Jean Claude Maun

Artificial neural network approach to single-ended fault locator for transmission lines

This paper describes the application of an artificial neural network-based algorithm to the single-ended fault location of transmission lines using voltage and current data. From the fault location equations, similar to the conventional approach, this method selects phasors of prefault and superimposed voltages and currents from all phases of the transmission line as inputs of the artificial neural network. The outputs of the neural network are the fault position and the fault resistance. With its function approximation ability, the neural network is trained to map the nonlinear relationship existing in the fault location equations with the distributed parameter line model. It can get both fast speed and high accuracy. The influence of the remote-end infeed on neural network structure is studied. A comparison with the conventional method has been done. It is shown that the neural network-based method can adapt itself to big variations of source impedances at the remote terminal. Finally, when the remote source impedances vary in small ranges, the structure of the artificial neural network has been optimized by the pruning method.

Influence of Saturation Level on the Effect of Broken Bars in Induction Motors Using Fundamental Electromagnetic Laws and Finite Element Simulations

This study is dedicated to faulty induction motors. These motors are often used in industrial applications and the monitoring of their condition becomes important for production maximization. This paper gives precise understanding of the effect of the broken bar on the stator currents, a quantity more and more used for fault detection and quantification. This study uses 2-D finite element (FE) models for detailed field analysis and considers different saturation levels (voltage levels). Fundamental electromagnetic laws are used to interpret FE results. It is shown that the presence of local saturation tends to lower the effect of a broken bar on the stator currents. This effect increases with the voltage level and must be taken into consideration for precise quantification of the fault. Furthermore, on a local point of view, the presence of a broken bar leads to highly saturated regions in the neighborhood of the broken bar that will influence the progression of the fault.

A level-1 probabilistic risk assessment to blackout hazard in transmission power systems

The blackout risk in power systems is difficult to estimate by actual probabilistic methods because they usually neglect, or do not properly consider, the dependencies between failures and the dynamic evolution of the grid in the course of a transient. Our purpose is therefore to develop an integrated probabilistic approach to blackout analysis, capable of handling the coupling between events in cascading failure, and the dynamic response of the grid to stochastic initiating perturbations. This approach is adapted from dynamic reliability methodologies. This paper focuses on the modeling adopted for the first phase of a blackout, ruled by thermal transients. The goal is to identify dangerous cascading scenarios and better calculate their frequency. A Monte Carlo code specifically developed for this purpose is validated on a test grid. Some dangerous scenarios are presented and their frequency calculated by this method is compared with a more classical estimation neglecting thermal effects, showing significant differences. In particular, our method can reveal dangerous scenarios neglected or underestimated by the more classical method because they do not take into account the increase of failure rates in stress conditions.

Blackout Probabilistic Risk Assessment and Thermal Effects: Impacts of Changes in Generation

Renewable energy integration and deregulation imply that the electric grid will be operated near its limits in the future, and that the variability of cross-border flows will increase. Therefore, it is becoming more and more crucial to study the impact of these changes on the risk of cascading failures leading to blackout. We propose in this paper to emphasize important factors leading to blackouts, to review methodologies which were developed to simulate cascading failure mechanisms and to study specifically the impact of thermal effects on the risk of blackout for several changes in generation (variations in cross-border flows, wind farms penetration, shut-down of power plants). This is studied by applying to a test system the first level of a dynamic probabilistic blackout risk assessment developed previously. We show that taking into account thermal effects in cascading failures is important not only to have a good estimation of the risk of blackout in different grid configurations, but also to determine if a specific change in generation has a positive or a negative impact on the blackout risk.

Voltage-Stability Monitoring Using Wide-Area Measurement Systems

A new monitoring method based on a wide-area network of Phaser Measurement Units (PMU) is proposed. This tool detects on-line and in real-time an incipient voltage instability in the power system. A geographical visualization is introduced to monitor efficiently the state of the network. The method is general and can be implemented in any part of a network prone to voltage instability. It can be used to trigger corrective actions to avoid a collapse. The hypotheses and the results of the new method will be discussed in regard with those given by the Thevenins equivalent computed at the load bus.

A new computer based Phasor Measurement Unit framework

A Phasor Measurement Unit (PMU) is one of the most important measuring devices that can provide synchronized phasor measurements of voltages and currents in real time from widely spread locations in an electric grid. Indeed this device is crucial to the detection of disturbances and characterization of transient swings. In this work, a new PMU has been designed and implemented including the hardware architecture and the software program using a recent developed algorithm of phasor measurement and frequency estimation. The developed PMU is based on computer associated with an acquisition card AD512 using Matlab as software tool for developing its running program as well as its graphical user interface. Finally, this PMU has been tested in laboratory using network simulator that gives good measurements with an acceptable errors even at off-frequency.

Harmonic impedance measurement using voltage and current increments from disturbing loads

A device has been developed to measure the harmonic impedances of power network by using harmonic voltage and current increments at the point of common coupling between the network and a disturbing load. The key problem in the implementation of the method is the synchronization of phasors sampled asynchronously and at different times. This kind of synchronization is linked to the frequency measurement. A small error in the frequency will cause a great deviation of the impedance measurement. A formulation is given here to evaluate the error on the impedance in each individual measurement (event), and a statistical method is proposed to achieve a relatively high accuracy in the source impedance estimation. Laboratory and field tests results are both discussed as well as verified with the harmonic emission level estimation of the disturbing load.

Distribution system state estimation using unsynchronized phasor measurements

Accurate monitoring of the distribution system is performed using state estimation methods. The purpose of these methods is to estimate the most likely state of the grid given various types of redundant measurements. In this paper, we propose a three phase state estimation method that can handle accurately unsynchronized three phase phasor measurements. Unsynchronized phasor measurements, as opposed to synchrophasor measurements, consist in phasor measurements that do not have accurate time stamps. The use of such measurements could be very valuable in unbalanced distribution networks. To handle these measurements, we add unknown synchronizing operators to the state variables. The identification of these additional state variables allows considering any configuration of unsynchronized phasor measurement in a simple and intuitive way. The proposed method is illustrated on a simulated distribution network.

A Two-Level Probabilistic Risk Assessment of Cascading Outages

Cascading outages in power systems can lead to major power disruptions and blackouts and involve a large number of different mechanisms. The typical development of a cascading outage can be split in two phases with different dominant cascading mechanisms. As a power system is usually operated in N-1 security, an initiating contingency cannot entail a fast collapse of the grid. However, it can trigger a thermal transient, increasing significantly the likelihood of additional contingencies, in a “slow cascade.” The loss of additional elements can then trigger an electrical instability. This is the origin of the subsequent “fast cascade,” where a rapid succession of events can lead to a major power disruption. Several models of probabilistic simulations exist, but they tend to focus either on the slow cascade or on the fast cascade, according to mechanisms considered, and rarely on both. We propose in this paper a decomposition of the analysis in two levels, able to combine probabilistic simulations for the slow and the fast cascades. These two levels correspond to these two typical phases of a cascading outage. Models are developed for each of these phases. A simplification of the overall methodology is applied to two test systems to illustrate the concept.

Incorporation of data-mining in protection technology for high impedance fault detection

Modernizing the power distribution system implies improving the reliability and performance of protection devices. By incorporating data-mining in the process of designing protection functions, the limits of performance are extended. We propose a method that uses data-mining, able to detect high impedance faults (HIFs) in multi-grounded distribution networks when conventional devices are insufficient. HIFs are produced when overhead lines contact a quasi-isolated surface, such as a tree or the ground. The fault current can be lower than the residual current under normal conditions; hence overcurrent devices do not detect this fault. We describe a set of indicators that characterize HIFs and that can be used in data-mining to distinguish fault situations from other situations. The result is a HIF detection function whose development is based on pattern recognition analysis. The presented methodology can be applied to other fault detection problems to achieve more reliable protection devices.