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Li Y.Z.,South China University of Technology | Li M.S.,South China University of Technology | Wen B.J.,Guangdong Electrical Power Dispatching Center | Wu Q.H.,South China University of Technology | Wu Q.H.,University of Liverpool
IEEE Power and Energy Society General Meeting

This paper presents the mean-variance (MV) model to solve power system dispatch problems with wind power integrated. In the MV model, the profit and the risk are taken into account simultaneously under the uncertain wind power (speed) environment. To describe this uncertain environment, the Monte Carlo (MC) simulation method is used to sample uncertain wind speeds. The optimization algorithm, group search optimizer (GSO), then optimize the MV model by introducing the risk tolerance parameter. The simulation is conducted based on the IEEE 30-bus power system, and the results demonstrate the applicability of the proposed model into power system dispatch problems with wind power integrated. © 2014 IEEE. Source

Wen Y.F.,Zhejiang University | Wang Y.,Zhejiang University | Guo C.X.,Zhejiang University | Wu Q.H.,University of Liverpool | And 2 more authors.
IEEE Power and Energy Society General Meeting

Traditional preventive control strategies do not take into account the likelihood of each potential contingency, thus the dispatch results may often be conservative or radical. Aiming to achieve a reasonable tradeoff between economy and security, this paper develops a risk-oriented preventive control (ROPC) strategy. A three-state weather model is introduced to reflect the impact of weather conditions on the failure rate of transmission lines. Using the multi-objective optimization (MO) method, system security level associated with overload risk can be controlled in advance. A distance based multi-objective particle optimization (DSMOPSO) algorithm is adopted to solve the MO problem. Simulation results obtained on a six-bus system are analyzed in comparison with OPF and PSCOPF to show that the proposed ROPC could provide a useful decision-making tool to keep an optimal balance between system operational cost and overload risk in different weather conditions. © 2012 IEEE. Source

Ao L.,Zhejiang University | Wang H.,Zhejiang University | Zhang C.,Guangdong Electrical Power Dispatching Center | Li Y.,Guangdong Electrical Power Dispatching Center | He B.,Zhejiang University
Dianwang Jishu/Power System Technology

Regarding to the problems existing in condition based maintenance decision-making, such problems as single target model, ignorance of the decision makers' expects, individual rather than group decision-making, a decision-making model employing D-S evidence theory was proposed in this paper. At first, a maintenance decision-making framework was established, as well as a complete decision index system summarized from numerous maintenance objectives and strains. And the indices' quantitative methods were given. Then the indices were taken as the theory's evidences, the maintenance schemes which were combined of the maintenance types based on the maintenance guidelines and time intervals partitioned by two weeks from equipment's predicted average remaining life based on its current state were taken as the recognition framework, while the standardized indices' values and the comprehensive weights of the indices and decision makers were normalized to the mass functions, multi-objective and group decision-making result was obtained then. Finally, a numerical example was used to illustrate the proposed decision-making process, which demonstrates that the model can effectively realize the control of the decision-making process considering the decision makers' intentions. All indicators can achieve overall optimum. Source

Chen J.,Chongqing University | Yan W.,Chongqing University | Lu J.,Guangdong Electrical Power Dispatching Center | Li S.,Chongqing University | And 2 more authors.
Dianli Xitong Zidonghua/Automation of Electric Power Systems

Considering the problem of randomly changing estimated parameter values with the impact of measurement errors, a robust method for transformer reactance estimation is presented based on phasor measurement unit (PMU) or supervisory control and data acquisition (SCADA) system multi-period measurement information of three-winding transformer at three sides. Firstly, the transformer three sides' branch current phasor, neutral point voltage and reactance are regarded as state variables, the paper establishes the three-winding transformer multi-period PMU or SCADA measurement equations and corresponding extended least squares reactance estimation model, which can be solved by Gauss-Newton method. Secondly, the least squares estimated values of transformer reactance is regarded as the sample and the variance coefficient threshold of random sample as the convergence criterion, the average value of the transformer reactance's least squares estimated value by iterative calculation is obtained as the final estimated parameter values. Because the average estimation is used and the proper average samples are chosen, the proposed method has excellent superiority in robustness. With different load levels of a typical 500 kV three-winding transformer, the random characteristics of its parameter values obtained by least squares estimation is analyzed. The model and method proposed are also verified. © 2011 State Grid Electric Power Research Institute Press. Source

Yan W.,Chongqing University | Song L.,Chongqing University | Yu J.,Chongqing University | Lu J.,Guangdong Electrical Power Dispatching Center | And 2 more authors.
Dianli Xitong Zidonghua/Automation of Electric Power Systems

Existing methods can not effectively identify and estimate multiple erroneous parameters along with bad telemetry measurements, which is referred to as bad data for short. To solve this problem, a divisional identification and estimation method of network parameter errors based on weight function is presented. Firstly, the network is divided into radial network, simple meshed network and complex meshed network to form many independent subareas. Secondly, Lagrange identification approach is carried out to identify bad data in each subarea. Lastly, if a parameter is identified as erroneous in each subarea, a function weighted least squares (FWLS) augmented method is proposed to estimate its value. The interaction of bad data between different subareas is avoided and the effect of bad data during parameter estimation is inhibited by this method. Thus, the effectiveness of identifying multiple bad data and the accuracy of estimating erroneous parameter are improved. The validity of the method proposed is verified by simulations on the IEEE 30-bus system and IEEE 118-bus system. © 2011 State Grid Electric Power Research Institute Press. Source

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