Evaluation of electrical load estimation in Diyala governorate (Baaquba city) based on fuzzy inference system

The evaluation of electrical load estimation is requisitely of any electrical power system. This manner is needed for system obligation, economical distribution and maintenance time of electrical system. In this paper, we propose electrical load estimation method based on fuzzy inference system which gives accurate results for estimated loads in Iraq (Diyala governorateBaaquba city). And it can assist the electrical generation and distribution system that depends on important parameters (temperature, humidity and the speed of the wind). By considering the parameters temperature, humidity and the speed of the wind. These parameters are applied as inputs to the fuzzy logic control system to obtain the normalize estimated load as output by electing membership functions. It is exceptionally valuable to form a choice by taking into consideration these assessed readings that come to from the proposed FIS that displayed in this paper with precision of 0.969 from the real stack request.

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Effective Electricity Theft Detection in Power Distribution Grids Using an Adaptive Neuro Fuzzy Inference System

Energies ◽

10.3390/en13123110 ◽

2020 ◽

Vol 13 (12) ◽

pp. 3110

Author(s):

Konstantinos V. Blazakis ◽

Theodoros N. Kapetanakis ◽

George S. Stavrakakis

Keyword(s):

Artificial Intelligence ◽

Electric Power ◽

Power Distribution ◽

Fuzzy Inference System ◽

Distribution System ◽

Fuzzy Inference ◽

Electrical Power ◽

Electrical Power System ◽

Inference System ◽

Neuro Fuzzy

Electric power grids are a crucial infrastructure for the proper operation of any country and must be preserved from various threats. Detection of illegal electricity power consumption is a crucial issue for distribution system operators (DSOs). Minimizing non-technical losses is a challenging task for the smooth operation of electrical power system in order to increase electricity provider’s and nation’s revenue and to enhance the reliability of electrical power grid. The widespread popularity of smart meters enables a large volume of electricity consumption data to be collected and new artificial intelligence technologies could be applied to take advantage of these data to solve the problem of power theft more efficiently. In this study, a robust artificial intelligence algorithm adaptive neuro fuzzy inference system (ANFIS)—with many applications in many various areas—is presented in brief and applied to achieve more effective detection of electric power theft. To the best of our knowledge, there are no studies yet that involve the application of ANFIS for the detection of power theft. The proposed technique is shown that if applied properly it could achieve very high success rates in various cases of fraudulent activities originating from unauthorized energy usage.

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Increasing the Reliability of an Electrical Power System in a Big European Hospital through the Petri Nets and Fuzzy Inference System Mamdani Modelling

Applied Sciences ◽

10.3390/app11062604 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2604

Author(s):

Constâncio António Pinto ◽

José Torres Farinha ◽

Sarbjeet Singh ◽

Hugo Raposo

Keyword(s):

Power System ◽

Petri Nets ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Electrical Power ◽

Electric Power System ◽

Electrical Power System ◽

Inference System ◽

Reliable Design ◽

Generator Sets

The big hospitals’ electricity supply system’s reliability is discussed in this article through Petri nets and Fuzzy Inference System (FIS). To simulate and analyse an electric power system, the FIS Mamdani in MATLAB is implemented. The advantage of FIS is that it uses human experience to provide a faster solution than conventional techniques. The elements involved are the Main Electrical Power, the Generator sets, the Automatic Transfer Switches (ATS), and the Uninterrupted Power Supply (UPS), which are analysed to characterize the system behaviour. To evaluate the system and identified the lower reliability modules being proposed, a new reliable design model through the Petri Nets and Fuzzy Inference System approach. The resulting approach contributes to increasing the reliability of complex electrical systems, aiming to reduce their faults and increase their availability.

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Leveraging a Genetic Algorithm for the Optimal Placement of Distributed Generation and the Need for Energy Management Strategies Using a Fuzzy Inference System

Electronics ◽

10.3390/electronics10020172 ◽

2021 ◽

Vol 10 (2) ◽

pp. 172

Author(s):

Sunny Katyara ◽

Muhammad Fawad Shaikh ◽

Shoaib Shaikh ◽

Zahid Hussain Khand ◽

Lukasz Staszewski ◽

...

Keyword(s):

Genetic Algorithm ◽

Energy Management ◽

Fuzzy Inference System ◽

Distribution System ◽

Fuzzy Inference ◽

Management Strategies ◽

Test System ◽

Optimal Placement ◽

Inference System ◽

Simulated Environment

With the rising load demand and power losses, the equipment in the utility network often operates close to its marginal limits, creating a dire need for the installation of new Distributed Generators (DGs). Their proper placement is one of the prerequisites for fully achieving the benefits; otherwise, this may result in the worsening of their performance. This could even lead to further deterioration if an effective Energy Management System (EMS) is not installed. Firstly, addressing these issues, this research exploits a Genetic Algorithm (GA) for the proper placement of new DGs in a distribution system. This approach is based on the system losses, voltage profiles, and phase angle jump variations. Secondly, the energy management models are designed using a fuzzy inference system. The models are then analyzed under heavy loading and fault conditions. This research is conducted on a six bus radial test system in a simulated environment together with a real-time Power Hardware-In-the-Loop (PHIL) setup. It is concluded that the optimal placement of a 3.33 MVA synchronous DG is near the load center, and the robustness of the proposed EMS is proven by mitigating the distinct contingencies within the approximately 2.5 cycles of the operating period.

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Adaptive Neuro Fuzzy Inference System based power estimation method for CMOS VLSI Circuits

International Journal of Electronics ◽

10.1080/00207217.2017.1357763 ◽

2017 ◽

Cited By ~ 1

Author(s):

Govindaraj Vellingiri ◽

Ramesh Jayabalan

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

Estimation Method ◽

Power Estimation ◽

Vlsi Circuits ◽

Inference System ◽

Neuro Fuzzy ◽

Cmos Vlsi

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The Effect of External Factors on Consumption Electricity Loads Forecasting using Fuzzy Takagi-Sugeno Kang

MATICS ◽

10.18860/mat.v9i1.3968 ◽

2017 ◽

Vol 9 (1) ◽

pp. 1

Author(s):

Gayatri Dwi Santika ◽

Wayan F Mahmudy

Keyword(s):

Fuzzy Inference System ◽

Historical Data ◽

Fuzzy Inference ◽

External Factors ◽

Electrical Load ◽

Inference System ◽

Takagi Sugeno

<strong>This study applied Fuzzy Inference System Sugeno to forecast electrical load by considering the external factors. To see the accuracy of forecasting using Fuzzy Inference System Sugeno, then a comparison between the forecasting results of Fuzzy Inference System Sugeno using historical data with Fuzzy Inference System Sugeno using external factors was done. By using external factors method, resulted the smallest RMSE of 0762 and using historical data obtained error (RMSE) of 1028. The results of the study came to the conclusion that Fuzzy Inference System Sugeno method using external factors to forecast the consumption of electrical load gives a better result than Fuzzy Inference System Sugeno using only historical data.</strong>

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Application of Composite Method for Determining Fault Location on Electrical Power Distribution Lines

Journal of Electrical and Computer Engineering ◽

10.1155/2020/2836940 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Chinweike Okoli ◽

Boniface Anyaka ◽

Chidiogo Nwokedi ◽

Victor Anya

Keyword(s):

Power Distribution ◽

Distribution System ◽

Fault Location ◽

Fuzzy Inference ◽

Electrical Power ◽

Current Data ◽

Inference System ◽

Composite Method ◽

Distribution Lines ◽

Distribution Line

Distribution line is one of the most important components of the distribution system. Troubleshooting faults on these lines are often a tedious task requiring service vehicles and personnel moving from one place to another in order to locate the fault and fix the problem. The study, therefore, is on how a composite fault location technique can be applied to predict the location of faults on the distribution lines. The calculations for the estimation of the fault location are performed using one terminal voltage and current data of the distribution line. A composite method that combines the impedance-based method and the fuzzy inference system method is used in the fault location algorithm. The presented algorithm has been extensively tested using the MATLAB-Simulink model of a 33 KV 40-kilometer distribution line. The simulation result demonstrates good accuracy and robustness of the algorithm.

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