# Intelligent Identification of Embedded System Equipment State Based on Internet of Things

Authors: Ying Xu
Journal: Mobile Information Systems
 (2022)

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## Abstract

IoT (Internet of things) technology will be more widely used in the manufacturing industry, which will bring new trends to the development of the manufacturing industry. With the continuous advancement of IoT technology in the power field, the integration of IoT and smart grid has become a new topic. In the process of monitoring and troubleshooting the whole EPSE (Embedded Power System Equipment), it is very important to conduct a comprehensive analysis and routine testing on specific equipment and systems, so that the power system can work stably. In this paper, the knowledge sharing and transfer model of EPSE based on IoT is analyzed, and an intelligent identification method of EPSE state based on the GA-C_HMM (Genetic Algorithm-Coupled Hidden Markov Model) algorithm is proposed. After the GA parameters are optimized, the state model library is constructed by fitting each state data of the equipment to C_HMM, and the state of the equipment is determined by calculating the maximum probability value of the signal to be diagnosed. The experimental results show that when the recognition time is 12 hours, the recognition accuracy rate of the existing recognition method is 69.7%, and that of the recognition method in this paper is 98.3%, which shows that the recognition accuracy rate of the recognition method adopted in this study is higher than that of the existing methods and the recognition ability is stronger.


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## Body

## 1. Introduction

With the innovation of science and technology, the maturity and development of monitoring technology, embedded technology, communication, network, and other technologies, the existing monitoring system has been increasingly unable to meet the requirements of the information age industry. One of the hotspots in the field of monitoring technology is the multifunctional monitoring system based on information technology. The Internet of things (IoT) platform has breathed new life into the manufacturing industry and given intelligent manufacturing a powerful boost [1]. Image-based 3D reconstruction technology [2], for example, is used to monitor transmission line icing in real time. The operation state of the substation is one of the key factors for the safe operation of the power grid, as it is an important part of it. When EPSE (Embedded Power System Equipment) is in operation, it is disrupted by a variety of external factors, resulting in abnormal EPSE and affecting the overall power system’s normal operation. As a result, EPSE’s safe and stable operation is attracting interest in this field.With the advancement of IoT technology, the intelligent factory, which is based on the intelligent manufacturing system, has gradually realized new production management integration modes on the IoT platform, such as project information tracking, node wireless communication, and intelligent scheduling. Lee et al. used image shadow removal and color model binarization, then used the Hough transform to find the switch, and finally identified and compared the switch state [3]. To determine the state of the switch, Kim et al. found the switch symbol associated with the circuit breaker switch and performed gray-scale conversion, binarization, smoothing, character extraction, and character pattern matching in the map area of the symbol [4]. Jalil et al. proposed a method for evaluating the state of power grid equipment online using multiple measurements. The initial operation weight of power grid facilities is obtained using the analytic hierarchy process, and the optimal value of fuzzy measurement is solved using a directional matrix [5]. Kong et al. proposed a method for evaluating national power facilities based on the overall health index [6]. For the methods, a comprehensive evaluation model of power equipment operation status was designed, and the status index of power facilities was analyzed and corrected, using the health index as the evaluation index of power equipment status. To detect the transition or transition region of power system events, Cui et al. used a Kalman filter to estimate the signal and analyzed the residual components of the original measured signal and estimated signal [7]. However, all of the methods listed above have flaws such as low EPSE identification and positioning stability, weak anti-interference ability, and high false alarm rate. Existing methods are more targeted and have not yet formed the detection and identification of other switchgear types.With the equipment status monitoring and monitoring technology, monitoring data can be sent from the lower computer in the field to the upper computer via the computer platform’s remote desktop, allowing for better integration and use of the upper computer for hierarchical information management and production management services. This not only ensures the smooth operation of the industrial production process but also increases enterprise labor productivity, lowers production costs, and improves core competitiveness. Because the above methods have flaws, this paper proposes an intelligent EPSE state identification method based on the GA-C_HMM (Genetic Algorithm-Coupled Hidden Markov Model) algorithm. Wireless sensor networks provide real-time monitoring and information transmission of power transmission and transformation facilities, as well as timely notification of abnormal facilities to the maintenance planning team.Based on the above analysis, the main innovations of this paper are as follows:(1)
IoT technology is an effective means to realize intelligent and automatic monitoring and management of substation equipment. In this paper, the information, work, and technical methods of EPSE condition detection are analyzed, the primary and secondary equipment is studied in detail, and the strategy and method of power equipment condition maintenance are established based on IoT technology(2)
An intelligent identification method of EPSE state based on the GA-C_HMM algorithm is proposed to address the problems of low monitoring accuracy and long time delay of traditional EPSE operation state monitoring methods. The EPSE state characteristics are extracted using the GA-C_HMM algorithm, and then, EPSE state monitoring and optimization characteristic analysis are performed. The results of the experiments show that this method can effectively identify EPSE status and has some advantagesThe intelligent identification of embedded system equipment status is the main research topic of this paper, and the chapters are organized as follows.The first chapter introduces the research background and significance before moving on to the paper’s main work. The second chapter focuses on the embedded system technologies that are related to them. The third chapter outlines the paper’s specific methods and implementation. The fourth chapter verifies the research model’s superiority and feasibility. The full text summary is found in the fifth chapter.


## 2. Related Work

### 2.1. Research Status of Embedded System

An embedded system is a set of computer hardware and software (along with possibly other mechanical or electronic components) that are used to perform a specific task [8]. Embedded systems first appeared in the 1970s and were initially used in industrial control and national defense systems that required a high level of real-time security. Sha and colleagues used a typical multicore architecture with an isomorphic symmetric shared L2 cache design and an intercore communication mode of a high-speed bus [9]. Rossi and colleagues [10] used homogeneous multicore processors with different functions assigned to each processor. Device processors and application processors are the two functional groups of processors, and each device processor corresponds to a specific device, such as a network card or disk controller. By dynamically allocating operating systems on multicore processor cores, Malek et al. realized running multiple operating systems on a multicore CPU [11]. The operating system on the processor core can be controlled dynamically using this design. Singh and colleagues developed a bare-metal dual system based on the ZYNQ-7000 and an AMP architecture motion controller based on Linux [12]. Tang et al. [13] use architecture, specifically Linux+Nuttx architecture, to achieve robot control on a dual-core processor.Although multicore processors have many advantages over single-core processors in terms of architecture and system management, efficient design and deployment of real-time systems on multicore platforms remain a challenge. The research on multicore systems for general computing was summarized by Ordonez and colleagues. The design and analysis of multicore systems for real-time computing, on the other hand, have not been thoroughly investigated [14]. Assaouy et al. research shows that other applications running in parallel on other cores have a significant impact on application execution performance, leading to denial of service [15]. Tsai et al. conduct experiments on quadcore processors, and the results show that the interference of shared cache among cores increases task completion time when compared to the task completion time when running alone.


### 2.2. Research on Condition Monitoring of Power System Equipment

The operation and maintenance costs of related equipment can be reduced, and the entire system can run more safely, by monitoring the state of power equipment and making scientific and reasonable diagnoses. The main purpose of condition monitoring in the power system is to detect the status of the main equipment. The mechanism of a series of failures, such as insulation failure and mechanical failure, has not been thoroughly investigated, and effective monitoring of major equipment failures is technically difficult.Power equipment monitoring has become more comprehensive and intelligent as a result of continuous innovation in power equipment condition monitoring technology. With the continued use of advanced technology, it is now possible to obtain health data that are not readily available in conventional equipment, such as some nonelectrical and atypical monitoring parameters of the equipment. Transmission, analysis, calculation, processing, and analysis of equipment monitoring data have all become much easier since the introduction of computer and communication technology. Yu and Xiong used the principal component analysis method to construct the state evaluation matrix of the studied index parameters, as well as the principal component analysis method to eliminate index correlation and reduce the number of evaluation indexes [16]. Liu et al. used the linear discriminant analysis method to solve the grid problem and set the repetition problem between the index weights of evaluation indexes [17]. They chose index parameters related to the viability of the grid structure from four aspects of grid backbone evaluation. Zhang et al. investigated power system transient stability evaluation parameters and selected closely related index parameters to describe the stable operation of power systems, with the goal of improving classification accuracy [18]. Oliveira and colleagues created an IoT-based online monitoring system for substations in order to meet the growing demand for smart grids. The system has high running stability, good real-time data transmission performance, and high popularization and application value, according to the test results and transmission experiments [19]. Parajuli et al. [20] created the EPSE state intelligent identification system [21–23], which enabled remote intelligent identification of switch and breaker equipment state in a substation via data collection and information interaction with the monitoring system.At present, there are still many problems in the condition monitoring of power equipment, especially the processing of condition monitoring data, too many condition monitoring data, and a lot of overlap and repetition between data, and the useful information is hidden, so the monitoring data can be processed and analyzed in real time.


## 2.1. Research Status of Embedded System

An embedded system is a set of computer hardware and software (along with possibly other mechanical or electronic components) that are used to perform a specific task [8]. Embedded systems first appeared in the 1970s and were initially used in industrial control and national defense systems that required a high level of real-time security. Sha and colleagues used a typical multicore architecture with an isomorphic symmetric shared L2 cache design and an intercore communication mode of a high-speed bus [9]. Rossi and colleagues [10] used homogeneous multicore processors with different functions assigned to each processor. Device processors and application processors are the two functional groups of processors, and each device processor corresponds to a specific device, such as a network card or disk controller. By dynamically allocating operating systems on multicore processor cores, Malek et al. realized running multiple operating systems on a multicore CPU [11]. The operating system on the processor core can be controlled dynamically using this design. Singh and colleagues developed a bare-metal dual system based on the ZYNQ-7000 and an AMP architecture motion controller based on Linux [12]. Tang et al. [13] use architecture, specifically Linux+Nuttx architecture, to achieve robot control on a dual-core processor.Although multicore processors have many advantages over single-core processors in terms of architecture and system management, efficient design and deployment of real-time systems on multicore platforms remain a challenge. The research on multicore systems for general computing was summarized by Ordonez and colleagues. The design and analysis of multicore systems for real-time computing, on the other hand, have not been thoroughly investigated [14]. Assaouy et al. research shows that other applications running in parallel on other cores have a significant impact on application execution performance, leading to denial of service [15]. Tsai et al. conduct experiments on quadcore processors, and the results show that the interference of shared cache among cores increases task completion time when compared to the task completion time when running alone.


## 2.2. Research on Condition Monitoring of Power System Equipment

The operation and maintenance costs of related equipment can be reduced, and the entire system can run more safely, by monitoring the state of power equipment and making scientific and reasonable diagnoses. The main purpose of condition monitoring in the power system is to detect the status of the main equipment. The mechanism of a series of failures, such as insulation failure and mechanical failure, has not been thoroughly investigated, and effective monitoring of major equipment failures is technically difficult.Power equipment monitoring has become more comprehensive and intelligent as a result of continuous innovation in power equipment condition monitoring technology. With the continued use of advanced technology, it is now possible to obtain health data that are not readily available in conventional equipment, such as some nonelectrical and atypical monitoring parameters of the equipment. Transmission, analysis, calculation, processing, and analysis of equipment monitoring data have all become much easier since the introduction of computer and communication technology. Yu and Xiong used the principal component analysis method to construct the state evaluation matrix of the studied index parameters, as well as the principal component analysis method to eliminate index correlation and reduce the number of evaluation indexes [16]. Liu et al. used the linear discriminant analysis method to solve the grid problem and set the repetition problem between the index weights of evaluation indexes [17]. They chose index parameters related to the viability of the grid structure from four aspects of grid backbone evaluation. Zhang et al. investigated power system transient stability evaluation parameters and selected closely related index parameters to describe the stable operation of power systems, with the goal of improving classification accuracy [18]. Oliveira and colleagues created an IoT-based online monitoring system for substations in order to meet the growing demand for smart grids. The system has high running stability, good real-time data transmission performance, and high popularization and application value, according to the test results and transmission experiments [19]. Parajuli et al. [20] created the EPSE state intelligent identification system [21–23], which enabled remote intelligent identification of switch and breaker equipment state in a substation via data collection and information interaction with the monitoring system.At present, there are still many problems in the condition monitoring of power equipment, especially the processing of condition monitoring data, too many condition monitoring data, and a lot of overlap and repetition between data, and the useful information is hidden, so the monitoring data can be processed and analyzed in real time.


## 3. Methodology

### 3.1. The Framework of EPSE State Awareness System Based on IoT

Real-time accurate identification of the equipment running state is the premise of intelligent remote/automatic control and the foundation of intelligent unmanned power system implementation. The existing EPSE monitoring and monitoring system maintains the visual representation level of sensor data and simple threshold analysis (exceeding the threshold of signal value and exceeding the limit). There are some problems in the EPSE system, such as missing defective samples, incomplete data collection, large noise interference, and various working conditions, which make the working condition diagnosis more difficult.IoT is another wave of technology after computer, Internet, and mobile Internet, with great potential. From the perspective of architecture, IoT can generally be divided into three layers (perception layer, network layer, and application layer) from bottom to top [16]. The perception layer is the core of the physical network, which not only refers to the physical device object itself but also includes its edge devices and sensors. It is the middle layer of IoT in the network layer, which is mainly responsible for information transmission, routing, and control. The supported transmission technologies include the public network, WIFI, Bluetooth, and infrared. The application layer, also known as the processing layer, is located at the top of the structure and is mainly responsible for information processing and human-computer interaction. For the data sent by the network layer, different types of information are calculated, processed, and analyzed in the application layer, such as big data calculation, data mining, and data analysis. This layer also uses data storage and middleware technology.Information is not transmitted unilaterally between the three layers of the Internet of things, but it can be exchanged, and the types of information are complex and varied. Despite the fact that IoT has a variety of application scenarios in fields such as smart factories, intelligent transportation, smart homes, medical care, and shopping, the basic structural framework of various fields and industries is sufficient for the cognitive layer, transport layer, and application. Each data parameter has a function in the health inspection of power equipment, and each data information reflects one aspect of the power equipment problem. Judge whether the power equipment is running, abnormal, and abnormal and hidden faults, based on the collected data information, and judge parameter comparison and visualization based on the characteristics to improve the online real-time detection quality. Furthermore, due to the mechanical principle of generator equipment, ultrasonic sensors can be used to monitor the air gap parameter values between the rotor and the stator, or speed sensors can be installed on the equipment to collect vibration data.Data must be transmitted after it has been collected effectively, but in practice, the data transmission process is easily hampered by the surrounding environment. Related signals can be compressed and then transmitted to improve transmission speed and real-time data transmission due to other specific transmission time and distance requirements. The rapid transmission of big data is now possible thanks to the advancement of wireless information transmission technology. The data transmission time is shortened, and the detection system’s reliability [24, 25] is improved by continuously optimizing related data transmission technologies.The lag of EPSE status identification and analysis technology limits the development of intelligent equipment management, predictive maintenance, and robot automation control technology. Accurate identification and prediction analysis of power system health must solve three problems.(1)
Accurate perception and knowledge expression of equipment operation status information(2)
Evaluation of the rationality of working conditions and real-time diagnosis of abnormal problems (clearly identify abnormal working conditions)(3)
Early fault diagnosis and degradation analysis of main components of equipment (identification of hidden premature damage)As shown in Figure1, the framework of the EPSE state identification and intelligent identification system of IoT mode can be divided into multisource information identification of power system, IoT edge calculation and online diagnosis, intelligent analysis of power level big data and group level, big data fusion analysis [26], and multidimensional knowledge sharing.Figure 1
IoT-based EPSE state awareness system.By using the massive multisource sensor data accumulated in the long-term operation of the equipment and the artificial intelligence algorithm model, the correlation analysis between the change of equipment operation state and the data is realized. The application model of deep analysis and mining of big data in power systems is established, and the long-term accumulated laws and tacit knowledge of power system state evolution are mined to realize tracking, prediction, and early warning of power system operation state evolution.The working flow chart of the EPSE state intelligent identification system is shown in Figure2.Figure 2
Work flow chart of EPSE state intelligent identification system.The EPSE status intelligent identification system mainly realizes the following functions: intelligent identification of equipment status. It is mainly used for intelligent image recognition of equipment status such as switch and breaker in substation and intelligent detection of equipment health, which mainly realizes intelligent patrol of daily working status of equipment. System management functions mainly to achieve past work query, report query, video display, alarm, statistical analysis of historical alarm records, etc.The built-in dynamic test analyzer collects data from field devices using the analyzer and the detection path information that was downloaded. Data can be analyzed in the field using the analyzer’s signal analysis technology or uploaded to a server for analysis. Delete the analyzer’s data from the network’s corresponding measuring point directory at the same time. After being authorized and certified, equipment managers or experts in the remote monitoring center can download online uploaded equipment data from the server, analyze the running state of the equipment at the client using various signal analysis methods provided by system signal analysis, and then diagnose faults and make appropriate maintenance decisions.


### 3.2. Intelligent Identification of EPSE State

#### 3.2.1. Feature Extraction of Power Equipment Condition Monitoring Indicators

It can be seen that through the introduction of power equipment status monitoring data, the importance of monitoring index attributes to determine the status of power equipment in the equipment status monitoring data is different. Some of these attributes are absolutely essential, and the absence of these attributes may lead to errors in determining the health status of the whole equipment.Continuous discretization of attributes is a process of dividing the range and region of state monitoring data in order to obtain a simplified decision-making system based on the premise of minimizing information loss in decision-making systems. When the attribute judgment result is accurate, we must do our best to ensure the accurate discretization of state monitoring data during the discretization process. When rough set theory is applied to data from equipment condition monitoring, it reduces not only the number of rules but also the number of attributes. The key to studying the influence of decision-making and reducing the comparison between attributes and original attributes is to figure out how to get the simplest set of attributes.If there are multiple reduced attribute subsets in attribute setB, the intersection of all reduced attribute subsets is defined as the core of B, denoted as CoreB, and the expression is as follows:
(1)CoreB=∩RedB.CoreB contains the core of all reduced attribute subsets of B, which is the common part they have and is an indispensable important and core attribute set in knowledge base B.LetQ and P be equivalent relation clusters on the domain of discourse, and the positive domain of P of Qis denoted as PosPQ, which is expressed as follows:
(2)PosPQ=∪X∈U/QPX.LetP and Q be equivalent relations on the universe of discourse, a∈P, if there is a relation:
(3)PosIndPIndQ=PosIndP−aIndQ.Then,a is the redundant attribute of Q in P; otherwise, a is the necessary attribute of Q in P. If any relation a in P is Q-necessary, then P is called Q-independent. If the above relation does not exist, then a is irreducible about Q.In the field of computer vision [27, 28], the problem of object detection and recognition can be regarded as a labeling problem. The pixels or image blocks in the image can be represented as random variables in the random field model, and when solving the problem of object classification, the set of labels can be represented as different categories.It is necessary to model the joint probability distribution functionPX,YI of the random variable set X,Y for a given image block set P. According to the Bayesian theorem, the posterior probability can be expressed as
(4)PX,YI=PI,XY⋅PX,YPI,where PI is the probability of observing data I, when I is given, PI is a constant, I=Ii represents the set of image blocks, and Ii represents the feature vector set of each image block.PI,XY is the conditional probability distribution function of the observation data I, which is called the likelihood function of assigning X,Y when the observation data I is given. We assume that the observation data I is independent under the given assignment X,Y. Therefore, the expression of the conditional probability distribution function is shown in
(5)PI,XY=∏i=1NPIi,XY.Given the configuration ofY, the configuration of random field X can be determined by the maximum a posteriori probability criterion, so as to maximize PX,YI. Therefore, the problem of target detection can be transformed into the problem of solving the bipartite graph in Figure 3.Figure 3
Bipartite graph problem.When solving the maximum posterior probabilityPX,YI, the amount of calculation can be reduced by reducing the number of target hypotheses in Hough space, thus reducing the size of Hough space and the amount of calculation.The specific steps of feature extraction with EPSE monitoring indicators as the target object are as follows:(1)
Collect the status monitoring index parameters of power equipment, and build the index parameter system(2)
Discretize condition attribute setC and decision attribute set D as required(3)
According to the created decision table, select the appropriate attribute reduction method for variable reduction(4)
Finally, we get simplified measurement parameters of equipment condition monitoringIn this paper, the monitoring index system of power facilities status collected and constructed is selected and optimized without affecting the evaluation effect of facilities status, and the redundant monitoring indexes are selected and removed without affecting the evaluation effect of facilities status.


#### 3.2.2. Intelligent Identification Implementation

It is of practical significance to study the fault diagnosis methods of power system equipment, find faults in time, and prepare for maintenance. Because EPSE defect signals are often accompanied by high uncertainty and complex correlation [1], it is necessary to develop effective identification methods to improve the accuracy of defect diagnosis. Feature extraction [7] and state recognition are the core of fault diagnosis research, which is related to the reliability and accuracy of fault diagnosis. Therefore, we try to extract various features from vibration signals, improve the state identification method of fault diagnosis, and find that the high-sensitivity identification method can identify more signals when identifying signal states.We use C_HMM (Coupled Hidden Markov Model) for defect diagnosis, comprehensively consider the accuracy and sensitivity of defect diagnosis, and propose a C_HMM (GA-C_HMM) defect diagnosis method based on GA (genetic algorithm). On the basis of injecting small signal jammer into a steady-state operating point by using the small disturbance suppression method, combined with the small disturbance identification method, the disturbance in the process of intelligent identification of the EPSE state is suppressed, and the intelligent identification of the EPSE state is realized [15].The distribution phase of EPSE status intelligent information isWKkn, and the number of sensors for EPSE status monitoring is N=n−m−1τ, where m is the number of layers for equipment status monitoring and τ is the discrete time serial number. The low-frequency and high-frequency components for EPSE status monitoring are calculated as follows:
(6)Xk=∑n=0N−1xnWNkn,(7)X′k=∑n=0N/2−1xnWNkn+WNkn/2∑n=0N−1xn+N2WNkn.The encoding method is selected as gray code encoding, assuming that the binary codeX=xm⋯x2x1, its corresponding gray code Y=ym⋯y2y1, then
(8)ym=xm,yi=xi+1⊕xi,i=m−1,m−2,⋯,1.The objective function is set as the function ofN,L; that is, the recognition accuracy accN,L of C_HMM is calculated according to the generated N,L, and then, the fitness is calculated according to the accuracy accN,L,L, that is,
(9)fitN,L=α⋅accN,L+1−αLmax−LLmax,where α is the proportion of recognition accuracy in fitness, α reflects the importance of recognition accuracy, and 1−α reflects the importance of recognition sensitivity; Lmax−L is the maximum value in the range of values.According to the extraction of passband cutoff frequency of power equipment state operation, the reliability parameter distribution of power equipment state monitoring is obtained:(10)X2r=∑n=0N/2−1gnWN/2rn.The hidden state numberN and the data amount L contained in each group of observation vectors are the parameters that C_HMM needs to determine here. In order to find the parameter N,L that can make C_HMM accurate and sensitive, we adopt the parameter optimization algorithm GA, and the specific optimization process is shown in Figure 4.Figure 4
Parameter optimization flow chart.On the basis of the above analysis, the intelligent identification of EPSE status is realized.


## 3.1. The Framework of EPSE State Awareness System Based on IoT

Real-time accurate identification of the equipment running state is the premise of intelligent remote/automatic control and the foundation of intelligent unmanned power system implementation. The existing EPSE monitoring and monitoring system maintains the visual representation level of sensor data and simple threshold analysis (exceeding the threshold of signal value and exceeding the limit). There are some problems in the EPSE system, such as missing defective samples, incomplete data collection, large noise interference, and various working conditions, which make the working condition diagnosis more difficult.IoT is another wave of technology after computer, Internet, and mobile Internet, with great potential. From the perspective of architecture, IoT can generally be divided into three layers (perception layer, network layer, and application layer) from bottom to top [16]. The perception layer is the core of the physical network, which not only refers to the physical device object itself but also includes its edge devices and sensors. It is the middle layer of IoT in the network layer, which is mainly responsible for information transmission, routing, and control. The supported transmission technologies include the public network, WIFI, Bluetooth, and infrared. The application layer, also known as the processing layer, is located at the top of the structure and is mainly responsible for information processing and human-computer interaction. For the data sent by the network layer, different types of information are calculated, processed, and analyzed in the application layer, such as big data calculation, data mining, and data analysis. This layer also uses data storage and middleware technology.Information is not transmitted unilaterally between the three layers of the Internet of things, but it can be exchanged, and the types of information are complex and varied. Despite the fact that IoT has a variety of application scenarios in fields such as smart factories, intelligent transportation, smart homes, medical care, and shopping, the basic structural framework of various fields and industries is sufficient for the cognitive layer, transport layer, and application. Each data parameter has a function in the health inspection of power equipment, and each data information reflects one aspect of the power equipment problem. Judge whether the power equipment is running, abnormal, and abnormal and hidden faults, based on the collected data information, and judge parameter comparison and visualization based on the characteristics to improve the online real-time detection quality. Furthermore, due to the mechanical principle of generator equipment, ultrasonic sensors can be used to monitor the air gap parameter values between the rotor and the stator, or speed sensors can be installed on the equipment to collect vibration data.Data must be transmitted after it has been collected effectively, but in practice, the data transmission process is easily hampered by the surrounding environment. Related signals can be compressed and then transmitted to improve transmission speed and real-time data transmission due to other specific transmission time and distance requirements. The rapid transmission of big data is now possible thanks to the advancement of wireless information transmission technology. The data transmission time is shortened, and the detection system’s reliability [24, 25] is improved by continuously optimizing related data transmission technologies.The lag of EPSE status identification and analysis technology limits the development of intelligent equipment management, predictive maintenance, and robot automation control technology. Accurate identification and prediction analysis of power system health must solve three problems.(1)
Accurate perception and knowledge expression of equipment operation status information(2)
Evaluation of the rationality of working conditions and real-time diagnosis of abnormal problems (clearly identify abnormal working conditions)(3)
Early fault diagnosis and degradation analysis of main components of equipment (identification of hidden premature damage)As shown in Figure1, the framework of the EPSE state identification and intelligent identification system of IoT mode can be divided into multisource information identification of power system, IoT edge calculation and online diagnosis, intelligent analysis of power level big data and group level, big data fusion analysis [26], and multidimensional knowledge sharing.Figure 1
IoT-based EPSE state awareness system.By using the massive multisource sensor data accumulated in the long-term operation of the equipment and the artificial intelligence algorithm model, the correlation analysis between the change of equipment operation state and the data is realized. The application model of deep analysis and mining of big data in power systems is established, and the long-term accumulated laws and tacit knowledge of power system state evolution are mined to realize tracking, prediction, and early warning of power system operation state evolution.The working flow chart of the EPSE state intelligent identification system is shown in Figure2.Figure 2
Work flow chart of EPSE state intelligent identification system.The EPSE status intelligent identification system mainly realizes the following functions: intelligent identification of equipment status. It is mainly used for intelligent image recognition of equipment status such as switch and breaker in substation and intelligent detection of equipment health, which mainly realizes intelligent patrol of daily working status of equipment. System management functions mainly to achieve past work query, report query, video display, alarm, statistical analysis of historical alarm records, etc.The built-in dynamic test analyzer collects data from field devices using the analyzer and the detection path information that was downloaded. Data can be analyzed in the field using the analyzer’s signal analysis technology or uploaded to a server for analysis. Delete the analyzer’s data from the network’s corresponding measuring point directory at the same time. After being authorized and certified, equipment managers or experts in the remote monitoring center can download online uploaded equipment data from the server, analyze the running state of the equipment at the client using various signal analysis methods provided by system signal analysis, and then diagnose faults and make appropriate maintenance decisions.


## 3.2. Intelligent Identification of EPSE State

### 3.2.1. Feature Extraction of Power Equipment Condition Monitoring Indicators

It can be seen that through the introduction of power equipment status monitoring data, the importance of monitoring index attributes to determine the status of power equipment in the equipment status monitoring data is different. Some of these attributes are absolutely essential, and the absence of these attributes may lead to errors in determining the health status of the whole equipment.Continuous discretization of attributes is a process of dividing the range and region of state monitoring data in order to obtain a simplified decision-making system based on the premise of minimizing information loss in decision-making systems. When the attribute judgment result is accurate, we must do our best to ensure the accurate discretization of state monitoring data during the discretization process. When rough set theory is applied to data from equipment condition monitoring, it reduces not only the number of rules but also the number of attributes. The key to studying the influence of decision-making and reducing the comparison between attributes and original attributes is to figure out how to get the simplest set of attributes.If there are multiple reduced attribute subsets in attribute setB, the intersection of all reduced attribute subsets is defined as the core of B, denoted as CoreB, and the expression is as follows:
(1)CoreB=∩RedB.CoreB contains the core of all reduced attribute subsets of B, which is the common part they have and is an indispensable important and core attribute set in knowledge base B.LetQ and P be equivalent relation clusters on the domain of discourse, and the positive domain of P of Qis denoted as PosPQ, which is expressed as follows:
(2)PosPQ=∪X∈U/QPX.LetP and Q be equivalent relations on the universe of discourse, a∈P, if there is a relation:
(3)PosIndPIndQ=PosIndP−aIndQ.Then,a is the redundant attribute of Q in P; otherwise, a is the necessary attribute of Q in P. If any relation a in P is Q-necessary, then P is called Q-independent. If the above relation does not exist, then a is irreducible about Q.In the field of computer vision [27, 28], the problem of object detection and recognition can be regarded as a labeling problem. The pixels or image blocks in the image can be represented as random variables in the random field model, and when solving the problem of object classification, the set of labels can be represented as different categories.It is necessary to model the joint probability distribution functionPX,YI of the random variable set X,Y for a given image block set P. According to the Bayesian theorem, the posterior probability can be expressed as
(4)PX,YI=PI,XY⋅PX,YPI,where PI is the probability of observing data I, when I is given, PI is a constant, I=Ii represents the set of image blocks, and Ii represents the feature vector set of each image block.PI,XY is the conditional probability distribution function of the observation data I, which is called the likelihood function of assigning X,Y when the observation data I is given. We assume that the observation data I is independent under the given assignment X,Y. Therefore, the expression of the conditional probability distribution function is shown in
(5)PI,XY=∏i=1NPIi,XY.Given the configuration ofY, the configuration of random field X can be determined by the maximum a posteriori probability criterion, so as to maximize PX,YI. Therefore, the problem of target detection can be transformed into the problem of solving the bipartite graph in Figure 3.Figure 3
Bipartite graph problem.When solving the maximum posterior probabilityPX,YI, the amount of calculation can be reduced by reducing the number of target hypotheses in Hough space, thus reducing the size of Hough space and the amount of calculation.The specific steps of feature extraction with EPSE monitoring indicators as the target object are as follows:(1)
Collect the status monitoring index parameters of power equipment, and build the index parameter system(2)
Discretize condition attribute setC and decision attribute set D as required(3)
According to the created decision table, select the appropriate attribute reduction method for variable reduction(4)
Finally, we get simplified measurement parameters of equipment condition monitoringIn this paper, the monitoring index system of power facilities status collected and constructed is selected and optimized without affecting the evaluation effect of facilities status, and the redundant monitoring indexes are selected and removed without affecting the evaluation effect of facilities status.


### 3.2.2. Intelligent Identification Implementation

It is of practical significance to study the fault diagnosis methods of power system equipment, find faults in time, and prepare for maintenance. Because EPSE defect signals are often accompanied by high uncertainty and complex correlation [1], it is necessary to develop effective identification methods to improve the accuracy of defect diagnosis. Feature extraction [7] and state recognition are the core of fault diagnosis research, which is related to the reliability and accuracy of fault diagnosis. Therefore, we try to extract various features from vibration signals, improve the state identification method of fault diagnosis, and find that the high-sensitivity identification method can identify more signals when identifying signal states.We use C_HMM (Coupled Hidden Markov Model) for defect diagnosis, comprehensively consider the accuracy and sensitivity of defect diagnosis, and propose a C_HMM (GA-C_HMM) defect diagnosis method based on GA (genetic algorithm). On the basis of injecting small signal jammer into a steady-state operating point by using the small disturbance suppression method, combined with the small disturbance identification method, the disturbance in the process of intelligent identification of the EPSE state is suppressed, and the intelligent identification of the EPSE state is realized [15].The distribution phase of EPSE status intelligent information isWKkn, and the number of sensors for EPSE status monitoring is N=n−m−1τ, where m is the number of layers for equipment status monitoring and τ is the discrete time serial number. The low-frequency and high-frequency components for EPSE status monitoring are calculated as follows:
(6)Xk=∑n=0N−1xnWNkn,(7)X′k=∑n=0N/2−1xnWNkn+WNkn/2∑n=0N−1xn+N2WNkn.The encoding method is selected as gray code encoding, assuming that the binary codeX=xm⋯x2x1, its corresponding gray code Y=ym⋯y2y1, then
(8)ym=xm,yi=xi+1⊕xi,i=m−1,m−2,⋯,1.The objective function is set as the function ofN,L; that is, the recognition accuracy accN,L of C_HMM is calculated according to the generated N,L, and then, the fitness is calculated according to the accuracy accN,L,L, that is,
(9)fitN,L=α⋅accN,L+1−αLmax−LLmax,where α is the proportion of recognition accuracy in fitness, α reflects the importance of recognition accuracy, and 1−α reflects the importance of recognition sensitivity; Lmax−L is the maximum value in the range of values.According to the extraction of passband cutoff frequency of power equipment state operation, the reliability parameter distribution of power equipment state monitoring is obtained:(10)X2r=∑n=0N/2−1gnWN/2rn.The hidden state numberN and the data amount L contained in each group of observation vectors are the parameters that C_HMM needs to determine here. In order to find the parameter N,L that can make C_HMM accurate and sensitive, we adopt the parameter optimization algorithm GA, and the specific optimization process is shown in Figure 4.Figure 4
Parameter optimization flow chart.On the basis of the above analysis, the intelligent identification of EPSE status is realized.


## 3.2.1. Feature Extraction of Power Equipment Condition Monitoring Indicators

It can be seen that through the introduction of power equipment status monitoring data, the importance of monitoring index attributes to determine the status of power equipment in the equipment status monitoring data is different. Some of these attributes are absolutely essential, and the absence of these attributes may lead to errors in determining the health status of the whole equipment.Continuous discretization of attributes is a process of dividing the range and region of state monitoring data in order to obtain a simplified decision-making system based on the premise of minimizing information loss in decision-making systems. When the attribute judgment result is accurate, we must do our best to ensure the accurate discretization of state monitoring data during the discretization process. When rough set theory is applied to data from equipment condition monitoring, it reduces not only the number of rules but also the number of attributes. The key to studying the influence of decision-making and reducing the comparison between attributes and original attributes is to figure out how to get the simplest set of attributes.If there are multiple reduced attribute subsets in attribute setB, the intersection of all reduced attribute subsets is defined as the core of B, denoted as CoreB, and the expression is as follows:
(1)CoreB=∩RedB.CoreB contains the core of all reduced attribute subsets of B, which is the common part they have and is an indispensable important and core attribute set in knowledge base B.LetQ and P be equivalent relation clusters on the domain of discourse, and the positive domain of P of Qis denoted as PosPQ, which is expressed as follows:
(2)PosPQ=∪X∈U/QPX.LetP and Q be equivalent relations on the universe of discourse, a∈P, if there is a relation:
(3)PosIndPIndQ=PosIndP−aIndQ.Then,a is the redundant attribute of Q in P; otherwise, a is the necessary attribute of Q in P. If any relation a in P is Q-necessary, then P is called Q-independent. If the above relation does not exist, then a is irreducible about Q.In the field of computer vision [27, 28], the problem of object detection and recognition can be regarded as a labeling problem. The pixels or image blocks in the image can be represented as random variables in the random field model, and when solving the problem of object classification, the set of labels can be represented as different categories.It is necessary to model the joint probability distribution functionPX,YI of the random variable set X,Y for a given image block set P. According to the Bayesian theorem, the posterior probability can be expressed as
(4)PX,YI=PI,XY⋅PX,YPI,where PI is the probability of observing data I, when I is given, PI is a constant, I=Ii represents the set of image blocks, and Ii represents the feature vector set of each image block.PI,XY is the conditional probability distribution function of the observation data I, which is called the likelihood function of assigning X,Y when the observation data I is given. We assume that the observation data I is independent under the given assignment X,Y. Therefore, the expression of the conditional probability distribution function is shown in
(5)PI,XY=∏i=1NPIi,XY.Given the configuration ofY, the configuration of random field X can be determined by the maximum a posteriori probability criterion, so as to maximize PX,YI. Therefore, the problem of target detection can be transformed into the problem of solving the bipartite graph in Figure 3.Figure 3
Bipartite graph problem.When solving the maximum posterior probabilityPX,YI, the amount of calculation can be reduced by reducing the number of target hypotheses in Hough space, thus reducing the size of Hough space and the amount of calculation.The specific steps of feature extraction with EPSE monitoring indicators as the target object are as follows:(1)
Collect the status monitoring index parameters of power equipment, and build the index parameter system(2)
Discretize condition attribute setC and decision attribute set D as required(3)
According to the created decision table, select the appropriate attribute reduction method for variable reduction(4)
Finally, we get simplified measurement parameters of equipment condition monitoringIn this paper, the monitoring index system of power facilities status collected and constructed is selected and optimized without affecting the evaluation effect of facilities status, and the redundant monitoring indexes are selected and removed without affecting the evaluation effect of facilities status.


## 3.2.2. Intelligent Identification Implementation

It is of practical significance to study the fault diagnosis methods of power system equipment, find faults in time, and prepare for maintenance. Because EPSE defect signals are often accompanied by high uncertainty and complex correlation [1], it is necessary to develop effective identification methods to improve the accuracy of defect diagnosis. Feature extraction [7] and state recognition are the core of fault diagnosis research, which is related to the reliability and accuracy of fault diagnosis. Therefore, we try to extract various features from vibration signals, improve the state identification method of fault diagnosis, and find that the high-sensitivity identification method can identify more signals when identifying signal states.We use C_HMM (Coupled Hidden Markov Model) for defect diagnosis, comprehensively consider the accuracy and sensitivity of defect diagnosis, and propose a C_HMM (GA-C_HMM) defect diagnosis method based on GA (genetic algorithm). On the basis of injecting small signal jammer into a steady-state operating point by using the small disturbance suppression method, combined with the small disturbance identification method, the disturbance in the process of intelligent identification of the EPSE state is suppressed, and the intelligent identification of the EPSE state is realized [15].The distribution phase of EPSE status intelligent information isWKkn, and the number of sensors for EPSE status monitoring is N=n−m−1τ, where m is the number of layers for equipment status monitoring and τ is the discrete time serial number. The low-frequency and high-frequency components for EPSE status monitoring are calculated as follows:
(6)Xk=∑n=0N−1xnWNkn,(7)X′k=∑n=0N/2−1xnWNkn+WNkn/2∑n=0N−1xn+N2WNkn.The encoding method is selected as gray code encoding, assuming that the binary codeX=xm⋯x2x1, its corresponding gray code Y=ym⋯y2y1, then
(8)ym=xm,yi=xi+1⊕xi,i=m−1,m−2,⋯,1.The objective function is set as the function ofN,L; that is, the recognition accuracy accN,L of C_HMM is calculated according to the generated N,L, and then, the fitness is calculated according to the accuracy accN,L,L, that is,
(9)fitN,L=α⋅accN,L+1−αLmax−LLmax,where α is the proportion of recognition accuracy in fitness, α reflects the importance of recognition accuracy, and 1−α reflects the importance of recognition sensitivity; Lmax−L is the maximum value in the range of values.According to the extraction of passband cutoff frequency of power equipment state operation, the reliability parameter distribution of power equipment state monitoring is obtained:(10)X2r=∑n=0N/2−1gnWN/2rn.The hidden state numberN and the data amount L contained in each group of observation vectors are the parameters that C_HMM needs to determine here. In order to find the parameter N,L that can make C_HMM accurate and sensitive, we adopt the parameter optimization algorithm GA, and the specific optimization process is shown in Figure 4.Figure 4
Parameter optimization flow chart.On the basis of the above analysis, the intelligent identification of EPSE status is realized.


## 4. Experiment and Results

Because C_HMM can effectively represent the characteristics of vibration changes and obtain a system with higher sensitivity than frame feature extraction, the learning rate of this recognition system in C_HMM is slightly slower than that in C_HMM after feature extraction, but it can save the time of feature extraction.The initial range is set toN∈2,9, L∈5,120 and the parameter α=0.87. According to the training data of GA, the number of hidden states N and the amount of data L in each group are optimized. The hidden state is N=6, and the data amount is L=80. The specific fitness change is shown in Figure 5.Figure 5
GA fitness curve.In the process of optimizing the parameters of the GA algorithm, there will be many iterative calculations. In this experiment, there are 166 nonrepetitive operations, accounting for 2410 operations, and the traversal optimization calculation is 166 times, which is 13.27 times that of this optimization method, which can effectively reduce the operations.In order to verify the correctness of the conclusion and explore the influence ofL,N on the recognition effect of C_HMM in the observation vector, we use N∈2,9, L∈5,120 for C_HMM recognition test results as shown in Figure 6.Figure 6
C_HMM test results.With the increase ofN, the recognition accuracy decreases, and with the increase of L, the peak value of recognition accuracy gradually moves in the direction of decreasing N. N∈2,9 shows that with the increase of data L of each group, the recognition accuracy is stable at 90%, and then, it reduces the accuracy. However, when N=2 is used, the accuracy fluctuates greatly, and the recognition accuracy is 96.3%, which shows that the optimization result of GA parameters is not the highest accuracy, but the accuracy and sensitivity are considered.Find out the relationship between equipment status and index parameters through association rules, analyze the support and confidence of index parameters to equipment status, and determine the relationship between indicators and corresponding equipment status. A small index excludes that correlation, so we need to study and extract the index parameters. Taking insulation dehumidification as an example, when insulation dehumidification defect occurs in the transformer, there will be six phenomena: volume resistivity decreases, polarization index decreases, insulation resistance absorption decreases, and insulation resistance increases. The water content and impact breakdown voltage of oil decrease, while theH2 content increases. The frequency of insulation failure due to damp is 105 times. When failure occurs, the failure statistics are shown in Table 1.Table 1
Statistical data of insulation damp fault.
Index parameterExceeding standard timesTotal number of indexes exceeding the standardMedium oil water content101188Oil breakdown voltage96152Absorptance8799Polarization index9391Volume resistivity7173H2 content90118In order to verify the reliability of the proposed method, the application performance of the proposed method in EPSE state intelligent identification is verified by simulation experiments. The simulation is carried out on the Matlab platform. The simulation operating system is Windows 7, with 16 GB of system memory and 3.6 GHz of CPU frequency. The data obtained are analyzed by SPSS 13.0 software. In this test, the working state of the transformer in a 220 kV substation at a specific location is identified. Figure7 shows the comparison results of recognition efficiency between this method and existing methods.Figure 7
Comparison of recognition efficiency.As can be seen from Figure7, the recognition efficiency of this method is always higher than that of traditional methods with the increasing number of experiments, which fully shows that this method can effectively improve the recognition efficiency of EPSE.After a certain period of operation, the recognition accuracy is shown in Figure8.Figure 8
Recognition accuracy.Figure8 shows that when the recognition time is 2 hours, the existing recognition method’s recognition accuracy is 38.7%, while the present recognition method’s recognition accuracy is 73.6 percent. When the recognition time is 6 hours, the existing recognition method’s recognition accuracy is 73.6 percent, while the recognition accuracy of the present recognition method is 89.6 percent, and the recognition accuracy of the present recognition method is 89.6 percent. This paper’s recognition method is 96.8%, the recognition time is 12 hours, the existing recognition method’s recognition accuracy is 69.7%, and this method’s recognition accuracy is 98.3 percent. Based on the above data, it can be concluded that the intelligent EPSE state recognition method based on the GA-C_HMM algorithm studied in this paper outperforms existing methods in terms of recognition accuracy and efficiency. It can effectively address the shortcomings of existing identification methods while also meeting EPSE’s detection requirements.In order to further verify the feasibility of the proposed method, the identification of the proposed method, the multiple measures-based online status assessment method of power grid equipment (multiple measures), and the comprehensive health index-based power equipment status assessment method (comprehensive health index) was analyzed experimentally. For time consumption, the simulation results are shown in Figure9.Figure 9
Identification of time consumption comparison.From the analysis of Figure9, it can be seen that the time consumption of EPSE status identification using three methods is different. Among them, the average recognition time of the proposed method is lower than that of the other two methods, and the change is relatively stable, while the recognition time of the other two methods fluctuates constantly, thus verifying the effectiveness of the proposed method.Take the main transformer’s power cut-off operation as an example. After the equipment health intelligent identification system is started, the operator can automatically identify the system equipment status remotely without checking and confirming the equipment status. It can complete the shutdown operation of equipment, save the shutdown operation time of substation equipment, and improve the efficiency of shutdown operation. The specific switching uptime statistics are given in Table2.Table 2
Time consumption statistics of switching operation during main transformer blackout.
Operation itemTime consumption before commissioning (min)Time consumption after commissioning (min)Equipment changes from operation to hot standby73Equipment changes from hot standby to cold standby41Equipment from cold standby to maintenance105Equipment from cold standby to maintenance84When the system is running, the operator does not need to check the state of the switch and knife switch on the equipment site, so as to prevent the damage caused by short-circuit explosion of the equipment and ensure personal safety. The system also realizes the functions of regular inspection and customized inspection, further saving manpower, solving the problem of low inspection quality caused by manual inspection, and improving the operation and maintenance quality of substation equipment.


## 5. Conclusions

The emergence and development of embedded platform information technology have had a significant impact on modern industrial production, and an increasing number of production activities have been or will be based on it. Analyze the EPSE state knowledge sharing and migration model in an IoT system, improve the value of power big data utilization, and boost the level of transparency in power safety production management. The GA-C_HMM algorithm is used to propose an intelligent method for identifying the EPSE state. The optimal allocation of Hough space is solved using the maximum posterior probability criterion, the Hough transform is transformed into a bipartite graph problem, and the problem is finally solved using a greedy algorithm, which avoids the great suppression process. The results show that C_HMM can be used to model the original vibration data while maintaining a good recognition effect, omitting feature extraction steps, and allowing C_HMM to be used for more sensitive diagnosis. The existing recognition method’s recognition accuracy is 69.7% after 12 hours, while the method proposed in this paper’s recognition accuracy is 98.3%. The EPSE state intelligent recognition method based on the GA-C_HMM algorithm has a higher recognition accuracy and a stronger recognition ability than conventional methods.



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*Source: 3076432-2022-05-19.xml*