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Transmission line impedance parameter estimation forms an essential part in modeling and monitoring of electricity networks. The accuracy of the estimated parameters has a direct impact on various functions of network operation, such as state estimation, fault location, and dynamic thermal line rating. Impedance parameters can be estimated from voltage and current measurements taken at the ends of the transmission line. Previous research has produced a range of methods with the purpose of maximizing the accuracy of the estimated values. However, the input measurements from the line ends can contain systematic errors that are introduced by the instrumentation channel and significantly reduce impedance parameter accuracy, which is not taken into account by most of the existing methods. In this paper, a novel method is presented that estimates correction factors for the systematic errors and, thus, increases the accuracy of impedance parameter values. The performance of the new method is compared with an existing one in a case study on laboratory measurements.

There is a growing interest in measuring human activities via worn inertial sensors, and situating two accelerometers on a body segment allows accessing rotational kinematic information, at a significantly lower energy cost when compared with gyroscopes. However, the placement of sensors has not been widely considered in the literature. In practice, dual-accelerometer systems should be built as compact as possible to ensure long-term wearability. In this paper, the impact of sensor placement and nature of human activity on signal quality is quantified by individual and differential signal-to-noise ratios (SNRs). To do so, noise-free signals are described by a 2-D kinematic model of a body segment as a function of kinematic variables and sensor location on the segment. Measurements are modelled as kinematic signals disturbed by zero mean additive Gaussian noise. Depending on the accuracy needed, one can choose a minimal SNR to achieve, with such dual-accelerometer arrangement. We estimate SNR and minimal sensor separations for three data sets, two from the public domain and one collected for this paper. The data sets give arm motion profiles for reaching, inertial data collected during locomotion on a treadmill and during activities of daily life. With a dual-accelerometer arrangement, we show that it is possible to achieve a good differential SNR for the analysis of various human activities if the separation between the two sensors and their placement is well chosen.

Real-time estimation of power transmission line impedance parameters has become possible with the availability of synchronized phasor (synchrophasor) measurements of voltage and current. If sufficiently accurate, the estimated parameter values are a powerful tool for improving the performance of a range of power system monitoring, protection, and control applications, including fault location and dynamic thermal line rating. The accuracy of the parameter estimates can be reduced by unknown errors in the synchrophasors that are introduced in the measurement process. In this paper, a method is proposed with the aim of obtaining accurate estimates of potentially variable impedance parameters, in the presence of systematic errors in voltage and current measurements. The method is based on optimization to identify correction constants for the phasors. A case study of a simulated transmission line is presented to demonstrate the effectiveness of the new method, which is better in comparison with a previously proposed method. The results, as well as limits, and the potential extensions of the new method are discussed.

A method for calculating the power demand of the hoist motor in rubber-tired gantry (RTG) cranes with nonparallel cables has been developed to measure the energy consumption in a typical lift cycle. From measurements taken at the Port of Felixstowe, it has been observed that the peculiar geometrical characteristics of the hoisting mechanism cause the power demand to increase with the container height in constant rotor speed conditions. The change in the angle of the hoisting ropes causes an increase in torque load and power consumption. By using information extracted from the crane's geometry, it has been possible to calculate the potential energy increase given the weight and vertical position of the container. The load torque on the hoist motor and the vertical speed of the mass have also been calculated, allowing for the modeling of the hoist motor power consumption when lifting containers with constant rotational speed. The proposed model has been compared to a constant power demand approximation, showing a higher accuracy for masses below 40 t.