Accuracy improvement of various short-term load forecasting models by a novel and unified statistical data-filtering method

Bùi, Dương Minh; Le, Phuong; Cao, Minh Tien; Pham, Trang; Phạm, Duy Anh

doi:10.1080/15435075.2020.1761810

Cited by 12 publications

(8 citation statements)

References 30 publications

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“…Theo các nghiên cứu của Duong Minh Bui và Phuc Duy Le et al (2020) [11][12][13] , mặc dù bộ dữ liệu phụ tải được thu thập bằng hệ thống quản lý thời gian thực (RTMS-Real-time Management System) có độ chính xác cao, nhưng vẫn chứa khá nhiều dữ liệu gây nhiễu ngẫu nhiên do các nguyên nhân gồm: i) đặc tính vận hành ngẫu nhiên của phụ tải, ii) sự dao động của nguồn lưới hoặc của các nguồn DG, iii) khi LĐPP xảy ra mất điện do sự cố; iv) kế hoạch bảo trì định kỳ; v) đóng/cắt tụ bù; hoặc vi) do đường truyền kết nối không ổn định. Do đó, độ tin cậy của bộ dữ liệu phụ tải thường khó có thể đạt mức độ tin cậy cao nhất là 100%.…”

Section: Phương Pháp Loại Bỏ Dữ Liệu Nhiễu để Xác địNh Ngưỡng Dao độNg Của Phụ Tải Tuyến Dâyunclassified

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An improved fault analysis method based on bus-voltage thresholds determination for distribution networks with distributed generators

Le¹,

Bùi²,

Đoàn³

et al. 2021

Sci. Tech. Dev. J.- Eng. Tech.

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Integration of distributed generators (DGs) into a distribution network (DN) can impact on the reliability operation and stability of protection systems. Depending on DG types, their installed locations, and operation states, fault current values in the distribution network can be significantly changed such that protective devices can inaccurately operate. Traditional fault analysis methods have not considered the presence of distributed generators and their operation characteristics, bidirectional power flows and change in bus-voltage values in the DN. Therefore, improvement of fault current calculation methods is necessary to fast, automatically and accurately determine fault current values in the DN with distributed generators. Hence, this paper develops an improved fault analysis method based on bus-voltage thresholds determination for distribution networks with DGs. The bus-voltage thresholds can be evaluated by determining load power confidence intervals (LPCI), and fault current thresholds will be then calculated in the DN with DGs. Based on a developed LPCI calculation tool and E-terra distribution software, achieved simulation results have validated the effectiveness of fault analysis method based on bus-voltage thresholds for distribution networks in case of considering types, locations and operation states of various distributed generators.

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Section: Phương Pháp Loại Bỏ Dữ Liệu Nhiễu để Xác địNh Ngưỡng Dao độNg Của Phụ Tải Tuyến Dâyunclassified

“…Trong đó, z là dữ liệu ngõ vào có trọng số đối với lớp ngõ ra; P(z) là hàm transfer PL của lớp ngõ ra; z' là dữ (11).…”

Section: Mô Hình Dự Báo Annunclassified

An improved fault analysis method based on bus-voltage thresholds determination for distribution networks with distributed generators

Le¹,

Bùi²,

Đoàn³

et al. 2021

Sci. Tech. Dev. J.- Eng. Tech.

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“…Based on the confidence intervals of bus-voltages in the distribution network, an adaptable fault analysis technique depicted in Section of An Adaptable Fault Analysis Technique is proposed to calculate the min-max confidence intervals of fault currents contributed by different DG units and fault current contributed by the utility which are used for effectively selecting tripping and pick-up thresholds of definite-time and inverse-time OC functions in the same OCR. In general, this adaptable fault analysis method contains four main steps: (i) forecasting a min-max confidence interval of load power at each load bus on a feeder of distribution network [81,82]; (ii) calculating a min-max confidence interval of load current at each load bus on the feeder; (iii) doing the current-injection-based power flow analysis to find a min-max confidence interval of voltage at each bus where has already installed the OC relay; and (iv) performing an adaptable fault analysis method to determine a min-max confidence interval of fault currents for each fault type in the DG-contained distribution network.…”

Section: Calculating the Min-max Confidence Interval Of Fault Currents In The Dn Having Dgsmentioning

confidence: 99%

“…Figure 2 shows a flow diagram of forecasting a min-max confidence interval of load power at each load bus in the DN. According to the work [82], there are seven main steps to forecast a min-max confidence interval of load power at each load bus on a feeder of distribution network as the following.…”

Section: (I) Forecasting a Min-max Confidence Interval Of Load Power At Each Load Busmentioning

confidence: 99%

An Adaptive and Scalable Protection Coordination System of Overcurrent Relays in Distributed-Generator-Integrated Distribution Networks

Bùi

Nguyễn

et al. 2021

Applied Sciences

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Integration of distributed generators (DGs) into a distribution network (DN) can cause coordination challenges of overcurrent relays (OCRs) because of different fault-current contributions of DGs as well as the directional change in fault currents. Therefore, the OCRs should be properly coordinated to maintain their adaptability and scalability to protect the DG-integrated distribution network. In this study, an adaptive and scalable protection coordination (ASPC) approach has been developed for the OCRs in a DG-contained distribution network based on two implementation stages. At the first stage, the reliability improvement of fault-current calculation results is performed by determining the min-max confidence interval of fault current for each different fault type, which is the basis for properly selecting tripping and pick-up thresholds of definite-time and inverse-time OC functions in the same OCR. At the second stage, optimization algorithms are used for calculating protection-curve coefficients and Time-Dial Setting (TDS) multiplier for the inverse-time OC functions in the OCR. A real 22 kV DG-integrated distribution network which is simulated by ETAP software is considered a reliable test-bed to validate the proposed ASPC system of OCRs in the multiple-DG-contained distribution network. In addition, the coordination results of OCRs can be obtained by three common optimization algorithms, Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Genetic Algorithm (GA). These relay coordination results have shown an effective protection combination of the definite-time OC functions (50P and 50G) and the inverse-time OC functions (51P and 51G) in the same OCR to get the adaptable and scalable DN protection system.

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“…Here, the solar photovoltaic modules are all horizontally arranged on the ship deck, and the photovoltaic modules on the ship will move together with the ship's swing. While sailing in the ocean, due to the effects of wind, waves, and ocean currents, this will produce six degrees of freedom (DOF) movements, namely, surge, sway, heave, roll, pitch, and yaw [17]. This section introduces the calculation model of the spatial motion variation of shipborne photovoltaic panels under the influence of rolling and pitching when the ship is sailing.…”

Section: Model Of Ship Photovoltaic Panel Swing Motionmentioning

confidence: 99%

Ultrashort-Term Power Fluctuation Forecasting Based on the Prediction of the Shipborne Panel Tilt Angle

et al. 2020

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When a solar ship is navigating in the ocean, the swaying motion of a photovoltaic panel will affect the output power of the photovoltaic (PV) power generation system more frequently and violently. In addition to considering multiple climatic factors, this paper also adopts a ship swaying motion and radiation level of sunlight to establish a suitable calculation model for the output power of photovoltaic systems, which are rarely considered at the same time in previous studies, and also to make ultrashort-term power predictions. Furthermore, this paper proposes a multilayer heterogeneous particle swarm optimization (PSO) algorithm to design the weights and thresholds of a long short-term memory (LSTM) neural network to improve the accuracy of forecasting the changes of a photovoltaic panel’s angle, which is used for accurate power output prediction for the purpose of power planning. The case analysis shows the effectiveness of the algorithm, which provides a more reliable method for designing a power prediction system.

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Accuracy improvement of various short-term load forecasting models by a novel and unified statistical data-filtering method

Cited by 12 publications

References 30 publications

An improved fault analysis method based on bus-voltage thresholds determination for distribution networks with distributed generators

An improved fault analysis method based on bus-voltage thresholds determination for distribution networks with distributed generators

An Adaptive and Scalable Protection Coordination System of Overcurrent Relays in Distributed-Generator-Integrated Distribution Networks

Ultrashort-Term Power Fluctuation Forecasting Based on the Prediction of the Shipborne Panel Tilt Angle

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