Day-Ahead Probabilistic Load Flow Analysis Considering Wind Power Forecast Error Correlation

Short-term power flow analysis has a significant influence on day-ahead generation schedule. This paper proposes a time series model and prediction error distribution model of wind power output. With the consideration of wind speed and wind power output forecast error’s correlation, the probabilistic distributions of transmission line flows during tomorrow’s 96 time intervals are obtained using cumulants combined Gram-Charlier expansion method. The probability density function and cumulative distribution function of transmission lines on each time interval could provide scheduling planners with more accurate and comprehensive information. Simulation in IEEE 39-bus system demonstrates effectiveness of the proposed model and algorithm.

SMS Notifying Design in Intra-Day Scheduling Security Check System

In order to master the future operation and stability of power grid exactly, and gasp the weak point accurately, the requirement of power data quality become strict, and the data timeliness of power gird change into outstandingly more and more, because of this, in this paper propose the SMS notifying method of intra-day scheduling data based on safely data principle. The principle is mainly complied with the data source existed or not, the data is coincident to the power grid model, the data is unbroken or not and it is reasonable with the physical reality, then it can obtain better convergence and reasonable intra-day check power data. In order to accelerate the information and network pace of the power grid, the SMS notifying can monitoring data quality without time delay. It dredge the vast path for the future power market into use with the wide range, then, can more effective to ensure the convergence and accuracy of safe check calculation, it provides an effective guarantee with the safe and stable operation of the power grid, in the same way, it is also an efficient method to provides effective guarantee for power grid safe operation from the data source.

Impacts of subsynchronous and supersynchronous frequency components on synchrophasor measurements

Phasor measurement units(PMU) are playing an increasingly important role in power system dynamic security monitoring and control. However, the wide-area deployments of the renewable energy sources and the high voltage direct current(HVDC) transmission bring a large number of inter-harmonics to the power grid, which may result in further power system security problems. The impacts of inter-harmonics on synchrophasor measurements are revealed. This paper derives the phasor expressions of the signal, which contains the fundamental component and the inter-harmonics. It is found that the inter-harmonics will lead to the subsynchronous oscillation of the phasor measurements. The frequency transmutation principle between the harmonic and the phasor oscillation is revealed. Then, the field PMU data recorded during a subsynchronous oscillation, which occurred in an area of China with a high concentration of wind farms and HVDC transmission lines, are studied. A geographical wiring diagram with the subsynchronous oscillation distribution depicts the severe consequences of the inter-harmonics. In addition, the correctness of the theoretical derivation and the possibility of the inter-harmonics monitoring are verified.

A Hybrid FCW-EMD and KF-BA-SVM Based Model for Short-term Load Forecasting

This paper proposes a hybrid short-term load forecasting method,which is based on the fuzzy combination weights as well as the empirical mode decomposition process(FCW-EMD),and support vector machine optimized via the Bat algorithm as well as the Kalman filtering process(KF-BA-SVM).The subjective weight is presented as a new theory and is applied to capture the inherent correlation effectively among hourly loads.Based on the proposed objective weights and subjective weights,the fuzzy combination weights theory(FCW)-a new similar day selection theory is presented,which improves the accuracy of the similar day selection,and correspondingly,makes the original data for EMD processing decrease dramatically.BA is introduced to optimize parameters of the SVM model for further improving the forecasting accuracy.Using the decomposed load series via empirical model decomposition(EMD)as inputs to SVM and further correcting the output of SVM via KF,a hybrid FCW-EMD and KF-BA-SVM short-term load forecasting method is established.Numerical case studies on the load forecasting of a transformer substation in south China show that the proposed hybrid forecasting model outperforms other forecasting methods and effectively improves the prediction accuracy.