IntelliSense technology in the new power systems

Xie, Haonan; Jiang, Meihui; Zhang, Dongdong; Goh, Hui Hwang; Ahmad, Tanveer; Liu, Hui; Liu, Tianhao; Wang, Shuyao; Wu, Thomas

doi:10.1016/j.rser.2023.113229

Cited by 37 publications

(13 citation statements)

References 118 publications

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“…The energy management system can control the energy absorption and release of HESS reasonably by collecting the SOC and output of each device to suppress wind power fluctuation. The energy flow relationship in the wind storage system is shown in (1) and (2).…”

Section: Wind Storage System Structurementioning

confidence: 99%

“…According to the calculation method in [22], the capacities of the battery and supercapacitor are 5.98 MWh and 1.69 MWh, respectively. In order to illustrate the effectiveness of the method proposed in this study, the lower and upper limits of SOC are set to 0.2 and 0.8, respectively, and two control methods are used to optimize the SOC of HESS, which are: 1 The fuzzy control method in literature [22]; 2 The fuzzy control method considering charge-discharge time sequence proposed in this study. The SOC curves of the battery and supercapacitor under different control strategies are shown in Figures 12 and 13.…”

Section: Analysis Of Soc Optimization Effect Of Hessmentioning

confidence: 99%

“…The direct grid connection of wind power has a significant impact on the stability of the power system. How to effectively suppress the fluctuation of wind power and improve its grid-connected operation ability is of great practical importance for the utilization of wind power [1][2][3][4]. The existing energy storage technologies consist of two categories: energy-type and power-type, among which energy-type energy storage has high energy density, low power density and short cycling life, which is not applicable for a fast charge-discharge; Power-type energy storage has low energy density and high power density, frequent charge-discharge will not damage its performance.…”

Section: Introductionmentioning

confidence: 99%

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Wavelet Packet-Fuzzy Optimization Control Strategy of Hybrid Energy Storage Considering Charge–Discharge Time Sequence

et al. 2023

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A hybrid energy storage system (HESS) can effectively suppress the high and low-frequency power fluctuations generated by wind farms under the intermittency and randomness of wind. However, for the existing power distribution strategies of HESS, power-type and energy-type energy storage have the problem of inconsistent charge–discharge states in the same time sequence, which makes it difficult to achieve optimal operation in terms of charge–discharge coordination and energy flow. To solve this problem, this study firstly adopts adaptive wavelet packet decomposition (WPD) to decompose the original wind power to acquire grid-connected power and HESS initial distribution power, to ensure that the supercapacitor and battery undertake the corresponding high and low-frequency power fluctuations, respectively; Then, for the inconsistent charge–discharge states, a charge–discharge time sequence optimization strategy based on the consistency index is proposed to correct the initial power distribution of HESS for the first time; Finally, aiming at the stage of charge (SOC) over-limit problem, the fuzzy optimization method is adopted to correct the HESS output power for the second time, which can reduce the unnecessary charge–discharge energy effectively. With typical daily output data of a 100 MW wind farm, the proposed control strategy is verified. The results show that it can make different energy storage technologies synchronously suppress wind power fluctuation in the same time sequence; compared with not considering charge–discharge time sequence optimization, the charge–discharge conversion times of the battery obtained by the proposed method are reduced from 71 to 14 times, and the charge–discharge conversion times of supercapacitor are reduced from 390 to 61 times; The cumulative reduction of unnecessary charge–discharge energy by HESS is 12.12 MWh. Besides, the SOC curves of HESS are controlled at a normal level, thus improving the economy and service life of HESS.

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Section: Wind Storage System Structurementioning

confidence: 99%

Section: Analysis Of Soc Optimization Effect Of Hessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Wavelet Packet-Fuzzy Optimization Control Strategy of Hybrid Energy Storage Considering Charge–Discharge Time Sequence

et al. 2023

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“…Since the capacitor capacitance of graphene material winding can only produce a small change under the applied electric field, in order to increase the magnitude of its capacitance change, the only way to increase the applied electric field strength, i.e., increase the voltage value of "output capacitor", the capacitor capacitance will decrease; apply the reverse voltage, the capacitor capacitance will increase [10].…”

Section: Hardware Structure Principlementioning

confidence: 99%

Study of graphene variable capacitance based on intelligent algorithm

Shi,

Wu,

Tan

et al. 2023

International Conference on Mechatronics and Intelligent Control (ICMIC 2023)

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“…Most countries around the world are witnessing rapid growth in building energy use; commercial and residential buildings account for more than one-third of the global energy consumption, while accounting for more than 40% of global carbon dioxide emissions (Yoon S H et al, 2018). In order to improve energy efficiency, it is necessary to adopt more suitable energy management techniques (Zhang D et al, 2021) or the use of smart devices to collect more detailed equipment data (Xie H et al, 2023). Since the 1990s, non-intrusive load monitoring (NILM) (Hart G W, 1992) has become one of the dominant frameworks in the field of energy consumption detection (Azizi E et al, 2021;Gillis J M et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

A Wasserstein-based distributionally robust neural network for non-intrusive load monitoring

et al. 2023

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Non-intrusive load monitoring (NILM) is a technique that uses electrical data analysis to disaggregate the total energy consumption of a building or home into the energy consumption of individual appliances. To address the data uncertainty problem in non-intrusive load monitoring, this paper constructs an ambiguity set to improve the robustness of the model based on the distributionally robust optimization (DRO) framework using the Wasserstein metric. Also, for the hard-to-solve semi-infinite programming problem, a novel and computationally efficient upper-layer approximation is used to transform it into an easily solvable regularization problem. Two different data feature extraction methods are used on two open-source datasets, and the experimental results show that the proposed model has good robustness and performs better in identifying devices with large fluctuations. The improvement is about 6% compared to that of the convolutional neural network model without the addition of distributionally robust optimization. The proposed method supports transfer learning and can be added to the neural network in the form of a single-layer net, avoiding unnecessary training times, while ensuring accuracy.

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IntelliSense technology in the new power systems

Cited by 37 publications

References 118 publications

Wavelet Packet-Fuzzy Optimization Control Strategy of Hybrid Energy Storage Considering Charge–Discharge Time Sequence

Wavelet Packet-Fuzzy Optimization Control Strategy of Hybrid Energy Storage Considering Charge–Discharge Time Sequence

Study of graphene variable capacitance based on intelligent algorithm

A Wasserstein-based distributionally robust neural network for non-intrusive load monitoring

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