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Short-term load forecasting (STLF) is the basis of power system planning and operation. With regard to the fast-growing load in China, a novel two-stage hybrid forecasting method is proposed in this paper. In the first stage, daily load is forecasted by time-series methods; in the second stage, the deviation caused by time-series methods is forecasted considering the impact of relative factors, and then is added to the result of the first stage. Different from other conventional methods, this paper does an in-depth analysis on the impact of relative factors on the deviation between actual load and the forecasting result of traditional time-series methods. On the basis of this analysis, an adaptive algorithm is proposed to perform the second stage which can be used to choose the most appropriate algorithm among linear regression, quadratic programming, and support vector machine (SVM) according to the characteristic of historical data. These ideas make the forecasting procedure more accurate, adaptive, and effective, comparing with SVM and other prevalent methods. The effectiveness has been demonstrated by the experiments and practical application in China. [ABSTRACT FROM PUBLISHER]

Failure probability of system components may vary with the changes of weather, environment and other operation conditions. The time-varying or condition-based failure probability can be represented using a fuzzy model. Based on credibility theory, this paper presents a novel operation risk assessment method to handle the two-fold uncertainty combining randomness and fuzziness in power system operations. In the proposed method, a random fuzzy model is developed to accommodate impacts due to various operation conditions and other factors on the failure probability of system components. The WECC nine-bus system, IEEE 14-bus system and an actual power system in Northeast China were used to demonstrate feasibility and applicability of the presented method. The operation risk assessment module using the proposed method has been embedded in the EMS system in a district control center of a power company to perform real-time operation risk assessment. [ABSTRACT FROM AUTHOR]

In this study, a locally weighted linear regression (LWLR) method is proposed to predict damping ratio of a dominant mode online. The LWLR method, which is nonparametric and data-oriented, is essentially proposed for nonlinear data fitting; therefore, it can track the nonlinear power system operations and help damping ratio prediction in real power systems, which is hardly achieved by the conventional linear regression. To successfully implement this method, the measurement of weighting value and the choice of weighting function as well as its parameter setting, related to prediction accuracy and numerical conditions, are extensively discussed. Simulations are carried out in a two-area four-machine system and a large complex system, China Southern Grid. Both results validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]