The distribution grid is undergoing profound changes nowadays, and state estimator has become an essential part in the control room to enable future smart grid. The newly deployed micro phase measurement units (μPMU) and advanced metering infrastructure (AMI) devices make the real-time state estimation possible for distribution system. Therefore, this paper studies the incorporation and process of the multi-source measurements and proposes a fast three-phase state estimation based on the hybrid measurements scheme. By the process of μPMU measurements, pseudo voltage measurements are added, which significantly increases the estimation redundancy. For AMI measurements, the harmonic components model is used to overcome the asynchronicity problem. An improved sequential state estimation based on the changed measurements is proposed to enable a fast estimation. IEEE 13-node and 390-node systems are simulated to verify the efficacy and efficiency of the proposed method.INDEX TERMS Advanced metering infrastructure (AMI), micro phase measurement units (μPMU), sequential state estimation, hybrid measurements scheme.
Compared to traditional measurement devices, the micro-synchrophasor measurement unit (D-PMU or μPMU) in the distribution power system has great differences in data acquisition frequency, data format, data dimension, time-stamped information, etc. Hence, it is imperative to research the integration mechanism of heterogeneous data from multiple sources. Based on the analysis of the current technology of multi-source information fusion, this paper proposes a novel approach, which considers two aspects: the interoperability of multi-source data and the real-time processing of large-scale streaming data. To solve the problem of data interoperability, we have modified the model of D-PMU data and established a unified information model. Meanwhile, an advanced distributed processing technology has been deployed to solve the problem of real-time processing of streaming data. Based on this approach, a smart distribution power system wide-area measurement and control station can be established, and the correctness and practicality of the proposed method are verified by an on-field project.
Mounting concerns pertaining to energy efficiency have led to the research of load monitoring. By Non-Intrusive Load Monitoring (NILM), detailed information regarding the electric energy consumed by each appliance per day or per hour can be formed. The accuracy of the previous residential load monitoring approach relies heavily on the data acquisition frequency of the energy meters. It brings high overall cost issues, and furthermore, the differentiating algorithm becomes much more complicated. Based on this, we proposed a novel non-Intrusive residential load disaggregation method that only depends on the regular data acquisition speed of active power measurements. Additionally, this approach brings some novelties to the traditionally used denoising Auto-Encoder (dAE), i.e., the reconfiguration of the overlapping parts of the sliding windows. The median filter is used for the data processing of the overlapping window. Two datasets, i.e., the Reference Energy Disaggregation Dataset (REDD) and TraceBase, are used for test and validation. By numerical testing of the real residential data, it proves that the proposed method is superior to the traditional Factorial Hidden Markov Model (FHMM)-based approach. Furthermore, the proposed method can be used for energy data, disaggregation disregarding the brand and model of each appliance.
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