An efficient algorithm for joint estimation of differential time delays and frequency offsets

Journal ArticleABSTRACT This paper introduces an efficient algorithm that jointly estimates differential time delays and frequency offsets between two signals. The approach is a two-step procedure. First, the differential frequency offsets are estimated from measurement of the autocorrelation functions of the received and transmitted signals. The time delays are estimated from estimates of the higher-order statistics of the two signals involved. The major advantage of the new approach is its remarkably reduced computational complexity over traditional approaches. The experimental resuits indicate that the algorithm performs better than the traditional methods in most cases of interest in spite of its reduced computational complexity

Electric utility planning methods for the design of one shot stability controls

Indiana University-Purdue University Indianapolis (IUPUI)Reliability of the wide-area power system is becoming a greater concern as the power grid is growing. Delivering electric power from the most economical source through fewest and shortest transmission lines to customers frequently increases the stress on the system and prevents it from maintaining its stability. Events like loss of transmission equipment and phase to ground faults can force the system to cross its stability limits by causing the generators to lose their synchronism. Therefore, a helpful solution is detection of these dynamic events and prediction of instability. Decision Trees (DTs) were used as a pattern recognition tool in this thesis. Based on training data, DT generated rules for detecting event, predicting loss of synchronism, and selecting stabilizing control. To evaluate the accuracy of these rules, they were applied to testing data sets. To train DTs of this thesis, direct system measurements like generator rotor angles and bus voltage angles as well as calculated indices such as the rate of change of bus angles, the Integral Square Bus Angle (ISBA) and the gradient of ISBA were used. The initial method of this thesis included a response based DT only for instability prediction. In this method, time and location of the events were unknown and the one shot control was applied when the instability was predicted. The control applied was in the form of fast power changes on four different buses. Further, an event detection DT was combined with the instability prediction such that the data samples of each case was checked with event detection DT rules. In cases that an event was detected, control was applied upon prediction of instability. Later in the research, it was investigated that different control cases could behave differently in terms of the number of cases they stabilize. Therefore, a third DT was trained to select between two different control cases to improve the effectiveness of the methodology. It was learned through internship at Midwest Independent Transmission Operators (MISO) that post-event steady-state analysis is necessary for better understanding the effect of the faults on the power system. Hence, this study was included in this research

Calculation of Crude Oil Price Risk Using HM-GARCH and MRS-GARCH Model

Crude oil is the main source of energy and accounts for about a third of world energy production. Turmoil in this market will have far-reaching economic and financial consequences. Because of this, investors attach great importance to predicting volatility when investing in crude oil markets to hedge risk and portfolio diversification. However, their investment strategies are often strongly influenced by volatility because, in different periods of crude oil markets, there are high and low fluctuations that are attributed to the movement of economic cycles. Accordingly, the present study compares the Markov Regime Switching (MRS) and Hidden Markov (HM) volatility models with the GJR-GARCH asymmetric model on their forecasting capabilities in the WTI and Brent crude oil markets. Empirical results show that the MRS-GJRGARCH model performs better than the HM_GJRGARCH model in predicting volatility in both markets. Accordingly, using the two criteria of value at risk and the expected deficit, the minimum loss and the expected loss for December 2021 were predicted. The results show that the expected shortfall from investing in the WTI market is greater than the Brent oil marke

ANGLE STABILITY PREDICTIONS

poster abstractThe variance of phase angle changes over the network is a good display of total stress and angle stability. The integral square generator angle (ISGA) changes had been recommended earlier to evaluate how severe the stable and unstable transient contingencies in simulation are. This project offers its addition to bus voltage angles (ISBA) which could be measured with synchronized phasor measurement units (PMUs) over a wide-ranging area. By restructuring continuous paths that go outside the boundary be-tween positive and negative 180 degrees before calculating the ISBA, the cutoff of bus angles at positive and negative 180 degrees is recovered. The project also directs the matter of obtaining the best angle stability index as the threshold between stable and unstable classes with use of simulation da-ta. This issue becomes more difficult by the fact that large databases might include a few events for which loss of synchronism happens toward the end of the simulation sequence

Memory- and time-efficient dense network for single-image super-resolution

Abstract Dense connections in convolutional neural networks (CNNs), which connect each layer to every other layer, can compensate for mid/high‐frequency information loss and further enhance high‐frequency signals. However, dense CNNs suffer from high memory usage due to the accumulation of concatenating feature‐maps stored in memory. To overcome this problem, a two‐step approach is proposed that learns the representative concatenating feature‐maps. Specifically, a convolutional layer with many more filters is used before concatenating layers to learn richer feature‐maps. Therefore, the irrelevant and redundant feature‐maps are discarded in the concatenating layers. The proposed method results in 24% and 6% less memory usage and test time, respectively, in comparison to single‐image super‐resolution (SISR) with the basic dense block. It also improves the peak signal‐to‐noise ratio by 0.24 dB. Moreover, the proposed method, while producing competitive results, decreases the number of filters in concatenating layers by at least a factor of 2 and reduces the memory consumption and test time by 40% and 12%, respectively. These results suggest that the proposed approach is a more practical method for SISR