scholarly journals A Data-Driven Modeling Strategy for Smart Grid Power Quality Coupling Assessment Based on Time Series Pattern Matching

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Hao Yu ◽  
Qingquan Jia ◽  
Ning Wang ◽  
Haiyan Dong
Keyword(s):  
Time Series ◽  
Smart Grid ◽  
Power Quality ◽  
Pattern Matching ◽  
Distribution System ◽  
Piecewise Linear ◽  
Extreme Points ◽  
Data Driven ◽  
Modeling Strategy ◽  
Data Driven Modeling

This study introduces a data-driven modeling strategy for smart grid power quality (PQ) coupling assessment based on time series pattern matching to quantify the influence of single and integrated disturbance among nodes in different pollution patterns. Periodic and random PQ patterns are constructed by using multidimensional frequency-domain decomposition for all disturbances. A multidimensional piecewise linear representation based on local extreme points is proposed to extract the patterns features of single and integrated disturbance in consideration of disturbance variation trend and severity. A feature distance of pattern (FDP) is developed to implement pattern matching on univariate PQ time series (UPQTS) and multivariate PQ time series (MPQTS) to quantify the influence of single and integrated disturbance among nodes in the pollution patterns. Case studies on a 14-bus distribution system are performed and analyzed; the accuracy and applicability of the FDP in the smart grid PQ coupling assessment are verified by comparing with other time series pattern matching methods.

scholarly journals Reparation of voltage disturbance using PR controller-based DVR in Modern power systems

2021 ◽  
Vol 27 (1) ◽  
pp. 16-29
Author(s):  
D. Danalakshmi ◽  
S. Prathiba ◽  
M. Ettappan ◽  
D. Mohan Krishna
Keyword(s):  
Smart Grid ◽  
Power Systems ◽  
Power Quality ◽  
Distribution System ◽  
Harmonic Distortion ◽  
Fast Response ◽  
Grid Environment ◽  
Dynamic Voltage Restorer ◽  
Pr Controller ◽  
Compensating Devices

Abstract The Smart Grid environment gives more benefits for the consumers, whereas the power quality is one of the challenging factors in the smart grid environment. To protect the system equipment and increase the reliability, different filter technologies are used. Even though, consumers’ expectations towards the power quality are not fulfilled. To overcome these drawbacks and enhance the system reliability, a new Custom Power Devices (CPD) are introduced in the system. Among different CPDs, the Dynamic Voltage Restorer (DVR) is one of the voltage compensating devices that is used to improve the power quality during distortions. When the distortions such as voltage swell and sag occur in the distribution system, the control strategy in the DVR plays a significant role. In this article, the DVR performance using Proportional Integral (PI), Proportional Resonant (PR) controllers are analyzed. A robust optimization algorithm called Self Balanced Differential Evolution (SBDE) is used to find the optimal gain values of the controllers in order to reach the target of global minimum error and obtain fast response. Then, a comparative analysis is performed between different controllers and verified that the performance of PR controller is superior than the other controllers. It has been found that the proposed PR controller strategy reduces the Total Harmonic Distortion (THD) values for all types of faults. The proposed SBDE optimized DVR with PR controller reduces the THD value less than 4% under voltage distoration condition. The DVR topology is validated in MATLAB/SIMULINK in order to detect the disturbance and inject the voltage to compensate the load voltage.

scholarly journals Online Coordination of Plug-In Electric Vehicles Considering Grid Congestion and Smart Grid Power Quality

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2187 ◽  
Author(s):  
Sara Deilami
Keyword(s):  
Smart Grid ◽  
Power Quality ◽  
Electric Vehicles ◽  
Distribution System ◽  
Smart Grids ◽  
Low Voltage ◽  
Maximum Sensitivity ◽  
Power Losses ◽  
Nonlinear Load ◽  
Harmonic Power

This paper first introduces the impacts of battery charger and nonlinear load harmonics on smart grids considering random plug-in of electric vehicles (PEVs) without any coordination. Then, a new centralized nonlinear online maximum sensitivity selection-based charging algorithm (NOL-MSSCA) is proposed for coordinating PEVs that minimizes the costs associated with generation and losses considering network and bus total harmonic distortion (THD). The aim is to first attend the high priority customers and charge their vehicles as quickly as possible while postponing the service to medium and low priority consumers to the off-peak hours, considering network, battery and power quality constraints and harmonics. The vehicles were randomly plugged at different locations during a period of 24 h. The proposed PEV coordination is based on the maximum sensitivity selection (MSS), which is the sensitivity of losses (including fundamental and harmonic losses) with respect to the PEV location (PEV bus). The proposed algorithm uses the decoupled harmonic power flow (DHPF) to model the nonlinear loads (including the PEV chargers) as current harmonic sources and computes the harmonic power losses, harmonic voltages and THD of the smart grid. The MSS vectors are easily determined using the entries of the Jacobian matrix of the DHPF program, which includes the spectrums of all injected harmonics by nonlinear electric vehicle (EV) chargers and nonlinear industrial loads. The sensitivity of the objective function (fundamental and harmonic power losses) to the PEVs were then used to schedule PEVs accordingly. The algorithm successfully controls the network THDv level within the standard limit of 5% for low and moderate PEV penetrations by delaying PEV charging activities. For high PEV penetrations, the installation of passive power filters (PPFs) is suggested to reduce the THDv and manage to fully charge the PEVs. Detailed simulations considering random and coordinated charging were performed on the modified IEEE 23 kV distribution system with 22 low voltage residential networks populated with PEVs that have nonlinear battery chargers. Simulation results are provided without/with filters for different penetration levels of PEVs.

Earthquake Prediction Research Based on the Mining in Time Series of Groundwater Temperature

2012 ◽  
Vol 532-533 ◽  
pp. 1016-1020 ◽  
Author(s):  
Jian Zhang ◽  
Yi Lin Lu ◽  
Shao Chun Wu
Keyword(s):  
Time Series ◽  
Earthquake Prediction ◽  
Time Series Data ◽  
Sequence Data ◽  
Piecewise Linear ◽  
Extreme Points ◽  
Linear Representation ◽  
Series Data ◽  
Groundwater Temperature ◽  
Symbol Sequence

Earthquake prediction has always been an extremely important and difficult research topic. A road map was proposed in this paper to capture useful information for earthquake prediction by exploring the time sequence data of groundwater temperature. Firstly, the triangle extreme points and the trend turning points are employed for the piecewise linear representation of the time series data. Then the segmentation is classified and symbolized by slope, and symbol sequence is simplified further according to the simplification rules. Finally, the earthquake catalogue data and the symbol sequence are jointly preprocessed with a new method to form transaction-like data, which then be treated by association analysis to extract earthquake prediction knowledge. The results of experiment show that this processing flow is an effective way to provide valuable information about earthquake prediction.

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