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Effect of high speed reclosing on fault induced delayed voltage recovery
Abstract
This paper presents the effect on fault induced delayed voltage recovery (FIDVR) of high speed reclosing onto a fault. As most of the faults on the system are temporary or transient faults, auto-reclosing is widely used across the industry to reduce the outage time. However, if the voltage recovery following clearing of a fault is slow and if the fault is permanent in nature, auto-reclosing on to a fault could increase voltage recovery time significantly. This is especially true for the case where the fault cannot be cleared instantaneously. This is may be because the line does not have a pilot protection scheme or the pilot is out of service and the fault is located outside the zone 1 reach of the reclosing end breaker. This paper presents the simulation of this scenario on some of the 230kV lines around the metro Atlanta area. Finally, this paper also discusses a few solution options, if auto-reclosing onto a fault poses a threat of a FIDVR event.
This chapter introduces voltage stability indices. These indices quickly provide an estimation of the distance from the current operating point to the power system maximum loadability limit. Voltage stability indices can be divided into two general categories: model‐ and measurement based. The chapter also introduces a number of important measurements‐based indices, including indices based on the equivalent Thevenin circuit, bus apparent‐power criterion, S different criterion, and DSY. It describes the fault‐induced delayed voltage recovery phenomenon and introduces indices to evaluate it. The chapter provides indices based on measuring and monitoring the trajectory of voltage variations. It presents the real‐world applications of measurement‐based Indices.
Transient stability analysis is becoming increasingly important for power systems engineers and researchers. Accurate dynamic models are required, but aggregate load models are an area of weakness. Measurement-based system identification methods based on least-square minimization have difficulty determining model parameters because the models do not satisfy injectivity: vastly different model parameters can produce the same output waveform for a given disturbance. One could argue that the parameters of a model are unimportant, as long as the simulation output waveforms are correct. While this is true for the disturbance(s) we used to determine the parameters, we show that the model fails when we try to use it to predict the result of other disturbances. Thus, we must attempt to regain injectivity. In this paper, we present two methods for doing so. First we try using multiple disturbances, but this does not have a significant impact. Second, we present an algorithm based on a maximum a-posteriori (MAP) estimator, which is much more robust than least-squares. The MAP estimator can take advantage of prior knowledge of the parameters of the grid. When we know a great deal about the model parameters beforehand, the MAP estimator is extremely robust to measurement noise, even down to SNRs approaching zero. When we do not, even if we simply guess that each of the five components in the complex load model comprises 20% of the total load, the MAP estimator still performs significantly better than least squares.
As a result of a multiple contingency fault and breaker failure event at two Metro Atlanta 230 kV substations in 1999, Southern Company experienced a wide area voltage depression lasting around 15 seconds. The event resulted in a 1900 MW load loss. Dynamic simulations utilizing standard static stability load models were not successful in replicating the event. However, the actual response of the transmission system was replicated utilizing dynamic simulations with aggregate load models that included the effect of induction motors and distribution system impedances. Since the event in 1999, load has grown exceptionally in the North Georgia region. As a result of the load growth, capital projects are being implemented in Southern Company to appropriately manage the exposure to both NERC reliability standard category B and D fault induced delayed voltage recovery events. Therefore, it is critical that the load models used in dynamic studies correctly represent the behavior of actual load. This is necessary to ensure that the timing and effectiveness of capital projects are appropriately quantified. Both the formulation of the aggregate load models used to replicate the 1999 event, and ongoing efforts to refine the load model used to assess future exposure to fault induced delayed voltage recovery are discussed in this paper.
This paper describes a multiple contingency fault and breaker
failure event at two Metro Atlanta 230 kV substations. After the fault
activity, the transmission system in the Metro Atlanta area experienced
a voltage depression lasting around 15 seconds, followed by a voltage
overshoot. Over 1900 MWs of load was lost. Evidence suggests that the
majority of the load was dropped due to the operation of induction motor
protection removing the motors from the extended low voltages. Dynamic
simulations using aggregate load models that included the effect of
induction motors and distribution system impedances were used to
recreate the response of the transmission system. Real world data,
including a plot from a data fault recorder, is presented and compared
to the dynamic simulations. This paper describes the event, and the
study techniques required to simulate the observed transmission system
behavior. The effectiveness of new bus arrangements, enhanced relaying,
and dynamic and static MVAr support toward mitigating the voltage
depression is also discussed