Po- Chen

Sensitivity Analysis of Voltage Sag Based Fault Location With Distributed Generation

The presence of distributed generation (DG) in distribution networks may seriously affect accuracy of the voltage sag based fault location method. An approach toward quantifying the adverse effect of DG on the fault location calculation is described. A series of realistic scenarios is used to illustrate how DG impacts synchrophasor measurements during disturbances. Alternative Transients Program-Electromagnetic Transients Program models are used to obtain steady-state solutions in the time domain, while Sobols approach to sensitivity analysis is used to quantify the effect of DG and imperfections of measuring instruments on fault location. Various test cases reveal that DG may adversely affect the voltage characteristic and therefore the accuracy of voltage sag based fault location.

Locating sub-cycle faults in distribution network applying half-cycle DFT method

It is necessary to accurately detect and locate sub-cycle faults in order to prevent unexpected outages. However, conventional fault location methods cannot locate these faults as typically data windows longer than the faults signature are used for phasor extraction. This paper presents an overall analysis of how the single-phase-ground sub-cycle fault in the distribution network can be located using voltage sag. The half-cycle Discrete Fourier transform is used for phasor extraction in the timedomain simulations. Our results reveal that the proposed approach is capable of accurately locating sub-cycle faults whose duration is between 0.5 and 1.0 cycles. The results also suggest that the placement of meters may significantly affect the capability of the proposed approach to locate sub-cycle faults.

Risk Assessment of a Transmission Line Insulation Breakdown Due to Lightning and Severe Weather

The transmission line insulation breakdown is typically assessed by performing insulator chain tests, and by conducting network modeling and simulation studies incorporating various stress conditions. This paper investigates how historical data coming from the lightning detection network and measurement stations capturing associated weather conditions can be utilized to provide a predicted assessment of risk of insulation breakdown for a given exposure and associated weather threats. The proposed analysis is enabled by the space and time correlation of the transient data recorded in the substations at the end of the lines, as well as by the assimilation of data obtained from the lightning detection network and weather stations. The proposed modeling and simulation tools are utilized to facilitate the time and space correlation analysis that leads to predication of the risk.

Sensitivity analysis of voltage sag based fault location algorithm

In this paper, the global, variance-based, sensitivity analysis is used to quantify the impact of measurement imperfections on voltage sag based fault location. This kind of fault location requires voltage phasor information from meters to be compared to simulated cases in order to locate faults. However, meters are prone to measurement imperfections. It is therefore critical that the impact of measurement imperfections, such as measurement and loading errors, are fully assessed to account for uncertainty in algorithm inputs. Sensitivity analysis was used to attribute responsibility for uncertainty in fault location to uncertainty in the inputs of the fault location algorithm. The results demonstrate that the single most detrimental factor to precise fault location is large fault resistance, both alone and in combination with other factors. Although accurately deduced by the algorithm, other impacts of this fault feature adversely impact accuracy.

Uncertainty of measurement error in intelligent electronic devices

This paper focuses on methodology to quantify uncertainty in measurements obtained from Intelligent Electronic Devices (IED). IEDs have emerged in distribution systems as a prevalent source of measurements in monitoring and protection, as well as for different kinds of applications beyond IEDs primary purposes. These measurement devices are installed across a system, from substations down to the customer locations, and provide measurements of a wide array of quantities. We report how IED measurements respond to external disturbances, which may lead to possible accuracy impacts in various applications. The example used to illustrate the approach is highly accurate fault location in distribution systems based on voltage sag measurements.

Analysis of voltage stability issues with distributed generation penetration in distribution networks

This paper presents an overall analysis of how the penetration of distributed generation in low-voltage secondary distribution networks affects voltage stability. It is critical that the voltage collapse point be carefully studied under different system operating points to prevent degradation of service. System components have been sophisticatedly modeled in ATP/EMTP. DGs are allocated in a probabilistic fashion to account for uncertainties in future allocation. A large number of experiments under both light and peak load conditions have been carried out to provide realistic results. Results indicate that voltage stability is positively correlated with penetration of DG, but large induction type DG may lower the voltage stability margin.

Voltage Stability Prediction Using Active Machine Learning

An active machine learning technique for monitoring the voltage stability in transmission systems is presented. It has been shown that machine learning algorithms may be used to supplement the traditional simulation approach, but they suffer from the difficulties of online machine learning model update and offline training data preparation. We propose an active learning solution to enhance existing machine learning applications by actively interacting with the online prediction and offline training process. The technique identifies operating points where machine learning predictions based on power system measurements contradict with actual system conditions. By creating the training set around the identified operating points, it is possible to improve the capability of machine learning tools to predict future power system states. The technique also accelerates the offline training process by reducing the amount of simulations on a detailed power system model around operating points where correct predictions are made. Experiments show a significant advantage in relation to the training time, prediction time, and number of measurements that need to be queried to achieve high prediction accuracy.

Fuzzy Logic Approach to Predictive Risk Analysis in Distribution Outage Management

Weather impacts are one of the main causes of distribution outages. To devise strategies to mitigate weather impacts, a fuzzy logic system for decision making is introduced. It allows utility operators to achieve more precise outage predictions and optimize real time operation and maintenance scheduling. A novel approach for weather-driven risk framework is applied to process the data and produce risk maps for better decision making. The use of weather data in reducing fault location time, an important performance improvement in outage management, is also presented.

Predicting weather-associated impacts in outage management utilizing the GIS framework

Weather-related impacts are at the top of all outage causes. Yet, traditional outage management (OM) approaches do not integrate all available and relevant weather-associated data automatically. This paper presents a predictive method that correlates different weather-associated data layers to provide predictive OM process implemented using the geographic information systems (GIS) framework. Examples for both transmission and distribution OM are demonstrated using vegetation, wind, and power system data in ArcGIS.

Sensitivity of voltage sag based fault location in distribution network to sub-cycle faults

Single-phase-to-ground sub-cycle faults in the distribution network can be located using voltage sag fault location. This paper illustrates how a sensitivity study of measurement imperfections can be used to quantify the impact of sub-cycle faults on voltage sag based fault location. Our results suggest that there is a complex relationship between factors influencing error in fault location because the design of the study covered a wide range of conditions. The more complicated, higher order interactions have a stronger influence on error than any particular input factor alone.