Non-Intrusive Power Load Monitoring And Disaggregation Technique

Electricity consumption details monitoring is of great significance for utilitiesoptimizing the operation, planning and management of the power grid, guiding theintelligent usage of electricity for consumers to save electricity consumption and fee,accelerating energy-efficiency technology innovation and inducing energy-efficiencymarket transformation, and improving the whole society of consciousness ofecological civilization and fulfilling it into practical action.A novel electricity consumption details monitoring technique, Non-IntrusivePower Load Monitoring and Decomposition (NILMD), is focused on with theobjective of obtaining the power consumption and operating state of each (or class of)electrical equipment within a total power load. The NILMD technology can breakthrough the bottleneck facing current smart meters that only the total powerconsumption information is collected and sent above to the data centers. Comparedwith intrusive power load monitoring technology, many advantages are provided bythe non-intrusive one, such as easy operation, low cost, high reliability, good dataintegrity, ease of rapid promotion and so on.Based on the review of the research status and development tend of the subject,some basic understanding is generalized：①The existence and repeatability of theLoad Signature (LS) are the fundamental assumption and necessary condition forimplementing NILMD.②Since it is reasonabe that the NILMD problem isformulated as the pattern recognition problem, the NILMD System can be establishedby the analogy with the pattern recognition system, and the necessary functionalmodules are further determined.③On the basis of the fact that every operating stateof electrical equipment corresponds to a set of LS, the basic task of NILMD is to seekthe mathematical model and solving method, based on which the power component ofeach (class of) electrical equipment can be estimated by the voltage and/or aggregatedcurrent at the customer entry of electrticity. Finally, several key technical problemswhich have not been successfully addressed are summarized.Based on the vectorized representation of the operating state of total load, aNILMD-method built on electric current pattern matching is proposed. In order tocomplete the optimal matching between the measured current pattern (harmoniccharacteristics) and the estimated one, the commom optimization algorithms aren’t utilized, but a novel table-looking method is established in view of the advantages ofthe instance-based learning method. A large number of cases prove that the proposedmethod can accurately monitor the operating state and power consumption of eachelectrical equipment, from which the electricity usage information of every class ofequipment can be easily derived. The proposed method solves the problem thatsimultaneous or aliasing load events would cause false identification, enables theeffective monitoring of finite state machine (FSM), continuous variable state machine(CVSM) and the "always-on" or long-period electrical equipment, and also improvesthe accuracy of discriminating the similar electrical equipment. Furthermore, therequirement of NILMD system for the microprocessor computational performance iseffectively lowered by the suggested method, which is beneficial for reasonablesystem cost reduction.