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Intelligent Renewable Energy Systems


Intelligent Renewable Energy Systems

Integrating Artificial Intelligence Techniques and Optimization Algorithms
1. Aufl.

von: Neeraj Priyadarshi, Akash Kumar Bhoi, Sanjeevikumar Padmanaban, S. Balamurugan, Jens Bo Holm-Nielsen

173,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 29.12.2021
ISBN/EAN: 9781119786283
Sprache: englisch
Anzahl Seiten: 480

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Beschreibungen

<b>INTELLIGENT RENEWABLE ENERGY SYSTEMS</b> <p><b>This collection of papers on artificial intelligence and other methods for improving renewable energy systems, written by industry experts, is a reflection of the state of the art, a must-have for engineers, maintenance personnel, students, and anyone else wanting to stay abreast with current energy systems concepts and technology.</b> <p>Renewable energy is one of the most important subjects being studied, researched, and advanced in today’s world. From a macro level, like the stabilization of the entire world’s economy, to the micro level, like how you are going to heat or cool your home tonight, energy, specifically renewable energy, is on the forefront of the discussion. <p>This book illustrates modelling, simulation, design and control of renewable energy systems employed with recent artificial intelligence (AI) and optimization techniques for performance enhancement. Current renewable energy sources have less power conversion efficiency because of its intermittent and fluctuating behavior. Therefore, in this regard, the recent AI and optimization techniques are able to deal with data ambiguity, noise, imprecision, and nonlinear behavior of renewable energy sources more efficiently compared to classical soft computing techniques. <p>This book provides an extensive analysis of recent state of the art AI and optimization techniques applied to green energy systems. Subsequently, researchers, industry persons, undergraduate and graduate students involved in green energy will greatly benefit from this comprehensive volume, a must-have for any library. <p><b>Audience</b> <p>Engineers, scientists, managers, researchers, students, and other professionals working in the field of renewable energy.
<p>Preface xv</p> <p><b>1 Optimization Algorithm for Renewable Energy Integration 1</b></p> <p><i>Bikash Das, SoumyabrataBarik, Debapriya Das and V Mukherjee</i></p> <p>1.1 Introduction 2</p> <p>1.2 Mixed Discrete SPBO 5</p> <p>1.2.1 SPBO Algorithm 5</p> <p>1.2.2 Performance of SPBO for Solving Benchmark Functions 8</p> <p>1.2.3 Mixed Discrete SPBO 11</p> <p>1.3 Problem Formulation 12</p> <p>1.3.1 Objective Functions 12</p> <p>1.3.2 Technical Constraints Considered 14</p> <p>1.4 Comparison of the SPBO Algorithm in Terms of CEC-2005 Benchmark Functions 17</p> <p>1.5 Optimum Placement of RDG and Shunt Capacitor to the Distribution Network 18</p> <p>1.5.1 Optimum Placement of RDGs and Shunt</p> <p>Capacitors to 33-Bus Distribution Network 25</p> <p>1.5.2 Optimum Placement of RDGs and Shunt Capacitors to 69-Bus Distribution Network 29</p> <p>1.6 Conclusions 33</p> <p>References 34</p> <p><b>2 Chaotic PSO for PV System Modelling 41</b></p> <p><i>Souvik Ganguli, Jyoti Gupta and Parag Nijhawan</i></p> <p>2.1 Introduction 42</p> <p>2.2 Proposed Method 43</p> <p>2.3 Results and Discussions 43</p> <p>2.4 Conclusions 72</p> <p>References 72</p> <p><b>3 Application of Artificial Intelligence and Machine Learning Techniques in Island Detection in a Smart Grid 79</b></p> <p><i>Soham Dutta, Pradip Kumar Sadhu, Murthy Cherikuri and Dusmanta Kumar Mohanta</i></p> <p>3.1 Introduction 80</p> <p>3.1.1 Distributed Generation Technology in Smart Grid 81</p> <p>3.1.2 Microgrids 81</p> <p>3.3.1.1 Problems with Microgrids 81</p> <p>3.2 Islanding in Power System 82</p> <p>3.3 Island Detection Methods 83</p> <p>3.3.1 Passive Methods 83</p> <p>3.3.2 Active Methods 85</p> <p>3.3.3 Hybrid Methods 86</p> <p>3.3.4 Local Methods 87</p> <p>3.3.5 Signal Processing Methods 87</p> <p>3.3.6 Classifer Methods 88</p> <p>3.4 Application of Machine Learning and Artificial Intelligence Algorithms in Island Detection Methods 89</p> <p>3.4.1 Decision Tree 89</p> <p>3.4.1.1 Advantages of Decision Tree 91</p> <p>3.4.1.2 Disadvantages of Decision Tree 91</p> <p>3.4.2 Artificial Neural Network 91</p> <p>3.4.2.1 Advantages of Artificial Neural Network 93</p> <p>3.4.2.2 Disadvantages of Artificial Neural Network 93</p> <p>3.4.3 Fuzzy Logic 93</p> <p>3.4.3.1 Advantages of Fuzzy Logic 94</p> <p>3.4.3.2 Disadvantages of Fuzzy Logic 94</p> <p>3.4.4 Artificial Neuro-Fuzzy Inference System 95</p> <p>3.4.4.1 Advantages of Artificial Neuro-Fuzzy Inference System 95</p> <p>3.4.4.2 Disadvantages of Artificial Neuro-Fuzzy Inference System 96</p> <p>3.4.5 Static Vector Machine 96</p> <p>3.4.5.1 Advantages of Support Vector Machine 97</p> <p>3.4.5.2 Disadvantages of Support Vector Machine 97</p> <p>3.4.6 Random Forest 97</p> <p>3.4.6.1 Advantages of Random Forest 98</p> <p>3.4.6.2 Disadvantages of Random Forest 98</p> <p>3.4.7 Comparison of Machine Learning and Artificial Intelligence Based Island Detection Methods with Other Methods 99</p> <p>3.5 Conclusion 99</p> <p>References 101</p> <p><b>4 Intelligent Control Technique for Reduction of Converter Generated EMI in DG Environment 111</b></p> <p><i>Ritesh Tirole, R R Joshi, Vinod Kumar Yadav, Jai Kumar Maherchandani and Shripati Vyas</i></p> <p>4.1 Introduction 112</p> <p>4.2 Grid Connected Solar PV System 113</p> <p>4.2.1 Grid Connected Solar PV System 113</p> <p>4.2.2 PhotoVoltaic Cell 114</p> <p>4.2.3 PhotoVoltaic Array 114</p> <p>4.2.4 PhotoVoltaic System Configurations 114</p> <p>4.2.4.1 Centralized Configurations 115</p> <p>4.2.4.2 Master Slave Configurations 115</p> <p>4.2.4.3 String Configurations 115</p> <p>4.2.4.4 Modular Configurations 115</p> <p>4.2.5 Inverter Integration in Grid Solar PV System 115</p> <p>4.2.5.1 Voltage Source Inverter 116</p> <p>4.2.5.2 Current Source Inverter 117</p> <p>4.3 Control Strategies for Grid Connected Solar PV System 117</p> <p>4.3.1 Grid Solar PV System Controller 117</p> <p>4.3.1.1 Linear Controllers 117</p> <p>4.3.1.2 Non-Linear Controllers 117</p> <p>4.3.1.3 Robust Controllers 118</p> <p>4.3.1.4 Adaptive Controllers 118</p> <p>4.3.1.5 Predictive Controllers 118</p> <p>4.3.1.6 Intelligent Controllers 118</p> <p>4.4 Electromagnetic Interference 118</p> <p>4.4.1 Mechanisms of Electromagnetic Interference 119</p> <p>4.4.2 Effect of Electromagnetic Interference 120</p> <p>4.5 Intelligent Controller for Grid Connected Solar PV System 120</p> <p>4.5.1 Fuzzy Logic Controller 120</p> <p>4.6 Results and Discussion 121</p> <p>4.6.1 Generated EMI at the Input Side of Grid SPV System 122</p> <p>4.7 Conclusion 125</p> <p>References 125</p> <p><b>5 A Review of Algorithms for Control and Optimization for Energy Management of Hybrid Renewable Energy Systems 131</b></p> <p><i>Megha Vyas, Vinod Kumar Yadav, Shripati Vyas, R.R Joshi and Ritesh Tirole</i></p> <p>5.1 Introduction 132</p> <p>5.2 Optimization and Control of HRES 134</p> <p>5.3 Optimization Techniques/Algorithms 135</p> <p>5.3.1 Genetic Algorithms (GA) 136</p> <p>5.4 Use of Ga In Solar Power Forecasting 140</p> <p>5.5 PV Power Forecasting 142</p> <p>5.5.1 Short-Term Forecasting 143</p> <p>5.5.2 Medium Term Forecasting 144</p> <p>5.5.3 Long Term Forecasting 144</p> <p>5.6 Advantages 145</p> <p>5.7 Disadvantages 146</p> <p>5.8 Conclusion 146</p> <p>Appendix A: List of Abbreviations 146</p> <p>References 147</p> <p><b>6 Integration of RES with MPPT by SVPWM Scheme 157</b></p> <p><i>Busireddy Hemanth Kumar and Vivekanandan Subburaj</i></p> <p>6.1 Introduction 158</p> <p>6.2 Multilevel Inverter Topologies 158</p> <p>6.2.1 Cascaded H-Bridge (CHB) Topology 159</p> <p>6.2.1.1 Neutral Point Clamped (NPC) Topology 160</p> <p>6.2.1.2 Flying Capacitor (FC) Topology 160</p> <p>6.3 Multilevel Inverter Modulation Techniques 161</p> <p>6.3.1 Fundamental Switching Frequency (FSF) 162</p> <p>6.3.1.1 Selective Harmonic Elimination Technique for MLIs 162</p> <p>6.3.1.2 Nearest Level Control Technique 163</p> <p>6.3.1.3 Nearest Vector Control Technique 164</p> <p>6.3.2 Mixed Switching Frequency PWM 164</p> <p>6.3.3 High Level Frequency PWM 164</p> <p>6.3.3.1 CBPWM Techniques for MLI 164</p> <p>6.3.3.2 Pulse Width Modulation Algorithms Using Space Vector Techniques for Multilevel Inverters 167</p> <p>6.4 Grid Integration of Renewable Energy Sources (RES) 167</p> <p>6.4.1 Solar PV Array 167</p> <p>6.4.2 Maximum Power Point Tracking (MPPT) 169</p> <p>6.4.3 Power Control Scheme 170</p> <p>6.5 Simulation Results 171</p> <p>6.6 Conclusion 176</p> <p>References 176</p> <p><b>7 Energy Management of Standalone Hybrid Wind-PV System 179</b></p> <p><i>Raunak Jangid, Jai Kumar Maherchandani, Vinod Kumar and Raju Kumar Swami</i></p> <p>7.1 Introduction 180</p> <p>7.2 Hybrid Renewable Energy System Configuration</p> <p>& Modeling 180</p> <p>7.3 PV System Modeling 181</p> <p>7.4 Wind System Modeling 183</p> <p>7.5 Modeling of Batteries 185</p> <p>7.6 Energy Management Controller 186</p> <p>7.7 Simulation Results and Discussion 186</p> <p>7.7.1 Simulation Response at Impulse Change in Wind Speed, Successive Increase in Irradiance Level and Impulse Change in Load 187</p> <p>7.8 Conclusion 193</p> <p>References 194</p> <p><b>8 Optimization Technique Based Distribution Network Planning Incorporating Intermittent Renewable Energy Sources 199</b></p> <p><i>Surajit Sannigrahi and Parimal Acharjee</i></p> <p>8.1 Introduction 200</p> <p>8.2 Load and WTDG Modeling 204</p> <p>8.2.1 Modeling of Load Demand 204</p> <p>8.2.2 Modeling of WTDG 205</p> <p>8.3 Objective Functions 207</p> <p>8.3.1 System Voltage Enhancement Index (SVEI) 208</p> <p>8.3.2 Economic Feasibility Index (EFI) 208</p> <p>8.3.3 Emission Cost Reduction Index (ECRI) 211</p> <p>8.4 Mathematical Formulation Based on Fuzzy Logic 212</p> <p>8.4.1 Fuzzy MF for SVEI 212</p> <p>8.4.2 Fuzzy MF for EFI 213</p> <p>8.4.3 Fuzzy MF for ECRI 214</p> <p>8.5 Solution Algorithm 215</p> <p>8.5.1 Standard RTO Technique 215</p> <p>8.5.2 Discrete RTO (DRTO) Algorithm 217</p> <p>8.5.3 Computational Flow 219</p> <p>8.6 Simulation Results and Analysis 221</p> <p>8.6.1 Obtained Results for Different Planning Cases 223</p> <p>8.6.2 Analysis of Voltage Profile and Power Flow Under the Worst Case Scenarios: 230</p> <p>8.6.3 Comparison Between Different Algorithms 231</p> <p>8.6.3.1 Solution Quality 234</p> <p>8.6.3.2 Computational Time 234</p> <p>8.6.3.3 Failure Rate 234</p> <p>8.6.3.4 Convergence Characteristics 234</p> <p>8.6.3.5 Wilcoxon Signed Rank Test (WSRT) 236</p> <p>8.7 Conclusion 237</p> <p>References 239</p> <p><b>9 User Interactive GUI for Integrated Design of PV Systems 243</b></p> <p><i>SushmitaSarkar, K UmaRao, Prema V, Anirudh Sharma C A, Jayanth Bhargav and ShrikeshSheshaprasad</i></p> <p>9.1 Introduction 244</p> <p>9.2 PV System Design 245</p> <p>9.2.1 Design of a Stand-Alone PV System 245</p> <p>9.2.1.1 Panel Size Calculations 246</p> <p>9.2.1.2 Battery Sizing 247</p> <p>9.2.1.3 Inverter Design 248</p> <p>9.2.1.4 Loss of Load 249</p> <p>9.2.1.5 Average Daily Units Generated 249</p> <p>9.2.2 Design of a Grid-Tied PV System 250</p> <p>9.2.3 Design of a Large-Scale Power Plant 251</p> <p>9.3 Economic Considerations 252</p> <p>9.4 PV System Standards 252</p> <p>9.5 Design of GUI 252</p> <p>9.6 Results 255</p> <p>9.6.1 Design of a Stand-Alone System Using GUI 255</p> <p>9.6.2 GUI for a Grid-Tied System 257</p> <p>9.6.3 GUI for a Large PV Plant 259</p> <p>9.7 Discussions 260</p> <p>9.8 Conclusion and Future Scope 260</p> <p>9.9 Acknowledgment 261</p> <p>References 261</p> <p><b>10 Situational Awareness of Micro-Grid Using Micro-PMU and Learning Vector Quantization Algorithm 267</b></p> <p><i>Kunjabihari Swain and Murthy Cherukuri</i></p> <p>10.1 Introduction 268</p> <p>10.2 Micro Grid 269</p> <p>10.3 Phasor Measurement Unit and Micro PMU 270</p> <p>10.4 Situational Awareness: Perception, Comprehension and Prediction 272</p> <p>10.4.1 Perception 273</p> <p>10.4.2 Comprehension 274</p> <p>10.4.3 Projection 280</p> <p>10.5 Conclusion 280</p> <p>References 280</p> <p><b>11 AI and ML for the Smart Grid 287</b></p> <p><i>Dr M K Khedkar and B Ramesh</i></p> <p>Abbreviations 288</p> <p>11.1 Introduction 288</p> <p>11.2 AI Techniques 291</p> <p>11.2.1 Expert Systems (ES) 291</p> <p>11.2.2 Artificial Neural Networks (ANN) 291</p> <p>11.2.3 Fuzzy Logic (FL) 292</p> <p>11.2.4 Genetic Algorithm (GA) 292</p> <p>11.3 Machine Learning (ML) 293</p> <p>11.4 Home Energy Management System (HEMS) 294</p> <p>11.5 Load Forecasting (LF) in Smart Grid 295</p> <p>11.6 Adaptive Protection (AP) 297</p> <p>11.7 Energy Trading in Smart Grid 298</p> <p>11.8 AI Based Smart Energy Meter (AI-SEM) 300</p> <p>References 302</p> <p><b>12 Energy Loss Allocation in Distribution Systems with Distributed Generations 307 </b></p> <p><i>Dr Kushal Manohar Jagtap</i></p> <p>12.1 Introduction 308</p> <p>12.2 Load Modelling 311</p> <p>12.3 Mathematicl Model 312</p> <p>12.4 Solution Algorithm 317</p> <p>12.5 Results and Discussion 317</p> <p>12.6 Conclusion 341</p> <p>References 341</p> <p><b>13 Enhancement of Transient Response of Statcom and VSC Based HVDC with GA and PSO Based Controllers 345</b></p> <p><i>Nagesh Prabhu, R Thirumalaivasan and M.Janaki</i></p> <p>13.1 Introduction 346</p> <p>13.2 Design of Genetic Algorithm Based Controller for STATCOM 347</p> <p>13.2.1 Two Level STACOM with Type-2 Controller 348</p> <p>13.2.1.1 Simulation Results with Suboptimal Controller Parameters 349</p> <p>13.2.1.2 PI Controller Without Nonlinear State Variable Feedback 349</p> <p>13.2.1.3 PI Controller with Nonlinear State Variable Feedback 351</p> <p>13.2.2 Structure of Type-1 Controller for 3-Level STACOM 354</p> <p>13.2.2.1 Transient Simulation with Suboptimal Controller Parameters 357</p> <p>13.2.3 Application of Genetic Algorithm for Optimization of Controller Parameters 357</p> <p>13.2.3.1 Boundaries of Type-2 Controller Parameters in GA Optimization 359</p> <p>13.2.3.2 Boundaries of Type-1 Controller Parameters in GA Optimization 360</p> <p>13.2.4 Optimization Results of Two Level STATCOM with GA Optimized Controller Parameters 360</p> <p>13.2.4.1 Transient Simulation with GA Optimized Controller Parameters 361</p> <p>13.2.5 Optimization Results of Three Level STATCOM with Optimal Controller Parameters 362</p> <p>13.2.5.1 Transient Simulation with GA Optimized Controller Parameters 363</p> <p>13.3 Design of Particle Swarm Optimization Based Controller for STATCOM 364</p> <p>13.3.1 Optimization Results of Two Level STATCOM with GA and PSO Optimized Parameters 365</p> <p>13.4 Design of Genetic Algorithm Based Type-1 Controller for VSCHVDC 371</p> <p>13.4.1 Modeling of VSC HVDC 371</p> <p>13.4.1.1 Converter Controller 374</p> <p>13.4.2 A Case Study 375</p> <p>13.4.2.1 Transient Simulation with Suboptimal Controller Parameters 376</p> <p>13.4.3 Design of Controller Using GA and Simulation Results 378</p> <p>13.4.3.1 Description of Optimization Problem and Application of GA 378</p> <p>13.4.3.2 Transient Simulation 379</p> <p>13.4.3.3 Eigenvalue Analysis 379</p> <p>13.5 Conclusion 379</p> <p>References 386</p> <p><b>14 Short Term Load Forecasting for CPP Using ANN 391</b></p> <p><i>Kirti Pal and Vidhi Tiwari</i></p> <p>14.1 Introduction 392</p> <p>14.1.1 Captive Power Plant 394</p> <p>14.1.2 Gas Turbine 394</p> <p>14.2 Working of Combined Cycle Power Plant 395</p> <p>14.3 Implementation of ANN for Captive Power Plant 396</p> <p>14.4 Training and Testing Results 397</p> <p>14.4.1 Regression Plot 397</p> <p>14.4.2 The Performance Plot 398</p> <p>14.4.3 Error Histogram 399</p> <p>14.4.4 Training State Plot 399</p> <p>14.4.5 Comparison between the Predicted Load and Actual Load 401</p> <p>14.5 Conclusion 403</p> <p>14.6 Acknowlegdement 403</p> <p>References 404</p> <p><b>15 Real-Time EVCS Scheduling Scheme by Using GA 409</b></p> <p><i>Tripti Kunj and Kirti Pal</i></p> <p>15.1 Introduction 410</p> <p>15.2 EV Charging Station Modeling 413</p> <p>15.2.1 Parts of the System 413</p> <p>15.2.2 Proposed EV Charging Station 414</p> <p>15.2.3 Proposed Charging Scheme Cycle 414</p> <p>15.3 Real Time System Modeling for EVCS 415</p> <p>15.3.1 Scenario 1 415</p> <p>15.3.2 Design of Scenario 1 418</p> <p>15.3.3 Scenario 2 419</p> <p>15.3.4 Design of Scenario 2 421</p> <p>15.3.5 Simulation Settings 422</p> <p>15.4 Results and Discussion 424</p> <p>15.4.1 Influence on Average Waiting Time 424</p> <p>15.4.1.1 Early Morning 425</p> <p>15.4.1.2 Forenoon 425</p> <p>15.4.1.3 Afternoon 426</p> <p>15.4.2 Influence on Number of Charged EV 426</p> <p>15.5 Conclusion 428</p> <p>References 428</p> <p>About the Editors 435</p> <p>Index 437</p>
<p><b>Neeraj Priyadarshi, PhD,</b> works in the Department of Energy Technology, Aalborg University, Denmark, from which he also received a post doctorate. He received his M. Tech. degree in power electronics and drives in 2010 from the Vellore Institute of Technology (VIT), Vellore, India, and his PhD from the Government College of Technology and Engineering, Udaipur, Rajasthan, India. He has published over 60 papers in scientific and technical journals and conferences and has organized several international workshops. He is a reviewer for a number of technical journals, and he is the lead editor for four edited books, including Scrivener Publishing. <p><b>Akash Kumar Bhoi, PhD,</B> is an assistant professor in the Department of Electrical and Electronics Engineering at Sikkim Manipal Institute of Technology (SMIT), India. He is also a research associate at Wireless Networks (WN) Research Laboratory, Institute of Information Science and Technologies, National Research Council (ISTI-CRN) Pisa, Italy. He is a member of several technical associations and is an editorial board member for a number of journals. He has published several papers in scientific journals and conferences and is currently working on several edited volumes for various publishers, including Scrivener Publishing. <p><b>Sanjeevikumar Padmanaban, PhD,</b> is a faculty member with the Department of Energy Technology, Aalborg University, Esbjerg, Denmark and works with CTIF Global Capsule (CGC), Department of Business Development and Technology, Aarhus University, Denmark. He received his PhD in electrical engineering from the University of Bologna, Italy. He has almost ten years of teaching, research and industrial experience and is an associate editor on a number of international scientific refereed journals. He has published more than 300 research papers and has won numerous awards for his research and teaching. <p><b>S. Balamurugan</b> is the Head of Research and Development, QUANTS IS & Consultancy Services, India. He has authored or edited 40 books, more than 200 papers in scientific and technical journals and conferences and has 15 patents to his credit. He is either the editor-in-chief, associate editor, guest editor, or editor for many scientific and technical journals, from many well-respected publishers around the world. He has won numerous awards, and he is a member of several technical societies. <p><b>Jens Bo Holm-Nielsen</b> currently works at the Department of Energy Technology, Aalborg University and is head of the Esbjerg Energy Section. He helped establish the Center for Bioenergy and Green Engineering in 2009 and served as the head of the research group. He has served as technical advisor for many companies in this industry, and he has executed many large-scale European Union and United Nation projects. He has authored more than 300 scientific papers and has participated in over 500 various international conferences.
<p><b>This collection of papers on artificial intelligence and other methods for improving renewable energy systems, written by industry experts, is a reflection of the state of the art, a must-have for engineers, maintenance personnel, students, and anyone else wanting to stay abreast with current energy systems concepts and technology.</b></p> <p>Renewable energy is one of the most important subjects being studied, researched, and advanced in today’s world. From a macro level, like the stabilization of the entire world’s economy, to the micro level, like how you are going to heat or cool your home tonight, energy, specifically renewable energy, is on the forefront of the discussion. <p>This book illustrates modelling, simulation, design and control of renewable energy systems employed with recent artificial intelligence (AI) and optimization techniques for performance enhancement. Current renewable energy sources have less power conversion efficiency because of its intermittent and fluctuating behavior. Therefore, in this regard, the recent AI and optimization techniques are able to deal with data ambiguity, noise, imprecision, and nonlinear behavior of renewable energy sources more efficiently compared to classical soft computing techniques. <p>This book provides an extensive analysis of recent state of the art AI and optimization techniques applied to green energy systems. Subsequently, researchers, industry persons, undergraduate and graduate students involved in green energy will greatly benefit from this comprehensive volume, a must-have for any library. <p><b>Audience</b> <p>Engineers, scientists, managers, researchers, students, and other professionals working in the field of renewable energy.

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