FLC Technique in Smart Grid for Demand Side Management

2022 ◽  
pp. 1302-1316
Author(s):  
Kitty Tripathi ◽  
Sarika Shrivastava
Keyword(s):  
Smart Grid ◽  
Knowledge Base ◽  
Power Distribution ◽  
Fuzzy Inference ◽  
Inference System ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
Computational Intelligence Technique ◽  
Multi Agent ◽  
Qualitative Expression

The chapter discusses the general characteristics of smart grid, which combines different state-of-the-art technologies intended for operative power distribution when the generation is decentralized. Fault's existence in the power grid is entirely unanticipated. Fuzzy logic is the computational intelligence technique that integrates the knowledge base of experts that is either human or system using the qualitative expression. This technique can successfully be applied for end-user who is a prosumer and aims for low electricity bill as well as provide intelligent decision-making skill in the agents of the multi-agent system. Fuzzy inference system can be efficiently used in such systems due to its capability to deal with imprecision, incomplete data, and its strong knowledge base.

FLC Technique in Smart Grid for Demand Side Management

2020 ◽  
pp. 107-121
Author(s):  
Kitty Tripathi ◽  
Sarika Shrivastava
Keyword(s):  
Smart Grid ◽  
Knowledge Base ◽  
Power Distribution ◽  
Fuzzy Inference ◽  
Inference System ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
Computational Intelligence Technique ◽  
Multi Agent ◽  
Qualitative Expression

The chapter discusses the general characteristics of smart grid, which combines different state-of-the-art technologies intended for operative power distribution when the generation is decentralized. Fault's existence in the power grid is entirely unanticipated. Fuzzy logic is the computational intelligence technique that integrates the knowledge base of experts that is either human or system using the qualitative expression. This technique can successfully be applied for end-user who is a prosumer and aims for low electricity bill as well as provide intelligent decision-making skill in the agents of the multi-agent system. Fuzzy inference system can be efficiently used in such systems due to its capability to deal with imprecision, incomplete data, and its strong knowledge base.

Optimized neural network and adaptive neuro-fuzzy controlled dynamic voltage restorer for power quality performance

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Prashant Kumar ◽  
Sabha Raj Arya ◽  
Khyati D. Mistry
Keyword(s):  
Neural Network ◽  
Power Distribution ◽  
Fuzzy Inference ◽  
Dynamic Performance ◽  
Hybrid Approach ◽  
Voltage Regulation ◽  
Dynamic Voltage Restorer ◽  
Inference System ◽  
Intelligence System ◽  
Neuro Fuzzy

Abstract In this article, a hybrid approach is implemented namely, neural network training (NNT) based machine learning (ML) estimator inspired by artificial neural network (ANN) and self-adaptive neuro-fuzzy inference system (ANFIS) to tackle the voltage aggravations in the power distribution network (DN). In this work, potential of swarm intelligence technique namely particle swam optimization (PSO) is analysed to obtain an optimum prediction model with certain modifications in training algorithm parameters. In practice, when the systems are continuously subjected to parametric changes or external disturbances, then ample time is dedicated to tune the system to regain its stable performance. To improve the dynamic performance of the system intelligence-based techniques are proposed to overcome the shortcomings of conventional controllers. So, gain tuning process based on the intelligence system is a desirable choice. The statistical tools are used to proclaim the effectiveness of the controllers. The obtained MSE, RMSE, ME, SD and R were evaluated as 0.0015959, 0.039949, −0.00089838, 0.039941 and 1 in the training phase and 0.0015372, 0.039207, −0.0005657, 0.039203 and 1 in the testing phase, respectively. The results revealed that the ANFIS-PSO network model could accomplish a better DC voltage regulation performance when it is compared to the conventional PI. The proposed intelligence strategies confirm that the predicted DVR model based on NNT-ML and ANFIS has faster convergence speed and reliable prediction rate. Moreover, the simulation results show that the dynamic response is improved with proposed PSO based NNT based ML and ANFIS (Takagi-Sugeno) that significantly compensates the voltage based PQ issues. The proposed DVR is actualized in MATLAB/SIMULINK platform.

Modeling and Optimization of Multi-Performance Characteristics of Powder-Mixed EDM of Tungsten Carbide Alloy Using Intelligent Decision-Making Tools

2017 ◽  
Vol 16 (02) ◽  
pp. 101-128 ◽  
Author(s):  
Jagdeep Singh ◽  
Rajiv Kumar Sharma
Keyword(s):  
Decision Making ◽  
Tungsten Carbide ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Performance Characteristics ◽  
Inference System ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
Input Parameters ◽  
Pulse On Time

The main aim of this work is to propose a hybrid framework, which makes use of intelligent decision-making tools, that are gray, fuzzy, and ANFIS, to optimize the multi-performance characteristics (MPCs) of powder-mixed electrical discharge machining (PM-EDM) of tungsten carbide (WC). To perform the experimentation, four input parameters: (i) pulse-on time, (ii) current, (iii) powder concentration, and (iv) powder grain size are considered to investigate the MPCs such as material removal rate, tool wear rate, surface roughness, and micro-hardness. The proposed framework uses response surface methodology (RSM) with gray, gray-fuzzy, and gray-ANFIS approaches to obtain optimal solution and also to handle the element of uncertainty or fuzziness associated with the uncertain, multi-input, and discrete data. This method helps to generate the values of gray relational grade (GRG), gray-fuzzy reasoning grade (GFRG), and gray adaptive neuro-fuzzy inference system grade (G-ANFISG) for all the 30 experiments. Analysis of variance (ANOVA) is performed on GRG, GFRG, and G-ANFISG to identify the major contributing input parameters which may affect the MPCs. Finally, the theoretical prediction is done to verify the improvement in the performance characteristics obtained through the proposed approaches. Both the experimental and statistical results clearly demonstrate the success of proposed framework for the optimization of PM-EDM of WC alloy.

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