scholarly journals A real-time intelligent abnormity diagnosis platform in electric power system

2014 ◽  
Author(s):  
Feng Zhao ◽  
Guannan Wang ◽  
Chunyu Deng ◽  
Yue Zhao
Keyword(s):  
Power System ◽  
Electric Power ◽  
Real Time ◽  
Electric Power System

scholarly journals Application of Synchrophasor Measurements for Improving Situational Awareness of the Power System

2018 ◽  
Vol 55 (2) ◽  
pp. 3-10
Author(s):  
A. Obushevs ◽  
A. Mutule
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
Power System ◽  
Electric Power ◽  
Real Time ◽  
Situational Awareness ◽  
Renewable Energy Sources ◽  
Electric Power System ◽  
Energy Sources ◽  
Scada Systems

Abstract The paper focuses on the application of synchrophasor measurements that present unprecedented benefits compared to SCADA systems in order to facilitate the successful transformation of the Nordic-Baltic-and-European electric power system to operate with large amounts of renewable energy sources and improve situational awareness of the power system. The article describes new functionalities of visualisation tools to estimate a grid inertia level in real time with monitoring results between Nordic and Baltic power systems.

scholarly journals Development Of Software–Hardware System for Real Time Simulation of Electric Power System with Smart Grids

2018 ◽  
Vol 3 (4) ◽  
pp. 139
Author(s):  
A A Suvorov ◽  
A S Gusev ◽  
Y S Borovikov ◽  
A O Sulaymanov ◽  
M V Andreev ◽  
...  
Keyword(s):  
Power System ◽  
Electric Power ◽  
Real Time ◽  
Smart Grids ◽  
Electric Power System ◽  
Real Time Simulation ◽  
Hardware System ◽  
Time Simulation

.

scholarly journals Determination of Generator Capability Curve using Modified-Single Machine to Infinite Bus (M-SMIB) System Approach

2020 ◽  
Vol 188 ◽  
pp. 00022
Author(s):  
Rusilawati Rusilawati ◽  
Irrine Budi Sulistiawati ◽  
Naoto Yorino
Keyword(s):  
Power System ◽  
Electric Power ◽  
Real Time ◽  
Single Machine ◽  
System Approach ◽  
Electric Power System ◽  
Generator Unit ◽  
Load Location ◽  
Maximum Limit

The capability curve for each generator unit is usually provided by the generator manufacturer. But in practice, the generator can reach its maximum generation limit before reaching the maximum limit on the generator capability curve provided by the generator manufacturer. This might occur because of the load location is far from the generator or the varying of the loading value so that the maximum generation limit is smaller than the value given on the generator capability curve of the manufacturer. In this paper, the generator capability curve is determined using the Modified Single Machine to Infinite Bus (M-SMIB) system approach to determine the maximum generation limit every time there is a change in loading or change in the load location. After the maximum generation limit of each unit generator is known, the generator capability curve that is always in accordance with the real time situation can be formed. Thus, the operation limit of each generator can be recognized, determine the appropriate protection system setting and can prevent the electric power system disturbance. This method will be applied to generator units in the four bus IEEE system with two generators.

scholarly journals Real-Time LFO Damping Enhancement in Electric Networks Employing PSO Optimized ANFIS

Inventions ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 61
Author(s):  
Md Ilius Hasan Pathan ◽  
Md Juel Rana ◽  
Mohammad Shoaib Shahriar ◽  
Md Shafiullah ◽  
Md. Hasan Zahir ◽  
...  
Keyword(s):  
Power System ◽  
Electric Power ◽  
Real Time ◽  
Damping Ratio ◽  
Power System Stability ◽  
Synchronous Machine ◽  
Electric Power System ◽  
System Stability ◽  
Unified Power Flow Controller ◽  
Inference System

In recent years, machine learning (ML) tools have gained tremendous momentum and received wide-spread attention in different segments of modern-day life. As part of digital transformation, the power system industry is one of the pioneers in adopting such attractive and efficient tools for various applications. Apparently, a nonthreatening, but slow-burning issue of the electric power systems is the low-frequency oscillations (LFO), which, if not dealt with appropriately and on time, could result in complete network failure. This paper addresses the role of a prominent ML family member, particle swarm optimization (PSO) tuned adaptive neuro-fuzzy inference system (ANFIS) for real-time enhancement of LFO damping in electric power system networks. It adopts and models two power system networks where in the first network, the synchronous machine is equipped with only a power system stabilizer (PSS), and in the other, the PSS of the synchronous machine is coordinated with the unified power flow controller (UPFC), a second-generation flexible alternating current transmission system (FACTS) device. Then, it develops the proposed ML approach to enhance LFO damping for both adopted networks based on the customary practices of statistical judgment. The performance measuring metrics of power system stability, including the minimum damping ratio (MDR), eigenvalue, and time-domain simulation, were used to analyze the developed approach. Moreover, the paper presents a comparative analysis and discussion with the referenced works’ achieved results to conclude the proposed PSO-ANFIS technique’s ability to enhance power system stability in real-time by damping out the unwanted LFO.

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