Yu Xinjie

Cooperative Coevolutionary Genetic Algorithm for Digital IIR Filter Design

A novel algorithm for digital infinite-impulse response (IIR) filter design is proposed in this paper. The suggested algorithm is a kind of cooperative coevolutionary genetic algorithm. It considers the magnitude response and the phase response simultaneously and also tries to find the lowest filter order. The structure and the coefficients of the digital IIR filter are coded separately, and they evolve coordinately as two different species, i.e., the control species and the coefficient species. The nondominated sorting genetic algorithm-II is used for the control species to guide the algorithms toward three objectives simultaneously. The simulated annealing is used for the coefficient species to keep the diversity. These two strategies make the cooperative coevolutionary process work effectively. Comparisons with another genetic algorithm-based digital IIR filter design method by numerical experiments show that the suggested algorithm is effective and robust in digital IIR filter design

Optimization Design of SMES Solenoids Considering the Coil Volume and the Magnet Volume

Solenoids can be simply built and provide highly stored energy per unit of the conductor. A novel two-objective optimization design model of the superconducting magnetic energy storage (SMES) solenoid system has been suggested. The objectives include the minimization of the solenoid coil volume and the whole magnets volume. Three constraints are considered, i.e., the energy requirement, the stray field in the surrounding region, and the quench condition. Non-dominant sorting genetic algorithm (NSGA-II) is adopted for the two-objective optimization. The probability-of-objective-comparing, p_obj, has been defined to improve the search ability, and an adaptive method has been proposed to control the parameter p_obj. The adaptive parameter control guarantees the exploration in the early stage and the exploitation in the late stage. Numerical experiment results show that the suggested model and the improved algorithm can provide a set of Pareto solutions for the optimization design of SMES solenoids.

Pareto Competition Based Evolution Strategy for Two-Objective Optimization Design of SMES Solenoids

The superconducting magnetic energy storage (SMES) system has an attractive potential in power systems. Solenoid coil, one important configuration of SMES, can be simply built and provide highly stored energy per unit of the conductor. This paper suggests a novel two-objective optimization design model of solenoid coil. The objectives include the minimization of the solenoid volume and the uniformity of the magnetic field inside the magnet. Three constraints are considered, i.e., the energy requirement, the stray field in the surrounding region, and the quench condition. To optimize this highly constrained two-objective optimization design problem, Evolution Strategy (ES) has been used. The proposed sorting operator with Pareto competition divides the individuals into four groups: feasible Pareto (FP), infeasible Pareto (IP), feasible dominated (FD), and infeasible dominated (ID). FP solutions have the highest selection priority and ID solutions hold the lowest one. The annealing-like deterministic parameter control method for the acceptable tolerance of the energy error is also suggested to improve the constraints handling ability of ES. Numerical solutions show that the Pareto competition based ES can provide a set of feasible Pareto solutions for the two-objective optimization design model of SMES solenoids, among which the ldquobestrdquo one is to be extracted according to the designers experience.

Harmonic detection method for HVDC DC-side active power filter

One of the most critical issues associated with APF control is to find an efficient algorithm that can obtain an accurate harmonic reference signal for control purpose. This paper presented two novel methods of the harmonic detection in an advanced active power filter (APF) for the compensation of instantaneous harmonic current components in high voltage direct current (HVDC) transmission system. One is based on the ameliorated adaptive noise canceling theory and the other is grounded on mean value filtering method. The two methods are implemented on TMS320C32 DSP system. Both of them can efficiently determine the instantaneous harmonic reference signal in real time. The validity of two algorithms is substantiated via simulations and experiments.

Multi-peak Function Optimization Using a Hierarchical Clustering based Genetic Algorithm

A hierarchical clustering genetic algorithm which used hierarchical clustering based fitness sharing method was designed. Special designed inner-species selection and inner-species/inter-species crossover are used to improve the search ability. The results of several benchmark problems show that this algorithm is quite effective at the optimization of multi-peak functions by finding all peaks within the landscape.

Harmonic cancellation for HVDC using a novel control scheme

How to eliminate harmonics is one of the key technologies of high-voltage direct current (HVDC) transmission systems. In order to cancel harmonics, hybrid filters, consisting of passive filter (PF) and active power filter (APF), are employed in some recent projects. This paper proposes a novel scheme, which can take full advantage of passive filter, which is good at canceling low frequency harmonics with large power and active power filters, which is good at canceling high frequency harmonics with less power. What is more, the scheme can reduce the requirement of additional DC source in the DC part of active power filter and achieve expected effect.

Novel control schemes for the hybrid filter applied in HVDC according to the different harmonic measurement schemes

How to eliminate the harmonics is one of the key technologies of high-voltage direct current HVDC transmission systems. A hybrid filter, consisting of a double tune passive filter and an active filter, can take full advantage of these performances and is applied in some existing projects. This paper compare the operation of three control schemes due to adopting different harmonic measurement schemes.

An Improved Variable-Length Representation Approach for Knapsack Problem

One of the methods to solve knapsack problem is the variable-length representation approach based on genetic algorithm (GA). The original variable-length representation approach has the problem of low efficiency. In this paper we present a improved approach. In the early stage of the evolution, the heuristic searching method is applied to search better solutions rapidly, and in the latter part of evolution the random searching method is applied to improve the result. Through the simulation on some benchmark and two random knapsack problems, the results show that the improved variable-length representation approach has superiority in speed and accuracy over the standard method