scholarly journals Fuzzy Logic Controller Based Distributed Generation Integration Strategy for Stochastic Performance Improvement

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Jagdish Prasad Sharma ◽  
H. Ravishankar Kamath
Keyword(s):  
Fuzzy Logic ◽  
Distributed Generation ◽  
Fuzzy Logic Controller ◽  
Load Ratio ◽  
Time Interval ◽  
Distributed Generations ◽  
Voltage Unbalance ◽  
Load Models ◽  
The Impact ◽  
Zip Load

In the restructured environment, distributed generation (DG) is considered as a very promising option due to a high initial capital cost of conventional plants, environmental concerns, and power shortage. Apart from the above, distributed generation (DG) has also abilities to improve performance of feeder. Most of the distribution feeders have radial structure, which compel to observe the impact of distributed generations on feeder performance, having different characteristics and composition of time varying static ZIP load models. Two fuzzy-based expert system is proposed for selecting and ranking the most appropriated periods to an integration of distributed generations with a feeder. Madami type fuzzy logic controller was developed for sizing of distributed generation, whereas Sugeno type fuzzy logic controller was developed for the DG location. Input parameters for Madami fuzzy logic controller are substation reserve capacity, feeder power loss to load ratio, voltage unbalance, and apparent power imbalances. DG output, survivability index, and node distance from substation are chosen as input to Sugeno type fuzzy logic controller. The stochastic performance of proposed fuzzy expert systems was evaluated on a modified IEEE 37 node test feeder with 15 minutes characteristics time interval varying static ZIP load models.

scholarly journals Fuzzy Logic Controller

2019 ◽  
Vol 3 (3) ◽  
pp. 16-29
Author(s):  
Muhammad Saqlain ◽  
Kashaf Naz ◽  
Kashf Gaffar ◽  
Muhammad Naveed Jafar
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Fuzzy Numbers ◽  
Research Paper ◽  
Inference Process ◽  
Intuitionistic Fuzzy Numbers ◽  
Intuitionistic Fuzzy ◽  
Water Ph ◽  
The Impact

In this research paper, the impact of water pH on detergent was measured by constructing a Fuzzy Logic Controller (FLC) based on Intuitionistic Fuzzy Numbers (IFNs) by incorporating three linguistic inputs and one output as taken by Saeed. M. et al. [1]. The inference process was carried out using MATLAB fuzzy logic toolbox and the results were compared with FLC based on fuzzy numbers. The objective of the study was the comparison of FLC based on intuitionistic and fuzzy numbers. The results showed that FLC based on IFNs is approximately the same but has more precise values. So, IFNs based FLC can be used in the Instinctive Laundry System. 

A Heuristic Analysis Method for the Impact of DGs to Power System Reliability

2014 ◽  
Vol 986-987 ◽  
pp. 187-191
Author(s):  
Bo Zeng ◽  
Kai Wang ◽  
Xiang Yu Kong ◽  
Yi Zeng ◽  
Qun Yang
Keyword(s):  
Distributed Generation ◽  
System Reliability ◽  
Power Distribution ◽  
Distribution System ◽  
Simulation Analysis ◽  
Analysis Method ◽  
Distributed Generations ◽  
Power Distribution System ◽  
High Penetration ◽  
The Impact

With high penetration of distributed generation connected to the grid, distribution system will have some huge impacts, and system reliability calculation models and assessment methods are changing. Based on Monte-Carlo method, a heuristic reliability analysis method for distribution system with distributed generations was proposed in the paper, which focuses on the mode of distributed generation in parallel to system power supply. Functional role of distributed generation in the power distribution system failure and distributed power adapter with load strategies were analyzed in this method. Cases simulation analysis was used to verify its effectiveness.

scholarly journals Controller Design for Temperature Control of MISO Water Tank System

2021 ◽  
Vol 15 (4) ◽  
pp. 1-13
Author(s):  
Vishal Vishnoi ◽  
Sheela Tiwari ◽  
Rajesh Kumar Singla
Keyword(s):  
Fuzzy Logic ◽  
Temperature Control ◽  
Fuzzy Logic Controller ◽  
Controller Design ◽  
Water Tank ◽  
Single Output ◽  
Multiple Input ◽  
Tank System ◽  
Process Disturbances ◽  
The Impact

This article introduces the design of split range control and fuzzy logic control for temperature control of the MISO (multiple input single output) water tank scheme. A multiple input single output (MISO) system is considered for the proposed work as most of the practical systems comprise of numerous MISO system. Investigations are conducted on the impact of control parameters, system dynamics and process disturbances. From the simulation outcomes, it is clearly inferred that the fuzzy logic controller outperformed split range control over all parameters.

scholarly journals Distributed generation integration optimization using fuzzy logic controller

AIMS Energy ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 337-348 ◽  
Author(s):  
Santosh Kumar Sharma ◽  
◽  
D. K. Palwalia ◽  
Vivek Shrivastava
Keyword(s):  
Fuzzy Logic ◽  
Distributed Generation ◽  
Fuzzy Logic Controller

scholarly journals Distributionally Robust Distributed Generation Hosting Capacity Assessment in Distribution Systems

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2981 ◽  
Author(s):  
Mohammad Seydali Seyf Abad ◽  
Jin Ma ◽  
Ahmad Ahmadyar ◽  
Hesamoddin Marzooghi
Keyword(s):  
Robust Optimization ◽  
Distributed Generation ◽  
Optimization Model ◽  
Distribution Systems ◽  
Historical Data ◽  
Distribution Networks ◽  
Distributionally Robust Optimization ◽  
Distributed Generations ◽  
Distributionally Robust ◽  
The Impact

Uncertainties associated with the loads and the output power of distributed generations create challenges in quantifying the integration limits of distributed generations in distribution networks, i.e., hosting capacity. To address this, we propose a distributionally robust optimization-based method to determine the hosting capacity considering the voltage rise, thermal capacity of the feeders and short circuit level constraints. In the proposed method, the uncertain variables are modeled as stochastic variables following ambiguous distributions defined based on the historical data. The distributionally robust optimization model guarantees that the probability of the constraint violation does not exceed a given risk level, which can control robustness of the solution. To solve the distributionally robust optimization model of the hosting capacity, we reformulated it as a joint chance constrained problem, which is solved using the sample average approximation technique. To demonstrate the efficacy of the proposed method, a modified IEEE 33-bus distribution system is used as the test-bed. Simulation results demonstrate how the sample size of historical data affects the hosting capacity. Furthermore, using the proposed method, the impact of electric vehicles aggregated demand and charging stations are investigated on the hosting capacity of different distributed generation technologies.

scholarly journals Stochastic voltage stability margin in unbalance feeder with fuzzy based distributed generation placement

2018 ◽  
Vol 7 (2.21) ◽  
pp. 53
Author(s):  
Jagdish Prasad Sharma ◽  
H Ravishankar Kamath
Keyword(s):  
Distributed Generation ◽  
Voltage Stability ◽  
Stability Margin ◽  
Test System ◽  
Load Flow ◽  
Base Case ◽  
Time Interval ◽  
Voltage Stability Margin ◽  
The Impact ◽  
Distributed Generation Placement

In this paper, the impact of distributed generation (DG) integration on worst stochastic voltage stability margin is investigated for a modified IEEE 37 node test system. This unbalance test system has voltage sensitive load model for industrial, commercial and residential consumers and load flow computed in MATLAB environment with 15 minutes metering time interval for a whole day. DG integration is based on fuzzy expert system and integrated between 35 to 73 period of metering time interval. The stochastic voltage stability margin for all phase are evaluated under three different DG operational scenarios and compared with results obtained in the base case. The cause and consequence of unbalance phenomena is also broadly discussed in detail. 

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