Abstract
The exponential rise in the electrical power demand has led to various types of instability due to overburdening of the existing interconnected power systems. Voltage instability is one such phenomenon that can result in major blackouts. The primary cause of this problem is the collapsing of the transmission system due to the frequent and abrupt variations in the load, which gives rise to deviation in the normal operating conditions. In this chapter, the main focus is on detecting the most sensitive node (bus) in a modified IEEE-14 bus system and investigating a better and effective implementation of UPFC to improve the voltage profile and, thus, the voltage stability. For analyzing this condition, PSAT, a MATLAB-based Simulink and Simulation toolbox, which utilizes L-index method for voltage stability analysis and sensitive nodes determination, are used. The above setup is tested and compared for load variation in the step size of 5% from original power flow (OPF) to 40% load change with and without UPFC, and suitable conclusions are drawn from the results obtained so far.
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Abbreviations
- ACMV:
-
Air-conditioning and mechanical ventilation
- ANN:
-
Artificial neural network
- CO2:
-
Carbon dioxide
- CRAE:
-
Correlation attribute evaluation
- DE:
-
Differential evolution
- FNN:
-
Feedforward neural network
- GA:
-
Genetic algorithm
- GRAE:
-
Gain ratio attribute evaluation
- HR:
-
Humidity ratio
- HVAC:
-
Heating, ventilation, and air-conditioning
- IGAE:
-
Information gain attribute evaluation
- ORAE:
-
One R attribute evaluation
- PIR:
-
Passive infrared
- ppm:
-
Part per million
- PSO:
-
Particle swarm optimization
- RFAE:
-
Relief F attribute evaluation
- RH:
-
Relative humidity
- SA:
-
Simulated annealing
- StdDev:
-
Standard deviation
- SUAE:
-
Symmetrical uncertainty attribute evaluation
- Temp.:
-
Temperature
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Sinha, N.K., Devarapalli, R., Rao, P.N. (2022). Novel Approach for Power System Stability Analysis and Improvement: A Case Study Based on UPFC Application. In: Malik, H., Ahmad, M.W., Kothari, D. (eds) Intelligent Data Analytics for Power and Energy Systems. Lecture Notes in Electrical Engineering, vol 802. Springer, Singapore. https://doi.org/10.1007/978-981-16-6081-8_6
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