Chuan Wang

A hybrid topology scale-free Gaussian-dynamic particle swarm optimization algorithm applied to real power loss minimization

Abstract This paper proposes a hybrid topology scale-free Gaussian-dynamic particle swarm (HTSFGDPS) optimization algorithm for real power loss minimization problem of power system. The swarm population is divided into two parts: hybrid topology population and scale-free topology population. The novel hybrid topology is mixed with fully connected topology and ring topology. Then, it enables the particles to have stronger exploration ability and fast convergence rate at the same time. In the scale-free part, the topology will be gradually generated as the construction process and the optimization process progress synchronously. As a result, the topology exhibits disassortative mixing property, which can improve the swarm population diversity. This work focuses on a new combination of swarm intelligence optimization theory and complex network theory, as well as its application to electric power system. The presented method is tested on IEEE 14-Bus and 30-Bus power system. The numerical results, compared with other stochastic search algorithms, show that HTSFGDPS could find high-quality solutions with higher convergence speed and probability.

The elimination of leakage currents in the neutral point clamped photovoltaic grid-connected inverter by the improved space vector pulse width modulation method

In this paper, the reason of high-frequency leakage current in neutral point clamped photovoltaic grid-connected inverters that adopts traditional modulations is analysed. In order to solve the problem, this article puts forward two improved methods based on the three-level space vector pulse width modulation (SVPWM), which can reduce the leakage current by applying three medium vectors or using only two medium vectors and one specific zero vector to compose the reference vector. In addition, the system control method adopts the coordination control of the current inner loop and the DC voltage outer loop, which can reduce voltage fluctuation at the DC side. Thereby, the suppression of leakage current can be realised without requiring any modification on the multilevel inverter. Compared with the traditional three-level sinusoidal pulse width modulation (SPWM) and SVPWM, the two new methods are advantageous in having a stable common-mode voltage, low leakage current, and low harmonic component of grid current. The theoretical analysis and results show the effectiveness of the proposed methods.

Parallel Operation of Microgrid Inverters Based on Adaptive Sliding-Mode and Wireless Load-Sharing Controls

This study proposes a new solution for the parallel operation of microgrid inverters in terms of circuit topology and control structure. A combined three-phase four-wire inverter composed of three single-phase full-bridge circuits is adopted. Moreover, the control structure is based on adaptive three-order sliding-mode control and wireless load-sharing control. The significant contributions are as follows. 1) Adaptive sliding-mode control performance in inner voltage loop can effectively reject both voltage and load disturbances. 2) Virtual resistive-output-impedance loop is applied in intermediate loop to achieve excellent power-sharing accuracy, and load power can be shared proportionally to the power rating of the inverter when loads are unbalanced or nonlinear. 3) Transient droop terms are added to the conventional power outer loop to improve dynamic response and disturbance rejection performance. Finally, theoretical analysis and test results are presented to validate the effectiveness of the proposed control scheme.

Self-adapting hybrid strategy particle swarm optimization algorithm

Particle swarm optimization (PSO) algorithm has shown promising performances on various benchmark functions and engineering optimization problems. However, it is still difficult to achieve a satisfying trade-off between exploration and exploitation for all the optimization problems and different evolving stages. Furthermore, control parameters of some related mechanisms need pre-experience by the requirement of trial-and-error scheme. This paper presents a novel PSO algorithm, which adaptively adopts various search strategies, called Self-adapting Hybrid Strategy PSO (SaHSPS). Unlike some other peer PSO variants, this method dynamically changes the probabilities of different strategies according to their previous successful searching memories, without any additional control parameters. The probabilities of different strategies would be re-initialized according to a proposed dynamic probabilistic model to diverse the population. Besides, particles are updated by probabilistically selected strategies after niching PSO with Ring topology. Moreover, a dynamic updating mechanism by niching PSO is proposed to guarantee the parallel searching capability during the whole evolution process. Thus, this proposed algorithm might be problem-independent and search-stage-independent, yielding more satisfying solutions on various optimization problems. A comprehensive experimental study is conducted on 28 benchmark functions of CEC 2013 special session on real-parameter optimization, including shifted, rotated, multi-modal, high conditioned, expanded and composition problems, compared with several state-of-the-art variants of PSO and differential evolution (DE) algorithms. Comparison results show that SaHSPS obtains outstanding performances on the majority of the test problems. Moreover, a practical engineering problem, real power loss minimization of IEEE 30-bus power system, is used to further evaluate SaHSPS. The numerical results, compared with other stochastic search algorithms, show that SaHSPS could find high-quality solutions with higher probability.

Self-adaptive differential evolution algorithm with hybrid mutation operator for parameters identification of PMSM

Parameters identification of permanent magnetic synchronous motor (PMSM), which significantly influences the control performance of the drive system, is an important and challenging task of power electronic system. The problem requires both high solution quality and fast convergence speed due to the constraints of hardware. This paper presents a self-adaptive differential evolution algorithm with hybrid mutation operator (SHDE) for parameters identification problem. In this method, a novel mutation operator, called “current-to-archive-best,” is developed by mixing the best solutions randomly selected from archive set and current population. Thus, the algorithm could use the best searching memories so far to generate promising solutions, yielding a faster evolving procedure. Besides, the corresponding control parameters of SHDE are also self-adapted without tedious trial-and-error progress to get appropriate values. Moreover, the parameters estimation program is inserted into the PMSM simulation that is solved by using Newton–Raphson method without any pre-assumption and simplification. This framework, which may be used under any working conditions with large disturbance, is different from other publications, resulting in wider applications. The proposed method applied to parameters identification of PMSM is evaluated on a PMSM drive system with two different operations. The comprehensive results and statistical analyses, compared with other state-of-the-art algorithms, show that SHDE could find high-quality solutions with higher convergence speed and probability.

Design of Fuzzy-Neural-Network-Inherited Backstepping Control for Unmanned Underwater Vehicle

This paper presents a closed-loop trajectory tracking controller for an Unmanned Underwater VehicleUUV with five degrees of freedom. A backstepping control BSC methodology combined with Lyapunov theorem is adopted to design the controller of trajectory tracking. Then an online-tuning fuzzy neural network FNN framework is chosen to inherit the conventional BSC law. Moreover, the adaptive parameters tuning laws are derived in the sense of Lyapunov stability theorem and projection algorithm to ensure the network convergence as well as stable control performance. Finally, the simulation results on UUV verify that an excellent performance of the proposed controller can be obtained.

Control for Grid Connected Inverter under Unbalanced Grid Voltage Based on PI-QR Controller

Control strategy under unbalanced and distorted grid voltage is one of the most significant tasks for grid-connected inverter (GCI). In order to guarantee that the grid current meets the related IEEE Std.929-2000, a new control scheme was presented. Firstly, the typical model of GCI was presented. Secondly, the GCI control model based on PI-QR controller in synchronous reference frame (SRF) was built. Finally, the system’s simulation was conducted under distorted and unbalanced grid voltage. The results demonstrate the effectiveness of the presented control strategy.

Marine Asynchronous Propulsion Motor Parameter Identification Using Dynamic Particle Swarm Optimization

This paper proposes a new application of dynamic particle swarm optimization (PSO) algorithm for parameter identification of vector controlled asynchronous propulsion motor (APM) in electric propulsion ship. The dynamic PSO modifies the inertia weight, learning coefficients and two independent random sequences which affect the convergence capability and solution quality, in order to improve the performance of the standard PSO algorithm. The standard PSO and dynamic PSO algorithms use measurements of the mt-axis currents, voltages of APM as the inputs to parameter identification system. The experimental results obtained compare the identified parameters with the actual parameters. There is also a comparison of the solution quality between standard PSO and dynamic PSO algorithms. The results demonstrate that the dynamic PSO algorithm is better than standard PSO algorithm for APM parameter identification. Dynamic PSO algorithm can improve the performance of ship propulsion motor under abrupt load variation.

Reconfiguration of the Shipboard Power System Based on Particle Swarm Optimization with Non-Clique Topology

The modern shipboard power system integrates heavy loads and has less fault tolerance than the terrestrial power system. Therefore, a fast and high-performance reconfiguration is needed when a fault occurs. This paper proposes a method to achieve a rapid and optimal reconfiguration for shipboard power system. This method at first employs graph theory to generate the topology of particles, which can construct a non-Clique topology. Then, the particle swarm optimization algorithm with the non-Clique topology is used to improve the restoration scheme while considering constraints. The proposed Particle Swarm Optimization algorithm enables to find the optimal combination of loads that can be supplied after the occurrence of the fault.

The Residual Life of Shipboard Cable Forecasting Based on PSO and GM(1, 1)

In this paper, the Gray Theory is used to predict the residual life of shipboard cable. However, under the influence of multi-factors, the grayscale of the model will be enlarged, which will influence the accuracy of the prediction. Therefore, the optimized parameter of the GM (1, 1) model based on PSO, is introduced to improve the precision of the predication. The method is proved effectively by the experimental data and the Arrhenius Equation.