A Quasi-Oppositional Heap-Based Optimization Technique for Power Flow Analysis by Considering Large Scale Photovoltaic Generator

Abstract

Load flow analysis is an essential tool for the reliable planning and operation of interconnected power systems. The constant increase in power demand, apart from the increased intermittency in power generation due to renewable energy sources without proportionate augmentation in transmission system infrastructure, has driven the power systems to function nearer to their limits. Though the power flow (PF) solution may exist in such circumstances, the traditional Newton–Raphson based PF techniques may fail due to computational difficulties owing to the singularity of the Jacobian Matrix during critical conditions and faces difficulties in solving ill-conditioned systems. To address these problems and to assess the impact of large-scale photovoltaic generator (PVG) integration in power systems on power flow studies, a derivative-free quasi-oppositional heap-based optimization (HBO) (QOHBO) technique is proposed in the present paper. In the proposed approach, the concept of quasi-oppositional learning is applied to HBO to enhance the convergence speed. The efficacy and effectiveness of the proposed QOHBO-PF technique are verified by applying it to the standard IEEE and ill-conditioned systems. The robustness of the algorithm is validated under the maximum loadability limits and high R/X ratios, comparing the results with other well-known methods suggested in the literature. The results thus obtained show that the proposed QOHBO-PF technique has less computation time, further enhancement of reliability in the presence of PVG, and has the ability to provide multiple PF solutions that can be utilized for voltage stability analysis.

Heap Based Optimization with Deep Quantum Neural Network Based Decision Making on Smart Healthcare Applications

“…A roulette wheel can be applied to separate the population into p 1 , p 2 , and p 3 proportions to preserve the balance between exploration and exploitation; p 1 enables the search agent to upgrade the location [24]. Moreover, p 1 , p 2 , and p 3 proportions are estimated, whereby t defines the present iteration, and T specifies the maximal iteration count.…”

A Review of State-of-the-Art Techniques for Power Flow Analysis

“…Modern power flow analysis techniques are designed to satisfy the increasing demand of power system by making analysis faster, detailed and more reliable, one new approach proposed utilizes the concept of quasioppositional learning to augment the speed of convergence by applying it to HBO (Heap-Based Optimization). Basetti in his research [11] proposed this method to ameliorate the convergence speed of PF iteration, as a derivative-free method. In this research, the QOHBO power flow technique is applied to a standard IEEE and ill-conditioned systems to test the effectiveness of the technique.…”

“…Therefore, the new state of the art methods of PF analysis is discovered to generally give better results than the classical methods and give the power the room to flow perfectly, fitting the new grid changes like Decentralized Generation (DG) [6]. These State-of-the-Art methods include Direct Matrix-Current Application (DM-CA) and Direct Matrix-Impedance Approximation (DM-IA) [5], Particle Swamp Optimization (PSO) Algorithm for Optimal PF Incorporating Wind Farm [6], Hybrid Firefly and Particle Swarm Optimization (HFPSO) [7], Artificial Neural Networks (ANNs) [8], Quasi-Oppositional Heap-Based Optimization (QOHBO) Technique [9], Three Stage Semi-Implicit Approach (3S-SIA) [10], Mann Iteration Process (MIP) For Ill-Conditioned System [11], Hybrid of Currentbalance and Power-balance formulation using Rectangular Coordinates (HCPB) [12], Modified Gauss-Seidel (MGS) [13], Batched Fast Decoupled Method [14], Newton-Raphson Load Flow Analysis in Power System Networks with STATCOM in New Approach [14] and so on. This paper will therefore, focus on these state-of-the-art techniques.…”

Single- and Multi-Objective Modified Aquila Optimizer for Optimal Multiple Renewable Energy Resources in Distribution Network

“…The OBL is a mathematical operator which was introduced for improving the performance of modern optimization algorithms. As proposed by Tizhoosh, the OBL is based on the idea that the probability of the opposite numbers to obtain a fitter solution is higher than random numbers [36]. Various studies have shown that OBL has improved the performance of numerous population-based optimization algorithms in terms of solution accuracy and convergence speed.…”