Multi-time Scale Research Articles

Dynamic state awareness for distribution network based on robust adaptive cubature kalman filter

With the rapid development of the economy and society, the demand for power quality is constantly increasing. As a crucial part of grid situational awareness, distribution network state estimation plays a vital role in providing critical data support for other advanced applications, which is significant for ensuring the safe and reliable operation of the distribution network. Therefore, this paper proposes a dynamic state awareness method for distribution networks based on the robust adaptive cubature Kalman filter (RACKF). The proposed solution, grounded on the core computational concept of the cubature Kalman filter (CKF), constructs a robust noise statistical estimator (NSE) composed of a biased NSE and an unbiased NSE to adapt to the unknown and time-varying process noise parameters in the dynamic state estimation process. The proposed solution can ensure that the calculated process noise parameters always meet the constraints and guarantee the robustness of the algorithm. In addition, an estimation strategy for the fusion of multi-time scale measurement data is developed according to the RACKF-based dynamic state estimation features in order to realize system state corrections and updates. The results of simulation experiments and comparisons with traditional CKF methods demonstrate the accuracy and superiority of the proposed solution.

Research on multi-time scale Volt/VAR optimization in active distribution networks based on NSDBO and MPC approach

To explore the potential of all-round and multiangle voltage and reactive power control to reduce losses in a new power system with a high proportion of distributed resources connected to a distribution network, this study proposes a Volt/VAR optimization method for distribution networks using a nondominated sorting dung beetle optimizer (NSDBO) and model predictive control (MPC). According to the characteristics of the various adjustable resources in the network, they are divided into day-ahead and intraday optimizations. The optimization process in the day-ahead stage uses the NSDBO to create a discrete equipment scheduling plan. The optimization process in the intraday stage combines the discrete equipment scheduling plan with the MPC to create the scheduling plan for photovoltaics, energy storage, and vehicle charging stations. Two-stage optimization scheduling is achieved with the lowest discrete equipment regulation cost and minimization of network loss and node voltage deviation penalty cost as the optimization goal. The feasibility of this method for coordinating various resources in the network, processing discrete and continuous variables, and coping with the volatility and uncertainty of high-proportion distributed resources is verified through case analysis. The effectiveness of this method in improving the security and economy of distribution networks is demonstrated. The superiority of the solution speed and quality is also confirmed.

Adaptive multi-time scale integration of the high-speed train fault samples

Adaptive multi-time scale integration of the high-speed train fault samples

Generation of the Quasi‐Biweekly Oscillation in the Surface Potential Vorticity Over the Tibetan Plateau During Boreal Summer: A Case Study of 2014

AbstractThe atmospheric circulation around the Tibetan Plateau (TP) exhibits a substantial 10–20‐day quasi–biweekly oscillation (QBWO), profoundly impacting weather and climate locally and remotely. Understanding the factors influencing the generation of QBWO over the TP (QBWOTP) and its physical mechanism is crucial. This study has investigated the influence of multi–timescale and land–atmosphere interactions on the generation of the QBWOTP in surface potential vorticity (SPV), a valuable tool for characterizing the mechanical and thermal variabilities in mountain forcing, based on a 2014 case study. Results indicate that in the free atmosphere, the summer monsoon onset over the Bay of Bengal induces a northward shift in the westerly jet toward the TP, manifested as an increase in low‐frequency zonal winds. This shift facilitates the propagation of wave trains, leading to atmospheric quasi–biweekly potential temperature anomalies (θa) over the TP through a multi‐timescale interaction. Additionally, the TP's surface thermal forcing and arrival of wave trains trigger anomalous upward motion and increase cloud cover. The resultant decrease in net short‐wave radiations and increase in net long‐wave radiations contribute to variations in surface potential temperature (θs) over the TP. As θa and θs evolve, the difference between them enlarges, resulting in the generation of the SPV QBWOTP. Given the relationship between the QBWOTP and downstream rainfall, this study could provide novel insights into understanding and predicting downstream rainfall QBWO.

Distributed Optimization Strategy for New Energy Stations and Energy Storage Stations Considering Multiple Time Scales

The “dual carbon” goal has made it a mainstream trend for new energy stations (NESs) and energy storage stations (ESSs) to jointly participate in market regulation. This paper proposes a multiple time scale distributed optimization method for NESs and ESSs based on the alternate direction multiplier method (ADMM). By first considering the uncertainty of new energy output and the volatility of electricity market prices, a multi time scale revenue model is constructed for day-ahead, intraday, and real-time markets. Then, the objective function is built by maximizing the comprehensive market revenues and is simplified using the synergistic effect of NESs and ESSs. Next, the simplified objective function is solved by the ADMM, and the revenues are maximized while each energy meets the relevant constraints. Lastly, the 33-node network topology is used to illustrate the feasibility of the proposed method. The simulation results show that after optimization, the output of NESs and ESSs can coordinate work in day-ahead, intraday, and real-time markets, while the abandonment power of wind and light is significantly improved.

The spillover and comovement of downside and upside tail risks among crude oil futures markets

Combining efficient transfer entropy and DY spillover indices, this study first explores the contagion effects of downside and upside tail risks among INE, Brent, and WTI. Then we utilize wavelet coherence to capture the comovement characteristics of tail risks at multi-time scales. Furthermore, by mapping wavelet coherence information into comovement networks, the diversity and regularity of mode conversions of tail interdependency are investigated in depth. The results show that INE is reactively susceptible to the tail risk spillovers from Brent and WTI, and in terms of the magnitude of spillover to INE, Brent surpasses WTI prominently. The coherence between the tail risks of INE and Brent or WTI is much weaker than that of the Brent-WTI pair, especially in the short term or in the upside tail case. The evolution of downside tail risk comovement is more difficult to stabilize. A few comovement modes show a significantly higher probability of occurrence than others, and they also tend to link each other. Of special note are some comovement modes with low probability of occurrence yet behave as the key “bridges” in the mode transformation process.

Voltage regulation and energy loss minimization for distribution networks with high photovoltaic penetration and EV charging stations using dual-stage model predictive control

The widespread integration of photovoltaic (PV) units and controllable loads like electric vehicles (EVs) might cause uncertain voltage fluctuations quite frequently. The reactive power from distributed generation (DG) units and EV charging stations (EVCSs) can effectively be used along with conventional devices like on-load tap changer (OLTC) to successfully control network voltages in real-time, with multi-time scale coordination. However, the control of a large number of available resources, with reliable communication among them becomes a complex task. Moreover, the substantial real-time data set amassed through the deployment of measurement devices across the network increases both cost and computational intricacies. This paper presents a novel approach, employing a time decomposition-based dual-stage model predictive control (MPC) with a reduced model control framework for voltage control and energy loss minimization in active distribution networks (ADNs), by significantly reducing the number of measuring devices. A minimum global set of available control resources is identified for real-time control, aiming to mitigate control complexity and minimize the demand for heavy communication. The proposed control strategy is validated on the 33-bus distribution network and modified IEEE 123-bus distributed network, under high PV penetration and EV charging stations. It is seen that the proposed reduced model framework with very limited measuring devices and control equipment can effectively regulate the voltages with a standard deviation of 0.0059 p.u. and 0.0035 p.u. as compared to the full order system model, for 33-bus network and IEEE 123-bus network, respectively. Furthermore, there is a net 23.24% reduction in energy losses when power loss minimization is considered along with the minimization of voltage deviations in the 33-bus network.

Multi-timescale feature extraction method of wastewater treatment process based on adaptive entropy

In wastewater treatment systems, extracting meaningful features from process data is essential for effective monitoring and control. However, the multi-time scale data generated by different sampling frequencies pose a challenge to accurately extract features. To solve this issue, a multi-timescale feature extraction method based on adaptive entropy is proposed. Firstly, the expert knowledge graph is constructed by analyzing the characteristics of wastewater components and water quality data, which can illustrate various water quality parameters and the network of relationships among them. Secondly, multiscale entropy analysis is used to investigate the inherent multi-timescale patterns of water quality data in depth, which enables us to minimize information loss while uniformly optimizing the timescale. Thirdly, we harness partial least squares (PLS) for feature extraction, resulting in an enhanced representation of sample data and the iterative enhancement of our expert knowledge graph. The experimental results show that the multi-timescale feature extraction algorithm can enhance the representation of water quality data and improve monitoring capabilities.

Discriminative feature learning based on multi-view attention network with diffusion joint loss for speech emotion recognition

In speech emotion recognition, existing models often struggle to accurately classify emotions with high similarity. In this paper, we propose a novel architecture that integrates a multi-view attention network (MVAN) and diffusion joint loss to alleviate confusion by placing a stronger focus on emotions that are challenging to classify accurately. First, we use logarithmic Mel-spectrograms (log-Mels), deltas, and delta-deltas of log-Mels as three-dimensional features to minimize external interference. Then, we design the MVAN to extract effective multi-time scale emotion features, where the channel and spatial attention are used to selectively localize the regions in the input features related to the target emotion. A Multi-time view bidirectional long and short-term memory network is used to extract the shallow edge features and deep semantic features, and multi-scale self-attention fuses these features through cross-scale attention fusion to obtain multi-time scale emotion features. Finally, a diffusion joint loss strategy is introduced to distinguish the emotional embeddings with high similarity by the generated complex emotion triplets in a diffusing fashion. We evaluated our proposed method on the Interactive Emotional Mood Binary Motion Capture (IEMOCAP), Chinese Academy of Sciences Automation Institute of Automation (CASIA), and Berlin German Emotion Speech Bank (EMODB) corpus. The results show significant improvements over existing methods, achieving 86.87% WA, 86.60% UA, and 86.82% WF1 on IEMOCAP; 70.74% WA, 70.74% UA, and 70.25% WF1 on CASIA; and 93.65% WA, 91.13% UA, and 92.26% WF1 on EMODB. These results confirm the superiority of our method. Our code and model are available at https://github.com/Littleznnz/MVAN-DiffSEG.

Recovery of synchronized oscillations on multiplex networks by tuning dynamical time scales

The heterogeneity among interacting dynamical systems or variations in the pattern of their interactions occur naturally in many real complex systems. Often they lead to partially synchronized states like chimeras or oscillation suppressed states like in-homogeneous or homogeneous steady states. In such cases, it is a challenge to get synchronized oscillations in spite of prevailing heterogeneity. In this study, we present a formalism for controlling multi layer, multi timescale systems and show how synchronized oscillations can be restored by tuning the dynamical time scales between the layers. Specifically, we use the model of a multiplex network, where the first layer of coupled oscillators is multiplexed with an environment layer, that can generate various types of chimera states and suppressed states. We show that by tuning the time scale mismatch between the layers, we can revive the synchronized oscillations. We analyse the nature of the transition of the system to synchronization from various dynamical states and the role of time scale mismatch and strength of inter layer coupling in this scenario. We also consider a three-layer multiplex system, where two system layers interact with the common environment layer. In this case, we observe anti synchronization and in-homogeneous steady states on the system layers and by tuning their time scale difference with the environment layer, they undergo transition to synchronized oscillations.

A novel temperature drift compensation method based on LSTM for NMR sensor

Nuclear Magnetic Resonance (NMR) sensors have become one of the best choices for angular velocity sensors in future Inertial Navigation Systems (INS) due to their small size and high precision. The performance of NMR sensors can be affected by temperature changes, resulting in temperature drift. Therefore, temperature compensation is essential. Accurate temperature compensation is challenging due to the lack of direct temperature observations in the core sensitive area (cell) and the complex temperature drift model. Considering the time correlation of temperatures inside and outside the cell, a new temperature drift compensation method based on the Long-Short Term Memory (LSTM) network is proposed. The memory characteristics of the LSTM enable it to fully utilize current and previous data to learn complex models. The main contributions of this study are as follows: (1) the mechanism of temperature drift in NMR sensors was analyzed; (2) a multi-time scale input–output model for temperature changes, temperature change rates, and ambient temperature versus sensor output bias was established; (3) a temperature drift model identification method based on LSTM was designed. Experimental results show that, compared to traditional polynomial fitting and memory-free methods, the proposed method improves temperature compensation accuracy by 26.0% to 47.0%. This method effectively reduces the adverse impact of temperature changes on the NMR sensor.

Co-estimation and definition for states of health and charge of lithium-ion batteries using expansion

Accurate and efficient estimation of state of charge (SOC) and state of health (SOH) is crucial for the safe and stable operation of lithium-ion batteries (LIBs). However, achieving accurate SOC–SOH co-estimation remains a challenging task. Meanwhile, the states are primarily defined by capacity, which cannot be directly measured and requires extensive calculations. Hence, a SOH–SOC co-estimation method based on multi-time scale long short-term memory (LSTM) is proposed, which utilizes the expansion characteristics. Additionally, definitions of SOH and SOC are presented based on expansion for the first time, which can obtain the LIBs’ states by direct measurement. To evaluate the necessity of expansion measurements for battery state estimation and definition, the University of Michigan Battery Laboratory (UMBL) dataset is utilized. Then, the relationships between expansion characteristics and SOH/SOC are deeply analyzed. The experimental results demonstrate that the proposed co-estimation method estimates SOC and SOH with high accuracy. The root mean square error (RMSE) and mean absolute error (MAE) are within 0.68 % and 0.51 %, respectively. Moreover, compared with the traditional definitions based on capacity, the MAE/RMSE of new definitions for SOH and SOC are 0.59 %/0.78 % and 5.2 %/6.6 %, respectively.

Online optimal tracking control of unknown nonlinear singularly perturbed systems using single network adaptive critic with improved learning

This study is the first time devoted to seek an online optimal tracking solution for unknown nonlinear singularly perturbed systems based on single network adaptive critic (SNAC) design. Firstly, a novel identifier with more efficient parametric multi-time scales differential neural network (PMTSDNN) is developed to obtain the unknown system dynamics. Then, based on the identification results, the online optimal tracking controller consists of an adaptive steady control term and an optimal feedback control term is developed by using SNAC to solve the Hamilton–Jacobi–Bellman (HJB) equation online. New learning law considering filtered parameter identification error is developed for the PMTSDNN identifier and the SNAC, which can realize online synchronous learning and fast convergence. The Lyapunov approach is synthesized to ensure the convergence characteristics of the overall closed loop system consisting of the PMTSDNN identifier, the SNAC and the optimal tracking control policy. Three examples are provided to illustrate the effectiveness of the investigated method.

Multi-time Scale Variation of Atmospheric Ammonia Concentration and Dry Deposition in a Paddy Rice Region in Subtropical China

Meteorological factors and anthropogenic activities significantly affect atmospheric ammonia （NH3） concentration and its dry deposition. Former studies have examined the spatial and temporal variability in atmospheric NH3 concentrations at monthly scales. However, the characteristics of atmospheric concentrations at finer time scales such as hourly and daily scales and the influencing factors remain unclear. In this study, atmospheric NH3 concentration and related meteorological factors were continuously monitored online for one year in a double cropping rice region in subtropical China, and atmospheric NH3 concentration and its meteorological influencing factors as well as dry deposition were analyzed at different time scales （hourly, daily, and monthly）. The main results were as follows： The annual average daily concentration of NH3 in the rice area varied from 0.01 to 58.0 μg·m-3 （in N, same below）, and the annual average concentration was 5.3 μg·m-3. On the hourly scale, the 24-hour dynamics of atmospheric NH3 concentration showed a unimodal pattern, and the time of the NH3 peak appearance in different seasons was different； the time of the peak that appeared in winter lagged behind that in the other seasons. From the perspective of daily scale, NH3 concentration was mainly affected by fertilization in the paddy fields, peaking at 1-3 days after fertilization and then gradually decreasing. On the monthly scale, NH3 concentration peaked at 12.8 μg·m-3 in July and was the lowest in October at 1.6 μg·m-3. On the hourly scale, NH3 concentration varied seasonally due to the influences of meteorological factors, mainly as follows： NH3 concentration showed significant positive correlations with air temperature and solar radiation in all four seasons and with wind speed in spring and summer, whereas it showed significant negative correlations with relative humidity except in winter. On the daily scale, NH3 concentration showed a significant positive correlation with air temperature, rainfall, and solar radiation, whereas it showed a significant negative correlation with relative humidity. On the monthly scale, no significant correlation existed between each meteorological factor and NH3 concentration. The annual dry deposition flux （in N） calculated from the hourly average NH3 concentration was 8.5 kg·（hm2·a）-1, which was 11.6% higher than the annual flux calculated from the daily average and 12.4% higher than the annual flux calculated from the monthly average. In summary, there were significant daily and seasonal variations in atmospheric NH3 concentration in the paddy rice region in subtropical China, and conducting hourly-scale observations of NH3 concentration can help to reveal the multi-time scale variations in NH3 concentration and to quantify NH3 dry deposition more accurately.

Multi-time scales prediction of aggregated schedulable capacity of electric vehicle fleets based on enhanced Prophet-LGBM algorithm

The fast-increasing applications of renewable energy and electric vehicles (EVs) pose significant challenges to the safe and economic operation of the power grid. However, through Virtual Power Plant (VPP) technology, EV fleets can be aggregated into large-scale EV generalized energy storage systems, providing scarce dispatchable flexible resources for the future power system. Rapid and accurate prediction of the Aggregated Schedulable Capacity of EV (EVASC) fleets is a crucial technology for VPP with Electric Vehicles (EV-VPP) to participate in various ancillary services of the power system. In this paper, orienting for various dispatch scenarios in the power system, a multi-time scale prediction model of the EVASC for EV-VPPs is presented based on the enhanced Prophet algorithm combining a light gradient boosting machine (LGBM) algorithm. The enhanced Prophet-LGBM algorithm is initially proposed here by incorporating the LGBM algorithm into the traditional Prophet algorithm in series and adding extra sequential features in its input, such as temperature, weather, and alike. In this way, the problem of overfitting and low capability in addressing additional input features in the traditional Prophet is overcome. The proposed enhanced Prophet-LGBM-based prediction method of the EVASC for EV-VPPs is validated using 1.8 million actual charging records of over 4000 charging piles for half a year at the provincial level. The 30-day simulation results of the day ahead and intraday predictions of the EVASC for EV-VPPs show that much higher performances in the prediction accuracy can be achieved, specifically in holidays.

Research on multi-time scale cooperative optimal scheduling of source, load, and storage in microgrid with flexible resources

Research on multi-time scale cooperative optimal scheduling of source, load, and storage in microgrid with flexible resources

Multitime Scale Optimization of Urban Micro-Grids Considering High Penetration of PVs and Heterogeneous Energy Storage Systems

Multitime Scale Optimization of Urban Micro-Grids Considering High Penetration of PVs and Heterogeneous Energy Storage Systems

Multi-period optimal capacity expansion planning scheme of regional integrated energy systems considering multi-time scale uncertainty and generation low-carbon retrofit

With increasingly challenging climate crisis, carbon neutrality has become a necessary path for the whole world. However, there still lacks a comprehensive investigation on the optimal capacity expansion planning for a low-carbon transformation while considering multiple uncertainties. In this paper, a multi-period optimal capacity expansion planning scheme for regional integrated energy systems considering multi-time scale uncertainties and generation low-carbon retrofit is proposed. First, to better describe the short-time scale uncertainty caused by renewable energy and load fluctuations, a multi-dimensional tempo-spatial correlated scenario generation method is proposed to more accurately capture stochastic features with the least amounts of representative scenarios. Second, a multi-period optimal planning model considering generation low-carbon retrofit and short-/long-time scale uncertainties is developed. The proposed model combines information gap decision theory and chance constraints to stress both uncertainties simultaneously, which is further linearized to facilitate the computation. Third, an improved bilinear Benders decomposition (IBBD) method is utilized to efficiently solve the proposed large-scale optimal planning problem. Finally, numerical experiments of a real world test system of WF city show: 1) the maximum errors of PDF and CDF between generated scenarios and historical data are 0.108 and 0.016, validating the effectiveness of the proposed scenario generation method; 2) the low-carbon retrofit reduces system carbon emissions by 14.12 × 107 kg, while the total system cost is $956,411,387 with additional energy coupling units and renewables installed, indicating the reliability of the multi-period planning model; 3) the IBBD algorithm demonstrates applicability and efficiency in solving the proposed multi-period optimal expansion planning problem, reducing computation time by over 80.39 % compared to commercial solvers, such as GUROBI.

Research on Voltage Coordinated and Stability Control of Condensers Based on Reactive Power Replacement

Abstract In this research, we investigated condenser voltage coordination and stability control systems by analyzing voltage coordinated control mode among excitation, voltage coordination and stability as well as automatic-voltage reactive power control systems. By using dynamic excitation regulation and steady-state regional voltage source control replacement, dynamic reactive power quickly output under fault condition as well as optimized regional reactive power under steady state were realized. The correctness and practicability of the proposed strategy was verified through engineering application. Adopting the reactive power displacement of voltage coordination control system to solve local voltage instability problem after UHV DC blocking. The proposed method realized multi-time scale emergency, recovery and steady-state control of the system under steady and dynamic states.

Research on Coordinated Control and Hierarchical Partition Control Strategy of Distributed Generation in New Power System

Abstract In recent years, the structure of urban power systems has become more complex and the power flow is more changeable, which seriously hinders the optimal scheduling of urban power systems. Therefore, it is necessary to realize the coordinated control of distributed generation, flexible load, and reactive power equipment globally and locally, improve the utilization rate of distributed generation, give full play to the source and load of electric vehicles and energy storage devices, and realize the optimal scheduling of urban power system. This paper proposes a multi-time scale distributed power coordinated control and hierarchical partition control model, which realizes the coordinated optimal control of the new power system for distributed power, load, and energy storage. The effectiveness of the model in this paper is verified by IEEE33 node simulation.