Improvement to observability measures of LFO modes in power systems with DFIGs

Observation of the low-frequency oscillation (LFO) modes in power systems is important to design the damping scheme. The state equations of the power system with the doubly-fed induction generators (DFIGs) are derived to find the LFO modes related to the synchronous generator (SGs) and the DFIGs. The definition of the observability measure is improved to consider the initial output and the attenuation speed of the modes. The sensitivities of the observability measures to the control parameters are derived. The numerical results from the small and large-disturbance validate the LFO modes caused by the DFIGs, and different observability measures are compared. Adjustment of the control parameters is chosen based on the sensitivity model to improve the observability and damping ratio of the LFO mode, and the stability of the wind power system.

Maximal Integral over Observable Measures

Let f be a continuous transformation on a compact, finite-dimensional manifold M, and φ a continuous function on M. This paper establishes the following formula：ess sup lim sup n→∞1/nφn（x）=sup{∫φdμ｜μ∈Of}≤lim sup n→∞1/n ess supφn（x）,where ess sup denotes the essential supremum taken against the Lebesgue measure,φn（x）=∑i=0^n-1φ（f^ix）and Of is the set of observable measures. Examples are provided to illustrate that the inequality could be an equality or strict. Moreover, if μ is the unique maximizing observable measure for φ, it is weakly statistical stable.

Bearing-only Visual SLAM for Small Unmanned Aerial Vehicles in GPS-denied Environments

This paper presents a hierarchical simultaneous localization and mapping（SLAM） system for a small unmanned aerial vehicle（UAV） using the output of an inertial measurement unit（IMU） and the bearing-only observations from an onboard monocular camera.A homography based approach is used to calculate the motion of the vehicle in 6 degrees of freedom by image feature match.This visual measurement is fused with the inertial outputs by an indirect extended Kalman filter（EKF） for attitude and velocity estimation.Then,another EKF is employed to estimate the position of the vehicle and the locations of the features in the map.Both simulations and experiments are carried out to test the performance of the proposed system.The result of the comparison with the referential global positioning system/inertial navigation system（GPS/INS） navigation indicates that the proposed SLAM can provide reliable and stable state estimation for small UAVs in GPS-denied environments.

Multi-objective allocation of measuring binary particle swarm optimization

Due to the size and complexity of power network and the cost of monitoring and telecommunication equipment, it is unfeasible to monitor the whole system variables. All system analyzers use voltages and currents of the network. Thus, monitoring scheme plays a main role in system analysis, control, and protection. To monitor the whole system using distributed measurements, strategic placement of them is needed. This paper improves a topological circuit observation method to minimize essential monitors. Besides the observability under normal condition of power networks, the observability of abnormal network is considered. Consequently, a high level of system reliability is carried out. In terms of reliability constraint, identification of bad measurement data in a given measurement system by making theme sure to be detectable is well done. Furthermore, it is maintained by a certain level of reliability against the single-line outages. Thus, observability is satisfied if all possible single line outages are plausible. Consideration of these limitations clears the role of utilizing an optimization algorithm. Hence, particle swarm optimization （PSO） is used to minimize monitoring cost and removing unobser-vable states under abnormal condition, simultaneously. The algorithm is tested in IEEE 14 and 30-bus test systems and Iranian （Mazandaran） Regional Electric Company.