Power Quality Prediction, Early Warning, and Control for Points of Common Coupling with Wind Farms

Bai, Jingjing; Gu, Wei; Yuan, Xiaodong; Qun, LI; Xue, Feng; Wang, Xuchong

doi:10.3390/en8099365

Cited by 17 publications

(6 citation statements)

References 24 publications

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“…The kinetic energy value embedded in a two-dimension space is used to distinguish PQ events. In [30], a PQ prediction approach for the common coupling points between wind farms and the network is proposed. To obtain PQ data a trend analysis model is established, which incorporates a distance-based cluster analysis, probability distribution analysis based on polynomial fitting, pattern matching based on similarity, and Monte Carlo random sampling.…”

Section: Introductionmentioning

confidence: 99%

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

et al. 2016

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Abstract:In electric power systems, there are always power quality disturbances (PQDs). Usually, noise contamination interferes with their detection and classification. Common methods perform frequency or time-frequency analyses on the power distribution signal for detecting and classifying a limited number of PQDs with some difficulties at low signal-to-noise ratio (SNR). In this regard, recently proposed methodologies for PQD detection estimate several parameters and apply distinct signal processing techniques to improve the detection of PQD. In this work, a novel methodology that merges empirical mode decomposition (EMD), the moments of a random variable, and an artificial neural network (ANN) is proposed for detecting and classifying different PQD. The proposed method estimates skewness, kurtosis, and Shannon entropy from the EMD of one-phase voltage/current signal. Then, an ANN is in charge of classifying the input signal into one of nine different classes for PQD, receiving these parameters as inputs. The effectiveness of the proposed method was verified through computer simulations and experimentation with real data. Obtained results demonstrate its high effectiveness reaching an outstanding 100% of accuracy in detecting and classifying all treated PQD through a few number of parameters, outperforming most of previously proposed approaches.

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Section: Introductionmentioning

confidence: 99%

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

et al. 2016

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“…Off‐grid systems using RE are usually supported by detached energy storage to regulate the power supply. This leads to additional needs for PQ optimization when considering the recharge state compared with conventional grids 4 . The intermittent nature of RE power can cause undesirable spikes in PQ with consequent system instability to a greater extent than using traditional energy 5 .…”

Section: Introductionmentioning

confidence: 99%

Power quality statistical predictions based on differential, deep and probabilistic learning using off‐grid and meteo data in 24‐hour horizon

Zjavka

2021

Intl J of Energy Research

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Off-grid systems using renewable energy (RE) are dependent on the stochastic power supply, which results in a high level of uncertainty, noise, and variability in the operational conditions. Power quality (PQ) norms specify allowable variations in relevant parameters of grid systems, necessary to maintain certain limits to guarantee their fault-tolerant states. 24-hour PQ prediction is vital in planning power consumption and utilization in smart off-grid houses based on RE. PQ data for all possible combinations of grid-attached household appliances and variable outside conditions cannot be measured completely or described exactly by physical equations. PQ predictions on daily bases using artificial intelligence (AI) models are requisite because atmospheric fluctuations and anomalies in local weather primarily influence the induced power and potential operation mode in real off-grids. Load specifics and possible collisions of the switched-on power consumers together with alterations in RE production can lead to additional disturbances in PQ and the consequent instability of the autonomous system. An automatic selection algorithm can give preliminary suggestion or correct several eventual energy consumption variants based on load time-shifting to allow efficient utilization of the predicted photovoltaic power (PVP). PQ models can after examine feasible daily load scenarios, offered by the system or re-defined by the users, scheduling selected equipment in the optimal switch intervals to ensure the system stability primarily. Users will have several practical options to choose from or combine the best ones to meet their demand in the optimal PQ and load planning strategy. This 2-step self-determination and verification procedure can help in effective operation of smart-grids in consolidation of their future state, balancing its power demands with local RE potential. The AI statistical models were evolved with the pre-assessed lengths of daily data periods. After that, they are applied to the last unseen data series, used in testing, to predict one-step sequences of the target PQ parameters in the trained all-day horizon.Parametric C++ application software with applied PQ and weather data is available for free to allow reproducibility of the results.

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“…Researches have been performed to eliminate the detrimental impacts on the power quality by EV chargers . A unified power quality conditioner (UPQC), which combines a serial active power filter with a parallel active power filter, is an integrated compensator that improves power quality . However, its utilization in local distribution networks is limited because of its prohibitive cost and complicated control.…”

Section: Introductionmentioning

confidence: 99%

“…9,18 A unified power quality conditioner (UPQC), which combines a serial active power filter with a parallel active power filter, is an integrated compensator that improves power quality. [19][20][21] However, its utilization in local distribution networks is limited because of its prohibitive cost and complicated control. EV charging stations support UPQC to local power distribution networks, which will contribute to high rates of return investments and improved power quality.…”

Section: Introductionmentioning

confidence: 99%

Electric vehicle charging station microgrid providing unified power quality conditioner support to local power distribution networks

Zhong

Xia

Chiang

2016

Int. Trans. Electr. Energ. Syst.

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Power grids will benefit when electric vehicle (EV) charging stations provide more services in addition to charging. This paper presents a new configuration of the unified power quality conditioner (UPQC) that uses a microgrid in an EV charging station. A double‐layer direct current structure is used in the microgrid to connect EVs in parallel and restrain direct current voltage variations in the UPQC. Control of the UPQC is modified to address the charging of EVs by using power from the grid directly while compensating for power quality issues. Recommended control parameters for the UPQC are derived from small signal analysis. An equal time ratio discharging control for EVs is proposed when a charging station UPQC acts as an emergency power supply to compensate for power interruptions. The performance of the proposed system was analyzed through simulations. Results show that the proposed system can compensate for harmonics, voltage sag and swell, voltage unbalance, and voltage interruption while ensuring EV charging.

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Power Quality Prediction, Early Warning, and Control for Points of Common Coupling with Wind Farms

Cited by 17 publications

References 24 publications

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

Power quality statistical predictions based on differential, deep and probabilistic learning using off‐grid and meteo data in 24‐hour horizon

Electric vehicle charging station microgrid providing unified power quality conditioner support to local power distribution networks

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