Explore the vast range of titles available.

This paper analyzes the key defining features of modern electric power distribution networks of industrial enterprises. It is shown that the requirements set by industrial enterprises with respect to power quality parameters (PQPs) at the points of their connection to external distribution networks of utilities have been becoming increasingly strict in recent years. This is justified by the high sensitivity of critical electrical loads and distributed generation facilities to distortions of currents and voltages from a pure sine wave. Significant deviations of PQPs lead to significant damage at the consumer end due to the shutdown of electrical equipment by electrical and process protections as a result of overheating and increased wear and tear of individual elements of process lines. This necessitates the implementation of continuous monitoring systems at industrial enterprises, or sampling-based monitoring of PQPs at the boundary bus with an external distribution network. When arranging sampling-based monitoring of PQPs at certain time intervals, only those parameters that are critical for specific electrical loads should be calculated. We provide a rationale for the transition from the monitoring of a set of individual PQPs to a generalized PQP with the arrangement of simultaneous monitoring of several parameters. The joint use of the results of simulation and data from PQP monitoring systems for PQP analysis using the sampling-based procedure produces the desired effect. We present an example of a sequential decision-making process in the analysis of a generalized PQP based on Wald’s sequential analysis procedure. This technique makes it possible to adapt the PQP monitoring procedure to the features of a specific power distribution network of an industrial enterprise. We present the structural diagram of the device developed by the authors, which implements the sampling-based monitoring procedure of the generalized PQP. We put forward an approach for determining the average number of sampling data points required to make a decision about the power quality in the implementation of the sequential analysis procedure.

The γ-FDP and k-FWER multiple testing error metrics, which are tail probabilities of the respective error statistics, have become popular recently as alternatives to the FDR and FWER. We propose general and flexible stepup and stepdown procedures for testing multiple hypotheses about sequential (or streaming) data that simultaneously control both the type I and II versions of γ-FDP, or k-FWER. The error control holds regardless of the dependence between data streams, which may be of arbitrary size and shape. All that is needed is a test statistic for each data stream that controls the conventional type I and II error probabilities, and no information or assumptions are required about the joint distribution of the statistics or data streams. The procedures can be used with sequential, group sequential, truncated, or other sampling schemes. We give recommendations for the procedures' implementation including closed-form expressions for the needed critical values in some commonly-encountered testing situations. The proposed sequential procedures are compared with each other and with comparable fixed sample size procedures in the context of strongly positively correlated Gaussian data streams. For this setting we conclude that both the stepup and stepdown sequential procedures provide substantial savings over the fixed sample procedures in terms of expected sample size, and the stepup procedure performs slightly but consistently better than the stepdown for γ-FDP control, with the relationship reversed for k-FWER control.

We consider testing for multiple structural changes in cointegrated systems and derive the limiting distribution of the sup-Wald test under mild conditions on the errors and regressors for a variety of testing problems. We show that even if the coefficients of the integrated regressors are held fixed but the intercept is allowed to change, the limit distributions are not the same as would prevail in a stationary framework. We also propose a sequential procedure that permits consistent estimation of the number of breaks present. We show via simulations that our tests maintain the correct size in finite samples and are much more powerful than the commonly used LM tests, which suffer from important problems of nonmonotonic power in the presence of serial correlation in the errors.

In this paper we provide sharp bounds on the Lp-norms of randomly stopped U-statistics. These bounds consist mainly of decoupling inequalities designed to reduce the level of dependence between the U-statistics and the stopping time involved. We apply our results to obtain Wald's equation for U-statistics, moment convergence theorems and asymptotic expansions for the moments of randomly stopped U-statistics. The proofs are based on decoupling inequalities, symmetrization techniques, the use of subsequences and induction arguments.