This paper presents a method for contactless measurement of instantaneous voltage and waveform reconstruction for use in a medium voltage (MV) indoor substation. Voltage waveforms are reconstructed by artificial neural network (ANN) using the signals originating from electric field sensors located under MV bus. The method is validated by the experiment in a typical indoor MV substation. Depending on the selection of ANN architecture and data used for training the root mean squared error of waveforms, reconstruction as low as 0.3% to 2.1% can be achieved in a steady state. The practical advice on the application of the proposed measurement method in a production environment is also given.

The new, noninvasive method of measurement for power system harmonic impedance is presented in the paper. The method is based on the observation of variations of voltage and current harmonics caused by natural load variability. The novelty of the method lays in a way the method rejects segments of data containing insufficient or false information about the identified power system. Segment selection/rejection uses logical conditions applied to RMS values of measured voltage and current. Harmonic impedance of the power grid is then calculated using modified power spectral method. The method was validated in a field test conducted at 30 kV feeder bus of HV/MV substation. Details of the experiment and the results are discussed the paper. Obtained results confirm that the method copes with temporal, band-limited excitation as well as with deep, sudden changes of utility background voltage.

Measurements of medium and high voltages in a power grid are normally performed with large and bulky voltage transformers or capacitive dividers. Besides installation problems, these devices operate in a relatively narrow frequency band, which limits their usability in modern systems that are saturated with power electronic devices. A sensor that can be installed directly on a wire and can operate without a galvanic connection to the ground may be used as an alternative voltage measurement device. This type of voltage sensor can complement current sensors installed on a wire, forming a complete power acquisition system. This paper presents such a sensor. Our sensor is built using two dielectric elements with different permeability coefficients. A finite element method simulation is used to estimate the parameters of a constructed sensor. Besides simulations, a laboratory model of a sensor was built and tested in a medium-voltage substation. Our results provide a proof of concept for the presented sensor. Some errors in voltage reconstruction have been traced to an oversimplified data acquisition and transmission system, which has to be improved during the further development of the sensor.

This paper describes a new method of power-grid signal variation measurement, consisting of a demodulation algorithm and a set of measures (indices). The measures that will be presented are the abstract functionals, operating on the complex envelope of a signal. This paper presents a demodulation algorithm, definitions of proposed functionals, results of simulations, as well as laboratory experiments.

The paper concerns important problem: estimation of power system equivalent parameters. Knowledge of these parameters is needed to determine disturbances propagation ability through power grid. Authors present new techniques, which may significantly improve accuracy of noninvasive power system identification. These are: voltage and current coherent resampling algorithm, which ensures that sampling frequency match actual fundamental frequency of power system, so the DFT analysis is almost leakage-free; envelope detection algorithm which allows to estimate equivalent impedance of the load and its variability for fundamental frequency; the method of load change detection ensuring that only fragments having sufficient amount of information for power system identification will be used in parameters estimation. The proposed methods were tested using laboratory power system model of known parameters. Presented results of laboratory experiments confirmed correctness of the described methods.