Mahendra Patel

Identifying coherent sub-graphs in networks is important in many applications. In power systems, large systems are divided into areas and zones to aid in planning and control applications. But not every partitioning is equally good for all applications; different applications have different goals, or attributes, against which solutions should be evaluated. This paper presents a hybrid method that combines a conventional graph partitioning algorithm with an evolutionary algorithm to partition a power network to optimize a multi-attribute objective function based on electrical distances, cluster sizes, the number of clusters, and cluster connectedness. Results for the IEEE RTS-96 show that clusters produced by this method can be used to identify buses with dynamically coherent voltage angles, without the need for dynamic simulation. Application of the method to the IEEE 118-bus and a 2383-bus case indicates that when a network is well partitioned into zones, intra-zone transactions have less impact on power flows outside of the zone; i.e., good partitioning reduces loop flows. This property is particularly useful for power system applications where ensuring deliverability is important, such as transmission planning or determination of synchronous reserve zones.

A number of impedance-based fault location algorithms have been developed for estimating the distance to faults in a transmission network. Each algorithm has specific input data requirements and makes certain assumptions that may or may not hold true in a particular fault location scenario. Without a detailed understanding of the principle of each fault-locating method, choosing the most suitable fault location algorithm can be a challenging task. This paper, therefore, presents the theory of one-ended (simple reactance, Takagi, modified Takagi, Eriksson, and Novosel et al.) and two-ended (synchronized, unsynchronized, and current-only) impedance-based fault location algorithms and demonstrates their application in locating real-world faults. The theory details the formulation and input data requirement of each fault-locating algorithm and evaluates the sensitivity of each to the following error sources: 1) load; 2) remote infeed; 3) fault resistance; 4) mutual coupling; 5) inaccurate line impedances; 6) DC offset and CT saturation; 7) three-terminal lines; and 8) tapped radial lines. From the theoretical analysis and field data testing, the following criteria are recommended for choosing the most suitable fault-locating algorithm: 1) data availability and 2) fault location application scenario. Another objective of this paper is to assess what additional information can be gleaned from waveforms recorded by intelligent electronic devices (IEDs) during a fault. Actual fault event data captured in utility networks is exploited to gain valuable feedback about the transmission network upstream from the IED device, and estimate the value of fault resistance.

OBJECTIVE: The authors rated periventricular and subcortical signal hyperintensities on magnetic resonance imaging (MRI) scans in elderly patients with depression and in normal subjects with similar demographic features to examine whether such changes discriminate patients with depression from normal subjects and whether they are associated with any clinical variables. METHOD: Two established hyperintensity rating systems were used to compare the MRI brain scans of 48 elderly patients with depression diagnosed according to DSM-III-R with the scans of 39 normal elderly subjects. RESULTS: Elderly depressed patients manifested significantly more severe hyperintensity ratings in the subcortical gray matter than age-matched comparison subjects. Significant differences were not identified between patients with similar current ages and cerebrovascular disease risk who had early-onset or late-onset depression. CONCLUSIONS: These findings support those of neuroimaging studies implicating the basal ganglia in depression and geriatric depression. The data suggest that the relationship observed in some reports between late-onset depression and MRI hyperintensities is most likely a function of cerebrovascular disease risk and age.

BACKGROUND AND PURPOSE: Increased frequency and severity of signal hyperintensities have been regularly reported in elderly depressed patients compared with normal subjects, however, greater neuroanatomic localization of lesions has been limited. METHODS: T2-weighted MRI scans in elderly depressed patients (n = 35) and normal comparison subjects (n = 31) were assessed for signal hyperintensities in lateralized discrete brain regions. RESULTS: Logistic regression revealed that left frontal deep white matter (P<.005) and left putaminal (P<.04) hyperintensities significantly predicted depressive group assignment. CONCLUSIONS: Findings suggest that greater neuroanatomic localization of hyperintensities than heretofore appreciated may relate to late-life depression.