Evaluation and trends of power quality indices in distribution system

In recent years, the power quality problem has become more complicated in power grids because of the extensive usage of power electronics and multisource multitransformation features. The method, based on physical characteristics such as time domain, frequency domain and transform domain, is facing challenges in terms of adaptability, algorithm efficiency and accuracy for the recognition of complex disturbance recognition. The bidirectional long short-term memory network is an algorithm in deep learning. It is based on data for characterization learning, which can effectively overcome the problem of information loss and generalization ability of physical methods. Moreover, it has the characteristics of memory, which can simultaneously consider historical information and future information and can better learn data features with time series characteristics. Aiming at the transient voltage sag time series data, this paper proposes a recognition method of the voltage sag causes based on the bidirectional long short-term memory network's extraction eigenvalue, the full-connection layer's high-dimensional feature extraction and the Softmax network layer's classification. The experiment uses simulation data and measured data to prove that the model has good recognition ability and good antinoise performance in the recognition of voltage sag causes and can be reliably applied in practical engineering.

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“…boxes receiving the inputs. In each time step, an input vector is fed into LSTM, and the output is computed according to (2).…”

Section: Bi-lstm Network Layer 321 Lstm Formula Derivation Lstm Comentioning

confidence: 99%

“…At the same time, in power transmission, there are also a large number of nonlinear devices. All of these make modern power quality concepts a complex area covering multiple topics [1,2]. Voltage sag is one of the most important topics in the field.…”

Section: Introductionmentioning

confidence: 99%

Recognition method of voltage sag causes based on Bi‐LSTM

Zheng

Wang

et al. 2019

IEEJ Transactions Elec Engng

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“…There is concern among network operators and others that the growing numbers of electronic equipment will result in unacceptably high levels of voltage and current distortion. Some documents claim that the general trend of the harmonic content is increasing [1], [2] or at least during the time-span of the survey. This paper reports a survey done at totally five "computer festivals" where the first was documented in 2002 and the latest in 2008.…”

Section: Introductionmentioning

confidence: 98%

Waveform distortion at computer festivals; 2002 to 2008

Larsson

Wahlberg

Bollen

et al. 2008

2008 13th International Conference on Harmonics and Quality of Power

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This paper shows measurements of the harmonic emission by large groups of state-of-the-art computers measured at five different occasions from 2002 to 2008. The emission was measured during LAN-festivals where the participants use mainly the newest models of computer. This allows insight in the change in emission of computers during the last 6 years. The results from these measurements show that the distortion level generated by computers is decreasing while the power consumption does not show any significant change.

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“…Therefore the trend of harmonics has been interesting to follow and some reports show the tendency of an increase of harmonic content over the years, or at least during the survey [1], [2]. However these surveys are made upon the voltage.…”

Section: Introductionmentioning

confidence: 99%