Hybrid Bare Bones Fireworks Algorithm for Load Flow Analysis of Islanded Microgrids

2022 ◽  
pp. 293-324
Author(s):  
Saad Mohammad Abdullah ◽  
Ashik Ahmed

In this chapter, a hybrid bare bones fireworks algorithm (HBBFWA) is proposed and its application in solving the load flow problem of islanded microgrid is demonstrated. The hybridization is carried out by updating the positions of generated sparks with the help of grasshopper optimization algorithm (GOA) mimicking the swarming behavior of grasshoppers. The purpose of incorporating GOA with bare bones fireworks algorithm (BBFWA) is to enhance the global searching capability of conventional BBFWA for complex optimization problems. The proposed HBBFWA is applied to perform the load flow analysis of a modified IEEE 37-Bus system. The performance of the proposed HBBFWA is compared against the performance of BBFWA in terms of computational time, convergence speed, and number of iterations required for convergence of the load flow problem. Moreover, standard statistical analysis test such as the independent sample t-test is conducted to identify statistically significant differences between the two algorithms.

Author(s):  
Saad Mohammad Abdullah ◽  
Ashik Ahmed

In this chapter, a hybrid bare bones fireworks algorithm (HBBFWA) is proposed and its application in solving the load flow problem of islanded microgrid is demonstrated. The hybridization is carried out by updating the positions of generated sparks with the help of grasshopper optimization algorithm (GOA) mimicking the swarming behavior of grasshoppers. The purpose of incorporating GOA with bare bones fireworks algorithm (BBFWA) is to enhance the global searching capability of conventional BBFWA for complex optimization problems. The proposed HBBFWA is applied to perform the load flow analysis of a modified IEEE 37-Bus system. The performance of the proposed HBBFWA is compared against the performance of BBFWA in terms of computational time, convergence speed, and number of iterations required for convergence of the load flow problem. Moreover, standard statistical analysis test such as the independent sample t-test is conducted to identify statistically significant differences between the two algorithms.


2019 ◽  
Vol 3 (1) ◽  
pp. 26 ◽  
Author(s):  
Vishnu Sidaarth Suresh

Load flow studies are carried out in order to find a steady state solution of a power system network. It is done to continuously monitor the system and decide upon future expansion of the system. The parameters of the system monitored are voltage magnitude, voltage angle, active and reactive power. This paper presents techniques used in order to obtain such parameters for a standard IEEE – 30 bus and IEEE-57 bus network and makes a comparison into the differences with regard to computational time and effectiveness of each solver


2018 ◽  
Vol 27 (3) ◽  
pp. 377-391
Author(s):  
Vipin Kumar ◽  
Shubham Swapnil ◽  
V. R. Singh

Abstract This paper presents a fast and efficient method for load flow analysis of radial distribution networks. Here, an adaptive algorithm is proposed to analyze the load flow problem of distribution systems. An adaptive algorithm is the combination of backward/forward (BW/FW) sweep and cuckoo search (CS) algorithms. In the proposed method, the optimum load flow analysis of the radial distribution system is attained, while optimizing the voltage and current computation of the BW/FW sweep algorithm. Now, by the CS, the output voltage of the BW/FW sweep algorithm is compared with the standard voltage and optimized. From the optimized voltage and current, load flow parameters like power loss and real and reactive power flow are assessed. The proposed method is implemented using the MATLAB platform and tested into the IEEE 33 bus radial distribution system. The effectiveness of the proposed technique is determined by comparing with the BW/FW algorithm and genetic algorithm-based BW/FW algorithm.


Author(s):  
Lea Tien Tay ◽  
William Ong Chew Fen ◽  
Lilik Jamilatul Awalin

<p>The determination of power and voltage in the power load flow for the purpose of design and operation of the power system is very crucial in the assessment of actual or predicted generation and load conditions. The load flow studies are of the utmost importance and the analysis has been carried out by computer programming to obtain accurate results within a very short period through a simple and convenient way. In this paper, Newton-Raphson method which is the most common, widely-used and reliable algorithm of load flow analysis is further revised and modified to improve the speed and the simplicity of the algorithm. There are 4 Newton-Raphson algorithms carried out, namely Newton-Raphson, Newton-Raphson constant Jacobian, Newton-Raphson Schur Complement and Newton-Raphson Schur Complement constant Jacobian. All the methods are implemented on IEEE 14-, 30-, 57- and 118-bus system for comparative analysis using MATLAB programming. The simulation results are then compared for assessment using measurement parameter of computation time and convergence rate. Newton-Raphson Schur Complement constant Jacobian requires the shortest computational time.</p>


Author(s):  
Shenghu Li

The induction generators (IGs) are basic to wind energy conversion. They produce the active power and consume the reactive power, with the voltage characteristics fragile compared with that of the synchronous generators and doubly-fed IGs. In the stressed system states, they may intensify var imbalance, yielding undesirable operation of zone 3 impedance relays.In this paper, the operation characteristics of the zone 3 relays in the wind power systems is studied. With the theoretical and load flow analysis, it is proved that the equivalent impedance of the IGs lies in the 2nd quadrature, possibly seen as the backward faults by the mho relays, i.e. the apparent impedance enters into the protection region from the left side. The undesirable operation may be caused by more wind power, larger load, less var compensation, and larger torque angle.


2021 ◽  
pp. 177-196
Author(s):  
P. Sivaraman ◽  
C. Sharmeela ◽  
S. Elango

Sign in / Sign up

Export Citation Format

Share Document