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Fuzzy logic controller for energy savings in a smart LED lighting system considering lighting comfort and daylight

ENERGY AND BUILDINGS 2016年第Sep.期127卷 95-104页

作者： Liu, Jingyu Zhang, Wen Chu, Xiaodong Liu, Yutian Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control 17923 Jingshi Rd Jinan 250061 Peoples R China

In commercial buildings, lighting constitutes a large proportion of energy consumption. Saving lighting energy in commercial buildings has aroused great interest among researchers. Achieving energy savings and satisfying lighting comfort are the two primary objectives in designing a lighting system. In this paper, a fuzzy logic controller was designed that considered daylight, movement information and lighting comfort. The DALI protocol was used to communicate the controller with LED luminaires. The simulation results demonstrate that lighting system without control can provide sufficient illumination. The lighting system provides wider controllability to make lighting environment operating at the most energy-saving state. The experimental results show that by using the designed controller, significant lighting energy can be saved. The office where the smart LED lighting system is installed can regulate lighting output automatically based on users' movements and allow users to choose their own lighting preferences. (C) 2016 Elsevier B.V. All rights reserved.

关键词： LED Daylight Energy savings Lighting comfort Fuzzy logic controller

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Week-ahead Predictions of Wind power Based on Weather Classification

Week-ahead Predictions of Wind Power Based on Weather Classi...

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2020 IEEE/IAS Industrial and Commercial power system Asia, I and CPS Asia 2020

作者： Ding, Tingting Yang, Ming Yu, Yixiao Yan, Fangqing Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan China

ISBN: (纸本)9781728143033

At present, the researches on short-term wind power prediction mostly focus on three days, but the maintenance schedule of wind farm and the generation schedule of hydro-thermal power system both hope to get the wind power forecasting results week-ahead. Exploring the law of wind power fluctuation becomes one of the most crucial issues to extend the prediction time scale. This paper proposes a combined probabilistic prediction approach based on weather classification to forecast the wind power week-ahead. This approach considers that wind power has a similar fluctuation law under the same weather type, and the combination model is used to improve forecasting accuracy. In the approach, the GK fuzzy clustering algorithm based on subtraction clustering is used to divide the weather into several typical types. And the combination probabilistic forecasting model formed by the sparse Bayesian learning, kernel density estimation, and beta distribution estimation is trained for each weather type. Meanwhile, the Gaussian Case-based Reasoning is used to classify the forecasting sample into corresponding weather type and the corresponding combined model is used to make the forecast. Case studies employ the proposed approach as well as several benchmarks to achieve week-ahead predictions of a wind farm located in China and the superior performance demonstrates the effectiveness of the proposed approach. © 2020 IEEE.

关键词： Wind power

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Application of Soft-Switching Cell With Inherent Redundancy Properties for Enhancing the Reliability of Boost-Based DC-DC Converters

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IEEE TRANSACTIONS ON power ELECTRONICS 2021年第11期36卷 12342-12354页

作者： Faraji, Rasoul Ding, Lei Rahimi, Tohid Kheshti, Mostafa Shandong Univ Minist Educ Sch Elect Engn Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China

In this article, the inherent fault-tolerant capability of the soft-switching cell for boost-based dc-dc converters is investigated. The main switch of boost-based dc-dc converters withstands high-temperature stress, which is expected to be failed with a higher probability than other devices. Providing soft-switching condition for the main switch and employing fault-tolerant capability are effective approaches to solve this problem. When a fault occurs for the main switch, the proposed converter can be reconfigured such that the soft-switching cell can change its duty to continue the normal operation of the converter without interruption. Inherent redundancy, low failure rate, high integration, and high efficiency are the specifications of the presented converter. The reliability analyses are presented and the functionality of the active soft-switching cell to provide soft-switching condition and fault-tolerant capability are verified by experimental results.

关键词： Switches Circuit faults Soft switching Fault tolerant systems Reliability Integrated circuit reliability Thyristors Active soft-switching cell boost dc-dc converter fault-tolerant capability inherent redundancy feature reliability

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Improved AAVM and Small-Signal Model of Hybrid MMC Considering Dynamic Differences Between Full-Bridge and Half-Bridge Submodules

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IEEE TRANSACTIONS ON power ELECTRONICS 2024年第7期39卷 8215-8228页

作者： Guo, Meng Hao, Quanrui Ding, Lei Shandong Univ Sch Elect Engn Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China

The hybrid modular multilevel converter (MMC), which consists of half-bridge submodules ( HBSMs ) and full-bridge submodules (FBSMs), is the general form of MMC. The conventional arm average value model (AAVM) of hybrid MMC assumes that the dynamics of HBSMs and FBSMs are identical and does not consider capacitor voltage differences between FBSM and HBSM, so it cannot describe the dynamics of hybrid MMC accurately. This article proposes an improved AAVM of a hybrid MMC, which includes an equivalent circuit and an arm modulation signal allocation algorithm: first, considering the dynamic difference between HBSM and FBSM, the equivalent circuit of improved AAVM was derived;second, an allocation algorithm that describes the nonlinear distribution of arm modulation signal between HBSM and FBSM was proposed. Considering the influence of high-order harmonics, the differential equation of the electrical part of hybrid MMC in the d-q frame was derived. Based on the nonlinear allocation algorithm of reference voltage and the piecewise solution method, the expressions of HBSM and FBSM equivalent reference voltages in the d-q frame were derived. A modular small-signal model of a hybrid MMC in the d-q frame was established. The correctness and accuracy of the proposed models are verified by simulation and experimental results.

关键词： Arm average value model (AAVM) equivalent circuit modular multilevel converter (MMC) small-signal model

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Iterative infinitesimal generator discretization-based method for eigen-analysis of large delayed cyber-physical power system

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ELECTRIC power systemS RESEARCH 2017年 143卷 389-399页

作者： Ye, Hua Gao, Weikang Mou, Qianying Liu, Yutian Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control 17923 Jingshi Rd Jinan 250061 Peoples R China

To efficiently analyze the stability of large delayed cyber-physical power systems (DCPPS) incorporating wide-area damping controllers, an iterative infinitesimal generator discretization-based method (IIGD) for computing critical eigenvalues of the system is presented. IIGD contains three core techniques to guarantee efficiency and scalability. First, the sparsity of the infinitesimal generator's discretized matrix, which possesses identical spectrum to DCPPS, is explored by reformulating its blocks into Kronecker products. Especially, the dominant block is factorized as sum of Kronecker products of constant Lagrange vectors and system state matrices, which lays the basis of further utilizing the sparsities in the augmented state matrices of DCPPS. Second, the shift-invert preconditioning technique is applied to transform the required eigenvalues into those dominated in moduli. Third, the inverse iteration of the discretized matrix involved in sparse eigenvalue computation is iteratively achieved by utilizing the induced dimension reduction method (IDR(s)). Subsequently, the discretized matrix-vector product embedded in the method is efficiently implemented by exploiting the unique property of Kronecker product and the inherent sparsities in augmented system state matrices. The correctness, accuracy, efficiency and scalability of IIGD are extensively studied and thoroughly validated on the two-area four-machine test system and a real-life large transmission grid. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Cyber-physical power system Eigen-analysis Infinitesimal generator discretization Small signal stability Time delay Wide-area damping control Wide-area measurement system

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Multitime Scale Co-Optimized dispatch for Integrated Electricity and Natural Gas system Considering Bidirectional Interactions and Renewable Uncertainties

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2022年第4期58卷 5317-5327页

作者： Zhang, Zhenwei Wang, Chengfu Chen, Shuai Zhao, Yufei Dong, Xiaoming Han, Xueshan Shandong Univ Sch Elect Engn Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China

This article presents a model predictive control (MPC) based multitime scale co-optimized dispatch for integrated electricity and natural gas system (IEGS) considering the bidirectional interactions and renewable uncertainties. In the proposed model, optimal dispatch is extended into three substages optimization problems of day-ahead, intraday, and real-time to coordinate the economy and accuracy of operations. The different optimization strategy is designed in each dispatching stage according to the operating characteristics of multienergy coupled units and high-penetration intermittent wind power. In the day-ahead stage, the unit commitment is tackled to arrange the start-stop plan of slow-response units, maintained in intraday and real-time stages to ensure full-term optimal economic operation. In the intraday stage, MPC-based rolling optimization is implemented to obtain the unit output scheme with the lowest operating cost target. Furthermore, the output scheme is rolling adjusted in the real-time stage based on the ultrashort-term forecast value, and a closed-loop feedback mechanism is introduced to achieve accurate power balance. In each stage, stochastic decision-making is executed based on the stochastic scenario method, efficiently capturing the uncertainty in IEGS. Besides, the proposed nonlinear model is transformed into a mixed-integer second-order cone programming problem using McCormick relaxation, which can be efficiently solved by commercial solvers. Simulation results on IEEE39-Gas20 and IEEE118-GAS40 test systems demonstrate the superiority of the proposed method in operational economy and wind power utilization, and also verify the effectiveness of the method to accurately track random fluctuations without obvious computational burden.

关键词： Wind power generation Natural gas Predictive control Real-time systems Pipelines Optimization dispatching Integrated electricity and natural gas system (IEGS) mixed-integer second-order cone programming problem (MISOCP) model predictive control (MPC) multitime scale co-optimization stochastic scenario method

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Research on Demand Forecast of power Grid Emergency Materials Based on CBR Method Improved by Information Update 8

Research on Demand Forecast of Power Grid Emergency Material...

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8th IEEE International Conference on Advanced power system Automation and Protection, APAP 2019

作者： Qingfa, Chai Wei, Cong Jian, Chen Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Jinan China

ISBN: (纸本)9781728117225

The grid emergency caused by natural disasters has the characteristics of complexity, urgency, and information incompleteness, making it difficult to make emergency material dispatching. Based on the study of historical fault-meteorology-repair statistics of power grids, this paper proposes a CBR (case-based reasoning) method for the demand of emergency materials in power grids, combining intuitionistic fuzzy sets with case-based reasoning, considering meteorological elements, geological elements, and accidents. The characteristic factors are corrected with the information update, the iterative calculation of the prediction process is realized, and the material demand is accurately predicted. The example shows that this method can accurately predict the demand for emergency materials in grid emergencies. © 2019 IEEE.

关键词： Iterative methods

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Multi-stage power Source and Grid Coordination Planning Method Considering Grid Load Uniformity

Multi-stage Power Source and Grid Coordination Planning Meth...

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2020 IEEE/IAS Industrial and Commercial power system Asia, I and CPS Asia 2020

作者： Li, Zhiqi Han, Xueshan Yang, Ming Ma, Yiran Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Jinan China

ISBN: (数字)9781728143033

ISBN: (纸本)9781728143033

In view of the increasing uncertainty of power supply and load, the concept of power source and grid coordination uniformity planning is proposed, and the standard deviation of transmission line load rate is taken as the uniformity evaluation index of power source and grid planning. A multi-stage and multi-objective optimization model of power source and grid expansion planning is established to minimize the comprehensive cost of the whole planning cycle. The improved PSO-GA hybrid algorithm is used to solve the model, which improves the efficiency of solution. The analysis of a simple IEEE garver-6 node system shows that the model and the solution method are effective and feasible, and are suitable for the coordinated planning of the power source and grid under the diversified development of power supply and load. © 2020 IEEE.

关键词： Multiobjective optimization

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A Voltage Adaptive Dynamic Harmonic Model of Nonlinear Home Appliances

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2020年第5期67卷 3607-3617页

作者： Sun, Yuanyuan Xie, Xiangmin Zhang, Linghan Li, Shurong Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Sch Elect Engn Jinan 250061 Peoples R China

The widespread application of nonlinear power electronic devices in residential loads has resulted in significant harmonic distortions. Harmonic source modeling is of great importance since it is a prerequisite to estimate the harmonic pollution. In this paper, a dynamic harmonic coupled matrix model (HCMM) is proposed for the nonlinear home appliances. This model is adaptive to the load supply voltage conditions;therefore, the dynamic harmonic generating characteristics of nonlinear home appliances can be demonstrated by the proposed HCMM. Moreover, the HCMM derivation process is based on measurement, independent of the load equivalent circuit, which makes the proposed method most suitable for nonlinear home appliances with unknown circuit topologies. The widely linear complex partial least squares method, which has a fast speed and high robustness, is used to estimate the HCMM. A procedure to establish the HCMM for multistate home appliances is also proposed in this paper. lab experiments were performed to verify the accuracy and validity of the proposed methodology. The aggregate harmonics caused by multiple nonlinear residential loads have been analyzed based on the proposed HCMM.

关键词： Harmonic analysis Home appliances Load modeling power system harmonics Integrated circuit modeling Adaptation models Equivalent circuits Aggregate harmonics dynamic harmonic characteristics harmonic modeling home appliances

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power Ramp Extremes Two Stage Forecast based on Auto Encoder and Bi-LSTM

Power Ramp Extremes Two Stage Forecast based on Auto Encoder...

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2024 IEEE IAS Industrial and Commercial power system Asia, I and CPS Asia 2024

作者： Wang, Chuanqi Yang, Ming Yu, Yixiao Li, Menglin Shan, Shuaijie Zhang, Yuanhe Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Jinan China

ISBN: (纸本)9798350352290

power ramp is one of the stages with the largest error of renewable energy power forecast. The deviation in the forecast of the time and values of the start-end points will lead to a significant forecast error to the whole ramp process. Therefore, accurate power ramp forecast can effectively improve the renewable energy power forecast accuracy. In this paper, SDAE is applied on encoding the meteorological features of the power ramp process. Then, the coded part is then spliced with a classifier and fine-tune, promote the output with clear category identity. Finally, the coding is fed into a bidirectional neural network to forecast the power at the start-end points. In case study, the proposed method in this paper demonstrated an improvement in the accuracy of extreme forecast of start-end points power by 4.94%in comparison to the power forecast based ramp recognition method. © 2024 IEEE.

关键词： Clean energy

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