WO2017211288A1 - 非侵入式在线实时电力负荷识别方法及识别系统 - Google Patents

非侵入式在线实时电力负荷识别方法及识别系统 Download PDF

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WO2017211288A1
WO2017211288A1 PCT/CN2017/087413 CN2017087413W WO2017211288A1 WO 2017211288 A1 WO2017211288 A1 WO 2017211288A1 CN 2017087413 W CN2017087413 W CN 2017087413W WO 2017211288 A1 WO2017211288 A1 WO 2017211288A1
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real
signal
time
time power
intrusive
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English (en)
French (fr)
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李祖毅
鲍镇
庄蒙蒙
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李祖毅
鲍镇
庄蒙蒙
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05F—SYSTEMS FOR REGULATING ELECTRIC OR MAGNETIC VARIABLES
    • G05F1/00—Automatic systems in which deviations of an electric quantity from one or more predetermined values are detected at the output of the system and fed back to a device within the system to restore the detected quantity to its predetermined value or values, i.e. retroactive systems
    • G05F1/66—Regulating electric power
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B15/00—Systems controlled by a computer
    • G05B15/02—Systems controlled by a computer electric
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/26—Pc applications
    • G05B2219/2642—Domotique, domestic, home control, automation, smart house
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems

Definitions

  • the invention belongs to the field of Internet of Things and big data technology, and relates to power consumption monitoring and state detection, in particular to a non-intrusive online real-time power load identification method and identification system.
  • Load identification was proposed by the Massachusetts Institute of Technology Hart in the 1880s. In order to alleviate global energy problems and environmental problems, recent load identification technologies are receiving more and more attention.
  • the purpose of load identification is to detect the energy consumption and real-time status of each appliance in the home. Load identification can help grid companies to provide load side response services and home appliance fault detection services for residents.
  • load identification is divided into intrusive load monitoring technology and non-intrusive load identification technology.
  • intrusive load monitoring sensors are installed on each household appliance to measure the power consumption and state of the appliance in real time.
  • Non-intrusive load identification technology only needs to measure the total real-time power consumption of the home, and identify all home appliances status and power consumption through machine learning and artificial intelligence algorithms.
  • non-intrusive load identification has a low cost and easy installation. At present, most of the solutions are to use the clustering algorithm, the stealth Markov model, the neural network and the support vector machine to complete the recognition process.
  • the load identification technology has the following limitations: it requires a large number of labeled samples for training; it can only identify large-load electrical equipment, such as refrigerators, air conditioners, etc.; due to the lack of a large number of training samples, the recognition accuracy is relatively low; the hardware cost is high Can not be deployed in residential residences; the algorithm is not efficient and usually cannot achieve real-time and online effects.
  • people have conducted long-term exploration and proposed various solutions.
  • the Chinese patent literature discloses a non-intrusive power load monitoring and decomposition technique.
  • the implementation architecture [application number: 201410389560.0] includes: a non-intrusive power load monitoring and decomposition technical service management module deployed at one end of the technical service provider, and a non-intrusive power load monitoring and decomposition function module deployed at the user end. , distributed network expansion function module and two-way communication network transmission module.
  • the present invention attempts to establish a system that can support the implementation of NILMD technology and non-intrusive power load monitoring and decomposition technology based on the practical needs of NILMD technology.
  • Architecture NILMDSI
  • the invention can fill the research gap in this field in the field, and can guide the technology provider to develop effective and systematic implementation plan for the practicalization and promotion of NILMD technology, in order to solve the NILMD technology practically.
  • the object of the present invention is to provide the above problem, and to provide a low-cost, low-volume sample that can be trained, is also very sensitive to small-load electrical appliances, and can solve the problem of electric energy oscillation and ensure the accuracy of load recognition.
  • Non-intrusive online real-time power load identification method is to provide the above problem, and to provide a low-cost, low-volume sample that can be trained, is also very sensitive to small-load electrical appliances, and can solve the problem of electric energy oscillation and ensure the accuracy of load recognition.
  • Another object of the present invention is to provide a non-cost, low-volume sample that can be trained, is also very sensitive to small-load electrical appliances, and can solve the problem of electric energy oscillation and ensure the accuracy of load recognition.
  • Intrusive online real-time power load identification system is also very sensitive to small-load electrical appliances, and can solve the problem of electric energy oscillation and ensure the accuracy of load recognition.
  • the present invention adopts the following technical solution: the non-intrusive online real-time power load identification method, characterized in that the method comprises the following steps:
  • A get real-time power signal: real-time collection of real-time power number of residential side distribution box And then converting the collected real-time power data to obtain a real-time power signal;
  • Non-intrusive load identification analysis Perform real-time power signal wavelet transform denoising, kernel density estimation detection event, judge whether there is periodic signal and calculate period, de-cycle signal and extract trend signal, cluster power signal, extract power Signal characteristics, thereby obtaining power consumption data and real-time status information of each household appliance corresponding to the residential-side distribution box;
  • the set error e i obeys a Gaussian distribution with a mean of 0, and sets a threshold to denoise the real-time power signal;
  • N is the signal length
  • wavelet transform denoising uses time-frequency analysis to remove high-frequency noise signals and preserve low-frequency signals.
  • nuclear density estimation detection event the denoised real-time power signal is estimated by the kernel density estimation method
  • the density function is as follows:
  • K is the density function
  • y is the original signal
  • x i is the expected value of the density function
  • h is the bandwidth of the density function
  • the Hodrick–Prescott filter optimization algorithm is used to solve the de-periodic signal.
  • the specific implementation process is as follows:
  • Clustering power signal According to the extracted trend signal, the algorithm based on density clustering is used to solve the outliers.
  • the essence of the outliers is the transient signal of the event.
  • the specific process is as follows: mark all points as Core point, boundary point or noise point; remove noise points; assign an edge between all core points within the threshold; each group is connected The core points form a cluster; each boundary point is assigned to a cluster of core points associated with it; thereby implementing a density-based clustering method to separate the transient signal and the steady state signal to realize positioning of the transient signal;
  • Extracting power signal characteristics Feature learning using depth learning and unsupervised density-based clustering algorithm for feature recognition.
  • the real-time power data includes a real-time voltage and a real-time current; and the real-time power data is converted into real-time active power and real-time reactive power. signal.
  • the real-time power signal is transmitted to the cloud through wireless and/or wired communication, and then transmitted from the cloud to the background server through wireless and/or wired communication.
  • the non-intrusive load identification analysis is performed in a background server.
  • the real-time power signal is transmitted to the cloud through an Ethernet and/or WiFi communication protocol, and then transmitted from the cloud to the background server through the Internet.
  • a non-intrusive online real-time electric load identification system applying the above non-intrusive online real-time electric load identification method, characterized in that the system comprises at least one embedded on a residential-side distribution box and used for obtaining real-time power signal embedding Device terminal, the embedded device terminal is connected to the cloud through wireless and/or wired communication, and the cloud is backstage capable of non-intrusive load identification analysis on real-time power signals through wireless and/or wired communication.
  • the server is connected, and the background server is connected with a data storage device and can transmit the analysis result to the terminal device corresponding to the residential-side distribution box by wireless and/or wired communication.
  • the real-time power signal is transmitted to the cloud through an Ethernet and/or WiFi communication protocol, and then transmitted from the cloud to the background server through the Internet.
  • the embedded There are multiple input device terminals and are connected to the cloud in a distributed connection mode.
  • the terminal device is a mobile terminal device and/or a PC.
  • the home appliance includes a large-load power device and/or a small-load power device.
  • the non-intrusive online real-time electric load identification method and the identification system have the advantages that the use of the resident side user is low in cost, does not require a large number of labeled samples, and can be trained for small-load electric appliances. Sensitive, at the same time able to solve the problem of power fluctuations, and ensure the accuracy of load identification; and then provide the overall energy solution for the family; in addition, the algorithm efficiency can achieve online and real-time effects.
  • FIG. 1 is a structural block diagram of the present invention.
  • FIG. 2 is a schematic diagram of a flow of an event detection algorithm provided by the present invention.
  • FIG. 3 is a schematic diagram of a structure of a deep learning network frame provided by the present invention.
  • FIG. 4 is a diagram of an original power signal provided by the present invention.
  • FIG. 5 is a diagram showing the effect of a nuclear method determination event provided by the present invention.
  • FIG. 6 is a partial structural block diagram of an embedded device terminal provided by the present invention.
  • FIG. 7 is a partial circuit diagram of an embedded device terminal provided by the present invention.
  • the residential side distribution box 1 the AC voltage source 11, the AC current source connection 12, the cloud 2, the background server 3, the embedded device terminal 4, the first operation circuit 41, the second operation circuit 42, the power supply 43 Transformer 44, rectifier circuit 45, filter circuit 46, voltage regulator circuit 47, smoothed output voltage circuit 48, data memory 6, and terminal device 7.
  • the non-intrusive online real-time power load identification method includes the following steps:
  • A. Obtain real-time power signal collect real-time power data of the residential side distribution box 1 in real time, and then convert the collected real-time power data to obtain real-time power signals; wherein the real-time power data includes operational data such as real-time voltage and real-time current; Real-time power data is converted to real-time active power and real-time reactive power signals.
  • Non-intrusive load identification analysis Perform real-time power signal wavelet transform denoising, kernel density estimation detection event, judge whether there is periodic signal and calculate period, de-cycle signal and extract trend signal, cluster power signal, extract power Signal characteristics, thereby obtaining power consumption data and real-time status information corresponding to each household appliance of the residential-side distribution box 1;
  • the real-time power signal is transmitted to the cloud 2 through wireless and/or wired communication, and then transmitted from the cloud 2 to the background server 3 through wireless and/or wired communication.
  • the non-intrusive load identification analysis is in the background server 3. get on.
  • the real-time power signal is transmitted to the cloud 2 via the Ethernet and/or WiFi communication protocol, and then transmitted from the cloud 2 to the background server 3 via the Internet.
  • the set error e i obeys a Gaussian distribution with a mean of 0, and sets a threshold to denoise the real-time power signal;
  • N is the signal length
  • wavelet transform denoising uses time-frequency analysis to remove high-frequency noise signals and preserve low-frequency signals.
  • nuclear density estimation detection event the denoised real-time power signal is estimated by the kernel density estimation method
  • the density function is as follows:
  • K is the density function
  • y is the original signal
  • x i is the expected value of the density function
  • h is the bandwidth of the density function
  • the Hodrick–Prescott filter optimization algorithm is used to solve the de-periodic signal.
  • the specific implementation process is as follows:
  • Clustering power signal According to the extracted trend signal, the algorithm based on density clustering is used to solve the outliers.
  • the essence of the outliers is the transient signal of the event.
  • the specific process is as follows: mark all points as Core point, boundary point or noise point; remove noise point; assign an edge between all core points whose distance is within the threshold; each group of connected core points form a cluster; assign each boundary point to one The cluster of associated core points; thereby realizing the use of density-based clustering method to separate the transient signal and the steady state signal to realize the positioning of the transient signal;
  • Extracting power signal characteristics Feature learning using depth learning and unsupervised density-based clustering algorithm for feature recognition.
  • a non-intrusive online real-time electric load identification system applying a non-intrusive online real-time electric load identification method, characterized in that the system comprises at least one embedded on the residential-side distribution box 1 and used for obtaining real-time power signal embedding Device terminal 4, the embedded device terminal 4 is connected to the cloud 2 by wireless and/or wired communication, and the cloud 2 can perform non-intrusive load on the real-time power signal through wireless and/or wired communication.
  • the background server 3 for identification analysis is connected, and the data server 6 is connected to the background server 3 and the analysis result can be transmitted to the terminal device 7 corresponding to the resident-side distribution box 1 by wireless and/or wired communication.
  • the real-time power signal is transmitted to the cloud 2 via the Ethernet and/or WiFi communication protocol, and then transmitted from the cloud 2 to the background server 3 via the Internet.
  • the terminal device 4 is a mobile terminal device and/or a PC.
  • Household Electrical appliances include heavy-duty electrical equipment and/or small-load electrical equipment, namely refrigerators, air conditioners, mobile phone chargers, lighting, computers, etc.
  • This application does not require a large number of labeled samples for training; it can identify large and small load electrical equipment; with a small number of training samples, the recognition accuracy is relatively high; the hardware cost is low, and it is easy to deploy in residential residences.
  • the patent application is based on signal processing, machine learning, artificial intelligence and other technologies for non-intrusive load identification, and then provides an overall energy solution for the family.
  • the core technologies included are mainly wavelet analysis denoising, nuclear density detection events, autocorrelation coefficients and optimization methods to degenerate periodic signals and extract trend information, and density-based clustering methods to separate transient signals and steady-state signals.
  • the power signal characteristics and the like are extracted by the sparse self-encoding technique in deep learning.
  • the embedded device terminal 4 includes a first operation circuit 41 and a second operation circuit 42 which are connected to each other, and the first operation circuit 41 and the second operation circuit 42 are both connected to a power supply 43.
  • the first arithmetic circuit 41 and the second arithmetic circuit 42 are respectively connected to the residential-side distribution box 1 (that is, the first arithmetic circuit 41 and the second arithmetic circuit 42 are respectively connected to the alternating-current voltage source 11 and the alternating current source 12).
  • the AC voltage source 11 is connected in turn with a transformer 44, a rectifier circuit 45, a filter circuit 46, a voltage stabilizing circuit 47, and a smoothed output voltage circuit 48.
  • the terms power supply 43, transformer 44, rectifier circuit 45, filter circuit 46, voltage regulator circuit 47, smoothed output voltage circuit 48, data memory 6, terminal device 7, etc. do not preclude the possibility of using other terms. These terms are only used to more easily describe and explain the present invention. Essence; interpreting them as any additional restrictions is contrary to the spirit of the invention.

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Abstract

一种非侵入式在线实时电力负荷识别方法及识别系统，该识别方法包括下述步骤：A、获得实时电力信号；B、非侵入式负荷识别分析；C、结果反馈。该识别系统包括至少一个连接在居民侧配电箱（1）上的嵌入式设备终端（4），嵌入式设备终端（4）与云端（2）相连，云端（2）与后台服务器（3）相连，后台服务器（3）上连接数据存储器（6）且能将分析结果传送至对应于居民侧配电箱（1）的终端设备（7）。该识别方法及识别系统的优点在于：对于居民侧用户使用成本低廉、不需要大量有标签的样本即可进行训练、对小负荷电器也非常敏感，同时能解决电能震荡问题、且保证负荷识别的准确率；能为家庭提供整体的能源解决方案；算法效率可以达到在线和实时的效果。

Description

非侵入式在线实时电力负荷识别方法及识别系统 技术领域
本发明属于物联网和大数据技术领域，涉及电能消耗监测和状态检测，尤其是涉及一种非侵入式在线实时电力负荷识别方法及识别系统。
背景技术
负荷识别在19世纪80年代，由麻省理工学院Hart提出。为了减轻全球能源问题和环境问题，近期负荷识别技术越来越受到重视。负荷识别的目的是检测家庭中每个电器的能耗和实时状态。负荷识别能够帮助电网企业为居民侧提供负荷侧响应服务，家电故障检测服务。其中负荷识别又分为侵入式负荷监测技术和非侵入式负荷识别技术。对于侵入式负荷监测，是在每个家用电器上加装传感器以实时测量电器功耗和状态。非侵入式负荷识别技术仅需测量家庭总的实时用电功率，通过机器学习和人工智能算法来识别所有家用电器状态和功耗。相对于侵入式负荷监测，非侵入式负荷识别具有成本低廉，安装方便等有点。目前大部分解决方案是利用聚类算法，隐形马尔科夫模型，神经网络和支持向量机完成识别的过程。
目前负荷识别技术具有以下局限性：需要大量有标签的样本做训练；只能识别大负荷用电设备，例如冰箱，空调等；由于缺乏大量的训练样本，识别准确率相对较低；硬件成本高昂，无法部署在居民住所；算法效率不高，通常无法达到实时和在线的效果。为此，人们进行了长期的探索，提出了各种各样的解决方案。例如，中国专利文献公开了一种非侵入式电力负荷监测与分解技 术实施的体系架构[申请号：201410389560.0]，包括：部署在技术服务提供方一端的非侵入式电力负荷监测与分解技术服务管理模块，部署在用户一端的非侵入式电力负荷监测与分解功能模块，分布式网络拓展功能模块和双向通信网络传输模块。本发明从NILMD技术系统应用的层面和市场普及的角度出发，首次尝试在分析NILMD技术的实用化需求的基础上建立了能够支持NILMD技术广泛实用的非侵入式电力负荷监测与分解技术实施的体系架构(NILMDSI)，该发明可以填补本领域内这方面的研究空白，而且可以指导技术提供方为NILMD技术的实用化与推广制定切实有效的系统性实施方案，以期能够很好地解决NILMD技术实用化所遇到的相关问题。
上述方案虽然在一定程度上优化了非侵入式电力负荷监测与分解的硬件架构，但是并不能从根本上解决现有技术存在的技术问题。
发明内容
本发明的目的是针对上述问题，提供一种使用成本低廉、不需要大量有标签的样本即可进行训练、对小负荷电器也非常敏感、同时能解决电能震荡问题、且保证负荷识别的准确率的非侵入式在线实时电力负荷识别方法。
本发明的另一目的是提供一种使用成本低廉、不需要大量有标签的样本即可进行训练、对小负荷电器也非常敏感、同时能解决电能震荡问题、且保证负荷识别的准确率的非侵入式在线实时电力负荷识别系统。
为达到上述目的，本发明采用了下列技术方案：本非侵入式在线实时电力负荷识别方法，其特征在于，本方法包括下述步骤：
A、获得实时电力信号：实时采集居民侧配电箱的实时电力数 据，然后将采集到的实时电力数据进行转换以获得实时电力信号；
B、非侵入式负荷识别分析：将实时电力信号进行小波变换去噪、核密度估计检测事件、判断是否存在周期信号并计算周期、去周期性信号和提取趋势信号、聚类电力信号、提取电力信号特征，从而获得对应于居民侧配电箱的每个家用电器的功耗数据和实时状态信息；
C、结果反馈：将分析获得的居民侧配电箱的每个家用电器的功耗数据和实时状态信息反馈给与居民侧配电箱相应的居民侧用户。
在上述的非侵入式在线实时电力负荷识别方法中，在上述的步骤B中，
①小波变换去噪：设定实时电力信号yi与真实电力信号f(xi)关系为：yi＝f(xi)+ei,i∈{1,...,n}，其中，ei为误差，n为自然数；
根据小波变换的原理：
Figure PCTCN2017087413-appb-000001
Figure PCTCN2017087413-appb-000002
φ(x)＝I(0,1)(x)；
其中，
Figure PCTCN2017087413-appb-000003
为尺度系数，
Figure PCTCN2017087413-appb-000004
为细节系数，
Figure PCTCN2017087413-appb-000005
为基函数；
设定误差ei服从均值为0的高斯分布，设定阀值从而对实时电力信号去噪；
选取阀值为：
Figure PCTCN2017087413-appb-000006
其中，N为信号长度，
Figure PCTCN2017087413-appb-000007
为鲁棒估计量；小波变换去噪通过时频分析，去掉高频噪声信号，保留低频信号。
②核密度估计检测事件：将去噪后的实时电力信号用核密度估计方法估计信号分布情况，
密度函数如下：
Figure PCTCN2017087413-appb-000008
其中，K为密度函数，y是原始信号，xi是密度函数的期望值，h是密度函数的带宽，若信号分布具有两个或两个以上的峰值点，则判断结果为有事件发生，否则无事件；
③判断是否存在周期信号并计算周期、去周期性信号和提取趋势信号：对于有事件发生的实时电力信号，判断是否有周期性信号存在，
计算信号的自相关系数：
Figure PCTCN2017087413-appb-000009
若信号内部存在相关性，即自相关系数不小于0.95，则采用Hodrick–Prescott filter优化算法求解去周期性信号，具体实现过程如下：
Figure PCTCN2017087413-appb-000010
求解结果
Figure PCTCN2017087413-appb-000011
为去周期性信号，其中，y为原始信号，λ为惩罚系数；所述的去周期性信号去掉能量震荡信号，保留隐藏在能量震动下的趋势信号，从而实现提取趋势信号；
④聚类电力信号：根据提取到的趋势信号，再用基于密度聚类的算法求解出离群点，所述的离群点本质就是事件的暂态信号，具体过程如下：将所有点标记为核心点、边界点或噪声点；删除噪声点；为距离在阀值之内的所有核心点之间赋予一条边；每组连通的 核心点形成一个簇；将每个边界点指派到一个与之关联的核心点的簇中；从而实现用基于密度的聚类方法来分离暂态信号和稳态信号，实现定位暂态信号；
⑤提取电力信号特征：利用深度学习进行特征压缩及无监督的基于密度的聚类算法进行特征识别。
在上述的非侵入式在线实时电力负荷识别方法中，在上述的步骤A中，所述的实时电力数据包括实时电压和实时电流；所述的实时电力数据转换为实时有功功率和实时无功功率信号。
在上述的非侵入式在线实时电力负荷识别方法中，所述的实时电力信号通过无线和/或有线通讯方式传输至云端，再通过无线和/或有线通讯方式从云端传输至后台服务器，所述的非侵入式负荷识别分析在后台服务器中进行。
在上述的非侵入式在线实时电力负荷识别方法中，所述的实时电力信号通过以太网和/或WiFi通信协议传输至云端，再通过因特网从云端传输至后台服务器。
一种应用上述非侵入式在线实时电力负荷识别方法的非侵入式在线实时电力负荷识别系统，其特征在于，本系统包括至少一个连接在居民侧配电箱上且用于获得实时电力信号的嵌入式设备终端，所述的嵌入式设备终端通过无线和/或有线通讯方式与云端相连，所述的云端通过无线和/或有线通讯方式与能够对实时电力信号进行非侵入式负荷识别分析的后台服务器相连，所述的后台服务器上连接有数据存储器且能够通过无线和/或有线通讯方式将分析结果传送至对应于居民侧配电箱的终端设备。
在上述的非侵入式在线实时电力负荷识别系统中，所述的实时电力信号通过以太网和/或WiFi通信协议传输至云端，再通过因特网从云端传输至后台服务器。
在上述的非侵入式在线实时电力负荷识别系统中，所述的嵌 入式设备终端有多个且采用分布式连接方式与云端相连。
在上述的非侵入式在线实时电力负荷识别系统中，所述的终端设备为移动式终端设备和/或PC。
在上述的非侵入式在线实时电力负荷识别系统中，所述的家用电器包括大负荷用电设备和/或小负荷用电设备。
与现有的技术相比，非侵入式在线实时电力负荷识别方法及识别系统的优点在于：对于居民侧用户使用成本低廉、不需要大量有标签的样本即可进行训练、对小负荷电器也非常敏感、同时能解决电能震荡问题、且保证负荷识别的准确率；继而能为家庭提供整体的能源解决方案；此外，算法效率可以达到在线和实时的效果。
附图说明
图1为本发明提供的结构框图。
图2为本发明提供的事件检测算法流程原理图。
图3为本发明提供的深度学习的网架结构原理图。
图4为本发明提供的原始电力信号图。
图5为本发明提供的核方法判断事件效果图。
图6为本发明提供的嵌入式设备终端的部分结构框图。
图7为本发明提供的嵌入式设备终端的部分电路图。
图中，居民侧配电箱1、交流电压源11、交流电流源相连12、云端2、后台服务器3、嵌入式设备终端4、第一运算电路41、第二运算电路42、供电电源43、变压器44、整流电路45、滤波电路46、稳压电路47、平滑输出电压电路48、数据存储器6、终端设备7。
具体实施方式
如图1-7所示，本非侵入式在线实时电力负荷识别方法，包括下述步骤：
A、获得实时电力信号：实时采集居民侧配电箱1的实时电力数据，然后将采集到的实时电力数据进行转换以获得实时电力信号；其中实时电力数据包括实时电压和实时电流等运行数据；实时电力数据转换为实时有功功率和实时无功功率信号。
B、非侵入式负荷识别分析：将实时电力信号进行小波变换去噪、核密度估计检测事件、判断是否存在周期信号并计算周期、去周期性信号和提取趋势信号、聚类电力信号、提取电力信号特征，从而获得对应于居民侧配电箱1的每个家用电器的功耗数据和实时状态信息；
C、结果反馈：将分析获得的居民侧配电箱1的每个家用电器的功耗数据和实时状态信息反馈给与居民侧配电箱1相应的居民侧用户。
这里的实时电力信号通过无线和/或有线通讯方式传输至云端2，再通过无线和/或有线通讯方式从云端2传输至后台服务器3，所述的非侵入式负荷识别分析在后台服务器3中进行。实时电力信号通过以太网和/或WiFi通信协议传输至云端2，再通过因特网从云端2传输至后台服务器3。
更具体地说，在上述的步骤B中，
①小波变换去噪：设定实时电力信号yi与真实电力信号f(xi)关系为：yi＝f(xi)+ei,i∈{1,...,n}，其中，ei为误差，n为自然数；
根据小波变换的原理：
Figure PCTCN2017087413-appb-000012
Figure PCTCN2017087413-appb-000013
φ(x)＝I(0,1)(x)；
其中，
Figure PCTCN2017087413-appb-000014
为尺度系数，
Figure PCTCN2017087413-appb-000015
为细节系数，
Figure PCTCN2017087413-appb-000016
为基函数；
设定误差ei服从均值为0的高斯分布，设定阀值从而对实时电力信号去噪；
选取阀值为：
Figure PCTCN2017087413-appb-000017
其中，N为信号长度，
Figure PCTCN2017087413-appb-000018
为鲁棒估计量；小波变换去噪通过时频分析，去掉高频噪声信号，保留低频信号。
②核密度估计检测事件：将去噪后的实时电力信号用核密度估计方法估计信号分布情况，
密度函数如下：
Figure PCTCN2017087413-appb-000019
其中，K为密度函数，y是原始信号，xi是密度函数的期望值，h是密度函数的带宽，若信号分布具有两个或两个以上的峰值点，则判断结果为有事件发生，否则无事件；
③判断是否存在周期信号并计算周期、去周期性信号和提取趋势信号：对于有事件发生的实时电力信号，判断是否有周期性信号存在，
计算信号的自相关系数：
Figure PCTCN2017087413-appb-000020
若信号内部存在相关性，即自相关系数不小于0.95，则采用Hodrick–Prescott filter优化算法求解去周期性信号，具体实现过程如下：
Figure PCTCN2017087413-appb-000021
求解结果
Figure PCTCN2017087413-appb-000022
为去周期性信号，其中，y为原始信号，λ为惩罚系数；所述的去周期性信号去掉能量震荡信号，保留隐藏在能量震动下的趋势信号，从而实现提取趋势信号；
④聚类电力信号：根据提取到的趋势信号，再用基于密度聚类的算法求解出离群点，所述的离群点本质就是事件的暂态信号，具体过程如下：将所有点标记为核心点、边界点或噪声点；删除噪声点；为距离在阀值之内的所有核心点之间赋予一条边；每组连通的核心点形成一个簇；将每个边界点指派到一个与之关联的核心点的簇中；从而实现用基于密度的聚类方法来分离暂态信号和稳态信号，实现定位暂态信号；
⑤提取电力信号特征：利用深度学习进行特征压缩及无监督的基于密度的聚类算法进行特征识别。
一种应用非侵入式在线实时电力负荷识别方法的非侵入式在线实时电力负荷识别系统，其特征在于，本系统包括至少一个连接在居民侧配电箱1上且用于获得实时电力信号的嵌入式设备终端4，所述的嵌入式设备终端4通过无线和/或有线通讯方式与云端2相连，所述的云端2通过无线和/或有线通讯方式与能够对实时电力信号进行非侵入式负荷识别分析的后台服务器3相连，所述的后台服务器3上连接有数据存储器6且能够通过无线和/或有线通讯方式将分析结果传送至对应于居民侧配电箱1的终端设备7。实时电力信号通过以太网和/或WiFi通信协议传输至云端2，再通过因特网从云端2传输至后台服务器3。嵌入式设备终端4有多个且采用分布式连接方式与云端2相连。终端设备4为移动式终端设备和/或PC。例如智能手机、PAD、笔记本电脑等。家用 电器包括大负荷用电设备和/或小负荷用电设备，即冰箱，空调，手机充电器，点灯，电脑等。
本申请不需要大量有标签的样本做训练；能识别大负荷及小负荷用电设备；利用少量的训练样本，识别准确率相对较高；硬件成本低廉，容易部署在居民住所。本申请专利是基于信号处理，机器学习，人工智能等技术做非侵入式负荷识别，继而为家庭提供整体的能源解决方案。其中包括的核心技术主要有用小波分析去噪，核密度检测事件，用自相关系数和优化方法去周期性信号并提取趋势信息，用基于密度的聚类方法来分离暂态信号和稳态信号以及用深度学习中稀疏自编码技术提取电力信号特征等。
如图6和7所示，嵌入式设备终端4包括相互连接的第一运算电路41和第二运算电路42，所述的第一运算电路41和第二运算电路42均与供电电源43相连。第一运算电路41和第二运算电路42分别连接于居民侧配电箱1(即第一运算电路41和第二运算电路42分别与交流电压源11和交流电流源相连12)。交流电压源11依次连接有变压器44、整流电路45、滤波电路46、稳压电路47和平滑输出电压电路48。
本文中所描述的具体实施例仅仅是对本发明精神作举例说明。本发明所属技术领域的技术人员可以对所描述的具体实施例做各种各样的修改或补充或采用类似的方式替代，但并不会偏离本发明的精神或者超越所附权利要求书所定义的范围。
尽管本文较多地使用了居民侧配电箱1、交流电压源11、交流电流源相连12、云端2、后台服务器3、嵌入式设备终端4、第一运算电路41、第二运算电路42、供电电源43、变压器44、整流电路45、滤波电路46、稳压电路47、平滑输出电压电路48、数据存储器6、终端设备7等术语，但并不排除使用其它术语的可能性。使用这些术语仅仅是为了更方便地描述和解释本发明的 本质；把它们解释成任何一种附加的限制都是与本发明精神相违背的。

Claims (10)

  1. 一种非侵入式在线实时电力负荷识别方法，其特征在于，本方法包括下述步骤：
    A、获得实时电力信号：实时采集居民侧配电箱(1)的实时电力数据，然后将采集到的实时电力数据进行转换以获得实时电力信号；
    B、非侵入式负荷识别分析：将实时电力信号进行小波变换去噪、核密度估计检测事件、判断是否存在周期信号并计算周期、去周期性信号和提取趋势信号、聚类电力信号、提取电力信号特征，从而获得对应于居民侧配电箱(1)的每个家用电器的功耗数据和实时状态信息；
    C、结果反馈：将分析获得的居民侧配电箱(1)的每个家用电器的功耗数据和实时状态信息反馈给与居民侧配电箱(1)相应的居民侧用户。
  2. 根据权利要求1所述的非侵入式在线实时电力负荷识别方法，其特征在于，在上述的步骤B中，
    ①小波变换去噪：设定实时电力信号yi与真实电力信号f(xi)关系为：yi＝f(xi)+ei,i∈{1,...,n}，其中，ei为误差，n为自然数；
    根据小波变换的原理：
    Figure PCTCN2017087413-appb-100001
    Figure PCTCN2017087413-appb-100002
    φ(x)＝I(0,1)(x)；
    其中，
    Figure PCTCN2017087413-appb-100003
    为尺度系数，
    Figure PCTCN2017087413-appb-100004
    为细节系数，
    Figure PCTCN2017087413-appb-100005
    为基函数；
    设定误差ei服从均值为0的高斯分布，设定阀值从而对实时电力信号去噪；
    选取阀值为：
    Figure PCTCN2017087413-appb-100006
    其中，N为信号长度，
    Figure PCTCN2017087413-appb-100007
    为鲁棒估计量；
    ②核密度估计检测事件：将去噪后的实时电力信号用核密度估计方法估计信号分布情况，
    密度函数如下：
    Figure PCTCN2017087413-appb-100008
    其中，K为密度函数，y是原始信号，xi是密度函数的期望值，h是密度函数的带宽，若信号分布具有两个或两个以上的峰值点，则判断结果为有事件发生，否则无事件；
    ③判断是否存在周期信号并计算周期、去周期性信号和提取趋势信号：对于有事件发生的实时电力信号，判断是否有周期性信号存在，
    计算信号的自相关系数：
    Figure PCTCN2017087413-appb-100009
    若信号内部存在相关性，即自相关系数不小于0.95，则采用Hodrick–Prescott filter优化算法求解去周期性信号，具体实现过程如下：
    Figure PCTCN2017087413-appb-100010
    求解结果
    Figure PCTCN2017087413-appb-100011
    为去周期性信号，其中，y为原始信号，λ为惩罚系数；所述的去周期性信号去掉能量震荡信号，保留隐藏在能量震动下的趋势信号，从而实现提取趋势信号；
    ④聚类电力信号：根据提取到的趋势信号，再用基于密度聚类的算法求解出离群点，所述的离群点本质就是事件的暂态信号，具 体过程如下：将所有点标记为核心点、边界点或噪声点；删除噪声点；为距离在阀值之内的所有核心点之间赋予一条边；每组连通的核心点形成一个簇；将每个边界点指派到一个与之关联的核心点的簇中；从而实现用基于密度的聚类方法来分离暂态信号和稳态信号，实现定位暂态信号；
    ⑤提取电力信号特征：利用深度学习进行特征压缩及无监督的基于密度的聚类算法进行特征识别。
  3. 根据权利要求1或2所述的非侵入式在线实时电力负荷识别方法，其特征在于，在上述的步骤A中，所述的实时电力数据包括实时电压和实时电流；所述的实时电力数据转换为实时有功功率和实时无功功率信号。
  4. 根据权利要求1或2所述的非侵入式在线实时电力负荷识别方法，其特征在于，所述的实时电力信号通过无线和/或有线通讯方式传输至云端(2)，再通过无线和/或有线通讯方式从云端(2)传输至后台服务器(3)，所述的非侵入式负荷识别分析在后台服务器(3)中进行。
  5. 根据权利要求4所述的非侵入式在线实时电力负荷识别方法，其特征在于，所述的实时电力信号通过以太网和/或WiFi通信协议传输至云端(2)，再通过因特网从云端(2)传输至后台服务器(3)。
  6. 一种应用权利要求1-5中任意一项所述的非侵入式在线实时电力负荷识别方法的非侵入式在线实时电力负荷识别系统，其特征在于，本系统包括至少一个连接在居民侧配电箱(1)上且用于获得实时电力信号的嵌入式设备终端(4)，所述的嵌入式设备终端(4)通过无线和/或有线通讯方式与云端(2)相连，所述的云端(2)通过无线和/或有线通讯方式与能够对实时电力信号进行非侵入式负荷识别分析的后台服务器(3)相连，所述的后台服务器(3)上连接有数据存储器(6)且能够通过无线和/或有线通 讯方式将分析结果传送至对应于居民侧配电箱(1)的终端设备(7)。
  7. 根据权利要求6所述的非侵入式在线实时电力负荷识别系统，其特征在于，所述的实时电力信号通过以太网和/或WiFi通信协议传输至云端(2)，再通过因特网从云端(2)传输至后台服务器(3)。
  8. 根据权利要求7所述的非侵入式在线实时电力负荷识别系统，其特征在于，所述的嵌入式设备终端(4)有多个且采用分布式连接方式与云端(2)相连。
  9. 根据权利要求6或7或8所述的非侵入式在线实时电力负荷识别系统，其特征在于，所述的终端设备(4)为移动式终端设备和/或PC。
  10. 根据权利要求6或7或8所述的非侵入式在线实时电力负荷识别系统，其特征在于，所述的家用电器包括大负荷用电设备和/或小负荷用电设备。
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