While it is true that power systems are designed to be transiently stable, and many of the methods described above may be used to achieve this goal, in actual practice, systems may be prone to being unstable. This is largely due to uncertainties related to assumptions made during the design process.
These uncertainties result from a number of sources including:
A growing trend in system operations is to perform transient stability assessment on-line in near-real-time. In this approach, the power flow defining the system topology and the initial operating state is derived, at regular intervals, from actual system measurements via the energy management system (EMS) using state-estimation methods. The derived power flow together with other data required for transient stability analysis is passed to transient stability software residing on dedicated computers and the computations required to assess all credible contingencies are performed within a specified cycle time. Using advanced analytical methods and high-end computer hardware, it is currently possible to assess the transient stability of vary large systems, for a large number of contingencies, in cycle times typically ranging from 5 to 30 min. Since this on-line approach uses information derived directly from the actual power system, it eliminates a number of the uncertainties associated with load forecasting, generation forecasting, and prediction of system topology, thereby leading to more accurate and meaningful stability assessment.
These uncertainties result from a number of sources including:
- LOAD AND GENERATION FORECAST: The design process must use forecast information about the amount, distribution, and characteristics of the connected loads as well as the location and amount of connected generation. These all have a great deal of uncertainty. If the actual system load is higher than planned, the generation output will be higher, the system will be more stressed, and the transient stability limit may be significantly lower.
- SYSTEM TOPOLOGY: Design studies generally assume all elements in service, or perhaps up to two elements out-of-service. In actual systems, there are usually many elements out-of-service at any one time due to forced outages (failures) or system maintenance. Clearly, these outages can seriously weaken the system and make it less transiently stable.
- DYNAMIC MODELING: All models used for power system simulation, even the most advanced, contain approximations out of practical necessity.
- DYNAMIC DATA: The results of time-domain simulations depend heavily on the data used to represent the models for generators and the associated controls. In many cases, this data is not known (typical data is assumed) or is in error (either because it has not been derived from field measurements or due to changes that have been made in the actual system controls that have not been reflected in the data).
- DEVICE OPERATION: In the design process it is assumed that controls and protection will operate as designed. In the actual system, relays, breakers, and other controls may fail or operate improperly.
A growing trend in system operations is to perform transient stability assessment on-line in near-real-time. In this approach, the power flow defining the system topology and the initial operating state is derived, at regular intervals, from actual system measurements via the energy management system (EMS) using state-estimation methods. The derived power flow together with other data required for transient stability analysis is passed to transient stability software residing on dedicated computers and the computations required to assess all credible contingencies are performed within a specified cycle time. Using advanced analytical methods and high-end computer hardware, it is currently possible to assess the transient stability of vary large systems, for a large number of contingencies, in cycle times typically ranging from 5 to 30 min. Since this on-line approach uses information derived directly from the actual power system, it eliminates a number of the uncertainties associated with load forecasting, generation forecasting, and prediction of system topology, thereby leading to more accurate and meaningful stability assessment.
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