Substation Equipment Fault Identification Based on Infrared Image Analysis

With the development of new technologies for smart grid construction, the smart grid is gradually developing. Smart grid construction is an important field of next generation information technology, and its value and significance have been widely recognized. Smart grid construction not only provides development opportunities, but also brings great challenges. Security and privacy have become serious problems in the development of smart grid. Based on this, this paper discusses the system structure of the block chain, and puts forward the security diagnosis scheme of the smart grid according to the existing problems in the smart grid. Based on the theoretical basis of smart grid big data technology, this paper combines the grid data encryption process with the privacy protection process under the premise of protecting privacy and security, and develops the latest network security protocol. Then use block chain distributed storage to reduce the connection protection process in privacy, and use multi-level encryption. The experimental results show that the technology can improve the rate of sensitive data hiding and has good anti-cracking ability, which provides a theoretical reference for the construction of enterprise power grid.

At present, the development of smart grid has already formed a consensus in the world. As an energy depletion big country, the development of smart grid has very important significance in China. Transformer substation plays a very important role in the smart grid, so do semantic description of substation equipment and improve their interoperability can effectively raise the intelligence application of the transformer substation. The ontology technology based on semantic sharing has unique advantages in describing the concept and relationship. Therefore, to build up ontology for substation equipment and to describe it using the ontology description language-OWL can achieve power equipment information sharing, interaction and processing, which provides a new method to build smart grid.

With the implemeGEPI-173ntation of unattended and intelligent substation, the operation and maintenance personnel are far away from the substation. The cooling system lacks real-time monitoring, fault prediction, remote control and other functions. The number of transformer non-stop caused by cooler fault is further increased, which directly threatens the power grid security. In order to solve the above problems, a new intelligent air-cooling control method is proposed. Based on the large-scale signal acquisition method, a new intelligent air-cooling system design scheme is proposed. The characteristics, hardware system design, software system design, main program design, oil temperature control design, comprehensive switching design, data sharing center design message flow design and other key links of the new intelligent transformer cooler control system are introduced in detail. The results show that the scheme can better meet the needs of unattended substation and realize the intelligent operation of substation. It can further improve the security capacity of transformer production and play an important role in the construction and development of smart grid.

With the continuous development of strong smart grid construction, the existing real-time database cannot meet the requirements of tens of millions of measuring points and large capacity storage. This paper analyzes the objective demand of the massive data of the power grid and the technical bottleneck of the existing real-time database, introduces the distributed technology into the field of real-time database, studies and designs the distributed real-time database management system for the massive data of the power grid, which can provide technical support for the construction and development of a strong smart grid.

The construction of smart grid has inevitably become the direction of the development of the power grid, and the use of data mining technologies to improve the level of data analysis is an important part of the construction of smart grid. In power grid scheduling, as the basis for the dispatcher to record the operation status of the power grid, the scheduling log usually uses short text to record the current state of the power grid, accident handling and abnormal alarm. However, it is lack of further analysis of the log. Aiming at the automatic classification of power grid dispatching log faults, we firstly use the custom power word and word segmentation tool to preprocess the text. After obtaining the TF-IDF feature of the text and creating the Word2Vec feature model, we compare three kinds of text classification algorithms, the nearest neighbor algorithm, Naive Bayes and support vector machine respectively. It is found that the support vector machine method is very effective in classification with a correct rate of 93% in the log short text.

This paper presents an approach to implement vibration signals for fault diagnosis of the rolling bearing. Due to the noise and transient impacts, it is difficulty to accurately diagnosis the faults with traditional methods. So a new type of learning architecture for deep generative model called deep belief networks (DBN) is applied. Since the unsupervised learning ability in DBN, it can extract the features from the raw data layer by layer. This article mainly research how to construct the encoder using DBN which can minimize the energy between the output and input vibration signals. Compared with existing diagnosis techniques, the proposed method can learn a good representation of features with higher accuracy. The results show that DBN can more comprehensively retain the data features in pattern recognition.

Real-time and accurate fault diagnosis can provide early warning of system failure and support decision-making of maintenance and replacement processes, enhancing reliability of the dynamic system and reducing costs for maintenance. Deep belief networks, as one of the deep learning methods, can extract features from monitoring data and establish nonlinear relationship between extracted features and comprehensive system conditions. It has potentials for fault diagnosis. In this paper, a complete fault diagnosis framework starting from FFT(Fast Fourier Transform) to health condition prediction is proposed. Bearing vibration data is employed to verify the proposed approach. The results show that the proposed model has high and stable prediction accuracy. These results demonstrate the effectiveness, stability, and robustness of the fault diagnosis framework based on deep belief networks.

As the development of smart grid and energy internet, the amount of transmitted data in real time significantly increase. Due to the mismatch with communication networks that were not designed to carry high-speed and real time data, data losses and data quality degradation may happen constantly. For this problem, according to the strong spatial and temporal correlation and periodicity of electricity data which is generated by human’s actions and feelings, we treat the electricity data as a tensor where the three dimensional are user, weeks, days. We divide the electricity data tensor into the sum of multiple rank-1 tensors and use the known data to approximate the electricity data tensor and recover the lost electrical data. Based on the real electricity data, we analyze the sparseness of the electricity data tensor and perform the CP decomposition-based method on the real data. The experimental results verify the recovery efficiency of the proposed scheme.

Strong and smart is the essential requirement of the development of modern power grid and the twenty-first Century energy development driving force. Smart grid bearing in response to changing in the environment, improving energy efficiency, reducing carbon emissions is an important mission. Smart grid has become the new development trend and new directions of the world power grid. The effective coordination of demands interest group of Smart Grid, promote the orderly development of smart grid. The main purpose is to realize Smart Grid interoperability and to guarantee open framework of Smart Grid. The IEC (International Electrical Commission), NIST (National Institute of Standards and Technology) and SGCC (STATE GRID Corporation of China) issued technology standards roadmap of Smart Grid. From technology view of Smart Grid, the IEC include 13 domains. From view of interest group of Smart Grid, according to the United States market conditions, the NIST covers 7 domains. The SGCC covers 8 domains, according to Chinese power industry conditions. The future five years and fifteen years of IEC, NIST and SGCC smart grid technology standard trend focuses are given, in the way of the mathematical model of probability distribution, according to the technology standard requirements of maturity of the Smart Grid related fields of technology, products and market. It is likelihood the middle and long-term development focuses of future Smart Grid technical standards that two-way interactive service of power consumption, distribution management, electric storage, renewable energy generation. It is suggested that the Smart Grid technology advantage into standard advantage, in the promotion of Smart Grid equipment R & D and manufacturing level and promote the overall construction of Smart Grid at the same time, to strengthen our country voice in the Smart Grid international standards, to seize commanding heights of technical innovation and industrial development.

With the construction and development of smart grid of State Grid Corporation of China, intelligent promotion and application of electricity meters are accelerating. At present, replacing electricity meters requires users to operate after power failure, which will affect users' normal production and life. In order to ensure that users can use electricity normally in the process of changing the meter, we developed a plug and plug electric energy meter replacement device with continuous electricity. The device can be used to change the working mode of power outage meter change, can complete the electricity meter change work without power outage, shorten the operation time of the meter change, improve the work efficiency. The use of this device can also realize the process of changing the meter residents electricity is not lost, for the company to expand sales to create favorable conditions.

PurposeUp to date development in sentiment analysis has resulted in a symbolic growth in the volume of study, especially on more subjective text types, namely, product or movie reviews. The key difference between these texts with news articles is that their target is defined and unique across the text. Hence, the reviews on newspaper articles can deal with three subtasks: correctly spotting the target, splitting the good and bad content from the reviews on the concerned target and evaluating different opinions provided in a detailed manner. On defining these tasks, this paper aims to implement a new sentiment analysis model for article reviews from the newspaper.Design/methodology/approachHere, tweets from various newspaper articles are taken and the sentiment analysis process is done with pre-processing, semantic word extraction, feature extraction and classification. Initially, the pre-processing phase is performed, in which different steps such as stop word removal, stemming, blank space removal are carried out and it results in producing the keywords that speak about positive, negative or neutral. Further, semantic words (similar) are extracted from the available dictionary by matching the keywords. Next, the feature extraction is done for the extracted keywords and semantic words using holoentropy to attain information statistics, which results in the attainment of maximum related information. Here, two categories of holoentropy features are extracted: joint holoentropy and cross holoentropy. These extracted features of entire keywords are finally subjected to a hybrid classifier, which merges the beneficial concepts of neural network (NN), and deep belief network (DBN). For improving the performance of sentiment classification, modification is done by inducing the idea of a modified rider optimization algorithm (ROA), so-called new steering updated ROA (NSU-ROA) into NN and DBN for weight update. Hence, the average of both improved classifiers will provide the classified sentiment as positive, negative or neutral from the reviews of newspaper articles effectively.FindingsThree data sets were considered for experimentation. The results have shown that the developed NSU-ROA + DBN + NN attained high accuracy, which was 2.6% superior to particle swarm optimization, 3% superior to FireFly, 3.8% superior to grey wolf optimization, 5.5% superior to whale optimization algorithm and 3.2% superior to ROA-based DBN + NN from data set 1. The classification analysis has shown that the accuracy of the proposed NSU − DBN + NN was 3.4% enhanced than DBN + NN, 25% enhanced than DBN and 28.5% enhanced than NN and 32.3% enhanced than support vector machine from data set 2. Thus, the effective performance of the proposed NSU − ROA + DBN + NN on sentiment analysis of newspaper articles has been proved.Originality/valueThis paper adopts the latest optimization algorithm called the NSU-ROA to effectively recognize the sentiments of the newspapers with NN and DBN. This is the first work that uses NSU-ROA-based optimization for accurate identification of sentiments from newspaper articles.

Deep belief network (DBN) is an effective learning model based on deep learning. It can hierarchically transform the input data via stacked feature detectors. As a predictive model, DBN has shown a promising prospect in model predictive control (MPC). However, its successful application relies seriously on the suitable structure size (the numbers of hidden layers and neurons), which is challenging to determine. In this work, we present a theoretical bound on its minimum structure size in order to accurately approximate a desired control law of MPC, called DBN-MPC. First, according to the Markov assumption, a controlled system is equivalent to a quadratic program, which only depends on the current system state. Second, a universal theorem is proposed to give a bound on the minimum structure size of DBN from the perspective of piecewise affine function analysis. Third, a partial least square regression is used to fine-tune DBN to overcome the problems of local-minimum and time-consuming training process. Finally, we demonstrate the effectiveness of the proposed method through two classical experiments: 1) tracking control of a benchmark dynamical system and 2) temperature control of a practical second-order continuous stirred tank reactor (CSTR) system. The experimental results generally give an answer to the question that how deep is deep enough for DBN to approximate an MPC law. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The proposed deep belief network (DBN)-model predictive control (MPC) scheme presents an answer to the question that how deep is deep enough for DBN, from the perspective of approximating an MPC law. Given an industrial system, the proposed DBN-MPC scheme can give the best tracking control performance with minimal computational complexity through an effective self-growing DBN and optimal approximation theorem of MPC law. In practical applications, it is better for practitioners to obtain accurate historical input and output data. During the operation process, DBN-MPC will be more practical if more accurate initial structure size of DBN is provided in advance because the initial structure size of DBN is the starting point of the dynamic optimization.

Proton exchange membrane fuel cell (PEMFC) degradation prediction is essential, especially in the industrial power generation system, since it can provide decision supports for equipment maintenance. As degradation phenomena inside PEMFC are strongly nonlinear and inter-coupled, accurate degradation prediction is extremely challenging. Thus, a novel data-driven prediction method based on the adaptive variational mode decomposition (AVMD) and deep belief network (DBN) is proposed. Firstly, an improved AVMD method is employed to decomposes the original voltage series into multiple sub-signals, which helps to analyze the different degradation characteristics of PEMFC. Then, the sample entropy (SE) theory is employed to filter out the noise sub-signals and reconstruct the rest sub-signals to strengthen significant features. Finally, the predictions are performed for each reconstructed sub-signal by the DBN method that has powerful mining ability for latent degradation features. The overall prediction results of the AVMD-DBN method are in good agreement with real experimental data, which indicates that the proposed method has good practicality for the health management of the PEMFC power generation system in industrial applications.

In this paper, a deep belief network (DBN) is proposed as an effective method to predict the mortality of patients within the first 72 h of visiting an emergency room (ER), i.e., 72-h mortality. Previously, physicians used the quick sequential organ failure assessment (qSOFA) and systemic inflammatory response syndrome (SIRS) to determine the survival probability of patients. Although such prediction methods are convenient, there is room for improvement in their accuracy. To demonstrate the effectiveness of our proposed method, original data from a hospital was used. A single-center retrospective study was performed regarding adult ER patients who were admitted between January 2007 and December 2013 with an infection. The qSOFA and SIRS scores were calculated using primary vital signs and laboratory data. A DBN was then used to predict patients&#x2019; survival rates and to choose 65 clinical variables as an input. Utilizing the DBN, the joint probability distribution of the 65 clinical variables was calculated, and valid solutions of the decreased features were achieved. Results indicate that the DBN can predict the 72-h mortality of ER septic patients more accurately than the qSOFA or SIRS. This paper aims to design an effective prediction system to assist clinicians in their diagnosis using a DBN for early risk stratification and intervention.

In recent years, China's rapid economic growth promote the development of the industry. The power system put forward higher requirements; the construction of smart grid has become an inevitable trend of development. China's power grid automation and information level is relatively low, because of the low performance and poor reliability of the power distribution network communication system. But in the smart grid, electric business increased put forward higher requirements for the communication terminal. The development of smart grid construction plays an important role in the communication network of electric power system, this paper will focus on the necessity of the construction of smart grid, analysis of electric power communication and its role in the smart grid, and the power communication terminal access architectures are described, in order to ensure the orderly and efficient operation of the smart grid.