Bikash Kumar Saw, Aashish Kumar Bohre, Jalpa Thakkar, M. Kolhe
{"title":"Techno-Economic and Environmental Based Approach for Planning of SDG and DSTATCOM with Impact of Network Reconfiguration using APSO and TLBO","authors":"Bikash Kumar Saw, Aashish Kumar Bohre, Jalpa Thakkar, M. Kolhe","doi":"10.13052/dgaej2156-3306.38510","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.38510","url":null,"abstract":"A Multi Objective based Fitness Function (MOFF) is proposed for the optimum planning of multiple Solar Distributed Generation (SDG) and DSTATCOM with radial distribution network (RDN) reconfiguration impact for techno-economic and environmental benefit improvement. The Adaptive-Particle Swarm Optimization (APSO) and Teaching-Learning Based Optimization techniques (TLBO) are employed to accomplish this work. In the proposed MOFF, the Active Power Loss (APLoss), Reactive Power Loss (RPLoss), System Voltage Deviation (SVD), Fault-Current Level-of-Line (FCLLine), and System Service Reliability (SSR) are considered. The economic-benefit measures along with Environmental Emissions Components (EEC) impact have also been considered in light of various system costs such as Fixed Capital Recovery Cost (FCRCost), Energy Loss Cost (ELCost) and Energy Not Supplied Cost (ENSCost). The novelty in the MOFF is the simultaneous consideration of FCLLine with APLoss, RPLoss, SVD, and SSR along with EEC impact calculation. The IEEE 69 and 118 bus RDN is considered with three case studies to demonstrate the proposed methodology's usefulness. The result analysis reveals that better performances can be obtained based on the considered MOFF in terms of environment-friendly techno-economic perspective, consistency, convergence, and computation time using TLBO rather than APSO. ","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82288150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Grid-Connected Solar PV Fed BLDC Motor Drive for Water Pumping System","authors":"B. N. Kar, P. Samuel, A. Mallick, J. K. Pradhan","doi":"10.13052/dgaej2156-3306.3856","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3856","url":null,"abstract":"This article proposes a unidirectional power flow of a grid-connected brushless DC motor powered water pumping system fed by a photovoltaic array using a bridgeless power factor corrected (PFC) boost converter. The system consists of a bridgeless PFC converter, a boost converter, and a voltage source inverter to drive a brushless DC motor coupled with a pump. As a backup source, the grid is used. This system allows the water pump to run at maximum capacity regardless of the weather conditions. The grid will provide power if the photovoltaic array is unable to fulfil the required power demand. The unidirectional power flow through a conventional power factor corrected (PFC) boost converter causes conduction loss in the input bridge rectifier, thereby hurting efficiency, power factor, and THD. This paper presents a bridgeless PFC boost converter topology to reduce the conduction losses, thereby increasing the efficiency and obtaining a nearly unity power factor as well as lower total harmonic distortion (THD) of input current. The system is simulated using MATLAB /Simulink. The proposed system’s real-time validation is realized through the OPAL-RT simulator OP5700. The THD results obtained are well within the specified standard of IEC 61000-3-2 and IEEE 519-1992.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"85 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85984524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xifeng Xie, Deng Luo, Jiangwei Wang, Xuesong Wu, Tao Chen
{"title":"Dead-Time Effect and Compensation Technology for an Isolated Dual Active Bridge Converter","authors":"Xifeng Xie, Deng Luo, Jiangwei Wang, Xuesong Wu, Tao Chen","doi":"10.13052/dgaej2156-3306.3851","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3851","url":null,"abstract":"Aiming at deficiencies of output voltage distortion and circulating current generation caused by dead-time effect in the modulation process, a simple and feasible dead time compensation strategy is presented. Firstly, the influence of dead-time effect on the output voltage of bridge is analysed, and a dead time compensation strategy is added between the modulation signal and dead time procession. According to the current direction of the bridge, the rising edge or falling edge of the driving signal is selectively delayed to compensate for dead-time effect. Secondly, an Optimized Triple Phase-shift (OTPS) modulation strategy is adopted with minimizing leakage inductor current Root-Mean-Square (RMS) control, which minimizes current stress, achieves soft-switching operation, avoids phase-shift errors caused by Dead-Time Effect, and optimizes control performance of DC-DC converters. Finally, simulated and experimental results are added to verify the correctness and effectiveness of the proposed method.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83334628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zexi Chen, Pu Wang, Bin Li, E. Zhao, Zhigang Hao, Dongqiang Jia
{"title":"Artificial Neural Networks-Based Fault Diagnosis Model for Distribution Network","authors":"Zexi Chen, Pu Wang, Bin Li, E. Zhao, Zhigang Hao, Dongqiang Jia","doi":"10.13052/dgaej2156-3306.38513","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.38513","url":null,"abstract":"With many branch lines in radiant distribution networks, diagnosing faults in a distribution network is very difficult. It is of great significance to identify different types of faults quickly and accurately for the stable operation of the power grid. This research presents a fault identification model for a distribution network based on artificial neural networks. The principal component analysis first extracts features from transitory data in a distribution network. The resulting low-dimensional data is subsequently used to update the artificial neural network model. The artificial neural network may also identify the type of fault. The proposed model’s fault detection accuracy is improved over the traditional approach by examining distribution network fault data during the simulation test.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74560950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Han Jinglin, Hu Ping, Zhao Hui, He Chunguang, Hou Ruosong, Li Bo
{"title":"Multi-objective Collaborative Planning Method for Micro-energy Systems Considering Thermoelectric Coupling Clusters","authors":"Han Jinglin, Hu Ping, Zhao Hui, He Chunguang, Hou Ruosong, Li Bo","doi":"10.13052/dgaej2156-3306.38514","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.38514","url":null,"abstract":"As the continuous development of integrated energy system and distributed power supply, operation economy and regional energy Internet reliability, especially micro-energy system, are increasing. Therefore, it is necessary to build multi-energy complementary micro-energy system, innovate energy supply mode, realize collaborative and efficient utilization among multi-energy systems, improve energy utilization efficiency and absorb renewable energy. In this paper, the decision model of distribution network planning scheme including distributed generator supply is established from four aspects: investment and operation cost, extra reserve capacity, energy conservation, reliability cost. The decision model involves a lot of parameter calculation and selection judgment, so after the decision goal is determined, an decision framework based on DS-MAS is established, that is, parameters are automatically calculated and selected based on different scenarios. Model validity is proved via a practical decision project.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"107 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84979084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Optimal Power Dispatch of Multiple DGs Using a Hybrid Algorithm for Mitigating Voltage Deviations and Losses in a Radial Distribution System with Economic Benefits","authors":"Ankeshwarapu Sunil, C. Venkaiah, D. Kumar","doi":"10.13052/dgaej2156-3306.3858","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3858","url":null,"abstract":"In this research, a meta-heuristic-based hybrid algorithm was used to optimize the power dispatch of numerous Distributed Generators (DGs) in a Radial Distribution System (RDS) for hourly fluctuating seasonal loads in order to reduce losses and voltage variations while also saving money. With hourly seasonal load changes, renewable DGs like PV, Wind, and Hybrid (PV+Wind) were used. The HA is proposed in this paper as a way to achieve successful outcomes by merging two meta-heuristic algorithms. The findings of the HA are compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Shuffled Frog Leap Algorithm (SFLA), and Jaya Algorithm (JA) when they are applied to a standard IEEE 33 bus RDS and PG&E 69 bus RDS. According to the test findings simulated in the MATLAB environment, Hybrid Algorithm (HA) beat GA, PSO, SFLA, and JA in terms of optimal power dispatch of numerous DGs to minimise losses and voltage variations, as well as the cost-benefit analysis of renewable DGs energy generation.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"85 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75426918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Improved DCGAN for Solar Cell Defect Enhancement","authors":"Deng Hao, Yilihamu Yaermaimaiti","doi":"10.13052/dgaej2156-3306.3852","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3852","url":null,"abstract":"Aiming at the problems of serious overfitting and poor training results caused by too small a data set of solar cell defect images in the process of deep learning training, an improved DCGAN generation countermeasure network model is proposed. Firstly, CLAHE preprocessing is used to enhance the defect image features, which can improve the defect contrast and avoid excessive noise enhancement at the same time; Secondly, the NAM attention module is introduced into DCGAN to improve the quality of the defect image; Finally, S-RELU is used to replace Leaky Relu in DCGAN discriminator to avoid the influence of too much negative information with gradient on the decision of discriminator. The experimental results of classification and detection show that the data enhancement effect of the improved model is better. Compared with the original model, its accuracy is improved by 2.51%, and the mapped value is improved by 1.92%, which proves the effectiveness of the proposed algorithm.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86573933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zexi Chen, Li Yang, J. Tian, Zeng Chen, Xiaoye Xu, E. Zhao
{"title":"Real-time Monitoring Technology of Voltage Sag Disturbance in Distribution Network Based on TCN-Attention Neural Network and Flink Flow Computing","authors":"Zexi Chen, Li Yang, J. Tian, Zeng Chen, Xiaoye Xu, E. Zhao","doi":"10.13052/dgaej2156-3306.38512","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.38512","url":null,"abstract":"In the face of the challenges brought by the complexity of power grid, diversification of disturbance factors, isolation of monitoring points and other issues to the cause identification of voltage sag disturbance, this paper proposes a real-time monitoring technology for voltage sag disturbance in distribution network based on TCN-Attention neural network and Flink flow calculation, which has important practical significance for controlling voltage sag and reducing economic losses. This method uses Temporal Convolutional Network (TCN) to extract the cross time nonlinear characteristics of voltage sag time series data, which effectively solves the problems of long-term dependence on time series and low training output efficiency of existing time series models. In order to further improve the recognition accuracy of the model, Attention mechanism is introduced to mine the duration relationship in voltage sag data. At the same time, the method also constructs a parallel real-time monitoring platform based on Flink streaming computing framework, embeds the TCN-Attention voltage sag cause identification model generated by training, so as to realize real-time identification and monitoring analysis of voltage sag disturbances at each monitoring point of the distribution network. In this paper, various voltage sags are simulated on IEEE 14 bus system using PSCAD software, and the proposed method is verified and tested. The deep learning fusion model has high recognition accuracy for the cause of voltage sag, and the flow computing platform has excellent performance in time delay and throughput indicators, and can realize the parallel real-time monitoring and analysis of voltage sag causes in distribution network.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75861242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Enhancement of Weighting Coefficient Selection using Grey Relational Analysis for Model Predictive Torque Control of PMSM Drive: Analysis and Experiments","authors":"Avinash Vujji, R. Dahiya","doi":"10.13052/dgaej2156-3306.3854","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3854","url":null,"abstract":"Permanent magnet synchronous motor (PMSM) with model predictive torque control (MPTC) is popular for its simplified control structure and adaptable in incorporating control parameters into the control algorithm. However, in control technique the primary concern for objective function (OF) depends on the selection of appropriate weighting coefficient (WC). Basically, for weighting coefficient selection, empirical methods are used but it takes additional time and heuristic process. In this paper, Grey Relational Analysis (GRA) technique is introduced in optimization of objective function for selection of appropriate weighting coefficient. In this methodology, stator flux and torque having individual OF are modified from single-OF. This ensures that in each sampling period, selection of grey relational optimal control action is dependent on the preference given to the control parameters in OF. For each sampling, a Grey Relational Grade (GRG) is employed to determine the appropriate control action. The models for two-level inverter fed PMSM are developed in MATLAB/Simulink to test the various operations of PMSM drive and the results are validated on the experimental test bench using dSPACE-1104 R&D controller. In order to highlight the effectiveness of the proposed technique, the results are compared with DTFC and MPTC approach.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139360042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Kumar, V. Das, Ashutosh Kumar Singh, P. Karuppanan
{"title":"Levelized Cost of Energy-Based Economic Analysis of Microgrid Equipped with Multi Energy Storage System","authors":"P. Kumar, V. Das, Ashutosh Kumar Singh, P. Karuppanan","doi":"10.13052/dgaej2156-3306.38411","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.38411","url":null,"abstract":"Energy storage system (ESS) plays a critical role in maintaining the reliability of microgrids. ESS selection for microgrids depends on energy density, specific power, specific energy, and economics. This paper analyses the economic benefits of various combinations of short-, medium-, and long-term ESS, i.e., multi-energy storage systems (MESS) in a microgrid. The economic feasibility of the system is analyzed using Homer software. The net present cost (NPC), the Levelized cost of energy (LCOE), and pollutant gas emission are chosen as parameters for analyzing the economic feasibility of the microgrids. The results show that amongst all the scenarios, the system with Hydrogen Storage System (HSS) with Proton exchange membrane fuel cell (PEMFC) and electrolyzer is the most feasible solution with the lowest LCOE and pollutant emission.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84034038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}