Applied Mechanics and Materials Vols. 494-495

Abstract: This paper introduces an homotopy algorithm which has convergence stability to solve the alternating current optimal power flow problem. The complicated Alternating Current Power Flow (ACPF) can simplify as simple Direct Current Power Flow (DCPF). The homotopy participation factor is introduced into the linear DCPF to make DCPF convert back into ACPF gradually to realize Alternating Current Power Flow Homotopy method (ACPFH). The homotopy curves are generated to solve a series of nonlinear problems.The traditional method can not solve the unstable points,because the calculate process always turn up Jacobian matrix.But the Homotopy method can calculate all results. It is a superiority for Homotopy,and then can explore power system problem more entirety.This novel algorithm is different from Newton - Raphson method, because it isnt sensitive to the initial point selection and has the global convergence.The homotopy algorithm is applied to IEEE - 3, 9, 14, 30, 36, 57, 118 node testing systems for power flow optional calculation, the simulation results show that the novel algorithm can solve power flow problem better and its calculating speed is much faster than the traditional algorithm, it can calculate the optimal value more direct.

Abstract: For realizing highly accuracy load forecasting, a new method is proposed. Power load time series belongs to chaotic series. Firstly, for obtaining three parameters in chaotic theory, namely time delay, embedding dimension and the number of the nearest neighbors, self-correlation function method and G-P algorithm are used to reconstruct the phase space of chaotic time series. Secondly, ant colony optimization approach is introduced to more accurately acquire forecasting reference points, considering distance and relativity of phase points evolution in this paper. Finally, GM (1, 1) Model is applied to forecast daily load data. The actual forecasting results prove that the new approach has better forecasting accuracy and convergence.

Abstract: With the principle of dynamic description logic, which introduces the concept of grid domain ontologys knowledge representation, a system is built that can be shared by each businesses ontology knowledge base in smart grid. First, combine attributive concept language with complements language using dynamic description logic with ALC language to build a smart grid domain ontology. And then use the ontology knowledge to build the final ontology knowledge base that can greatly improve the efficiency of information query and clearly express the basic concepts, properties, processing methods and internal relations of the smart grid domain knowledge, which is conducive to promote China's smart grid construction.

Abstract: In the micro-power wireless transmission of the electric system, positions among modes are relatively fixed, power business data is reported at specific time points, and time distribution presents great differences. Key technologies of IEEE802.15.4 MAC layer protocol is expounded, shortages of collision detection and CSMA/CA on power business support, etc. are discussed, self-adaptive low-power consumption CSMA/CA algorithm which is more suitable for business of the electric system are designed and improved, and the algorithm goes through simulation experiment against the business characteristics of micro-power wireless network of the electric system. The simulation result demonstrates the algorithm may be greatly adapted to changes of network traffic under a relatively fixed environment of network topology on the premise of low power consumption.

Abstract: Short-term power load forecasting is very important for the electric power market, and the forecasting method should have high accuracy and high speed. A three-layer BP neural network has the ability to approximate any N-dimensional continuous function with arbitrary precision. In this paper, a short-term power load forecasting method based on BP neural network is proposed. This method uses the three-layer neural network with single hidden layer as forecast model. In order to improve the training speed of BP neural network and the forecasting efficiency, this method firstly reduces the factors which affect load forecasting by using rough set theory, then takes the reduced data as input variables of the BP neural network model, and gets the forecast value by using back-propagation algorithm. The forecasting results with real data show that the proposed method has high accuracy and low complexity in short-term power load forecasting.

Abstract: With the rapid economic development in Liaoning Province, very rapid growth in electricity load, especially steel and fused magnesium commercial power. After the implementation of TOU price, Liaoning power grid voltage fluctuation is increased by that some enterprises were concentrated in low hours of electricity. Some power users were affected by lacking the means of dynamic reactive power. To point the necessity of application of the SVC, the existing of 66kV and 220kV grid power quality status are analyzed and simulated. The SVC compensation capacity and filter parameters are systematically design, after the transformation of the system and the simulation proved that the grid can achieve the design requirements.

Abstract: For the power distribution network load transferring path optimization problem, this paper focuses on optimization scheme of load transferring path based on the fast non-dominated storing genetic algorithm (NSGA-II) with elitist strategy, this proposed method can avoid the selection of weight and the preference of the solution, and the solution sets can highlight the essence of optimization problems, then the fuzzy theory and the entropy weight are employed to extract the comprehensive optimal solution. IEEE33 node system simulation results verify the effectiveness of the model and algorithm.

Abstract: Optimal Scheduling is an important issue in the power system including wind power, thermal power and hydro power. In this paper, a model is built to minimize the energy consumption and operating costs, considering the spinning reserve for wind power and operating characteristics of the units. During the peak load period, a hydro-thermal scheduling strategy is considered due to the peak shaving ability of hydraulic power plants. The solving process is based on the particle swarm optimization algorithm, and in the case study, a scheduling scheme is obtained for coordinated operation of wind power integrated power systems.

Abstract: In the situation that the traditional energy shortage and environmental pollution problems have become increasingly grim, the new energy research has been widespread concern at home and abroad. In recent years, due to dual pressures, environmental pollution and energy reduction, photovoltaic industry has a rapid development. As the solar panels convert light energy into electrical energy efficiency by light intensity and ambient temperature, therefore, the establishment of a common solar panel model to study the light intensity and ambient temperature on the output characteristics of the solar panels is necessary.

Abstract: Sensitivity method is to get the level of sensitivity of dependent variable to independent variable by using differential relationship of some physical quantities in the system. It has been widely used in the analysis and control of power system. From the point of time, sensitiveness analysis method can be divided into two categories, static sensitivity and trajectory sensitivity. And according to the different considered variables, they are reviewed respectively in this paper. Application of sensitiveness analysis method in the power system reflects essential research value.