Abnormal Detection System For The Household Electricity

The traditional power system lacks the interactive function between the home user and the meter.When the household appliance fails or the circuit is abnormal,the user cannot detect and control it in time,and in serious cases,the electrical accident will be caused,which has greatly affects people's quality of life.In order to monitor household electricity flexibly and ensure the safety of household electricity,this thesis designed an abnormal detection system for household electricity.The system is mainly completed in two aspects: First,design a household electrical system to replace and increase the function of traditional electric meter.The second is to complete the detection of anomaly power,improve the harmonic detection algorithm and accuracy.STM32F301 series single-chip,AD7656 analog-to-digital converter,ESP8266 WiFi module are selected as the system main hardware circuit,combined with cloud server data storage system,mobile APP control terminal,completing the design of the household electricity information collecting system.This system not only can realize the function of collecting and reading voltage,current and calculating electric energy of traditional electricity meter,also complete the two-way communication between the mobile phone APP and the household electricity system,realizing remote monitor the household appliances.This thesis studies the application of joint waveform diagnosis strategy in power system based on Prony algorithm and wavelet transform algorithm.Combining Prony algorithm and wavelet transform algorithm,the shortcomings of each of them in harmonic detection are eliminated and the accuracy of harmonic detection is improved.MATLAB is used to analyze the joint waveform diagnosis strategy based on Prony algorithm and wavelet change algorithm,which prove the feasibility of the algorithm.Finally,the establishment of the hardware platform and the collection of real and effective household electrical appliance data are completed to realize the detection of abnormal household electricity.The first detection method is use APP to power off the abnormal state of household electricity.The second is to use the improved harmonic analysis algorithm to detect the harmonic of the collected power waveform to determine the content of each harmonic and its corresponding amplitude,attenuation factor and phase in the steady-state harmonics,and to precisely locate the abrupt point of unsteady-state harmonics.This improved algorithm not only provides an effective solution for detecting household electrical anomalies by analyzing harmonics,but also provides a theoretical basis for the later harmonics management.In addition,the subject uses the server to store the electricity information for a long-term,which provides a source of data for using big data technology to analyze user's behavior and improves power supply and power consumption environment.