PMU and FMU Based Investigation of the Effect of Static System State Estimation on the Performance of a Transmission Network
Year 2018,
Volume: 2 Issue: 4, 123 - 132, 28.12.2018
İfedayo Oladeji
,
Micheal Adu
Abstract
The
characterisation of a power system at every operating state is a requirement
for reliable and secured operation. This paper presents the evaluation of the
state variables of a power system with static state estimation under steady
state and transient conditions. Power flow for evaluation of the initial system
variables was obtained through Newton
Raphson algorithm. Real time system data is obtained through the phasor
measurement units (PMUs) installed at remote terminal units (RTUs) of each load
points. Weighted least square estimation method is applied on the initial state
parameters; power flows, voltage
magnitudes and angles and frequency obtained from the load flow evaluation to
obtain reliable and final system state parameters. The result obtained through
simulation of a 40 bus network on MATLAB (PSAT) shows 32.5% and 50% variances
of state variables above 1%. Static state estimation (SSE) with PMU also
provided improvement in the network performance through transmission
loss reduction and overall system stability. A real time data acquisition (PMU)
and data analysis system (SSE) is required to fully model, accurately
characterise and ensure secured operation of the network under steady state and
transient conditions.
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- Oladeji, I. R., and Oyinlola, A. O. (2017). Evaluation of the critical load parameter: Predicition of voltage collapse under steady state system operation. International Journal of Scientific and Engineering Research , 248-254.
- Saha, R. B., Sinha, A. K., and Pradhan, A. K. (2012). An Optimal PMU Placement Technique for Power System Observability. International Journal of Electrical Power and Energy Systems , 71-77.
- Saleh, S. A. (2016). The Analysis and Development of a Power Flow-Based Controller for Micro-Grid Systems. IEEE Transactions on Industry Applications , 1-12.
- Vedran, K., Srdan, S., and Dubravko, F. (2016). A State Estimator Using SCADA and Synchronized Phasor Measurements. International Journal of Electrical and Computer Engineering Systems , 61-69.
- Wenyuan, L. (2011). Probabilistic Transmission System Planning. New Jersey: John Wiley Inc.
Year 2018,
Volume: 2 Issue: 4, 123 - 132, 28.12.2018
İfedayo Oladeji
,
Micheal Adu
References
- Abe, R. (2016). Digital Grid: Full of Renewable Energy in the Future. International Conference on Consumer Electronics (pp. 1-2). Taiwan: Electric Power Network Innovation University of Tokyo.
- Ali, A., and Antonio, G. E. (2004). Power System State Estimation-Theory and Implementation. New York: Marcel Dekker Inc.
- Anderson, P. M., and Fouad, A. A. (Anderson). Power System Control and Stability. Iowa: Iowa State University Press.
- Anurag, K. S., Ramon, Z., Noel, N. S., Krishnanjan, G. R., and Vinoth, M. M. (2012). Modeling and Control of Sustainable Power Systems;Real Time Modeling and Control of Smart Grid Systems. Berlin: Springer.
- Arpit, K., Akash, S., and Tandon, A. (2017). Recent Development in Power System Dynamic State Estimation. International Journal of Emerging Research in Management and Technology , 161-166.
- Bindeshwar, S., Sharma, N. K., and Tiwari, A. N. (2011). Applications of Phasor Measurement units (PMUs) in Electric Power System Networks Incorporated with FACTS Controllers. International Journal of Engineering, Science and Technology , 64-82.
- Ebrahim, V. (2014). Practical Power System Operation. New Jersey: IEEE Press Wiley.
- Farid, G., and Benjamin, C. K. (2010). Automatic Control Systems. New Jersey: John Wiley and Sons Inc.
- Federico, M. (2016). Advances in Power System Modelling, Control and Stability Analysis. London: The Institution of Engineering and Technology.
- Gianluigi, M. (2013). Advanced Technologies for Future Transmission Grids. New York: Springer.
- Hossein, S., and Mohammad, S. S. (2011). Electric Power System Planning: Issues, Algorithms and Solutions. Berlin: Springer.
- Jan, M., Janusc, W. B., and James, R. B. (2008). Power System Dynamics: Stability and Control. West Sussex: John Wiley and Sons Ltd.
- Junjian, Q., Kai, S., and Wei, K. (2014). Optimal PMU Placement for Power System Dynamic State Estimation by Using EmpiricalObservability Gramian. IEEE Transactions on Power Systems , 1-14.
- Kiani, R. H., and Moravej, Z. (166-182). An Approach for simultaneous distribution, sub-transmission and transmission networks expansion planning. Technology and Resilience Case Studies , 2017.
- Leonard, L. G. (2006). Electric Power Engineering Handbook. London: CRC Press.
- Loi, L. L. (2001). Power System Restructuring and Deregulation: Trading, Performance and Information Technology. Chichester: John Wiley and Sons LTD.
- Oladeji, I. R., and Oyinlola, A. O. (2017). Evaluation of the critical load parameter: Predicition of voltage collapse under steady state system operation. International Journal of Scientific and Engineering Research , 248-254.
- Saha, R. B., Sinha, A. K., and Pradhan, A. K. (2012). An Optimal PMU Placement Technique for Power System Observability. International Journal of Electrical Power and Energy Systems , 71-77.
- Saleh, S. A. (2016). The Analysis and Development of a Power Flow-Based Controller for Micro-Grid Systems. IEEE Transactions on Industry Applications , 1-12.
- Vedran, K., Srdan, S., and Dubravko, F. (2016). A State Estimator Using SCADA and Synchronized Phasor Measurements. International Journal of Electrical and Computer Engineering Systems , 61-69.
- Wenyuan, L. (2011). Probabilistic Transmission System Planning. New Jersey: John Wiley Inc.