Comparison of lossless compression schemes for high rate electrical grid time series for smart grid monitoring and analysis

The smart power grid of the future will utilize waveform level monitoring with sampling rates in the kilohertz range for detailed grid status assessment. To this end, we address the challenge of handling large raw data amount with its quasi-periodical characteristic via lossless compression. We compare different freely available algorithms and implementations with regard to compression ratio, computation time and working principle to find the most suitable compression strategy for this type of data. Algorithms from the audio domain (ALAC, ALS, APE, FLAC & TrueAudio) and general archiving schemes (LZMA, Delfate, PPMd, BZip2 & Gzip) are tested against each other. We assemble a dataset from openly available sources (UK-DALE, MIT-REDD, EDR) and establish dataset independent comparison criteria. This combination is a first detailed open benchmark to support the development of tailored lossless compression schemes and a decision support for researchers facing data intensive smart grid measurement

Advanced Wide-Area Monitoring System Design, Implementation, and Application

Wide-area monitoring systems (WAMSs) provide an unprecedented way to collect, store and analyze ultra-high-resolution synchrophasor measurements to improve the dynamic observability in power grids. This dissertation focuses on designing and implementing a wide-area monitoring system and a series of applications to assist grid operators with various functionalities. The contributions of this dissertation are below: First, a synchrophasor data collection system is developed to collect, store, and forward GPS-synchronized, high-resolution, rich-type, and massive-volume synchrophasor data. a distributed data storage system is developed to store the synchrophasor data. A memory-based cache system is discussed to improve the efficiency of real-time situation awareness. In addition, a synchronization system is developed to synchronize the configurations among the cloud nodes. Reliability and Fault-Tolerance of the developed system are discussed. Second, a novel lossy synchrophasor data compression approach is proposed. This section first introduces the synchrophasor data compression problem, then proposes a methodology for lossy data compression, and finally presents the evaluation results. The feasibility of the proposed approach is discussed. Third, a novel intelligent system, SynchroService, is developed to provide critical functionalities for a synchrophasor system. Functionalities including data query, event query, device management, and system authentication are discussed. Finally, the resiliency and the security of the developed system are evaluated. Fourth, a series of synchrophasor-based applications are developed to utilize the high-resolution synchrophasor data to assist power system engineers to monitor the performance of the grid as well as investigate the root cause of large power system disturbances. Lastly, a deep learning-based event detection and verification system is developed to provide accurate event detection functionality. This section introduces the data preprocessing, model design, and performance evaluation. Lastly, the implementation of the developed system is discussed

Real-time D-PMU data compression for edge computing devices in digital distribution networks

The proliferation of distribution-level phasor measurement units (D-PMUs) with a high reporting rate brings a heavy transmission burden to communication systems of distribution networks, which necessitate efficient data compression on edge computing devices. This paper proposes a real-time D-PMU data compression algorithm, including three stages of prediction, quantization, and Bitpack. The current data frame is predicted by the adaptive normalized least mean square predictor based on the stochastic gradient descent algorithm. Then, the prediction errors are quantized as integers and Bitpack is established to extract significant bits and losslessly reduce the redundancy of the quantized data. For edge computing devices accessing multiple D-PMUs in distribution networks, a performance optimization mechanism is proposed. The spatial similarity of D-PMUs is explored to multiplex the predictor and reduce the computation burden. In addition, the compression performances can be adaptively adjusted to diminish the transmission delay in limited bandwidth conditions. Finally, the proposed method is validated and compared with the state-of-the-art methods using the field data and simulated data in normal and fault conditions of distribution networks. Moreover, the impacts and countermeasures of data quality are considered. The results demonstrate that the proposed method achieves accurate and efficient real-time compression in different scenarios

Adding power of artificial intelligence to situational awareness of large interconnections dominated by inverter‐based resources

Large-scale power systems exhibit more complex dynamics due to the increasing integration of inverter-based resources (IBRs). Therefore, there is an urgent need to enhance the situational awareness capability for better monitoring and control of power grids dominated by IBRs. As a pioneering Wide-Area Measurement System, FNET/GridEye has developed and implemented various advanced applications based on the collected synchrophasor measurements to enhance the situational awareness capability of large-scale power grids. This study provides an overview of the latest progress of FNET/GridEye. The sensors, communication, and data servers are upgraded to handle ultra-high density synchrophasor and point-on-wave data to monitor system dynamics with more details. More importantly, several artificial intelligence (AI)-based advanced applications are introduced, including AI-based inertia estimation, AI-based disturbance size and location estimation, AI-based system stability assessment, and AI-based data authentication

SOFTWARE BASED EXPERIMENTAL SYSTEM FOR ELECTRICAL POWER QUALITY MEASUREMENT USING THE WIRELESS SENSOR NETWORK MODULES

Experimental system for measurement of standard electrical power quality (PQ) parameters, based on wireless sensor network (WSN), is presented in this paper. System includes generator of reference voltage waveforms, software application for measurement of standard PQ parameters and two microcontroller based wireless sensor modules for transmitting and receiving of measurement results. Reference voltage signals are provided using signal generator with possibility for simulation of typical network disturbances, presented in some previously published papers. This PQ signal generator is functionally supported by the virtual instrumentation software and data acquisition card. Measurements of basic quality parameters for reference test signals are performed using the LabVIEW software application. Time interval for each measurement cycle is 1 s. For communication is used wireless sensor network based on communication standard IEEE 802.15.4 (Zigbee). Hardware configuration includes two wireless sensor modules SPaRCMosquito v.2, based on the microcontroller with Cortex M3 architecture. Transfer of measurement results, between computer and wireless sensor modules on transmitter and receiver points, are provided using standard USB interface