Iot-based power detection equipment management and control system

Jintao Chen; Jianfeng Jiang; Binruo Zhu

doi:10.1515/jisys-2022-0127

Article Open Access

Iot-based power detection equipment management and control system

Jintao Chen , Jianfeng Jiang and Binruo Zhu

Published/Copyright: December 15, 2022

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From the journal Journal of Intelligent Systems Volume 31 Issue 1

Abstract

The development and application scope of the Internet of Things is also becoming more and more extensive. Especially in the application of power testing improved systems, great progress has been made. This article aims to study how to analyze the system detection equipment based on the Internet of Things. This article describes the basic theoretical knowledge of the Internet of Things and power detection improved systems. A clustering analysis algorithm and a support vector machine algorithm based on the Internet of Things are proposed. In the experiment of this article, the scoring items of the expert’s traditional detection system include complex technology, inconvenient use, and incomplete intelligence. Among them, the highest score for complex technology is 8.6 points, the lowest score is 7 points; the highest score for inconvenience is 8.6 points, and the lowest is 8.3 points. It can be seen that related experts believe that the traditional power detection improved system is not only very complicated in technology, very inconvenient to use but also incompletely intelligent. Therefore, it is very necessary to study the system detection equipment based on the Internet of Things.

Keywords: Internet of Things; power detection improved system; cluster analysis algorithm; support vector machine

1 Introduction

The risk early warning system is based on the characteristics of the research object, by collecting relevant data and information, monitoring the changing trend of risk factors, and evaluating the degree of deviation of various risk states from the early warning line, sending early warning signals to the decision-making level and taking pre-control in advance: countermeasure system. Therefore, in order to build an early warning system, we must first construct an evaluation index system, and analyze and process the index categories; secondly, according to the early warning model, comprehensively evaluate the evaluation index system; finally, according to the evaluation results, set the early warning interval and take corresponding countermeasures.

In recent years, the construction of a strong smart grid has become an important symbol of national scientific and technological development and improvement of international competitiveness. The intelligentization of substation equipment is an important foundation of the smart grid, and the demand for comprehensive online monitoring and fault diagnosis technology of the smart grid is increasing. The latest online surveillance technology has attracted much attention. Research on power grid detection has become a hot topic discussed by scholars.

With the development of the twenty-first century, a new round of energy revolution centered on electricity production is developing faster and faster, and the power grid is facing more and more challenges in the development process. In order to promote the continuous development of the power industry, grid technology needs to carry out corresponding technological transformation and development according to the development of society, and further promote the innovation of grid technology, and optimize the allocation of energy. As more and more types of access to the power grid are required, the scale of the power grid needs to be gradually expanded to meet current social needs. Different types of energy require distributed access.

The innovations of this article are as follows: (1) The theoretical knowledge of the Internet of Things and power detection improved system is introduced, and the cluster analysis algorithm is used to analyze how the Internet of Things plays a role in the research of the power detection improved system; (2) analyze traditional detection algorithms and detection systems based on clustering algorithms. It is also used to experiment and analyze the defects and adverse effects of the traditional power testing improved system. Through experiments, the Internet of Things can facilitate the development of the modern power industry.

2 Related work

With the development of the electric power industry, the research of new power detection improved systems has become more and more important. In order to prevent problems in the detection of power equipment systems, Iida proposed the detection law of the horizontal range curve model. He considered the advanced detection of the target and derives the generalized formula of the lateral distance curve and the sweep width, and used this algorithm to detect the power-improved system. The scholar’s analysis did not simply describe the formula and how the formula is applied to the detection equipment system [1]. Shaik and Mahela found that renewable energy resources can be applied to the power system, but this poses a threat to the stability of the power system and the quality of power. Therefore, he proposed a method based on the Stockwell transformation, which can simultaneously evaluate the power quality of renewable energy and detect the security of the grid. But he did not conduct corresponding experiments on the method and did not verify it in practice, so it can be known that the veracity of this conclusion is not high enough [2]. Parvez et al. found that power quality assessment is an important performance measurement in smart grids. And utility companies are also very interested in power quality monitoring. In order to solve this problem, he suggested using a type of support vector machine (SVM) to solve the problem. He proposed a semi-supervised machine learning algorithm that can automatically detect the presence of any type of interference in real time. However, the scholar did not specify the model, and the concept was too abstract, so that the reliability of the model could not be verified [3]. In order to solve the short-circuit fault in the power grid, Jana S proposed a method based on waveform analysis, which can detect and classify the problems in the power grid. In order to reduce the computational burden when processing large amounts of waveform data, he used a novel area detection method. And he used the classifier based on an artificial neural network to find the fault. However, the scholar’s experiment has no experimental objects and no data support. It should be compared in the experiment to draw better conclusions [4]. Razzaque et al. found that the Internet of Things can be connected through appropriate information and communication technologies to realize a series of applications and services. The Internet of Things can make the development of diverse applications and services a very challenging task. He mainly discussed the wireless sensor network, a key component of the Internet of Things. But it did not explain what the content of the wireless sensor network is, which makes the concept of the Internet of Things difficult to understand [5]. Lin et al. found that edge computing can be applied to the Internet of Things to enable computing service equipment deployed at the edge of the network. The combination of the two is to improve user experience and service efficiency. Edge computing can provide a higher quality of service for IoT applications. Therefore, the Internet of Things based on edge computing can promote the development of the Internet of Things in the future. However, the scholar did not mention how edge computing is applied to the Internet of Things, and what is the relationship between edge computing and the Internet of Things [6]. Perera et al. discovered that the Internet of Things is a dynamic global information network composed of objects connected to the Internet. For example, radio frequency identification, sensors, actuators, etc. He found that the Internet of Things can be applied to many areas. In order to determine the technologies, functions, and applications used, he investigated more than 100 application cases of the Internet of Things on the market. He found that the Internet of Things can be applied to smart homes, smart wearables, smart supermarkets, and so on. Although the scholar investigated more than 100 application cases of the Internet of Things, he did not classify the application cases. The advantages of these events should be described again [7]. Singh et al. found that the “Internet of Things” can realize the aspirations of universal computing, but it faces the difficulty of technical application. Cloud computing can support a wide range of connections and data sharing, which is a good solution. Cloud computing also considers the security, privacy, and personal safety risks that occur outside of these subsystems and solves these problems and risks well. Although the scholar proposed cloud computing as a method to solve problems in the Internet of Things, it did not mention how to specifically solve the problem [8].

3 The basic concepts of the Internet of Things and power testing improved systems

The Internet of Things technology originated in the media field and is the third revolution of the information technology industry. The Internet of Things refers to the connection of any object with the network through the information sensing equipment and according to the agreed protocol, and the objects exchange and communicate information through the information dissemination medium to realize intelligent identification, positioning, tracking, supervision and other functions. Simply put, the Internet of Things is the information transfer and control between things and people and things. These are the following key technologies in IoT applications:

Sensor technology, which is also a key technology in computer applications: Most computers deal with digital signals. Since the advent of computers, sensors have been required to convert analog signals into digital signals that computers can process.
Embedded system technology: It is a complex technology that integrates computer software and hardware, sensor technology, integrated circuit technology, and electronic application technology. After decades of evolution, intelligent terminal products featuring embedded systems can be seen everywhere;
Intelligent technology: It is a variety of methods and means used to utilize knowledge in order to effectively achieve a certain intended purpose. By implanting an intelligent system in an object, the object can have a certain intelligence, and can actively or passively communicate with the user, which is also one of the key technologies of the Internet of Things.

With the development of the Internet of Things, intelligent technology has been applied in transportation, home life, medical care, etc. It has also been applied in industrial production, social environment, and service industries [9]. Its application is shown in Figure 1.

Figure 1

The application of the Internet of Things in the power grid.

As shown in Figure 1: The Internet of Things (IoT) refers to the real-time collection of any information that needs to be monitored, connected, and interacted with through various devices and technologies such as information sensors, radio frequency identification technology, global positioning systems, infrared sensors, and laser scanners. Object or process. The connotation of the Internet of Things architecture has undergone major changes since its introduction. From the very beginning, the Internet of Things is the sensor network, which means that the Internet of Things is the network transmission from machine to machine, and then, the communication that connects objects is the Internet of Things. Every change represents the leapfrog development of the Internet of Things [10].

This article investigates the development trends of IoT in 2015–2018, as shown in Table 1.

Table 1

The development trend of the Internet of Things in the power industry from 2015 to 2018

Years	Application rate (%)	Efficiency (%)
2015	78	87
2016	75	85
2017	72	88
2018	76	89

As shown in Table 1, The efficiency rate of the development trend of the Internet of Things in the power industry in 2015 was 87%, and the efficiency rate of the development trend of the Internet of Things in the power industry in 2018 was 89%. With the rapid development of the power industry and power supply technology, online monitoring technology for substation equipment has become more and more important. The traditional maintenance method is shown in Figure 2:

Figure 2

Traditional maintenance method.

As shown in picture 2, The Internet of Things connects various power sources and electrical appliances as a whole and is widely used in the field of smart grids. This is the main trend and direction of power system technology development today [11].

The overall design of an online monitoring system for substation equipment based on the Internet of Things includes four levels: process level, bay level, station control level, and provincial network level [12]. The system structure is flexible and simple, and it can form a system independently, integrate with the grid automation system, or integrate with the remote monitoring system. The block diagram of the system is shown in Figure 3.

Figure 3

Block diagram of the substation system.

As shown in Figure 3, as the basis for maintaining state, the wireless monitoring of power transmission and conversion equipment should be based on non-power use information technology, sensor technology, computer technology, and other related technologies to analyze the physical characteristics of electrical equipment [13].

Finally, information processing and related data and parameters are analyzed and collected. Through this trend, the wireless assessment status is analyzed.

As shown in Figure 4: The era of the Internet of Things is coming soon, and the whole world can pass the related technologies of the Internet of Things. By actively exchanging information on the Internet of Things, people can create a new dimension of communication on the basis of the Internet of Things. And based on this dimension, the equivalent exchange of information can be obtained at any time, place, and things [14].

Figure 4

Wireless detection and evaluation status.

The wireless monitoring network of power transmission and transformation equipment is affected by the complexity of the environment, the delay and reliability of the network, the analysis of data packet errors, and resource limitations. On this basis, the optimization of the wireless monitoring network of smart substation equipment based on the Internet of Things is proposed [15].The development of advanced sensing technology and wireless monitoring technology has promoted the development of power transmission and transformation equipment.

4 SVM algorithm and cluster analysis algorithm based on the Internet of Things

4.1 SVM algorithm based on Internet of Things

A SVM is a binary classification model that maps the feature vector of an instance to some points in space. The purpose of the SVM is to draw a line to “best” distinguish the two types of points. SVM is one of the most widely used classifiers in the field of computer vision. It looks for the segmentation hyperplane located in the “right middle” of the two types of training samples to make the positive and negative samples have a considerable separation distance from the hyperplane. The segmented hyperplane obtained in this way has the best tolerance to local noise [16].

For linear SVM classifiers, the steps in the prediction phase mainly involve the calculation of the inner product, as shown in formula equation (1):

(1) f ( a ) = ≺ w , a ≻ .

An algorithm that uses the weighted sum of multiple binarized vectors f(a) to approximate a vector. After such binarized approximation, the inner product ≺ w , a ≻ of the vector can be quickly calculated using simple operations. The binary image occupies a very important position. The binarization of the image greatly reduces the amount of data in the image, so that the outline of the target can be highlighted.

After the binarization approximation algorithm of w in formula (1), an approximate expression can be obtained, as in formula (2):

(2) w ≈ ∑ j = 1 N W β i a j .

After obtaining the binarized vector, it can be expressed as the sum of a binary vector and its complement a _j, as in formula (3):

(3) a j = a j + − a 1 .

In the target detection task, two tasks need to be completed, one is to judge whether the candidate frame is the target or the background, and the other is to return the accurate position of the target according to the candidate frame and features [17]. The loss function is used to evaluate the degree to which the predicted value of the model is different from the true value. The better the loss function, the better the performance of the model. When these two tasks are completed at the same time, the loss functions of the two tasks can be combined. Such as formula (4):

(4) L ( P i , t i ) = 1 N cls ∑ i = 1 L cls ( P i , P i ∗ ) .

Among them, i represents the subscript of the candidate frame in the batch gradient descent process, P _i represents the probability of the ith candidate frame being identified as the target, and P i ∗ is the category label.

L _cls represents the classification loss function, which is a log loss function for two categories of target and non-target. Such as formula (5):

(5) L cls ( P i , P i ∗ ) = − log P i P i ∗ .

The classification loss function used to classify the target and background and the classification loss function used to regress the target position are both fully connected layers. The number of parameters of the fully connected layer is very large [18], as shown in Figure 5:

Figure 5

SVD decomposition diagram of the fully connected layer.

As shown in Figure 5, SVD decomposition will be carried out, and the first t largest eigenvalues will be taken to approximate the original w matrix. Such as formula (6):

(6) W = U ∑ V T ≈ U ∑ t V T .

At this time, the forward propagation of the network becomes UΣV ^T, then the computational complexity is changed from u × v to u × t + t × v . If t is much smaller than min(u, v), then t(u + v) is much smaller than u × v [19]. This can significantly accelerate the forward propagation speed of the fully connected layer [20].

Activation function f(a) is a very important part of the deep learning system. It adds non-linear changes to the whole system, which makes the deep learning model 1 1 + e − a have more powerful expressive ability [21]. Such as formula (7):

(7) f ( a ) = 1 1 + e − a .

4.2 Clustering analysis algorithm based on the Internet of Things

The degree of informatization in more and more fields has continued to increase. How to effectively mine valuable information from these data, extract common characteristics, and provide an accurate theoretical basis for power system analysis, decision-making, and control. In this way, ensuring the safe, reliable, and economical operation of the power grid has become a key issue in today’s power monitoring.

Cluster analysis aims to divide data sets, patterns, or objects into meaningful or useful clusters. Considering that the short-distance measurement is Euclidean distance data, this article uses the sum of squared errors as the objective function to measure the quality of clustering. It defines the error of each data point as the Euclidean distance to the nearest centroid and calculates it. Euclidean distance generally refers to the Euclidean metric. In mathematics, the Euclidean distance or Euclidean metric is the “ordinary” (i.e., straight line) distance between two points in Euclidean space.

The smaller the error sum of squares, the more ideal the clustering result. Such as formula (8):

(8) SSE = ∑ i = 1 k ∑ a ∈ c i dist ( c i , a ) ,

c _i is the ith cluster, and c _i is the centroid of the cluster. The centroid of the ith cluster is as in formula (9):

(9) c i = 1 m ∑ a ∈ c i a ,

where m _i is the number of objects in the ith cluster, m is the number of objects in the data set, and a is the number of clusters. The objective function is the desired form of the objective expressed by the design variables, so the objective function is the function of the design variables, which is a scalar.

The sum of squared distances between each data point a _i and its nearest cluster center c _r is minimized, the centroid of each cluster recalculated, and the deviation calculated as formula (10):

(10) D = ∑ i = 1 n [ min r = 1 , … , k d ( a i , c r ) 2 ] .

In the clustering process, an objective function is usually selected as the evaluation function of the clustering effect, and the value of this function determines the similarity between objects in the data set.

Fuzzy clustering analysis methods can be roughly divided into two types: one is the fuzzy clustering method based on fuzzy relationships. Also known as systematic cluster analysis. The other is called non-systematic clustering method. It first roughly divides the samples and then classifies them according to their optimal principles. After many iterations, until the classification is reasonable, this method is also called the step-by-step clustering method. Fuzzy clustering algorithms are widely used in the field of image segmentation, mainly because the membership of image pixels requires the use of fuzzy theory. Such as formula (11):

(11) min ∑ m = 1 k ∑ n = 1 k u m n δ d 2 ( c m , c n ) .

Among them, δ is the fuzzification index, and u m n δ is the membership degree of the nth sample to the mth class. The determination of the number of clusters needs to traverse the possible range of values.

The basic idea of the hierarchical clustering method is to calculate the similarity between nodes by a certain similarity measure, sort them from high to low by similarity, and gradually reconnect each node. The advantage of this method is that the division can be stopped at any time, The aggregation rules in the hierarchical clustering algorithm are divided into: single connection aggregation rules-that is, the distance between classes is equal to the minimum distance between two types of objects; fully connected aggregation rule-that is, the distance between classes is equal to the maximum distance between two types of objects; average inter-class connection aggregation rule-that is, the inter-class distance is the average distance between two types of objects. Arbitrarily select five sets of two-dimensional coordinates as the data set, as shown in Table 2.

Table 2

Five sets of two-dimensional coordinates of the hierarchical clustering algorithm

Sample point	X coordinate	Y coordinate
P1	0.3003	0.4305
P2	0.3132	0.3764
P3	0.3564	0.3245
P4	0.2765	0.1653
P5	0.0686	0.4234

As shown in Table 2, according to the proximity matrix, the distance between P3 and P5 is the smallest. To calculate the distance between two combined data points and one combined data point, the three aggregation rules mentioned earlier can be used: full connection aggregation rules, single connection aggregation rules, and this article uses single connection aggregation rules. In single-join aggregation clustering, the distance between two clusters is defined as the shortest distance between two objects in each cluster, and this calculation rule makes clustering easy to implement.

Grid is an effective method, especially to organize data sets in low-dimensional space. The purpose of the grid-based clustering algorithm is to divide the possible values of each attribute into multiple adjacent intervals and create a group of grid cells.

As shown in Figure 6, in the grid-based clustering algorithm, there are many ways to divide the possible values of each attribute into multiple adjacent intervals, so there are many ways to define the grid unit. Clustering is a widely used exploratory data analysis technique. People’s first intuition about data is often by meaningfully grouping data, and by grouping objects, similar objects are grouped into one group.

Figure 6

Grid-based clustering algorithm.

Model-based clustering algorithms are mainly methods based on probability models and methods based on neural network models, especially methods based on probability models.

The Gaussian distribution model is as formula (12):

(12) N ( a , u , Σ ) = 1 2 π ∣ Σ ∣ exp − 1 2 ( a − u ) T Σ − 1 ( a − u ) .

Gaussian mixture model is to use Gaussian probability density function (normal distribution curve) to accurately quantify things. It is a model that decomposes things into several models based on the Gaussian probability density function (normal distribution curve). Where a is the column vector with dimension d, u is the model expectation, and Σ is the model variance.

The Gaussian mixture model is shown in equation (13):

(13) Pr ( a ) = ∑ k = 1 k π k N ( a , u k , Σ k ) .

In the model, K is the number of clusters, which needs to be determined in advance, and the number of clusters is determined to be consistent in K-Means. π _k is the weight factor.

Assuming that the sample size is N, the number of samples belonging to K categories is N ₁, N ₂, …, N _K respectively, and the sample set belonging to the Kth category is L(k). The calculation expression is as formula (14):

(14) π k = N k N .

A likelihood function is a function of parameters in a statistical model that represents the likelihood in the parameters of the model. Likelihood functions play an important role in statistical inference, such as applications between maximum likelihood estimation and Fisher information. In the case of unclassified samples, assuming that there are N data points, find a set of parameters θ; usually, this expression is called the likelihood function. The calculation formula is as formula (15):

(15) ∏ i = 1 N Pr ( a 1 , q ) .

Since the probability of a single data point is generally smaller, the result of the likelihood function will be smaller. For the convenience of calculation, the logarithm is generally calculated as formula (16):

(16) ∑ i = 1 N log Pr ( a , θ ) .

In order to maximize the value of the result of formula (16), the EM method can be used to solve it, so this method is called the expectation-maximization algorithm, where b is an implicit variable. The EM solution expression is equation (17):

(17) θ ∗ = arg max ∏ j = 1 ∣ a ∣ ∑ i = 1 n Pr ( A = a j , B = b , θ ) .

Initialize the distribution parameter Pr(A = a _j, B = b,θ) so that the likelihood function of the parameter is equal to its lower bound.

Cluster effectiveness can be divided into internal effectiveness indicators, external effectiveness indicators, and relative effectiveness indicators. For internal indicators, they are usually divided into three types: indicators based on the fuzzy division of data sets, indicators based on the geometric structure of the data set samples, and indicators based on the statistical information of the data set.

The index based on the fuzzy division of the data set describes the closeness between the clusters through the within-class dispersion matrix. The index is defined as formula (18):

(18) CH ( h ) = tr B ( h ) / ( h − 1 ) tr W ( h ) / ( n − h ) .

trB(h) is the deviation matrix between classes, and trW(h) is the trace of the deviation matrix within the class.

The index definition based on the geometric structure of the data set sample is as shown in formula (19):

(19) DB ( h ) = 1 k ∑ i = 1 k max w i + w j c i j .

w _i represents the distance from all samples in class c _ij to their cluster centers. j represents the average distance of all sample points in class c _ij to the center of class c _ij, and c _ij is the average distance of class. It can be seen that the smaller the index, the better the clustering result.

Under the action of wind pressure and hot pressure, air tightness is an important control index to ensure the stability of thermal insulation performance of building exterior windows, the smaller the heat loss. The tightness is measured by the average distance between samples within a class, and the minimum distance between samples between classes is used to measure the separation. The indicator is defined as formula (20):

(20) Sil = 1 m ∑ n = 1 m b ( n ) − a ( n ) max { b ( n ) − a ( n ) } ,

where a(n) is the average distance from sample n to other samples in the class, and b(n) is the minimum value of the average distance from sample n to samples in other classes.

5 Based on the experiment and analysis of the power detection improved system before and after the improvement of the Internet of Things

5.1 Experiment and analysis of traditional power detection improved system

Traditional detection systems are generally wired. Today’s intelligent detection systems mostly use radio frequency technology, which means that they can be plug and play. This makes up for the defects of the traditional intelligent management system that are complex, inconvenient to use, and incompletely intelligent. It has the characteristics of real-time status feedback, real-time remote control, or interactive intelligent control.

In this article, four electric power experts scored the deficiencies and defects of the traditional detection system in various aspects, as shown in Figure 7.

Figure 7

The shortcomings and shortcomings of traditional inspection systems in various aspects.

As shown in Figure 7, the special defects of the traditional inspection system include complex process, inconvenient use, and incomplete intelligence. The adverse effects caused by this are high cost, low comfort, weak safety, and so on. This article selects the load curve monitoring points of 5 electricity consumption units in the monitoring system as the test data set to test the actual clustering effect of the K-Means clustering algorithm when processing the data set. The scatter diagram of sample points is shown in Figure 8.

Figure 8

The effect comparison chart before and after using the clustering algorithm.

As shown in Figure 8, it can be seen from the figure that as the data becomes larger, the distance gradually becomes smaller, and the slope of the curve also gradually decreases. It can be seen that with the increase in the number of clusters, the decreasing trend of the data set distance tends to be slow. As the number of clusters increases, the scale of each data center will decrease. At the same time, each cluster center is relatively more compact, the distance will be reduced, and the clustering effect will be better. This article compares the traditional power detection system algorithm and the power detection system algorithm based on the clustering algorithm.

It can be seen from Tables 3 and 4 that the algorithm detection rate of the power detection system based on the clustering algorithm is between 77 and 86%. The lowest detection rate is 77%, and the highest detection rate is 86%; The false detection rate of the power detection system algorithm based on the clustering algorithm is between 10 and 13%. The lowest false detection rate is 10%, and the highest false detection rate is 13%. It can be seen that the power detection system algorithm based on the clustering algorithm makes the error of the detection power equipment system smaller, and this method is effective to detect the power system.

Table 3

Traditional power detection system algorithm

Data set	Detection rate (%)	False detection rate (%)
1	67	32
2	65	35
3	72	34
4	75	33
5	73	37

Table 4

Power detection system algorithm based on clustering algorithm

Data set	Detection rate (%)	False detection rate (%)
1	78	13
2	77	12
3	83	14
4	82	10
5	86	11

5.2 Experiment and analysis of the improved power testing improved system

The main equipment involved in this article includes sensors, thermometers, access control, cameras, energy controllers, and fire alarm controllers.

5.2.1 Basic ideas

The project focuses on upgrading the platform body, installing sensors and intelligent control terminals, to realize the “sensing, transmission and control” of the testing personnel, testing equipment, samples to be tested, and the laboratory environment in the metrology laboratory. The implementation status monitoring and risk early warning of the test process are realized, and the construction of a full-scenario, visualized smart laboratory is supported.

5.2.2 Main plan

Laboratory environmental monitoring: The temperature and humidity sensors and smoke sensors are installed in the laboratory to monitor the environmental information in the laboratory at all times and transmit the environmental data to the PC software in real time and display the current temperature and humidity on the display screen in the laboratory to achieve environmental maintenance. The temperature and humidity of the laboratory are kept within a reasonable range through remote control of the start and stop of the air conditioner, humidifier, and other equipment.
Monitoring of platform testing equipment: the appearance of the platform is unified, and the interface of the platform with the conditions is modified. In order to collect various data of the platform and monitor the operating status of the platform, the following modifications are made:

① The terminal temperature detection device is installed. The terminal temperature detection device is a high-precision and high-performance device designed based on the latest digital technology. The hardware is mainly composed of high-precision signal source, main control, 0.05-level digital standard electric energy meter, SPWM power source, and meter unit. The software is a PC control management software with a configurable test plan, which can be adjusted according to the different functional requirements of customers to provide a complete test plan for the verification of the electric energy meter. The device has the characteristics of high detection efficiency, high accuracy, wide measuring range, stability and reliability, and high-cost performance. The platform model can be built on the PC software, and the data at each terminal of each epitope can be displayed in real time.

② The smart circuit breaker is installed, and the smart circuit breaker is installed on the table body to realize protection functions such as overcurrent and leakage. It also continuously monitors the working status and duration of the platform, and has the function of remote control start and stop. It also adds additional protective functions to the table body to ensure the safety of equipment and personnel.

③ The operation monitoring unit is installed to monitor the voltage and current data of different meter positions in real time and compare with the data set in advance to prevent operation failure. Through the platform model on the PC-side software, various data are displayed intuitively. When the data exceeds the predetermined value, the color changes and an alarm is issued on the platform.

(3) Inspection personnel: increase the laboratory safety identification module, install the access card in the laboratory, and install the “operation monitoring unit” on the platform. After swiping the card, the table body and the corresponding detection function of the table body are used. The station body identity verification module, the operating mode of the console body, is increased, and each staff member holds a card. Before the test work, send the test plan process and so on to the card through the PC. The staff needs to swipe the card on the platform for authentication, and the running content of the platform will not exceed the set plan. If you need to change it, you need to re-issue and re-authenticate the card.

As shown in Figure 9, the second part is the monitoring of the various operating data of the platform, which is collected by the collection unit and uploaded to the PC.

Figure 9

Modification diagram of platform testing equipment.

(4) Monitoring of samples submitted for inspection: The samples submitted for inspection are mainly integrated into unified management through warehousing coding, file creation, intelligent storage, automatic circulation, detection matching, cycle control, and early warning.

Traditional detection systems are designed to detect and query the operating data of power equipment, and lack the effective analysis and mining of historical operating data, and cluster analysis-based load classification research, medium, and long-term load forecasting, and power consumption pattern analysis have emerged. According to the development direction and plan of the smart grid.

(5) Data acquisition and transmission plan

Taiwan body identity verification module: For routine testing of electric energy meters, the testing process and plan can be determined, and the control unit can strictly control the running time and current of the platform. If the plan exceeds the predetermined plan, an alarm will be issued, and the test can be re-tested after re-certification.
HPLC/wireless converter: monitor the temperature, voltage and current in real time through MUS/485, and store the data in the collection unit. The collection unit transmits the collected data to the energy controller through the HPLC dual-mode in real time, and the energy controller uploads the data to the master station through the 5 G module or Ethernet.
Main station: The main station side establishes the space model diagram of the platform and displays various data at the corresponding position on the way.

As shown in Figure 10: Through the experiments, it can be seen that the improved detection system of this project has not only improved safety but also greatly improved its accuracy.

Figure 10

Data transfer diagram.

In view of the goal of the development of smart grids, smart grid equipment management requires a fast interactive real-time network platform. It uses the online monitoring system of the equipment to obtain the real-time status of the equipment and sends out a warranty signal in time when the equipment fails, so that the maintenance personnel can perform maintenance in time, so as to ensure that the equipment is running in the best state at all times. The equipment management of the smart grid is a very important part of the normal and efficient operation of the smart grid. Standardizing the itinerant overhaul, daily maintenance and maintenance of the equipment in the system can greatly improve work efficiency.

The specific advantages of the improved power detection equipment are shown in Figure 11.

Figure 11

Comparison diagram of two power detection improved systems: (a) traditional power detection equipment improved system; (b) improved power detection equipment improved system.

As shown in Figure 11, this article mainly compares the safety, accuracy, and economy of the power detection equipment improved system and the traditional power detection improved system of the Internet of Things.

It can be seen from Figure 11 that the safety, accuracy, and economy of the traditional power detection improved system of the Internet of Things are not as good as the power detection equipment improved system and the traditional power detection improved system of the Internet of Things. Therefore, it can be seen that the power testing equipment improved system is more conducive to the testing development of today’s electrical industry.

6 Discussion

Based on the theoretical knowledge of the Internet of Things, this article has launched a detailed analysis of the research on the system detection equipment. It explores how to use the Internet of Things to study the system detection equipment, and studies the related theories of power detection improved systems and the Internet of Things. And it discusses the difference between the traditional power detection improved system and the power detection equipment improved system through experiments.

This article also makes reasonable use of the clustering analysis algorithm based on the Internet of Things and the target detection method. As the combined application range of these two algorithms becomes larger and larger, their importance also increases. According to this calculation, it is very meaningful to study the system detection equipment based on the Internet of Things. It can not only improve the efficiency and safety of detection but also save a lot of manpower and cost.

This article analyzes the defects and characteristics of the traditional power detection improved system and studies the advantages of the power detection equipment improved system: The traditional power detection improved system not only wastes a lot of resources but also has a low accuracy rate, which makes people’s lives very inconvenient. The power detection equipment improved system not only makes people’s lives more convenient but also saves a lot of resources.

7 Conclusions

The focus of this article is how to conduct research on the system detection equipment based on the Internet of Things. This article first introduces a comprehensive and clear theoretical knowledge of the development of the Internet of Things and the system testing equipment. It explains why it is necessary to conduct research on the system detection equipment based on the Internet of Things. On this basis, this study proposes a clustering analysis algorithm, an SVM algorithm, and a target detection method. In the method part, not only the theoretical knowledge of the three algorithms is introduced in detail, but also the formulas are explained. In the experimental part, the shortcomings of the traditional power detection improved system were investigated and researched, and it was found that the traditional power detection improved system not only increased the cost of detection, but also had low safety. Therefore, it is very necessary to study a new type of power detection equipment improved system. Then, the advantages of the new power detection improved system were tested and analyzed, and it was found that the new power detection improved system not only improved the safety of detection but also saved a lot of costs and increased efficiency. At the end of the experiment, the cluster analysis algorithm is compared with the traditional detection algorithm, and it is found that the detection system based on the cluster analysis algorithm makes the detection error rate lower.

Conflict of interest: Authors state no conflict of interest.

References

[1] Iida K. Inverse Nth power detection law for Washburn’s lateral range curve. J Oper Res Soc Jpn. 2017;36(2):90–101.10.15807/jorsj.36.90Search in Google Scholar

[2] Shaik AG, Mahela OP. Power quality assessment and event detection in hybrid power system. Electr Power Syst Res. 2018;161:26–44.10.1016/j.epsr.2018.03.026Search in Google Scholar

[3] Parvez I, Aghili M, Sarwat AI, Rahman S, Alam F. Online power quality disturbance detection by support vector machine in smart meter. J Mod Power Syst Clean Energy. 2019;7(5):1328–39.10.1007/s40565-018-0488-zSearch in Google Scholar

[4] Jana S, De A. A novel zone division approach for power system fault detection using ANN-based pattern recognition technique. Can J Electr Comput Eng. 2017;40(4):275–83.10.1109/CJECE.2017.2751661Search in Google Scholar

[5] Razzaque MA, Milojevic-Jevric M, Palade A, Clarke S. Middleware for internet of things: A survey. IEEE Internet Things J. 2017;3(1):70–95.10.1109/JIOT.2015.2498900Search in Google Scholar

[6] Lin J, Yu W, Zhang N, Yang X, Zhang H, Zhao W. A survey on internet of things: Architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things J. 2017;4(5):1125–42.10.1109/JIOT.2017.2683200Search in Google Scholar

[7] Perera C, Liu CH, Jayawardena S. The emerging internet of things marketplace from an industrial perspective: A survey. IEEE Trans Emerg Top Comput. 2017;3(4):585–98.10.1109/TETC.2015.2390034Search in Google Scholar

[8] Singh J, Pasquier T, Bacon J, Ko H, Eyers D. Twenty security considerations for cloud-supported internet of things. IEEE Internet Things J. 2017;3(3):269–84.10.1109/JIOT.2015.2460333Search in Google Scholar

[9] Mosenia A, Jha NK. A comprehensive study of security of Internet-of-Things. IEEE Trans Emerg Top Comput. 2017;5(4):586–602.10.1109/TETC.2016.2606384Search in Google Scholar

[10] Yang Y, Wu L, Yin G, Li L, Zhao H. A survey on security and privacy issues in Internet-of-Things. Internet Things J IEEE. 2017;4(5):1250–8.10.1109/JIOT.2017.2694844Search in Google Scholar

[11] Zhang Y, Wen J. The IoT electric business model: Using blockchain technology for the internet of things. Peer-to-Peer Netw Appl. 2017;10(4):983–94.10.1007/s12083-016-0456-1Search in Google Scholar

[12] Hahm O, Baccelli E, Petersen H, Tsiftes N. Operating systems for low-end devices in the internet of things: A survey. IEEE Internet Things J. 2017;3(5):720–34.10.1109/JIOT.2015.2505901Search in Google Scholar

[13] Kshetri N. Can blockchain strengthen the internet of things? It Professional. 2017;19(4):68–72.10.1109/MITP.2017.3051335Search in Google Scholar

[14] Zhang D, Yang LT, Chen M, Zhao S, Guo M, Zhang Y. Real-time locating systems using active RFID for Internet of Things. IEEE Syst J. 2017;10(3):1226–35.10.1109/JSYST.2014.2346625Search in Google Scholar

[15] Ni J, Zhang K, Lin X, Shen X. Securing fog computing for internet of things applications: Challenges and solutions. IEEE Commun Surv & Tutor. 2018;20(99):601–28.10.1109/COMST.2017.2762345Search in Google Scholar

[16] Mishra D, Gunasekaran A, Childe SJ, Papadopoulos T, Dubey R, Wamba S. Vision, applications and future challenges of Internet of Things: A bibliometric study of the recent literature. Ind Manag & Data Syst. 2017;116(7):1331–55.10.1108/IMDS-11-2015-0478Search in Google Scholar

[17] Mostafa H, Kerstin T, Regina S. Wearable devices in medical internet of things: Scientific research and commercially available devices. Healthc Inform Res. 2017;23(1):4–15.10.4258/hir.2017.23.1.4Search in Google Scholar PubMed PubMed Central

[18] Stojkoska B, Trivodaliev KV. A review of Internet of Things for smart home: Challenges and solutions. J Clean Prod. 2017;140(3):1454–64.10.1016/j.jclepro.2016.10.006Search in Google Scholar

[19] Iranmanesh S, Rodriguez-Villegas E. An ultralow-power sleep spindle detection system on chip. IEEE Trans Biomed Circuits Syst. 2017;11(4):858–66.10.1109/TBCAS.2017.2690908Search in Google Scholar PubMed

[20] Tsai CY, Yang GC, Lin JS, Chang CY, Glesk I, Kwong WC. Pulse-power-detection analysis of incoherent O-CDMA systems under the influence of fiber temperature fluctuations. J Lightwave Technol. 2017;35(99):1.10.1109/JLT.2017.2690992Search in Google Scholar

[21] Tsai CY, Yang GC, Lin JS, Chang CY, Glesk I, Kwong WC. Pulse-power-detection analysis of incoherent O-CDMA systems under the influence of fiber temperature fluctuations. J Lightwave Technol A Jt IEEE/OSA Publ. 2017;35(12):2366–79.10.1109/JLT.2017.2690992Search in Google Scholar

Received: 2022-03-01

Revised: 2022-04-11

Accepted: 2022-06-30

Published Online: 2022-12-15

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

https://doi.org/10.1515/jisys-2022-0127

Keywords for this article

Internet of Things; power detection improved system; cluster analysis algorithm; support vector machine

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BY 4.0