Artificial Intelligence for Smarter Power Systems: Fuzzy logic and neural networks
- Length: 274 pages
- Edition: 1
- Language: English
- Publisher: The Institution of Engineering and Technology
- Publication Date: 2021-09-13
- ISBN-10: 1839530006
- ISBN-13: 9781839530005
- Sales Rank: #0 (See Top 100 Books)
The urgent need to reduce carbon emissions is leading to growing use of renewable electricity, particularly from wind and photovoltaics. However, the intermittent nature of these power sources presents challenges to power systems, which need to ensure high and consistent power quality. Going forward, power systems also need to be able to respond to changes in loads, for example from EV charging. Neither production nor load changes can be predicted precisely, and so there is a degree of uncertainty or fuzziness. One way to meet these challenges is to use a kind of artificial intelligence – fuzzy logic.
Fuzzy logic uses variables that may be any real number between 0 and 1, rather than either 0 or 1. It has obvious advantages when used for optimization of alternative and renewable energy systems. The parametric fuzzy algorithm is inherently adaptive because the coefficients can be altered to accommodate requirements and data availability.
This book focuses on the use of fuzzy logic and neural networks to control power grids and adapt them to changing requirements. Chapters cover fuzzy inference, fuzzy logic-based control, feedback and feedforward neural networks, competitive and associate neural networks, and applications of fuzzy logic, deep learning and big data in power electronics and systems.
Cover Title Copyright Contents About the author Foreword Preface 1 Introduction 1.1 Renewable-energy-based generation is shaping the future of power systems 1.2 Power electronics and artificial intelligence (AI) allow smarter power systems 1.3 Power electronic, artificial intelligence (AI), and simulations will enable optimal operation of renewable energy systems 1.4 Engineering, modeling, simulation, and experimental models 1.5 Artificial intelligence will play a key role to control microgrid bidirectional power flow 1.6 Book organization optimized for problem-based learning strategies 2 Real-time simulation applications for future power systems and smart grids 2.1 The state of the art and the future of real-time simulation 2.1.1 Transient stability tools for off-line or near real-time analysis 2.1.2 Transient stability simulation tools for real-time simulation and HIL testing 2.1.3 Electromagnetic transient simulation (EMT) tools__amp__#8212;off-line applications 2.1.4 Electromagnetic transient simulation (EMT) tools__amp__#8212;real-time HIL applications 2.1.5 Shift in power system architecture with increased challenges in system performance assessment 2.1.6 EMT simulation to improve dynamic performance evaluation 2.1.7 Fast EMT RTS as an enabler of AI-based control design for the smart grid 2.2 Real-time simulation basics and technological considerations 2.2.1 Notions of real time and simulation constraints 2.2.2 Concept of hard real time 2.2.3 Real-time simulator architecture for HIL and its requirements 2.2.4 Time constraints of RTS technologies 2.2.5 Accelerated simulation: faster-than-real-time and slower-than-real-time 2.3 Introduction to the concepts of hardware-in-the-loop testing 2.3.1 Fully digital real-time simulation__amp__#8212;a step before applying HIL techniques 2.3.2 RCP connected to a physical plant 2.3.3 RCP connected to a real-time plant model through RTS I/O signals 2.3.4 Controller hardware-in-the-loop (CHIL, or Often HIL) 2.3.5 Power-hardware-in-the-loop 2.3.6 Software-in-the-loop 2.3.7 HIL as a standard industry practice: the model-based method and the V-cycle 2.3.8 Bandwidth, model complexity, and scalability considerations for RTS applications 2.3.9 Transient stability and electromagnetic transient simulation methods 2.3.10 Smart-grid testbed attributes and real-time simulation fidelity 2.3.11 Importance of model data validation and verification 2.3.12 Test scenario determination and automation 2.4 RTS testing of smart inverters 2.4.1 Smart inverters at the heart of the smart distribution system: control architecture in smart distribution systems 2.4.2 Smart inverter design and testing using HIL 2.4.3 Smart inverter control development using rapid control prototyping (RCP) 2.4.4 Smart inverter control validation using controller HIL (CHIL) 2.4.5 Smart-inverter-power-system-level validation using Power HIL (PHIL) 2.5 RTS testing of wide area monitoring, control, and protection systems (WAMPACS) 2.6 Digital twin concepts and real-time simulators 2.6.1 RTS-based digital twins: DT background, key requirements and use cases 2.6.2 Model parameter tuning and adaptivity 2.6.3 Cyber-physical surveillance and security assessment 2.6.4 Predictive simulation and operator decision support 2.6.5 Detecting equipment malfunction 2.6.6 RTS as a key enabler toward implementing AI-based digital twins and control systems Contributors to this chapter 3 Fuzzy sets 3.1 What is an intelligent system 3.2 Fuzzy reasoning 3.3 Introduction to fuzzy sets 3.4 Introduction to fuzzy logic 3.4.1 Defining fuzzy sets in practical applications 3.5 Fuzzy sets kernel 4 Fuzzy inference: rule based and relational approaches 4.1 Fuzzification, defuzzification, and fuzzy inference engine 4.1.1 Fuzzification 4.1.2 Defuzzification 4.1.3 Fuzzy inference engine (implication) 4.2 Fuzzy operations in different universes of discourse 4.3 Mamdani__amp__#8217;s rule-based Type 1 fuzzy inference 4.4 Takagi__amp__#8211;Sugeno__amp__#8211;Kang (TSK), Type 2 fuzzy inference, parametric fuzzy, and relational-based 4.5 Fuzzy model identification and supervision control 5 Fuzzy-logic-based control 5.1 Fuzzy control preliminaries 5.2 Fuzzy controller heuristics 5.3 Fuzzy logic controller design 5.4 Industrial fuzzy control supervision and scheduling of conventional controllers 6 Feedforward neural networks 6.1 Backpropagation algorithm 6.2 Feedforward neural networks__amp__#8212;a simple binary classifier 6.3 Artificial neural network architecture__amp__#8212;from the McCulloch__amp__#8211;Pitts neuron to multilayer feedforward networks 6.3.1 Example of backpropagation training 6.3.2 Error measurement and chain-rule for backpropagation training 6.4 Neuron activation transfer functions 6.5 Data processing for neural networks 6.6 Neural-network-based computing 7 Feedback, competitive, and associative neural networks 7.1 Feedback networks 7.2 Linear Vector Quantization network 7.3 Counterpropagation network 7.4 Probabilistic neural network 7.5 Industrial applicability of artificial neural networks 8 Applications of fuzzy logic and neural networks in power electronics and power systems 8.1 Fuzzy logic and neural-network-based controller design 8.2 Fuzzy-logic-based function optimization 8.3 Fuzzy-logic-and-neural-network-based function approximation 8.4 Neuro-fuzzy ANFIS__amp__#8212;adaptive neural fuzzy inference system 8.5 AI-based control systems for smarter power systems 8.6 Artificial intelligence for control systems 9 Deep learning and big data applications in electrical power systems 9.1 Big data analytics, data science, engineering, and power quality 9.2 Big data for smart-grid control 9.3 Online monitoring of diverse time scale fault events for non-intentional islanding 9.4 Smart electrical power systems and deep learning features 9.5 Classification, regression, and clustering with neural networks 9.6 Classification building blocks: Instar and Outstar 9.7 Classification principles with convolutional neural networks 9.8 Principles of recurrent neural networks 9.8.1 Backpropagation through time-based recurrent neural networks 9.8.2 Long short-term memory (LSTM)-based recurrent neural networks 9.8.3 Fuzzy parametric CMAC neural network for deep learning Bibliography Index
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