The Kalman filter has been found to be useful in vast areas. However, it is well known that the successful use of the standard Kalman filter is greatly restricted by the strict requirements on a priori information of the model structure and statistics information of the process, and measurement noises. Generally speaking, the covariance matrix of process noise is harder to be determined than that of the measurement noise by routine experiments, since the statistical property of process noise cannot be obtained directly by collecting a large number of sensor data due to the intrinsic coupling of process noise and system dynamics. Considering such background of wide applications, this paper introduces one algorithm, recursive covariance estimation (RCE) algorithm, to estimate the unknown covariance matrix of noise from a sample of signals corrupted with the noise. Based on this idea, for a class of discrete-time linear-time-invariant systems where the covariance matrix of process noise is completely unknown, a new Kalman filtering algorithm named, Kalman filter with RCE, is presented to resolve this challenging problem of state estimation without the statistical information of process noise, and the rigorous stability analysis is given to show that this algorithm is optimal in the sense that the covariance matrix and state estimations are asymptotically consistent with the ideal Kalman filter when the exact covariance matrix of process noise is completely known a priori. Extensive simulation studies have also verified the theoretical results and the effectiveness of the proposed algorithm.

Bolt defect inspection is an important work in transmission line inspection. Due to the small size of bolts in the transmission line inspection images, existing algorithms are difficult to extract valuable features and achieve poor performance on bolt defect detection. This paper proposed an ultra-small bolt defect detection model(UBDDM) based on a deep convolutional neural network(DCNN), including an ultra-small object perception module(UOPM) and a local bolt detection module(LBDM). In this paper, UOPM is first constructed to realize coarse region recognition for the salient region of bolts in the inspection images, and the high-resolution image blocks are obtained from the original image according to the recognition results. Then, LBDM is constructed to intelligently identify the bolt defects from the high-resolution image blocks. Considering that the features of ultra-small targets are difficult to extract, feature extraction networks are constructed based on ResNet-50, and the hybrid attention mechanism and multi-scale feature fusion are introduced to further improve the network’s ability to extract shallow features. This method uses two-stage detection to realize end-to-end bolt defect detection but only needs to provide a single-stage target detection label, which greatly reduces the workload of data labeling. Experimental results show that the proposed method achieves excellent performance on bolt defect detection in inspection images.

The multienergy interaction characteristic of regional integrated energy systems can greatly improve the efficiency of energy utilization. This paper proposes an energy prediction strategy for multienergy information interaction in regional integrated energy systems from the perspective of horizontal interaction and vertical interaction. Firstly, the multienergy information coupling correlation of the regional integrated energy system is analyzed, and the horizontal interaction and vertical interaction mode are proposed. Then, based on the long short‐term memory depth neural network time series prediction, parallel long short‐term memory multitask learning model is established to achieve horizontal interaction among multienergy systems and based on user‐driven behavioral data to achieve vertical interaction between source and load. Finally, uncertain resources composed of wind power, photovoltaic, and various loads on both sides of source and load integrated energy prediction are achieved. The simulation results of the measured data show that the interactive parallel prediction method proposed in this article can effectively improve the prediction effect of each subtask.

This paper documents the condition-based maintenance (CBM) of power transformers, the analysis of which relies on two basic data groups: structured (e.g., numeric and categorical) and unstructured (e.g., natural language text narratives) which accounts for 80% of data required. However, unstructured data comprised of malfunction inspection reports, as recorded by operation and maintenance of the power grid, constitutes an abundant untapped source of power insights. This paper proposes a method for malfunction inspection report processing by deep learning, which combines the text data mining–oriented recurrent neural networks (RNN) with long short-term memory (LSTM). In this paper, the effectiveness of the RNN-LSTM network for modeling inspection data is established with a straightforward training strategy in which we replicate targets at each sequence step. Then, the corresponding fault labels are given in datasets, in order to calculate the accuracy of fault classification by comparison with the original data labels and output samples. Experimental results can reflect how key parameters may be selected in the configuration of the key variables to achieve optimal results. The accuracy of the fault recognition demonstrates that the method we proposed can provide a more effective way for grid inspection personnel to deal with unstructured data.

Time delay is a major problem in the acoustic communication technology. Considering such a background, a new dynamic model is proposed for an improved error estimation algorithm in this paper. And error propagation equations are constructed for an inertial navigation system/Doppler velocity log integrated navigation system. The time delay problem is reconsidered for acoustic communication with an ultra-short baseline acoustic positioning system that uses a multi-autonomous underwater vehicle (AUV) cooperative navigation process in master-slave mode. The characteristics of time delays in acoustic communication are considered and the time delay is converted into a measurement bias within an observation equation for the slave AUV platform. Under the framework of a standard Kalman filter, an improved error estimation algorithm is presented to address the problem that occurs when random measurement bias exists in the dynamic linear system model. Based on the Monte Carlo method, the simulation results illustrate that compared with traditional methods, the error estimation algorithm proposed in this paper can effectively decrease positioning errors caused by time delays in acoustic communication.