During an automatic power transmission line inspection, a large number of images are collected by unmanned aerial vehicles (UAVs) to detect existing defects in transmission line components, especially insulators. However, with twin insulator strings in the inspection images, when the umbrella skirts of the rear string are obstructed by the front string, defect detection becomes difficult. To solve this problem, we propose a method to detect self-shattering defects of insulators based on spatial features contained in images. Firstly, the images are segmented according to the particular color features of glass insulators, and the main axes of insulator strings in the images are adjusted to the horizontal direction. Then, the connected regions of insulators in the images are marked. After that, the vertical lengths of the regions, the number of insulator pixels in the regions, as well as the horizontal distances between two adjacent connected regions are selected as spatial features, based on which defect discriminants are formulated. Finally, experiments are performed using the proposed formula to detect self-shattering defects in the insulators, using the spatial distribution of the connected regions to locate the defects. The experiment results indicate that the proposed method has good detection accuracy and localization precision.

The selective catalytic reduction (SCR) decomposition of nitrogen oxide (de-NO _x ) process in coal-fired power plants not only displays nonlinearity, large inertia and time variation but also a lag in NO _x analysis; hence, it is difficult to obtain an accurate model that can be used to control NH ₃ injection during changes in the operating state. In this work, a novel dynamic inferential model with delay estimation was proposed for NO _x emission prediction. First, k -nearest neighbour mutual information was used to estimate the time delay of the descriptor variables, followed by reconstruction of the phase space of the model data. Second, multi-scale wavelet kernel partial least square was used to improve the prediction ability, and this was followed by verification using benchmark dataset experiments. Finally, the delay time difference method and feedback correction strategy were proposed to deal with the time variation of the SCR de-NO _x process. Through the analysis of the experimental field data in the steady state, the variable state and the NO _x analyser blowback process, the results proved that this dynamic model has high prediction accuracy during state changes and can realize advance prediction of the NO _x emission.

Recently, a population-based intelligent optimization algorithm research has been combined with multiple algorithms or algorithm components in order to improve the performance and robustness of an optimization algorithm. This paper introduces the idea into real world application. Different from traditional algorithm research, this paper implements this idea as a general framework. The combination of multiple algorithms or algorithm components is regarded as a complex multi-behavior population, and a unified multi-behavior combination model is proposed. A general agent-based algorithm framework is designed to support the model, and various multi-behavior combination algorithms can be customized under the framework. Then, the paper customizes a multi-behavior combination algorithm and applies the algorithm to solve the economic load dispatch problems. The algorithm has been tested with four test systems. The test results prove that the multi-behavior combination idea is meaningful which also indicates the significance of the framework.

The accurate measurement of NOx concentration is the basis of accurate ammonia injection in selective catalytic reduction (SCR) system of thermal power unit. Excessive or too little ammonia injection will cause ammonia escape or exceeding the standard of NOx emission, and cause environmental pollution. There exist a large measurement lag and errors during the purging process in the measurement of NOx concentration in the SCR system of thermal power units. Therefore, to enable the intelligent control of the SCR system, it is necessary to carry out soft sensing of NOx emissions. This paper proposes a soft sensing algorithm for NOx emissions of thermal power units based on an adaptive Gaussian Mixture Model (GMM) two-step clustering algorithm and ensemble learning. Adaptive GMM two-step clustering (AGTSC) algorithm is used to softly divide the historical data into working conditions. Temporal Pattern Attention Long Short-Term Memory (TPA-LSTM) is also adopted to construct individual learners for each working condition. Here we use the parameter regression algorithm to train the combiner based on the output of the individual learner and the membership signal of the working condition as the input of the combiner. The clustering algorithm is tested with three datasets. The results show that this algorithm addresses the shortcomings of GMM including not being applicable to the clustering of non-convex datasets and manual selection of the number of clusters. The soft sensing algorithm is verified by using the historical data of the SCR system of 1000 MW thermal power units. The results show that the proposed method has higher accuracy and stronger generalization ability than the conventional method, hence providing an effective method for soft sensing of NOx emissions of thermal power units.

The differential evolution (DE) algorithm is a popular and efficient evolutionary algorithm that can be used for single objective real-parameter optimization. Its performance is greatly affected by its parameters. Generally, parameter control strategies involve determining the most suitable value for the current state; there is only a little research on parameter combination and parameter distribution which is also useful for improving algorithm performance. This paper proposes an idea to use parameter region division and parameter strategy combination to flexibly adjust the parameter distribution. Based on the idea, a group-based two-level parameter combination framework is designed to support various modes of parameter combination, and enrich the parameter distribution characteristics. Under this framework, two customized parameter combination strategies are given for a single-operation DE algorithm and a multi-operation DE algorithm. The experiments verify the effectiveness of the two strategies and it also illustrates the meaning of the framework.