Voltage Angle Measurements Research Articles

A method for monitoring the load margin of power systems under load growth variations

The load margin is an important safety measure used in operation centers to assess the voltage stability of power systems. Usually this measure is calculated considering a single load growth direction and voltage stability requirements. However, oscillation modes with low damping rates can compromise the small-signal stability of power systems and different load growth directions can also affect load margin estimates. Thus, these two factors must be considered to provide adequate load margin for power system operation. This paper proposes a load margin monitoring scheme based on Artificial Neural Networks (ANN) considering voltage stability requirements and small-signal stability and load growth variations. Thus, the power system operator will have real-time load margin information considering voltage stability requirements and small-signal stability requirements. ANN uses as input data the voltage magnitude and voltage angle measurements provided by Phasor Measurement Units (PMUs) installed in system buses. A method to select a reduced set of buses for the monitoring system is proposed and evaluated. Case studies are presented and the results achieved provide indications of the applicability of the proposed scheme in the real-time operation of power systems.

Prediction and control of transient stability using system integrity protection schemes

Prediction of the transient stability in real-time can be a boon in preventing the occurrence of catastrophic events in a particular area that could potentially affect the entire grid and cause blackouts. Synchronised voltage angle measurements procured from the installed phasor measurement units in the network have been used to devise an index for assessment of transient stability in the system. The behaviour of the trend of the acceleration at the instant of the critical clearing angle has been used in this study as the basis for differentiating between a stable and an unstable power swing. Upon detection of an unstable condition, the generator, which is most likely to be going out of step is detected, and appropriate control action is suggested for mitigating the predicted out of step condition. Moreover, the distance relay maloperations caused during a stable power swing can also be a reason for triggering cascading outages. Suitable remedial measures to avoid such maloperations of the distance relay have been proposed. The efficacy of the proposed philosophy has been tested on a single machine infinite bus system, WSCC-9 bus system and New England 39 bus system modelled in Power Factory/DIgSILENT.

Non‐synchronous generation impact on power systems coherency

The increasing integration of renewables into the grid based on power electronics converters interfaces is affecting the power systems dynamics, requiring effective monitoring and visualisation to provide appropriate assessment during extreme events. The coherent group identification in power systems is of importance for dynamic studies and transmission capability improvement. This study analyses the coherency based on the mode shapes, the application of the Koopman mode analysis (KMA) and a Prony analysis (PA) on-line variation for the identification of coherent groups in power systems. KMA and PA use the voltage angle measurements obtained from simulation. The clustering of the coherent groups are evaluated in two test systems. The coherency methods are also evaluated taking into account the impact of a large gradual scale inclusion of non-synchronous generation under different penetration level cases. From the comparison and visualisation of the different methods it is possible to observe the impact of the large inclusion of non-synchronous generation on the coherency.

Line outage identification-based state estimation in a power system with multiple line outages

Line outages in power systems cause data centers to miss some measurements thus making parts of networks unobservable. They also change the network topology and, consequently, classical state estimators do not converge. This paper proposes a new state estimation methodology based on the concepts of sparse vector recovery and line outage modeling. The proposed method first uses the pre- and post-outage voltage angle measurements to find the changes in network topology. Then it uses either the post-contingency data from PMUs and updated line flows or post-contingency measurements from PMUs and conventional PQ meters, as well as the updated topology, to provide an estimate of the power system state. The performance and accuracy of the proposed method are evaluated for the IEEE 24-, 57- and 118-bus systems. The simulation results show that the method provides good results for the three networks.