PNNL

PNNL team is developing a new multi-scale state estimation framework that effectively integrates the SSE and DSE together, enabling the development of next generation of EMS, as shown in Fig. 1. The proposed frame work includes the following two basic items: (1) when to switch between static state estimation and dynamic state estimation; (2) how to switch. In the first one, we need to address event detection using measurement data and judge if the disturbance is big enough to lunch dynamic state estimation when an event is detected. In contrast, if we need to switch from dynamic state estimation to static state estimation, we need to develop criteria to judge if the system goes into static or quasi-static state. In the second one, we need to address how to determine the initial values for dynamic state estimation if we need to switch from static state estimation to dynamic state estimation.

The singular spectrum analysis (SSA)-based change point detection approach is developed to detect the changes of system operational conditions and identify the dominant dynamics so that proper estimators can be selected and combined to obtain the optimal performance. The connections and logics between SSE and DSE are also developed. Specifically, if no event is detected by the SSA, the system is operated under normal conditions and the robust SSE using both SCADA and PMU measurements is executed. Otherwise, the event is declared and the results obtained from recent SSE are used to derive the initial condition for DSE. During the transient process, only PMU-based DSE is executed for power grid monitoring. The DSE will be terminated when no change point of the system is detected. After that, the DSE results are forwarded for SSE initialization and bus voltage magnitude and angle estimations. The developed framework is able to track the changes of system states over wide temporal and spatial ranges and enhances significantly the operator’s situational awareness, yielding better system monitoring, protection and control.

We tested the proposed event detection method through field measurement data (Fig. 1) that was recorded right before the power outage happened in the western interconnection on Aug 10, 1996. The performance of the proposed approach is shown in Fig. 2. The results show that the proposed approach is able to detect the two events effectively.

To validate the effectiveness of the proposed framework in transition between SSE and DSE interchangeably, extensive simulations were carried out on the IEEE 10-machine 39-bus test system. At 20s, one line in the test system is tripped and another 70s time-domain simulations are used for test. The SSA-based event detection results are shown in Fig. 4. It is observed that the transmission line tripping induced transients have been effectively detected by the SSA method. After that, the SSE results obtained at 20s are used to calculate the dynamic states for initialization and the DSE is activated. The DSE results are displayed in Fig. 5. Thanks to the good initialization, the DSE is able to track the true dynamic states of the system at the very beginning of the transient process. According to the SSA-based change point detection results, the system is operating under steady-state at 42s.

Then, the DSE results are leveraged to calculate the bus voltage magnitude and angles of the system. These are used for the initialization of SSE. The estimated magnitude and angle at a specific bus are shown for illustration purpose. Its results are shown in Fig. 6. It can found that with the proper initialization via the DSE results, there is no convergence issue. Furthermore, the estimated results match well the true states. This further demonstrates the effectiveness of SSA method for system change point detection. This is because if there are undetected system changes, the non-synchronized SCADA measurements would not reflect the actual system operations and therefore, the estimated results will be far away from the true states.