Abstract
Phasor Measurement Units are the most advanced instruments for power network monitoring, since they allow phasors, frequency and rate of change of frequency (ROCOF) to be measured in predetermined time instants with respect to an absolute time reference. The employed estimation algorithm plays a key role in overall performance under off-nominal conditions; the challenge to be faced is combining high steady-state accuracy with fast responsiveness in dynamic conditions, small reporting latency and reduced computational burden. Under regular operation, AC power networks are weakly unbalanced three-phase systems. Based on this consideration, the recent literature has proposed native three-phase estimation algorithms that effectively exploit this property to accurately identify the positive sequence synchrophasor, frequency and ROCOF. In this respect, the present paper describes three among the most promising three-phase algorithms based on the Space Vector transformation. By means of numerical simulations, it compares the achieved performance in terms of response time and estimation error both under steady-state and dynamic conditions. All the considered approaches enable a flexible design that allows balancing accuracy and responsiveness. For this analysis, the reporting latency has been limited to about one and half nominal cycles, i.e., 30 ms at 50 Hz; the P-class algorithm suggested by IEC/IEEE Std 60255-118-1 has also been included as comparison benchmark.
Highlights
In recent years, modern power networks are experiencing an ever increasing integration of renewable energy sources and distributed generation, characterized by higher volatility and faster dynamics [1,2]
We present a comparison among three algorithms for estimating positive sequence synchrophasor, frequency and rate of change of frequency (ROCOF) starting from a three-phase signal
The second term is generated by the counter-rotating image component in (3), and is represented by the same scale factor multiplied by the synchrophasor conjugate and an unitary vector, that is rotating clockwise in the complex plane with angular speed equal to 2ω0 ; its frequency content is confined around −2 f 0
Summary
Modern power networks are experiencing an ever increasing integration of renewable energy sources and distributed generation, characterized by higher volatility and faster dynamics [1,2]. Several PMU algorithms exploits this symmetry property by using the Clarke/Park transformation [21] or Principal Component Analysis and Maximum Likelihood estimators [22] In this context, the Space Vector (SV) -based approach [23] can be adopted for reducing computational burden without sacrificing performance. Sci. 2021, 11, 2261 knowledge, this review represents the first attempt of a comparative analysis for highlighting peculiarities and strengths of the different methods For this purpose, we analyze the algorithms’ performance in terms of response time and measurement accuracy, both under steady-state and dynamic conditions; the P-class reference algorithm provided by the IEC.
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