xue Li

Current research of C-Sight visual prosthesis for the blind

Electrical stimulation of the optic nerve with penetrating electrode array for visual recovery had been proposed by C-Sight group. This paper presents the latest progress of various component parts of visual prosthesis, including design and testing of neural stimulator, fabrication of multi-channel flexible microelectrode array. According to the experiment data, the linearity between practical stimulator output and the setting parameters has been validated. The temporal properties of EEP evoked by optic nerve stimulation with penetrating electrodes will be introduced briefly according to in vivo electrophysiological study.

Direct output voltage control of a STATCOM using PI controller based on multiple models

To solve the voltage control problem of the STATCOM, a PI control scheme based on multiple models is proposed in this paper. This control scheme combines the multiple models to the PI control. PI controller structure is applied, and the scheme has the advantage of simple structure, short calculate time and easy realization. Meanwhile, the method of multiple models is applied to meet the accuracy and speed of the voltage control. Simulation result shows that compared to the traditional PI controller, PI controller based on multiple models has a better ability to adapt to the change of voltage and a higher compensating precision.

Short-term wind power forecasting based on lifting wavelet transform and SVM

Short-term load forecasting is important for the safety and economic operation of the wind power system. In order to forecast the power load more accurately, the Support Vector Machines (SVM) combined with the lifting wavelet transform is proposed in this paper. The lifting wavelet transform is used to find out the characteristics of original signal while the SVM is utilized to improve the precision of forecasting. Finally, the data in September 2010 from a wind farm in North China are adopted. The result shows that wind power load forecasting based on method above is more effective than that of SVM only, thus proving the validity of the method above for power load forecasting.

Short-term wind power prediction based on wavelet decomposition and extreme learning machine

Wind energy has been widely used as a renewable green energy all over the world. Due to the stochastic character in wind, the uncertainty in wind generation is so large that power grid with safe operation is challenge. So it is very significant to design an algorithm to forecast wind power for grid operator to rapidly adjust management planning. In this paper, based on the strong randomness of wind and the short precision of BP network forecasting, Short-Term Power Prediction of a Wind Farm Based on Wavelet Decomposition and Extreme Learning Machine (WD-ELM) is proposed. Signal was decomposed into several sequences in different band by wavelet decomposition. Decomposed time series were analyzed separately, then building the model for decomposed time series with ELM to predict. Then the predicted results were added. Through a wind-power simulation analysis of a wind farm in Inner Mongolia, the result shows that the method in this paper has higher power prediction precision compared with other methods.

A novel recursive integral PI repetitive control for three-phase three-wire shunt Active Power Filter

Active Power Filter (APF) has been widely used to compensate the harmonic currents which are generated by the nonlinear load. Its compensation performance mainly depends on the design of controller. In this paper, a novel control method based on recursive integral PI algorithm and repetitive control strategy has been proposed. Compared with traditional PI control which can not get zero-steady-state error of output current due to its bandwidth limitation, this approach guarantees the steady-state accuracy with its embedded repetitive control, and improves the dynamic response performance of the APF system by its recursive integral PI algorithm. Moreover, it is more appropriate for digital signal processor implementation. The validity of the proposed method that the filtering performance is improved obviously is demonstrated by simulation analysis.

Direct Output Voltage Control Strategy for STATCOM Based on Multi-model and Neural Network PI Controller

Aiming to deal with the voltage control problems and the limitations of the conventional PI controller in the Static Synchronous Compensator (STATCOM), a direct output voltage control strategy based on multi-model and neural network PI controller is proposed. This control scheme applied the multi-model and neural network technology to the PI controller to meet the accuracy and speed of the voltage control under different impact loads. Meanwhile, the neural network technology was used to tune the PI controller parameter values according to an optimal control law, which can meet the requirements of full range working conditions and optimality. Simulation experiments show that compared to the traditional PI controller, PI controller based on multi-model and neural network is proved to be better capable of adapting to the change of voltage with a higher compensating precision.

Reactive Power Optimization for Distribution Network Based on Chaos Guide Particle Swarm Optimization Algorithm with Gold Criterion

Voltage is an important aspect to measure the security of power system and reactive power can relatively exert great influence on the voltage level. So planning for reactive power is an important part of network planning. In this chapter a new algorithm called Gold Criterion Chaos Guide Particle Swarm Optimization (GCCGPSO) is presented in reactive power optimization for distribution. Firstly, a mathematical model of reactive power optimization for distribution network by capacitance is established. And the cost of system active power loss and investment in equipment is treated as the optimization objective. Meanwhile the node voltage and reactive power of generator is dealt with penalty function when they pass over the limitation. Then GCCGPSO is proposed. It adopts not only chaos algorithm with gold criterion to guarantee that the particles are not easy to fall into local optimum and search the same place, but also the Neighbor domain optimal item to promote the ability of choosing path. Finally, the result of the simulation shows that the algorithm is useful and has sound performance.

Reactive Power Optimization for Wind Power System Based on Adaptive Weights Flight Adjustment Particle Swarm Optimization

In recent years, the uncertain output of wind power has had growing effects on the regional power grid. Reasonable reactive power optimization can effectively improve the adverse effects of wind power. In this chapter, an Adaptive Weights Flight Adjustment Particle Swarm Optimization (AWFAPSO) is proposed for the reactive power optimization of wind power system. First, it established a mathematic model in which system active power loss will be treated as objective function, and adopted penalty function to process node voltage cross-border and generator reactive power cross-border. Then AWFAPSO was presented. Using variable inertia factor, it can locally regulate the flight speed of the particle which leads to finding the optimal solution effectively and adopting adaptive flight time to guarantee the flight convergence in general, thus preventing particles from oscillating near optimal solution in the late of conventional particle swarm. Finally, the simulation shows that reactive power optimized by AWFAPSO can effectively reduce the system loss and improve the node voltage level.

Short-Term Wind Power Forecasting Based on Lifting Wavelet, SVM and Error Forecasting

In order to improve the forecasting accuracy, a novel forecasting method using wavelet, support vector machine (SVM), and error forecasting technology is presented in this chapter. Firstly, it utilizes lifting wavelet method to decompose data to extract the data’s main characteristics. And then it establishes the SVM forecasting model and error forecasting model to realize the wind power load forecasting, relative error forecasting, and wind load data correcting. Finally, the actual data is adopted for simulation. The experimental results show that the method based on lifting wavelet transform, SVM, and error forecasting can improve the forecasting accuracy greatly. The test shows that the method used for the wind power load forecast is feasible and effective.

Analysis and Simulation of Cascade STATCOM Based on PAM Inverter

In order to achieve similar harmonic elimination effect as Pulse Width Modulation (PWM) method at a lower switching frequency and solve the problem of DC capacitor voltage unbalance, the Pulse Amplitude Modulation (PAM) method and the method of pulse exchanging circularly are proposed in this chapter. By solving the optimal objective function, the angles of switching point can be worked out. It makes the low harmonic performance and the total output voltage optimal. The pulse generators rotate each fundamental frequency cycle time in the pulse distribution, and 10 fundamental cycles (200 ms) are needed to complete one cycle pulse rotation mechanism, which effectively improves the condition of the capacitor voltage difference. Then a neural network PI controller is designed to tune the parameters of the PI controller timely. The results of simulation show the correctness of the proposed method. It can adjust the changes through the simulation.