR thuộc hệ thống bảo vệ trên LĐPP có xem xét đến những đặc tính vận hành của nguồn DG nhằm đảm bảo tính phối hợp khi hoạt động là cần thiết. Trong nghiên cứu này, nhóm tác giả sẽ giới thiệu về một phương pháp điều phối bảo vệ OCPCO (Over-current Protection Coordination Optimization) dành cho hệ thống bảo vệ của LĐPP có tích hợp nguồn DG. Cụ thể, phương pháp OCPCO này được phát triển dựa vào việc sử dụng kết quả phân tích ngắn mạch kết hợp với giải thuật tìm kiếm GSA (Gravitational Search Algorithm) để xác định các hệ số điều phối A, B, C và TDS (Time Dial Setting) phù hợp với từng trạng thái vận hành của LĐPP có tích hợp nguồn DG, đặc biệt là sau khi LĐPP đã được tái cấu trúc để cách ly sự cố và khôi phục cung cấp điện. Thông qua hàm mục tiêu về tổng thời gian đảm bảo phối hợp CTI (Coordination Time Interval) cho phép giữa những OCPR liền kề nhau, phương án điều phối trị số chỉnh định sẽ được đề xuất và cập nhật đến từng OCPR thông qua hạ tầng mạng truyền dẫn thông tin. Mô hình LĐPP có tích hợp nguồn DG được nhóm tác giả xây dựng dựa vào LĐPP thực tế bằng phần mềm ETAP, để phục vụ cho việc phân tích ngắn mạch và kiểm tra tính khả thi của phương pháp OCPCO đề xuất. Hơn nữa, kết quả điều phối bảo vệ bằng giải thuật GSA sẽ được so sánh với những kết quả xuất ra từ giải thuật PSO&GSA và GA nhằm làm cơ sở kiểm tra tính phù hợp khi điều phối những OCPR trên LĐPP có tích hợp nguồn DG.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 343-372 ◽  
Author(s):  
Umit Can ◽  
Bilal Alatas
Keyword(s):  
Association Rules ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Search Method ◽  
Quantitative Association Rules ◽  
Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

scholarly journals A New “Good and Bad Groups-Based Optimizer” for Solving Various Optimization Problems

2021 ◽  
Vol 11 (10) ◽  
pp. 4382
Author(s):  
Ali Sadeghi ◽  
Sajjad Amiri Doumari ◽  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Pavel Trojovský ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Gray Wolf ◽  
Good Group ◽  
Good Ability ◽  
Whale Optimization

Optimization is the science that presents a solution among the available solutions considering an optimization problem’s limitations. Optimization algorithms have been introduced as efficient tools for solving optimization problems. These algorithms are designed based on various natural phenomena, behavior, the lifestyle of living beings, physical laws, rules of games, etc. In this paper, a new optimization algorithm called the good and bad groups-based optimizer (GBGBO) is introduced to solve various optimization problems. In GBGBO, population members update under the influence of two groups named the good group and the bad group. The good group consists of a certain number of the population members with better fitness function than other members and the bad group consists of a number of the population members with worse fitness function than other members of the population. GBGBO is mathematically modeled and its performance in solving optimization problems was tested on a set of twenty-three different objective functions. In addition, for further analysis, the results obtained from the proposed algorithm were compared with eight optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching–learning-based optimization (TLBO), gray wolf optimizer (GWO), and the whale optimization algorithm (WOA), tunicate swarm algorithm (TSA), and marine predators algorithm (MPA). The results show that the proposed GBGBO algorithm has a good ability to solve various optimization problems and is more competitive than other similar algorithms.

scholarly journals GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1190
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Štěpán Hubálovský
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Random Search ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Main Idea ◽  
Population Based ◽  
Grey Wolf Optimizer ◽  
Whale Optimization

There are many optimization problems in the different disciplines of science that must be solved using the appropriate method. Population-based optimization algorithms are one of the most efficient ways to solve various optimization problems. Population-based optimization algorithms are able to provide appropriate solutions to optimization problems based on a random search of the problem-solving space without the need for gradient and derivative information. In this paper, a new optimization algorithm called the Group Mean-Based Optimizer (GMBO) is presented; it can be applied to solve optimization problems in various fields of science. The main idea in designing the GMBO is to use more effectively the information of different members of the algorithm population based on two selected groups, with the titles of the good group and the bad group. Two new composite members are obtained by averaging each of these groups, which are used to update the population members. The various stages of the GMBO are described and mathematically modeled with the aim of being used to solve optimization problems. The performance of the GMBO in providing a suitable quasi-optimal solution on a set of 23 standard objective functions of different types of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal is evaluated. In addition, the optimization results obtained from the proposed GMBO were compared with eight other widely used optimization algorithms, including the Marine Predators Algorithm (MPA), the Tunicate Swarm Algorithm (TSA), the Whale Optimization Algorithm (WOA), the Grey Wolf Optimizer (GWO), Teaching–Learning-Based Optimization (TLBO), the Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA). The optimization results indicated the acceptable performance of the proposed GMBO, and, based on the analysis and comparison of the results, it was determined that the GMBO is superior and much more competitive than the other eight algorithms.

scholarly journals An Effective Method for Parameter Estimation of Solar PV Cell Using Grey-Wolf Optimization Technique

2021 ◽  
Vol 6 (3) ◽  
pp. 911-931
Author(s):  
Abhishek Sharma ◽  
Abhinav Sharma ◽  
Averbukh Moshe ◽  
Nikhil Raj ◽  
Rupendra Kumar Pachauri
Keyword(s):  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Parameter Extraction ◽  
Solar Pv ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Swarm Optimization ◽  
Pv Cell ◽  
Gravitational Search

In the field of renewable energy, the extraction of parameters for solar photovoltaic (PV) cells is a widely studied area of research. Parameter extraction of solar PV cell is a highly non-linear complex optimization problem. In this research work, the authors have explored grey wolf optimization (GWO) algorithm to estimate the optimized value of the unknown parameters of a PV cell. The simulation results have been compared with five different pre-existing optimization algorithms: gravitational search algorithm (GSA), a hybrid of particle swarm optimization and gravitational search algorithm (PSOGSA), sine cosine (SCA), chicken swarm optimization (CSO) and cultural algorithm (CA). Furthermore, a comparison with the algorithms existing in the literature is also carried out. The comparative results comprehensively demonstrate that GWO outperforms the existing optimization algorithms in terms of root mean square error (RMSE) and the rate of convergence. Furthermore, the statistical results validate and indicate that GWO algorithm is better than other algorithms in terms of average accuracy and robustness. An extensive comparison of electrical performance parameters: maximum current, voltage, power, and fill factor (FF) has been carried out for both PV model.

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