Power quality analysis issues, especially the measurement of harmonic and interharmonic in cyber-physical energy systems, are addressed in this paper. As new situations are introduced to the power system, the impact of electric vehicles, distributed generation and renewable energy has introduced extra demands to distributed sensors, waveform-level information and power quality data analytics. Harmonics and interharmonics, as the most significant disturbances, require carefully designed detection methods for an accurate measurement of electric loads whose information is crucial to subsequent analyzing and control. This paper gives a detailed description of the power quality analysis framework in networked environment and presents a fast and resolution-enhanced method for harmonic and interharmonic measurement. The proposed method first extracts harmonic and interharmonic components efficiently using the single-channel version of Robust Independent Component Analysis (RobustICA), then estimates the high-resolution frequency from three discrete Fourier transform (DFT) samples with little additional computation, and finally computes the amplitudes and phases with the adaptive linear neuron network. The experiments show that the proposed method is time-efficient and leads to a better accuracy of the simulated and experimental signals in the presence of noise and fundamental frequency deviation, thus providing a deeper insight into the (inter)harmonic sources or even the whole system.

Cyber-physical energy systems provide a networked solution for safety, reliability and efficiency problems in smart grids. On the demand side, the secure and trustworthy energy supply requires real-time supervising and online power quality assessing. Harmonics measurement is necessary in power quality evaluation. However, under the large-scale distributed metering architecture, harmonic measurement faces the out-of-sequence measurement (OOSM) problem, which is the result of latencies in sensing or the communication process and brings deviations in data fusion. This paper depicts a distributed measurement network for large-scale asynchronous harmonic analysis and exploits a nonlinear autoregressive model with exogenous inputs (NARX) network to reorder the out-of-sequence measuring data. The NARX network gets the characteristics of the electrical harmonics from practical data rather than the kinematic equations. Thus, the data-aware network approximates the behavior of the practical electrical parameter with real-time data and improves the retrodiction accuracy. Theoretical analysis demonstrates that the data-aware method maintains a reasonable consumption of computing resources. Experiments on a practical testbed of a cyber-physical system are implemented, and harmonic measurement and analysis accuracy are adopted to evaluate the measuring mechanism under a distributed metering network. Results demonstrate an improvement of the harmonics analysis precision and validate the asynchronous measuring method in cyber-physical energy systems.

Within the trend of smart grid, distributed and intelligent metering has gained its popularity among large scale residential power network. Supervising and assessing power quality in time is the method to guarantee secure and trustworthy energy for the demand side, and harmonics measurement and analysis are of necessity. Harmonics metering under the large-scale distributed metering architecture faces the out-of-sequence measurement (OOSM) problem which brings latency in communication and data fusion. This paper depicts a distributed measuring schedule for large scale harmonic analysis, in which compressive sensing (CS) is utilized to decrease the harmonic sampling length and transform packets, and Nonlinear Autoregressive model with Exogenous inputs (NARX) model is exploited to reorder the out-of-sequence measuring data at the meter data center. Experiments on a practical residential power utilization environment are implemented and harmonic identification accuracy is adopted to evaluate the measuring mechanism under distributed metering network. Results demonstrate a improvement of the harmonics analysis precision and validate the asynchronous measuring method in large scale residential power network.

Distributed harmonic measurement is one of the key characters for power quality evaluation in residential monitoring networks. Synchronization of the electrical sensing node of the network is important in distributed harmonic measurement. The synchronization method needs to be time-efficient, accurate and trustworthy. Pulse-coupled oscillators (PCO) has been imported as the synchronization algorithm for its fast synchronization rate and scalability. This paper theoretically analyzes the synchronization criteria for distributed networks and, to raise the measuring reliability, proposes a Quadratic Polynomial Model(QPM) for time-efficient and trustworthy synchronization. Factors that affect PCO performance are analysed, the PCO dynamic model optimized to improve the synchronize rate and parameters are verified with simulation. Experiment results demonstrate that the proposed model effectively reduced the elapsed time to reach synchronization without increasing energy cost and complexity. Finally the effectiveness of PCO is certificated through applying the method in distributed harmonic measurement.