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Chandrashekhar, P. K.
- Dynamic Stability Enhancement of Power System Using Fuzzy Power System Stabilizer Under Different Loading Conditions
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Authors
Affiliations
1 Senior Research Fellow, Power Systems Division, Central Power Research Institute, Bangalore – 560 080, Karnataka, IN
2 Associate Professor, Dept. of Electrical & Electronics Engg., R V College of Engineering, Bangalore – 560 059, Karnataka, IN
3 Engg. Officer. Gr.-4, Power Systems Division, Central Power Research Institute, Bangalore – 560 080, Karnataka, IN
4 Associate Professor, Dept. of Electrical & Electronics Engg.,Siddaganga Institute of Technology, Tumkur - 572 103, Karnataka, IN
1 Senior Research Fellow, Power Systems Division, Central Power Research Institute, Bangalore – 560 080, Karnataka, IN
2 Associate Professor, Dept. of Electrical & Electronics Engg., R V College of Engineering, Bangalore – 560 059, Karnataka, IN
3 Engg. Officer. Gr.-4, Power Systems Division, Central Power Research Institute, Bangalore – 560 080, Karnataka, IN
4 Associate Professor, Dept. of Electrical & Electronics Engg.,Siddaganga Institute of Technology, Tumkur - 572 103, Karnataka, IN
Source
Power Research, Vol 10, No 1 (2014), Pagination: 27-34Abstract
The power system is dynamic in nature and is constantly being subjected to disturbances. It enters into dynamic instability when there is an imbalance between generation and varying load demand which calls for use of Power System Stabilizer (PSS). PSS is a device which provides additional signal to the voltage regulator derived from speed deviation, excitation deviation and accelerating power for damping critical oscillations. The PSS used should be capable to produce appropriate stabilizing signals against a wide range of operating conditions and disturbances. For this purpose, a PSS based on fuzzy logic control under multi-operating conditions is proposed in this paper. The performance of Fuzzy Logic based PSS(FLPSS) applied to Single Machine Infinite Bus (SMIB) system is studied for three different operating conditions; nominal load, heavy load and fault condition in transmission line. For Fuzzy PSS, speed deviation and acceleration are taken as input. The system is simulated in SIMULINK platform and its dynamic response is analyzed for system without PSS and with PSS. The results are compared for system with Conventional PSS and Fuzzy Logic PSS.Keywords
Automatic Voltage Regulator (AVR), Fuzzy Logic Controller (FLC), Power System Stabilizer (PSS), Single Machine Infinite Bus (SMIB)
- Synchronized Measurements based Online Transient Stability Assessment using Gaussian Process Regression
Abstract Views :265 |
PDF Views:0
Authors
Affiliations
1 Ph.D. Scholar, Central Power Research Institute, Bengaluru – 560080, Karnataka, IN
2 Associate Professor, RV College of Engineering, Bengaluru - 560059, Karnataka, IN
1 Ph.D. Scholar, Central Power Research Institute, Bengaluru – 560080, Karnataka, IN
2 Associate Professor, RV College of Engineering, Bengaluru - 560059, Karnataka, IN
Source
Power Research, Vol 16, No 2 (2020), Pagination: 129-135Abstract
In this paper an online post-fault Transient Stability Assessment (TSA) method using synchronized measurements or PMU measurements and Gaussian Process Regression (GPR) is presented. A post-fault multi-machine system is converted into two machine groups (Critical and Non critical) then into a suitable OMIB system using Single Machine Equivalent (SIME) method. With the help of thus obtained OMIB Pa-δ trajectory, a normalized Transient Stability Margin (TSM) is proposed offline. By using pre and during fault synchronized measurements as input, different GPR models are trained offline to predict the normalized stability margin. Keeping RMSE as a measure, a best suitable model is chosen for prediction. After a fault, the synchronized measurements are used as input to this trained model to predict the stability margin online. If the predicted margin is negative, then the post-fault system said to be unstable. If the predicted margin is positive, then the system is stable. The proposed assessment method is tested using New England 39 bus test system. The results are compared with offline simulations. High prediction accuracy rates are observed for GPR models, making them suitable for online TSA.Keywords
Gaussian Process Regression (GPR), Regression Analysis, Single Machine Equivalent (SIME), Synchronized Measurements, Transient Stability Prediction, Transient Stability Margin (TSM).References
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