When high-impedance faults (HIFs) occur in resonant grounded distribution networks, the current that flows is extremely weak, and the noise interference caused by the distribution network operation and the sampling error of the measurement devices further masks the fault characteristics. Consequently, locating a fault section with high sensitivity is difficult. Unlike existing technologies, this study presents a novel fault feature identification framework that addresses this issue. The framework includes three key steps: ① utilizing the variable mode decomposition (VMD) method to denoise the fault transient zero-sequence current (TZSC); ② employing a manifold learning algorithm based on t-distributed stochastic neighbor embedding (t-SNE) to further reduce the redundant information of the TZSC after denoising and to visualize fault information in high-dimensional 2D space; and ③ classifying the signal of each measurement point based on the fuzzy clustering method and combining the network topology structure to determine the fault section location. Numerical simulations and field testing confirm that the proposed method accurately detects the fault location, even under the influence of strong noise interference.
当谐振接地配电网络中出现高阻抗故障（HIF）时，流过的电流非常微弱，配电网络运行造成的噪声干扰和测量设备的采样误差进一步掩盖了故障特征。因此，以高灵敏度定位故障段非常困难。与现有技术不同，本研究提出了一种新型故障特征识别框架来解决这一问题。该框架包括三个关键步骤：利用变模分解（VMD）方法对故障瞬态零序电流（TZSC）进行去噪；②采用基于 t 分布随机邻域嵌入（t-SNE）的流形学习算法，进一步减少去噪后 TZSC 的冗余信息，并在高维二维空间中实现故障信息的可视化；基于模糊聚类方法对各测点信号进行分类，并结合网络拓扑结构确定故障段位置。数值模拟和现场测试证实，即使在强噪声干扰的影响下，所提出的方法也能准确检测出故障位置。
{"title":"High-Impedance Fault Section Location for Distribution Networks Based on T-Distributed Stochastic Neighbor Embedding and Variable Mode Decomposition","authors":"Zhihua Yin;Yuping Zheng;Zhinong Wei;Guoqiang Sun;Sheng Chen;Haixiang Zang","doi":"10.35833/MPCE.2023.000225","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000225","url":null,"abstract":"When high-impedance faults (HIFs) occur in resonant grounded distribution networks, the current that flows is extremely weak, and the noise interference caused by the distribution network operation and the sampling error of the measurement devices further masks the fault characteristics. Consequently, locating a fault section with high sensitivity is difficult. Unlike existing technologies, this study presents a novel fault feature identification framework that addresses this issue. The framework includes three key steps: ① utilizing the variable mode decomposition (VMD) method to denoise the fault transient zero-sequence current (TZSC); ② employing a manifold learning algorithm based on t-distributed stochastic neighbor embedding (t-SNE) to further reduce the redundant information of the TZSC after denoising and to visualize fault information in high-dimensional 2D space; and ③ classifying the signal of each measurement point based on the fuzzy clustering method and combining the network topology structure to determine the fault section location. Numerical simulations and field testing confirm that the proposed method accurately detects the fault location, even under the influence of strong noise interference.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10345458","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-05DOI: 10.35833/MPCE.2023.000432
Behrouz Azimian;Shiva Moshtagh;Anamitra Pal;Shanshan Ma
Recently, we demonstrated the success of a time-synchronized state estimator using deep neural networks (DNNs) for real-time unobservable distribution systems. In this paper, we provide analytical bounds on the performance of the state estimator as a function of perturbations in the input measurements. It has already been shown that evaluating performance based only on the test dataset might not effectively indicate the ability of a trained DNN to handle input perturbations. As such, we analytically verify the robustness and trustworthiness of DNNs to input perturbations by treating them as mixed-integer linear programming (MILP) problems. The ability of batch normalization in addressing the scalability limitations of the MILP formulation is also highlighted. The framework is validated by performing time-synchronized distribution system state estimation for a modified IEEE 34-node system and a real-world large distribution system, both of which are incompletely observed by micro-phasor measurement units.
{"title":"Analytical Verification of Performance of Deep Neural Network Based Time-Synchronized Distribution System State Estimation","authors":"Behrouz Azimian;Shiva Moshtagh;Anamitra Pal;Shanshan Ma","doi":"10.35833/MPCE.2023.000432","DOIUrl":"10.35833/MPCE.2023.000432","url":null,"abstract":"Recently, we demonstrated the success of a time-synchronized state estimator using deep neural networks (DNNs) for real-time unobservable distribution systems. In this paper, we provide analytical bounds on the performance of the state estimator as a function of perturbations in the input measurements. It has already been shown that evaluating performance based only on the test dataset might not effectively indicate the ability of a trained DNN to handle input perturbations. As such, we analytically verify the robustness and trustworthiness of DNNs to input perturbations by treating them as mixed-integer linear programming (MILP) problems. The ability of batch normalization in addressing the scalability limitations of the MILP formulation is also highlighted. The framework is validated by performing time-synchronized distribution system state estimation for a modified IEEE 34-node system and a real-world large distribution system, both of which are incompletely observed by micro-phasor measurement units.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10345460","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141769487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-05DOI: 10.35833/MPCE.2023.000325
Sirwan Shazdeh;Hêmin Golpîra;Hassan Bevrani
This paper proposes an adaptive method based on fuzzy logic that utilizes data from phasor measurement units (PMUs) to assess and classify generating-side voltage trajectories. The voltage variable and its associated derivatives are used as the input variables of a fuzzy-logic block. In addition, the voltage trajectory is compared with the pre-selected pilot-bus voltage to make a reliable decision about the voltage operational state. Different types of short-term voltage dynamics are considered in the proposed method. The fuzzy membership functions are determined using a systematic method that considers the current situation of the voltage trajectory. Finally, the voltage status is categorized into four classes to determine appropriate remedial actions. The proposed method is validated on a IEEE 73-bus power system in a MATLAB environment.
{"title":"An Adaptive Data-Driven Method Based on Fuzzy Logic for Determining Power System Voltage Status","authors":"Sirwan Shazdeh;Hêmin Golpîra;Hassan Bevrani","doi":"10.35833/MPCE.2023.000325","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000325","url":null,"abstract":"This paper proposes an adaptive method based on fuzzy logic that utilizes data from phasor measurement units (PMUs) to assess and classify generating-side voltage trajectories. The voltage variable and its associated derivatives are used as the input variables of a fuzzy-logic block. In addition, the voltage trajectory is compared with the pre-selected pilot-bus voltage to make a reliable decision about the voltage operational state. Different types of short-term voltage dynamics are considered in the proposed method. The fuzzy membership functions are determined using a systematic method that considers the current situation of the voltage trajectory. Finally, the voltage status is categorized into four classes to determine appropriate remedial actions. The proposed method is validated on a IEEE 73-bus power system in a MATLAB environment.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10345459","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-29DOI: 10.35833/MPCE.2023.000024
Silu Zhang;Nian Liu;Jianpei Han
With the large-scale connection of 5G base stations (BSs) to the distribution networks (DNs), 5G BSs are utilized as flexible loads to participate in the peak load regulation, where the BSs can be divided into base station groups (BSGs) to realize inter-district energy transfer. A Stackelberg game-based optimization framework is proposed, where the distribution network operator (DNO) works as a leader with dynamic pricing for multi-BSGs; while BSGs serve as followers with the ability of demand response to adjust their charging and discharging strategies in temporal dimension and load migration strategy in spatial dimension. Subsequently, the presence and uniqueness of the Stackelberg equilibrium (SE) are provided. Moreover, differential evolution is adopted to reach the SE and the optimization problem in multi-BSGs is decomposed to solve the time-space coupling. Finally, through simulation of a practical system, the results show that the DNO operation profit is increased via cutting down the peak load and the operation costs for multi-BSGs are reduced, which reaches a win-win effect.
{"title":"Temporal and Spatial Optimization for 5G Base Station Groups in Distribution Networks","authors":"Silu Zhang;Nian Liu;Jianpei Han","doi":"10.35833/MPCE.2023.000024","DOIUrl":"10.35833/MPCE.2023.000024","url":null,"abstract":"With the large-scale connection of 5G base stations (BSs) to the distribution networks (DNs), 5G BSs are utilized as flexible loads to participate in the peak load regulation, where the BSs can be divided into base station groups (BSGs) to realize inter-district energy transfer. A Stackelberg game-based optimization framework is proposed, where the distribution network operator (DNO) works as a leader with dynamic pricing for multi-BSGs; while BSGs serve as followers with the ability of demand response to adjust their charging and discharging strategies in temporal dimension and load migration strategy in spatial dimension. Subsequently, the presence and uniqueness of the Stackelberg equilibrium (SE) are provided. Moreover, differential evolution is adopted to reach the SE and the optimization problem in multi-BSGs is decomposed to solve the time-space coupling. Finally, through simulation of a practical system, the results show that the DNO operation profit is increased via cutting down the peak load and the operation costs for multi-BSGs are reduced, which reaches a win-win effect.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10335160","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141769490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-29DOI: 10.35833/MPCE.2023.000047
Joice G. Philip;Jaesung Jung;Ahmet Onen
This paper proposes an empirical wavelet transform (EWT) based method for identification and analysis of sub-synchronous oscillation (SSO) modes in the power system using phasor measurement unit (PMU) data. The phasors from PMUs are preprocessed to check for the presence of oscillations. If the presence is established, the signal is decomposed using EWT and the parameters of the mono-components are estimated through Yoshida algorithm. The superiority of the proposed method is tested using test signals with known parameters and simulated using actual SSO signals from the Hami Power Grid in Northwest China. Results show the effectiveness of the proposed EWT-Yoshida method in detecting the SSO and estimating its parameters.
{"title":"Empirical Wavelet Transform Based Method for Identification and Analysis of Sub-synchronous Oscillation Modes Using PMU Data","authors":"Joice G. Philip;Jaesung Jung;Ahmet Onen","doi":"10.35833/MPCE.2023.000047","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000047","url":null,"abstract":"This paper proposes an empirical wavelet transform (EWT) based method for identification and analysis of sub-synchronous oscillation (SSO) modes in the power system using phasor measurement unit (PMU) data. The phasors from PMUs are preprocessed to check for the presence of oscillations. If the presence is established, the signal is decomposed using EWT and the parameters of the mono-components are estimated through Yoshida algorithm. The superiority of the proposed method is tested using test signals with known parameters and simulated using actual SSO signals from the Hami Power Grid in Northwest China. Results show the effectiveness of the proposed EWT-Yoshida method in detecting the SSO and estimating its parameters.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10335161","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-29DOI: 10.35833/MPCE.2023.000179
Bo Sun;Xi Wu;Xi Chen;Zixiao Zou;Qiang Li;Bixing Ren
In recent years, with increasing amounts of renewable energy sources connecting to power networks, sub-/super-synchronous oscillations (SSOs) have occurred more frequently. Due to the time-variant nature of SSO magnitudes and frequencies, as well as the mutual interferences among SSO modes with close frequencies, the accurate parameter estimation of SSO has become a particularly challenging topic. To solve this issue, this paper proposes an improved spectrum analysis method by improving the window function and a spectrum correction method to achieve higher precision. First, by aiming at the sidelobe characteristics of the window function as evaluation criteria, a combined cosine function is optimized using a genetic algorithm (GA). Furthermore, the obtained window function is self-convolved to extend its excellent characteristics, which have better performance in reducing mutual interference from other SSO modes. Subsequently, a new form of interpolated all-phase fast Fourier transform (IpApFFT) using the optimized window function is proposed to estimate the parameters of SSO. This method allows for phase-unbiased estimation while maintaining algorithmic simplicity and expedience. The performance of the proposed method is demonstrated under various conditions, compared with other estimation methods. Simulation results validate the effectiveness and superiority of the proposed method.
{"title":"Parameter Estimation of Sub-/Super-Synchronous Oscillation Based on Interpolated All-Phase Fast Fourier Transform with Optimized Window Function","authors":"Bo Sun;Xi Wu;Xi Chen;Zixiao Zou;Qiang Li;Bixing Ren","doi":"10.35833/MPCE.2023.000179","DOIUrl":"10.35833/MPCE.2023.000179","url":null,"abstract":"In recent years, with increasing amounts of renewable energy sources connecting to power networks, sub-/super-synchronous oscillations (SSOs) have occurred more frequently. Due to the time-variant nature of SSO magnitudes and frequencies, as well as the mutual interferences among SSO modes with close frequencies, the accurate parameter estimation of SSO has become a particularly challenging topic. To solve this issue, this paper proposes an improved spectrum analysis method by improving the window function and a spectrum correction method to achieve higher precision. First, by aiming at the sidelobe characteristics of the window function as evaluation criteria, a combined cosine function is optimized using a genetic algorithm (GA). Furthermore, the obtained window function is self-convolved to extend its excellent characteristics, which have better performance in reducing mutual interference from other SSO modes. Subsequently, a new form of interpolated all-phase fast Fourier transform (IpApFFT) using the optimized window function is proposed to estimate the parameters of SSO. This method allows for phase-unbiased estimation while maintaining algorithmic simplicity and expedience. The performance of the proposed method is demonstrated under various conditions, compared with other estimation methods. Simulation results validate the effectiveness and superiority of the proposed method.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10335162","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141769488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-22DOI: 10.35833/MPCE.2023.000653
Bin Liu;Julio H. Braslavsky;Nariman Mahdavi
Dynamic operating envelopes (DOEs), as a key enabler to facilitate distributed energy resource (DER) integration, have attracted increasing attention in the past years. However, uncertainties, which may come from load forecasting errors or inaccurate network parameters, have been rarely discussed in DOE calculation, leading to compromised quality of the hosting capacity allocation strategy. This letter studies how to calculate DOEs that are immune to such uncertainties based on a linearised unbalanced three-phase optimal power flow (UTOPF) model. With uncertain parameters constrained by norm balls, formulations for calculating robust DOEs (RDOEs) are presented along with discussions on their tractability. Two cases, including a 2-bus illustrative network and a representative Australian network, are tested to demonstrate the effectiveness and efficiency of the proposed approach.
动态运行包络（DOE）作为促进分布式能源资源（DER）集成的关键因素，在过去几年中受到越来越多的关注。然而，在 DOE 计算中很少讨论不确定性，这些不确定性可能来自负荷预测误差或不准确的网络参数，从而导致托管容量分配策略的质量受到影响。本文基于线性化不平衡三相最优功率流 (UTOPF) 模型，研究如何计算不受此类不确定性影响的 DOE。在不确定参数受规范球约束的情况下，本文提出了计算鲁棒 DOE（RDOE）的公式，并讨论了这些公式的可操作性。测试了两个案例，包括一个双母线示例网络和一个具有代表性的澳大利亚网络，以证明所提方法的有效性和效率。
{"title":"Linear OPF-Based Robust Dynamic Operating Envelopes with Uncertainties in Unbalanced Distribution Networks","authors":"Bin Liu;Julio H. Braslavsky;Nariman Mahdavi","doi":"10.35833/MPCE.2023.000653","DOIUrl":"10.35833/MPCE.2023.000653","url":null,"abstract":"Dynamic operating envelopes (DOEs), as a key enabler to facilitate distributed energy resource (DER) integration, have attracted increasing attention in the past years. However, uncertainties, which may come from load forecasting errors or inaccurate network parameters, have been rarely discussed in DOE calculation, leading to compromised quality of the hosting capacity allocation strategy. This letter studies how to calculate DOEs that are immune to such uncertainties based on a linearised unbalanced three-phase optimal power flow (UTOPF) model. With uncertain parameters constrained by norm balls, formulations for calculating robust DOEs (RDOEs) are presented along with discussions on their tractability. Two cases, including a 2-bus illustrative network and a representative Australian network, are tested to demonstrate the effectiveness and efficiency of the proposed approach.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10327678","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136042420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-22DOI: 10.35833/MPCE.2023.000205
Hongxia Wang;Bo Wang;Jiaxin Zhang;Chengxi Liu;Hengrui Ma
Taking the advantage of Internet of Things (IoT) enabled measurements, this paper formulates the event detection problem as an information-plus-noise model, and detects events in power systems based on free probability theory (FPT). Using big data collected from phasor measurement units (PMUs), we construct the event detection matrix to reflect both spatial and temporal characteristics of power gird states. The event detection matrix is further described as an information matrix plus a noise matrix, and the essence of event detection is to extract event information from the event detection matrix. By associating the event detection problem with FPT, the empirical spectral distributions (ESDs) related moments of the sample covariance matrix of the information matrix is computed, to distinguish events from “noises”, including normal fluctuations, background noises, and measurement errors. Based on central limit theory (CLT), the alarm threshold is computed using measurements collected in normal states. Additionally, with the aid of sliding window, this paper builds an event detection architecture to reflect power grid state and detect events online. Case studies with simulated data from Anhui, China, and real PMU data from Guangdong, China, verify the effectiveness of the proposed method. Compared with other data-driven methods, the proposed method is more sensitive and has better adaptability to the normal fluctuations, background noises, and measurement errors in real PMU cases. In addition, it does not require large number of training samples as needed in the training-testing paradigm.
{"title":"Free Probability Theory Based Event Detection for Power Grids Using IoT-Enabled Measurements","authors":"Hongxia Wang;Bo Wang;Jiaxin Zhang;Chengxi Liu;Hengrui Ma","doi":"10.35833/MPCE.2023.000205","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000205","url":null,"abstract":"Taking the advantage of Internet of Things (IoT) enabled measurements, this paper formulates the event detection problem as an information-plus-noise model, and detects events in power systems based on free probability theory (FPT). Using big data collected from phasor measurement units (PMUs), we construct the event detection matrix to reflect both spatial and temporal characteristics of power gird states. The event detection matrix is further described as an information matrix plus a noise matrix, and the essence of event detection is to extract event information from the event detection matrix. By associating the event detection problem with FPT, the empirical spectral distributions (ESDs) related moments of the sample covariance matrix of the information matrix is computed, to distinguish events from “noises”, including normal fluctuations, background noises, and measurement errors. Based on central limit theory (CLT), the alarm threshold is computed using measurements collected in normal states. Additionally, with the aid of sliding window, this paper builds an event detection architecture to reflect power grid state and detect events online. Case studies with simulated data from Anhui, China, and real PMU data from Guangdong, China, verify the effectiveness of the proposed method. Compared with other data-driven methods, the proposed method is more sensitive and has better adaptability to the normal fluctuations, background noises, and measurement errors in real PMU cases. In addition, it does not require large number of training samples as needed in the training-testing paradigm.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10327677","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-22DOI: 10.35833/MPCE.2022.000581
Xiaoyang Ma;Diwen Zheng;Xiaoyong Deng;Ying Wang;Dawei Deng;Wei Li
Non-intrusive load monitoring is a technique for monitoring the operating conditions of electrical appliances by collecting the aggregated electrical information at the household power inlet. Despite several studies on the mining of unique load characteristics, few studies have extensively considered the high computational burden and sample training. Based on low-frequency sampling data, a non-intrusive load monitoring algorithm utilizing the graph total variation (GTV) is proposed in this study. The algorithm can effectively depict the load state without the need for prior training. First, the combined $K$ -means clustering algorithm and graph signals are used to build concise and accurate graph structures as load models. The GTV representing the internal structure of the graph signal is introduced as the optimization model and solved using the augmented Lagrangian iterative algorithm. The introduction of the difference operator decreases the computing cost and addresses the inaccurate reconstruction of the graph signal. With low-frequency sampling data, the algorithm only requires a little prior data and no training, thereby reducing the computing cost. Experiments conducted using the reference energy disaggregation dataset and almanac of minutely power dataset demonstrated the stable superiority of the algorithm and its low computational burden.
{"title":"Non-Intrusive Load Monitoring Based on Graph Total Variation for Residential Appliances","authors":"Xiaoyang Ma;Diwen Zheng;Xiaoyong Deng;Ying Wang;Dawei Deng;Wei Li","doi":"10.35833/MPCE.2022.000581","DOIUrl":"https://doi.org/10.35833/MPCE.2022.000581","url":null,"abstract":"Non-intrusive load monitoring is a technique for monitoring the operating conditions of electrical appliances by collecting the aggregated electrical information at the household power inlet. Despite several studies on the mining of unique load characteristics, few studies have extensively considered the high computational burden and sample training. Based on low-frequency sampling data, a non-intrusive load monitoring algorithm utilizing the graph total variation (GTV) is proposed in this study. The algorithm can effectively depict the load state without the need for prior training. First, the combined \u0000<tex>$K$</tex>\u0000-means clustering algorithm and graph signals are used to build concise and accurate graph structures as load models. The GTV representing the internal structure of the graph signal is introduced as the optimization model and solved using the augmented Lagrangian iterative algorithm. The introduction of the difference operator decreases the computing cost and addresses the inaccurate reconstruction of the graph signal. With low-frequency sampling data, the algorithm only requires a little prior data and no training, thereby reducing the computing cost. Experiments conducted using the reference energy disaggregation dataset and almanac of minutely power dataset demonstrated the stable superiority of the algorithm and its low computational burden.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10327676","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-17DOI: 10.35833/MPCE.2023.000447
Yang Wang;Xiang Zhou;Junmiao Tang;Xianyong Xiao;Shu Zhang;Jiandong Si
The effects of nonlinear loads on voltage quality represent an emerging concern for islanded microgrids. Existing research works have mainly focused on harmonic power sharing among multiple inverters, which ignores the diversity of different inverters to mitigate harmonics from nonlinear loads. As a result, the voltage quality of microgrids cannot be effectively improved. To address this issue, this study proposes an adaptive harmonic virtual impedance (HVI) control for improving voltage quality of microgrids. Based on the premise that no inverter is overloaded, the main objective of the proposed control is to maximize harmonic power absorption by shaping the lowest output impedances of inverters. To achieve this, the proposed control is utilized to adjust the HVI of each inverter based on its operation conditions. In addition, the evaluation based on Monte Carlo harmonic power flow is designed to assess the performance of the proposed control in practice. Finally, comparative studies and control-in-the-loop experiments are conducted.
{"title":"Adaptive Harmonic Virtual Impedance Control for Improving Voltage Quality of Microgrids","authors":"Yang Wang;Xiang Zhou;Junmiao Tang;Xianyong Xiao;Shu Zhang;Jiandong Si","doi":"10.35833/MPCE.2023.000447","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000447","url":null,"abstract":"The effects of nonlinear loads on voltage quality represent an emerging concern for islanded microgrids. Existing research works have mainly focused on harmonic power sharing among multiple inverters, which ignores the diversity of different inverters to mitigate harmonics from nonlinear loads. As a result, the voltage quality of microgrids cannot be effectively improved. To address this issue, this study proposes an adaptive harmonic virtual impedance (HVI) control for improving voltage quality of microgrids. Based on the premise that no inverter is overloaded, the main objective of the proposed control is to maximize harmonic power absorption by shaping the lowest output impedances of inverters. To achieve this, the proposed control is utilized to adjust the HVI of each inverter based on its operation conditions. In addition, the evaluation based on Monte Carlo harmonic power flow is designed to assess the performance of the proposed control in practice. Finally, comparative studies and control-in-the-loop experiments are conducted.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10322690","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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