Search

Power distribution systems play an important role in modern society. When distribution system outages occur, fast and proper restorations are crucial to improve the quality of services and customer satisfaction. Proper usages of outage root cause identification tools are often essential for effective outage restorations. This paper reports on the investigation and results of two popular classification methods: logistic regression (LR) and artificial neural network (ANN) applied on power distribution fault cause identification. LR is seldom used in power distribution fault diagnosis, while ANN has been extensively used in power system reliability researches. This paper discusses the practical application problems, including data insufficiency, imbalanced data constitution, and threshold setting that are often faced in power distribution fault cause identification problems. Two major distribution fault types, tree and animal contact, are used to illustrate the characteristics and effectiveness of the investigated techniques. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to enable frequent computational cycles in online dynamic security assessment by using the terms of the transient energy function (TEF) as input features to a machine learning algorithm. The aim is to train a single classifier that is capable of classifying stable and unstable operating points independent of the contingency. The network is trained based on the current system topology and the loading conditions. The potential of the proposed approach is demonstrated with the New England 39-bus test power system model using the support vector machine as the machine learning technique. It is shown that the classifier can be trained using a small set of data when the terms of the TEF are used as input features. The prediction accuracy of the proposed scheme was tested under the balanced and unbalanced faults with the presence of voltage sensitive and dynamic loads for different operating points. [ABSTRACT FROM PUBLISHER]

Real-time transient stability simulation is of paramount importance for system security assessment and to initiate preventive control actions before catastrophic events such as blackouts happen. Transient stability simulation of realistic power systems involves the solution of a large set of nonlinear differential-algebraic equations in the time-domain which requires signi icant computational resources. Exploitation of parallel processing techniques can provide an efficient and cost-effective solution to this problem. This paper proposes a fully parallel method known as instantaneous relaxation (IR) for real-time transient stability simulation. To validate the proposed method, two test systems have been implemented on an advanced PC-cluster-based real-time simulator. A comparison of the captured real-time results with those from the PSS/E software shows high accuracy. [ABSTRACT FROM AUTHOR]

Power distribution systems have been significantly affected by many fault causing events. Effective outage cause identification can help expedite the restoration procedure and improve the system reliability. However, the data imbalance issue in many real- world data often degrades the outage cause identification performance. In this paper, artificial immune recognition system (AIRS), an immune-inspired algorithm for supervised classification task is applied to the Duke Energy outage data for outage cause identification using three major causes (tree, animal, and lightning) as prototypes. The performance of AIRS on these real-world imbalanced data is compared with an artificial neural network (ANN). The results show that AIRS can greatly improve the performance by as much as 163% when the data are imbalanced and achieve comparable performance with ANN for relatively balanced data. [ABSTRACT FROM AUTHOR]

This paper demonstrates the use of a slow-coherency-based generator grouping algorithm and a graph theoretic approach to form controlled islands as a last resort to prevent cascading outages following large disturbances. The proposed technique is applied to a 30 000-bus, 5000-generator, 2004 summer peak load, Eastern Interconnection data and demonstrated on the August 14, 2003 blackout scenario. Adaptive rate of frequency decline-based load shedding schemes are used in the load rich islands to control frequency. The simulation results presented show the advantage of the proposed method in containing the impact of the disturbance within the islands formed and in preventing the impact of the disturbance from propagating to the rest of the system. This is demonstrated by the significant reduction in line flows in the rest of the system and by improved voltage and relative angle characteristics. Based on the suggestion in the joint U.S.-Canadian task force final report on the blackout, load shedding without any islanding is also performed, and results obtained are compared with the proposed controlled islanding method. The islanding method outperforms the load shedding-only method in reducing the transmission line flows, but both methods have similar effects on voltage and relative angle behavior. [ABSTRACT FROM AUTHOR]

This paper proposes a probabilistic load flow method considering random branch outages as well as uncertainties of nodal power injections. Branch outages are simulated by fictitious power injections at the corresponding nodes. A unified procedure is given to deal with random branch outages, generating unit outages, and load uncertainties by their moments and cumulants. The variations of nodal voltages and line flows produced by normally and discretely distributed input variables are handled separately. The method proposed by Von Mises is employed to solve the discrete distribution part of each state and output variable. The final distribution of a desired variable is obtained by simply convoluting its continuous and discrete distribution part. Results of 24-bus IEEE Reliability Test System are analyzed and compared to those obtained by Monte Carlo simulation. A numerical test on a real power system shows the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Monitoring of events in the power system provides a great deal of insight into the behavior of the system. Events with impact on the entire power system typically occur in or near the transmission network and are therefore best monitored at transmission system substations. This puts great demands on the equipment and in general restricts the use of the data since it becomes property of the transmission system operator. An alternative is to measure at lower voltage levels. This paper documents the close agreement between voltage and frequency at low and high voltage levels during a remote and a nearby fault in the transmission network. The very high quality of the data suggests that valuable information about the transmission system can be obtained at lower voltages. [ABSTRACT FROM AUTHOR]