In our society, private and industrial activities increasingly rest on the implicit assumption that electricity is available at any time and at an affordable price. Even if operational data and feedback from the electrical sector is very positive, a residual risk of blackout or undesired load shedding in critical zones remains. The occurrence of such a situation is likely to entail major direct and indirect economical consequences, as observed in recent blackouts. Assessing this residual risk and identifying scenarios likely to lead to these feared situations is crucial to control and optimally reduce this risk of blackout or major system disturbance. The objective of this PhD thesis is to develop a methodology able to reveal scenarios leading to a blackout or a major system disturbance and to estimate their frequencies and their consequences with a satisfactory accuracy.<p><p>A blackout is a collapse of the electrical grid on a large area, leading to a power cutoff, and is due to a cascading failure. Such a cascade is composed of two phases: a slow cascade, starting with the occurrence of an initiating event and displaying characteristic times between successive events from minutes to hours, and a fast cascade, displaying characteristic times between successive events from milliseconds to tens of seconds. In cascading failures, there is a strong coupling between events: the loss of an element increases the stress on other elements and, hence, the probability to have another failure. It appears that probabilistic methods proposed previously do not consider correctly these dependencies between failures, mainly because the two very different phases are analyzed with the same model. Thus, there is a need to develop a conceptually satisfying probabilistic approach, able to take into account all kinds of dependencies, by using different models for the slow and the fast cascades. This is the aim of this PhD thesis.<p><p>This work first focuses on the level-I which is the analysis of the slow cascade progression up to the transition to the fast cascade. We propose to adapt dynamic reliability, an integrated approach of Probabilistic Risk Analysis (PRA) developed initially for the nuclear sector, to the case of transmission power systems. This methodology will account for the double interaction between power system dynamics and state transitions of the grid elements. This PhD thesis also introduces the development of the level-II to analyze the fast cascade, up to the transition towards an operational state with load shedding or a blackout. The proposed method is applied to two test systems. Results show that thermal effects can play an important role in cascading failures, during the first phase. They also show that the level-II analysis after the level-I is necessary to have an estimation of the loss of supplied power that a scenario can lead to: two types of level-I scenarios with a similar frequency can induce very different risks (in terms of loss of supplied power) and blackout frequencies. The level-III, i.e. the restoration process analysis, is however needed to have an estimation of the risk in terms of loss of supplied energy. This PhD thesis also presents several perspectives to improve the approach in order to scale up applications to real grids.<p> / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

In the early 1980s, relaying engineers conceived a technology allowing a huge step forward in the monitoring of power system behavior: the synchrophasor, i.e. the estimation of a phasor representation - amplitude and phase - of a sinusoidal waveform at a given point in time thanks to highly accurate time synchronization of a digital relay. By measuring synchrophasors across the power system several times per second, and centralizing the appropriate information in a hierarchical way through a telecommunication network link, it is now possible to continuously monitor the state of very large systems at a high refresh rate. <p><p>At the beginning, the phase angle information of synchrophasors was used to support or improve the performance of classic monitoring applications, such as state estimation and post-mortem analysis. Later, synchrophasors were found to be valuable for the detection and analysis of phenomena that were not monitored previously, such as system islanding and angular stability. This allows a better understanding of system behavior and the design of remedial actions in cases where system security appears to be endangered. Early detection and even prediction of instabilities, as well as validation and improvement of the dynamic models used for studies, have thus become possible.<p><p>However, a power system is rarely stationary and the assumptions behind the definition of “phasor” are not completely fulfilled because the waveforms' frequency and amplitude are not constant over a signal cycle at fundamental frequency. Therefore, accuracy of synchrophasor measurements during dynamic events is an important performance criterion. Furthermore, when discontinuities (phase jumps and high magnitude variations) and harmonics disturb the measured analog signals as a consequence of switching actions or external disturbances, measurements provided to the “user” (the operator or the algorithms that will take decisions such as triggering alarms and remedial actions) require a certain robustness. <p><p>The efforts underpinning this thesis have lead to the development of a method that ensures the robustness of the measurement. This scheme is described and tested in various conditions. In order to achieve a closer alignment between required and actual measurement performance, it is recommended to add an online indicator of phasor accuracy to the phasor data. <p><p>Fast automated corrective actions and closed-loop control schemes relying on synchrophasors are increasingly deployed in power systems. The delay introduced in the measurement and the telecommunication can have a negative impact on the efficiency of these schemes. Therefore, measurement latency is also a major performance indicator of the synchrophasor measurement. <p><p>This thesis illustrates the full measurement chain, from the measurement of analog voltages and currents in the power system to the use of these measurements for various purposes, with an emphasis on real-time applications: visualization, triggering of alarms in the control room or remedial actions, and integration in closed-loop controls. It highlights the various elements along this chain, which influence the availability, accuracy and delay of the data. <p><p>The main focus is on the algorithm to estimate synchrophasors and on the tradeoff between accuracy and latency that arises in applications for which measurements are taken during dynamic events and the data must be processed within a very limited timeframe. <p><p>If both fast phasors and slower, more accurate phasors are made available, the user would be able to select the set of phasors that are the most suitable for each application, by giving priority to either accuracy or a short delay.<p><p>This thesis also tentatively identifies gaps between requirements and typical measurements in order to identify current barriers and challenges to the use of wide area measurement systems. <p><p>A specific application, the continuous monitoring of oscillatory stability, was selected in order to illustrate the benefits of synchrophasors for the monitoring, analysis and control of power system behavior. This application requires a good phasor accuracy but can allow for some measurement delay, unless phasor data are used in an oscillation damping controller. In addition, it also relies on modal estimators, i.e. techniques for the online identification of the characteristics of oscillatory modes from measurements. This field of ongoing research is also introduced in this thesis. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished