Harmonic Mitigation by Optimal Allocation of Tuned Passive Filter in Distribution System

The purpose of this work is to determine the appropriate allocation of a single tuned passive filter “STPF” in a real distribution network at Tala city to prevent harmonic distortion caused by nonlinear loads. This design will cover the ideal filter parameter as well as the best filter position. The filter design is based on single and multi-objective functions that optimize the cost of the proposed filters, real power losses, total harmonic distortion “THD,” and individual harmonic distortion “IHD”. Upper and lower limitations for filter parameters, quality factor, voltage and harmonic distortion limits are determined using inequality constraints. This will be done under the equality constraint of power balance. The challenge of optimization is solved using two modern algorithms, jellyfish optimization technique “JFOT” and arithmetic optimization algorithm “AOA”, in which the placements of the filters and their parameters are selected. IEEE 33-bus radial system and an actual system as a part of the Egyptian network at Tala city are used for demonstrating the results obtained by the proposed techniques. The results reveal that the system’s harmonic distortion of current and voltage is reduced less than the limitations of the IEEE 519 standards.

Recognition of Frequency Modulation Signal Using Parameter Invariant Filter

To classify the frequency modulation signal, this paper employs a parameter invariant filter, which can transfer the frequency modulated information to variety of its envelope, and then extracts the histogram feature to classify the modulation type. This method can efficiently classify the type of a signal such as frequency modulation （FM）, binary frequency shift keyiing （BFSK）, quadrature frequency shift keying （QFSK）, 8-ary frequency shift keying （8FSK）, etc. It can easily be realized and is especially suitable to applications in software radio.

Modified robust finite-horizon filter for discrete-time systems with parameter uncertainties and missing measurements

A robust finite-horizon Kalman filter is designed for linear discrete-time systems subject to norm-bounded uncertainties in the modeling parameters and missing measurements.The missing measurements were described by a binary switching sequence satisfying a conditional probability distribution,the commonest cases in engineering,such that the expectation of the measurements could be utilized during the iteration process.To consider the uncertainties in the system model,an upperbound for the estimation error covariance was obtained since its real value was unaccessible.Our filter scheme is on the basis of minimizing the obtained upper bound where we refer to the deduction of a classic Kalman filter thus calculation of the derivatives are avoided.Simulations are presented to illustrate the effectiveness of the proposed approach.

ESTIMATE ACCURACY OF NONLINEAR COEFFICIENTS OF SQUEEZEFILM DAMPER USING STATE VARIABLE FILTER METHOD

The estimate model for a nonlinear system of squeeze film damper (SFD) is described.The method of state variable filter (SVF) is used to estimate the coefficients of SFD.The factors which are critical to the estimate accuracy are discussed

A NEW METHOD OF CHANNEL FRICTION INVERSION BASED ON KALMAN FILTER WITH UNKNOWN PARAMETER VECTOR

Channel friction is an important parameter in hydraulic analysis. A channel friction parameter inversion method based on Kalman Filter with unknown parameter vector is proposed. Numerical simulations indicate that when the number of monitoring stations exceeds a critical value, the so lution is hardly affected. In addition, Kalman Filter with unknown parameter vector is effective only at unsteady state. For the nonlinear equations, computations of sensitivity matrices are time-costly. Two simplified measures can reduce computing time, but not influence the results. One is to reduce sensitivity matrix analysis time, the other is to substitute for sensitivity matrix.

FrFT-CSWSF: Estimating cross-range velocities of ground moving targets using multistatic synthetic aperture radar

Estimating cross-range velocity is a challenging task for space-borne synthetic aperture radar（SAR）, which is important for ground moving target indication（GMTI）. Because the velocity of a target is very small compared with that of the satellite, it is difficult to correctly estimate it using a conventional monostatic platform algorithm. To overcome this problem, a novel method employing multistatic SAR is presented in this letter. The proposed hybrid method, which is based on an extended space-time model（ESTIM） of the azimuth signal, has two steps： first, a set of finite impulse response（FIR） filter banks based on a fractional Fourier transform（FrFT） is used to separate multiple targets within a range gate; second, a cross-correlation spectrum weighted subspace fitting（CSWSF） algorithm is applied to each of the separated signals in order to estimate their respective parameters. As verified through computer simulation with the constellations of Cartwheel, Pendulum and Helix, this proposed time-frequency-subspace method effectively improves the estimation precision of the cross-range velocities of multiple targets.