The structure of the distribution network will change with the continuous access of distributed generation, which will affect the distribution of power flow in the original system network. When a fault occurs in the distribution network, it will cause the short-circuit current of the protection equipment on the transmission line to change, so the analysis of the distribution network and power system becomes more complex. This paper introduces the entity extraction model of the characteristic spectrum to analyze the causes of prediction errors in the distribution network. The probability distribution model of prediction errors is established. Feature extraction is carried out on the processed samples. The weights of different input influence features are adjusted and optimized through algorithm optimization. Entity extraction is carried out while quantifying and comparing the risk levels to identify the types and levels of faults. The simulation results show that the neural network combination algorithm model has achieved excellent prediction accuracy and operation speed. It is proved that the method of mechanism-data combination can realize early visual warning. The algorithm system studied in this paper can scientifically evaluate the situation of the distribution network and ensure the level of stable operation of the distribution network.
Molten carbonate fuel cell/ gas turbine hybrid power system (MCFC/GT) output power should real-time response the demand of load. The auto-tuning ZieglerNichols tuned PI controller (AZNPIC) as a feedback controller for power corrective action is added to the hybrid power system. The optimal setpoints and feed forward control inputs of the controller are given by Multi-output Support Vector Machine Regression (MSVR) predictor for the hybrid system. Simulation results show that the power of hybrid system can be effectively close to the desired setpoints based on the control method.
This paper briefly introduced the research on synthesis load modeling with online statistical measurement-based method. Statistical measurement-based method provides a complementary advantage for synthesis method and overall statistics measurement method by their combination and improvement on the basis of history load data. It offers a better idea to put load modeling into practice, which enables us to realize long-term management of dynamic online load modeling and the time-variability and diversity of load can be adapted.
Considering the atrocious load property when the rocket is launched, including the huge variation of load inertia, unbalanced torque and the disturbance from the air current impulsion, an optimal PID position controller based on improved Elman network is presented. The context neurons of output layer are added to the original Elman network, and self-feedback gain coefficients are trained as connective weighting value, which could strengthen the adaptive ability of Elman network to the time-varying system. According to the load property of multi-rocket launcher, improved Elman network could adjust the PID parameters online to minify the influence of the change of system parameters and external disturbance. The neural network is trained in online phases and a back-propagation training composes this neural network. Since the total number of nodes is only ten, this system is realized easily by the general microprocessor. During normal operation, the input-output response is sampled and the weighting value is trained multi-times by a back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. A digital signal processor TMS320F2812 realizes this method. The basic DSP software is used to write C-program, which is compiled by using ANSI-C style function prototypes. Simulation and experimental results show that this method could improve the stability and anti-disturbance ability of multi-rocket launcher position servo system effectively.
In order to evaluate a switchgear insulation condition assessment with live detection data more objectively and effectively, an optimal grade clustering method with a multi-dimensional feature database is proposed. This paper first analyzes the mechanism of partial discharge (PD), and volatility indicators and a life factor are introduced to fully exploit the characteristics of PD, and are used to establish the multi-dimensional feature database. Using the established database, the proposed K-means clustering algorithm, which includes data cleaning and optimal grade, is presented to classify the switchgear insulation condition. The cluster-outlier algorithm with relative distance (RD) is used to realize data cleaning. The optimal grade is designed to solve the problem of picking the number of clusters subjectively by means of the sum of the squared errors (SSE). The feasibility of the algorithm is verified via live detection data, which provides a certain theoretical basis for the switchgear insulation assessment.
With the enlarging scale of distribution network and increasing access users, distribution network has more and more complicated wiring. The existing adjustment methods based on manual operation are not only inefficient, but are also difficult to provide optimal adjustment solution. In this paper, hierarchical reconfiguration of distribution network involving multiple indexes is proposed to establish a mathematical optimization model for network reconfiguration. Examples are analyzed using a typical regional distribution network. The examples indicate that this method can reduce part of unnecessary reconfiguration analysis, and when there is need for the network to perform reconfiguration calculation, the method achieves a good optimization effect in the calculation. This optimization method can improve and optimize the distribution network in terms of economy, safety, reliability, etc.
Quasi-steady model of the HVDC system is usually used in the studies on Sub-Synchronous Oscillation(SSO),even its suitability is still arguable.The sample-data model of HVDC converter can represent the dynamic response of switching circuits,however,the firing control scheme and phase-locked loop(PLL) are not modeled in details.An improved sample-data model of 12-pulse HVDC converter is presented.Based on the generally used equidistant firing control scheme and PLL control,a new model of HVDC system is presented.Under small disturbances,the output of firing controller will not act on the converter immediately because of the igniting delay.The process of igniting is considered in detail,such that the sample-data model is improved.The model is validated on the EPRI HVDC system by eigenvalue analysis and complex torque coefficient method.The studies show that the improved model is more accurate in studying the SSO of HVDC systems.
The applying range of test kits becomes more and more wide due to its convenient and quick analysis. The quality of test kits was directly related to the accuracy of test results. However,it needs an appropriate evaluation method to ensure that the performance of these kits could meet the requirement in usage. In this paper,an evaluation method was designed and applied to validate the streptomycin ELISA test kits. The results demonstrated that the program was suitable for discriminating the quality of the kits; it possesses high applicable value in the practice.
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