Instantaneous frequency measurement using two parallel I/Q modulators based on optical power monitoring

A scheme for instantaneous frequency measurement(IFM)using two parallel I/Q modulators based on optical power monitoring is proposed.The amplitude comparison function(ACF)can be constructed to establish the relationship between the frequency of radio frequency(RF)signal and the power ratio of two optical signals output by two I/Q modulators.The frequency of RF signal can be derived by measuring the optical power of the optical signals output by two I/Q modulators.The measurement range and measurement error can be adjusted by controlling the delay amount of the electrical delay line.The feasibility of the scheme is verified,and the corresponding measurement range and measurement error of the system under different delay amounts of the electrical delay line are given.Compared with previous IFM schemes,the structure of this scheme is simple.Polarization devices,a photodetector and an electrical power meter are not used,which reduces the impact of the environmental disturbance on the system and the cost of the system.In simulation,the measurement range can reach 0 GHz-24.5 GHz by adjusting the delay amount of the electrical delay lineτ=20 ps.The measurement error of the scheme is better at low frequency,and the measurement error of low frequency 0 GHz-9.6 GHz can reach-0.1 GHz to+0.05 GHz.

Design of Millimeter-Wave High-Power Power Monitoring Miter Bend Based on Aperture-Coupling

A directional coupler in a miter bend was designed to monitor the microwave power of the transmission line in an electron cyclotron resonance heating（ECRH） system. It is based on aperture-coupling theory and simulation of an electromagnetic（EM） field to design and optimize the 140 GHz high-power directional coupler. In addition, we simulated the double-aperture directional coupler by using three-dimensional（3D） EM simulation software, in which the central frequency is 140 GHz, coupling factor is about –70 dB, and directivity is greater than 17 dB. The results show that such a coupler is a viable tool for power measurement in high-power transmission systems.

Developing a power monitoring and protection system for the junction boxes of an experimental seafloor observatory network

A power monitoring and protection system based on an embedded processor was designed for the junction boxes(JBs) of an experimental seafloor observatory network in China. The system exhibits high reliability, fast response, and high real-time performance. A two-step power management method which uses metal-oxide-semiconductor field-effect transistors(MOSFETs) and a mechanical contactor in series was adopted to generate a reliable power switch, to limit surge currents and to facilitate automatic protection. Grounding fault diagnosis and environmental monitoring were conducted by designing a grounding fault detection circuit and by using selected sensors, respectively. The data collected from the JBs must be time-stamped for analysis and for correlation with other events and data. A highly precise system time, which is necessary for synchronizing the times within and across nodes, was generated through the IEEE 1588(precision clock synchronization protocol for networked measurement and control systems) time synchronization method. In this method, time packets were exchanged between the grandmaster clock at the shore station and the slave clock module of the system. All the sections were verified individually in the laboratory prior to a sea trial. Finally, a subsystem for power monitoring and protection was integrated into the complete node system, installed in a frame, and deployed in the South China Sea. Results of the laboratory and sea trial experiments demonstrated that the developed system was effective, stable, reliable, and suitable for continuous deep-sea operation.

Massive Power Device Condition Monitoring Data Feature Extraction and Clustering Analysis using MapReduce and Graph Model

Effective storage,processing and analyzing of power device condition monitoring data faces enormous challenges.A framework is proposed that can support both MapReduce and Graph for massive monitoring data analysis at the same time based on Aliyun DTplus platform.First,power device condition monitoring data storage based on MaxCompute table and parallel permutation entropy feature extraction based on MaxCompute MapReduce are designed and implemented on DTplus platform.Then,Graph based k-means algorithm is implemented and used for massive condition monitoring data clustering analysis.Finally,performance tests are performed to compare the execution time between serial program and parallel program.Performance is analyzed from CPU cores consumption,memory utilization and parallel granularity.Experimental results show that the designed framework and parallel algorithms can efficiently process massive power device condition monitoring data.

A power-quality monitoring and assessment system for high-speed railways based on train-network-data center integration

In recent years,with the rapid development of high-speed railways(HSRs),power interruptions or disturbances in traction power supply systems have become increasingly dangerous.However,it is often impossible to detect these faults immediately through single-point monitoring or collecting data after accidents.To coordinate the power quality data of both traction power supply systems(TPSSs)and high-speed trains(HSTs),a monitoring and assessing system is proposed to access the power quality issues on HSRs.By integrating train monitoring,traction substation monitoring and data center,this monitoring system not only realizes the real-time monitoring of operational behaviors for both TPSSs and HSTs,but also conducts a comprehensive assessment of operational quality for train-network systems.Based on a large number of monitoring data,the field measurements show that this real-time monitoring system is effective for monitoring and evaluating a traction-network system.

Economic Implications of Poor Power Quality

Poor power quality is really becoming an increasing phenomenon. Studies revealed that most companies, particularly multinationals in Nigeria, do not pay enough attention to the effects of poor power quality on their plant and equipment, and let the economic implications alone. The result is high production and maintenance cost. Findings also revealed that these companies loose an average of $50,000 annually owing to equipment damage （such as variable speed drives） caused by poor power quality. This paper focuses on the need for continuous monitoring and management of poor power quality disturbance parameters and the cost for economic implications of no addressing power quality issues.

Smart Meter Development for Cloud-Based Home Electricity Monitor System

Αcloud-based home electricity data-monitoring system,which is based on an Arduino Mega controller,is proposed for monitoring the electricity consumption(electrical power)and power quality(PQ)in home.This system is also capable of monitoring the fundamental frequency and supply-voltage transients to ensure that the appliances operate in a safe operation range.The measured data(voltage and current)are transmitted through a Wi Fi device between the Arduino controller and server.The transmission control protocol(TCP)server is set up to acquire the high-data transmission rate.The server system immediately displays the calculated parameters and the waveform of the acquired signal.A comparison with a standard measurement device shows that the proposed system,which can be built at a low cost,exhibits the same functions as a factory product.

Fault separation and detection algorithm based on Mason Young Tracy decomposition and Gaussian mixture models

Purpose-For the large-scale power grid monitoring system equipment,its working environment is increasingly complex and the probability of fault or failure of the monitoring system is gradually increasing.This paper proposes a fault classification algorithm based on Gaussian mixture model(GMM),which can complete the automatic classification of fault and the elimination of fault sources in the monitoring system.Design/methodology/approach-The algorithm first defines the GMM and obtains the detection value of the fault classification through a method based on the causal Mason Young Tracy(MYT)decomposition under each normal distribution in the GMM.Then,the weight value of GMM is used to calculate weighted classification value of fault detection and separation,and by comparing the actual control limits with the classification result of GMM,the fault classification results are obtained.Findings-The experiment on the defined non-thermostatic continuous stirred-tank reactor model shows that the algorithm proposed in this paper is superior to the traditional algorithm based on the causal MYT decomposition in fault detection and fault separation.Originality/value-The proposed algorithm fundamentally solves the problem of fault detection and fault separation in large-scale systems and provides support for troubleshooting and identifying fault sources.