Probability Distribution Of Load Research Articles

Power System Reliability Evaluation Using Stochastic Load Flows

This paper presents a simulation procedure for bulk power system reliability evaluation. The operation of the system under forced or scheduled outages of circuits and/or units is simulated via a stochastic load flow. An efficient stochastic load flow methodology is presented, which computes circuit loading and bus voltage probability distributions from a given load probability distribution. The output of the stochastic load flow is utilized to compute the conditional probability of system failure according to predefined criteria. Circuit overloads and bus undervoltages/overvoltages are utilized as system failure criteria. The frequency and duration method is employed to compute bulk power system reliability indices. Upper and lower bounds on the reliability indices are computed, which converge to the true values of the indices as the number of contingencies simulated increases. The paper presents simulation results with two systems: (a) a small 9-bus system to demonstrate data requirements, and (b) the 500 kV/230 kV system of Georgia Power Company to demonstrate the applicability of the method to actual systems.

Fluctuating Wind Pressures on Low Building Roofs

Quasi-steady theory can be used for the prediction of fluctuating wind pressure coefficients acting on the edge strip of flat or low-sloped roofs and the ridge strip of steeper roofs of low buildings. Comparisons with experimental data indicate that the quasi-steady model, expressing the root mean square pressure coefficients as functions of mean pressure coefficients and the turbulence intensity at eave height, predicts the fluctuating wind pressures for wind directions normal to the roof edge; for oblique wind directions the model holds for flat or low-sloped roofs but only for the edge strip away from the corners. Many older test results dealing only with mean pressures may now be used with the model suggested to yield root mean square pressure coefficients. The latter can lead to the prediction of expected peak pressures, which are necessary for design, if an appropriate load probability distribution is assumed.

Probability Distribution of Maximum Sustained Load

The purpose of this note is to present an analysis of the probability distribution of the maximum load sustained by a single floor of a building during its lifetime that does not require the adoption of a specific type of distribution. Analysis shows that if the load history is regarded as a random sample from the probability distribution of the load magnitude, the probability distribution of the maximum load sustained by the floor during its life cycle can be calculated using standard results from probability theory.

Statistical Model for Live Floor Loads

Theoretical considerations and analysis of survey data suggest that live loads on floors of buildings can be represented by a two-dimensional stochastic process with independent increments, the prototype of which is the compound poisson process. An essential assumption, which is vindicated by analysis of the data, is the statistical independence of the loads on nonoverlapping areas, provided the areas are not too small. This is closely analysed. Rational probabilistic design is based on the upper tail of the load probability distribution, and it is shown that the tail can be well approximated by the Pareto formula Cx -α . The fit to survey data obtained is compared with that of the alternatives of a normal and an exponential tail. From the representation of the tail distribution, rational load-reduction formulas are derived. A further consideration which has been so far neglected is the necessity to assure that none of the panels into which the floor area is divided collapse, and this leads to an increase of the design load with increasing floor area.