An intelligent analysis method of security and stability control strategy based on the knowledge graph

The security and stability control system is the guarantee of the security and stability operation of the power grid. With the increasing scale of distributed new energy access to the power grid, the security and stability control strategy of the power grid is becoming more complex, and it is becoming increasingly important to correctly analyze and implement the security and stability control strategy. In order to ensure the correctness of the security and stability control strategy implemented by the security and stability control device, it is necessary to analyze the security and stability control strategy in detail. Therefore, this article proposes an intelligent analysis method of the security and stability control strategy based on the knowledge graph. First, this article introduces the ontology design method of the security and stability control strategy based on the knowledge graph, combines the characteristics and applications of the knowledge graph, analyzes the relationship between the elements of the strategy, and designs a clear-structured knowledge network. Second, this article analyzes the automatic construction technology of the graph, constructs the six-element ontology model of the security and stability control strategy, and realizes the human–computer interaction functions such as auxiliary decision making, strategy reasoning, and intelligent search based on the knowledge graph. Using artificial intelligence technology, this article takes the security and stability control strategy of a certain area’s security and stability control system as an example to model and manage. The results show that it can assist the tester to quickly retrieve the strategy, effectively improve the detection efficiency of the security and stability control strategy, avoid the omission and ambiguity caused by the manual understanding of the strategy, and ensure the accuracy and comprehensiveness of the security and stability control strategy detection

More Accurate Learning of k-DNF Reference Classes

In machine learning, predictors trained on a given data distribution are usually guaranteed to perform well for further examples from the same distribution on average. This often may involve disregarding or diminishing the predictive power on atypical examples; or, in more extreme cases, a data distribution may be composed of a mixture of individually “atypical” heterogeneous populations, and the kind of simple predictors we can train may find it difficult to fit all of these populations simultaneously. In such cases, we may wish to make predictions for an atypical point by selecting a suitable reference class for that point: a subset of the data that is “more similar” to the given query point in an appropriate sense. Closely related tasks also arise in applications such as diagnosis or explaining the output of classifiers. We present new algorithms for computing k-DNF reference classes and establish much stronger approximation guarantees for their error rates

Composite power system risk evaluation considering the accurate model of renewable power output

The fluctuation of renewable energy will threaten the reliable operation of the composite transmission and generation power system, so the risk level of the composite system with renewable energy attached must be studied. However, the accuracy of the renewable energy model has typically been overlooked in current risk assessment approaches. Therefore, this study provides an accurate hybrid wind/solar power output model that takes the wind farm’s seasonal and spatial characteristics, as well as the correlation between wind and solar PV power into account. For the first, build the seasonal Weibull model of the wind speed and correct the speed captured by each wind turbine considering different spatial characteristics of the wind farm. Secondly, build the probability distribution function of the solar power based on the kernel density estimation method. In the next, construct the joint probability distribution function considering the correlation between wind and solar PV power based on copula theory. Then, using the suggested accurate renewable energy model, the sequential Cross-entropy (SCE) technique is utilized to evaluate the risk indices of the composite system with renewable energy. To validate the models and technique, the modified IEEE-RTS79 is studied. The findings indicate that the accuracy of the renewable energy model influences the risk assessment outcomes

Generalized Lagrange Coded Computing: A Flexible Computation-Communication Tradeoff for Resilient, Secure, and Private Computation

We consider the problem of evaluating arbitrary multivariate polynomials over a massive dataset containing multiple inputs, on a distributed computing system with a master node and multiple worker nodes. Generalized Lagrange Coded Computing (GLCC) codes are proposed to simultaneously provide resiliency against stragglers who do not return computation results in time, security against adversarial workers who deliberately modify results for their benefit, and information-theoretic privacy of the dataset amidst possible collusion of workers. GLCC codes are constructed by first partitioning the dataset into multiple groups, then encoding the dataset using carefully designed interpolation polynomials, and sharing multiple encoded data points to each worker, such that interference computation results across groups can be eliminated at the master. Particularly, GLCC codes include the state-of-the-art Lagrange Coded Computing (LCC) codes as a special case, and exhibit a more flexible tradeoff between communication and computation overheads in optimizing system efficiency. Furthermore, we apply GLCC to distributed training of machine learning models, and demonstrate that GLCC codes achieve a speedup of up to $2.5\text{--}3.9\times$ over LCC codes in training time, across experiments for training image classifiers on different datasets, model architectures, and straggler patterns

Experimental investigation on convective heat transfer of ferrofluids inside a pipe under various magnet orientations

Some experimental tests were conducted to reveal the enhancement of the ferrofluid heat transfer under a permanent magnetic field. This research aims to investigate the effect of various external magnetic fields on convective heat transfer characteristics of the ferrofluid (magnetic nanofluid). Comparison of theoretical predictions and experimental data were conducted to validate the rationality of the test results, and a good agreement with less than 10% deviations was found. The deviations from experimental data decrease with an increase of the Reynolds number (Re) from 391 to 805. Results from the case with 5 cannulas indicate that a continuous increase in the magnetic flux density (by increasing the quantity of the magnets) can improve the heat transfer enhancement significantly. The ferrofluids with a magnetic cannula shows heat transfer enhancements of 26.5% and 54.5% at Re = 391 and 805, respectively

Structure Optimization of Rib Drill Pipe Based on Gas-Solid Coupling and Orthogonal Experiment

At present, drilling rig is a common equipment for controlling gas outburst generated in underground coal mine, and rib drill pipe is an important component of drilling rig. Due to the insufficiency of slag discharge capacity, pipe-sticking accidents often occur during the drilling process, which greatly reduces the effect of gas control. In order to improve the capacity of slag discharge of rib drill pipe, the mechanism of slag removal was analyzed, and the process of slag discharge was simulated as a gas-solid two-phase flow coupling process. Utilizing the computational fluid dynamics method, the process of slag discharge was simulated on the Edem-Fluent cosimulation platform. The structural parameters of the drill pipe affecting the capacity of slag discharge were derived. Based on the analysis results, the structural parameters of rib drill pipe were optimized by orthogonal experiment method. The global optimal results were obtained as follows: its pitch, blade height, and blade width are 120 mm, 3 mm, and 15 mm, respectively. Therefore, the results of slag discharge experiment on the optimum structure of rig drill pipe show that the slag discharge efficiency is increased by 11.38%, which effectively resolves the pipe-sticking problem

Investigation of Speed Matching Affecting Contrarotating Fan’s Performance Using Wireless Sensor Network including Big Data and Numerical Simulation

This paper describes the investigations performed to better understand two-stage rotor speed matching in a contrarotating fan. In addition, this study develops a comprehensive measuring and communication system for a contrarotating fan using ZigBee network. The investigation method is based on three-dimensional RANS simulations; the RANS equations are solved by the numerical method in conjunction with a SST turbulence model. A wireless measurement system using big data method is first designed, and then a comparison is done with experimental measurements to outline the capacity of the numerical method. The results show that when contrarotating fan worked under designed speed, performance of two-stages rotors could not be matched as the designed working condition was deviated. Rotor 1 had huge influences on flow rate characteristics of a contrarotating fan. Rotor 2 was influenced by flow rates significantly. Under large flow rate condition, the power capability of rotor 2 became very weak; under working small flow rate condition, overloading would take place to class II motor. In order to solve the performance mismatch between two stages of CRF under nondesigned working conditions, under small flow rate condition, the priority shall be given to increase of the speed of rotor 1, while the speed of rotor 2 shall be reduced appropriately; under large flow rate condition, the speed of rotor 1 shall be reduced and the speed of rotor 2 shall be increased at the same time

Axial Spacing Effects on Rotor-Rotor Interaction Noise and Vibration in a Contra-Rotating Fan

Because of the potential technical advantages, the contra-rotation technology has become a renewed interest in aviation and other applications. Contra-rotation increases efficiency in comparison with the single-rotor design, but this advantage is not fully harnessed. The axial spacing of two-stage contra-rotating blade rows has a significant impact on a contra-rotating fan/compressor. The results show that with a contra-rotation pattern, the strong unsteadiness of two-stage rotors is caused by the rotor-rotor interaction. The unsteadiness of rotor 1 is caused by the potential disturbance, and the upstream wake leads to the strong unsteadiness of rotor 2. With the increase of axial spacing, the rotor-rotor interaction is weakened, while unsteady features of two-stage rotor blades tend to be consistent. The acoustic and vibration effects of axial spacing are studied. It is found that the axial spacing has great influence of aerodynamic noise. The mean value of sound pressure level decreases by 17.2 dB in total when the axial spacing increased to 1.1 chord from 0.3 chord. For the accuracy of calculation, the scattering effect of the casing wall should be considered in the prediction of the noise. The axial spacing does not have obvious effects on the natural frequencies of the two-stage rotor blades but has certain effect on blade deformation

A fully decentralized multi-agent system for intelligent restoration of power distribution network incorporating distributed generations

In the distribution network fault management, service restoration is a very important component. When a fault occurs, it is necessary to restore power to these deenergized loads as soon as possible. The restoration problem could be formulated as a multilevel, multi-objective optimization problem with constraints [1]. © 2005-2012 IEEE