Finding Optimal Load Dispatch Solutions by Using a Proposed Cuckoo Search Algorithm

Abstract

Optimal load dispatch (OLD) is an important engineering problem in power system optimization field due to its significance of reducing the amount of electric generation fuel and increasing benefit. In the paper, an improved cuckoo search algorithm (ICSA) is proposed for determining optimal generation of all available thermal generation units so that all constraints consisting of prohibited power zone (PPZ), real power balance (RPB), power generation limitations (PGL), ramp rate limits (RRL), and real power reserve (RPR) are completely satisfied. The proposed ICSA method performance is more robust than conventional Cuckoo search algorithm (CCSA) by applying new modifications. Compared to CCSA, the proposed ICSA approach can obtain high quality solutions and speed up the solution search ability. The ICSA robustness is verified on different systems with diversification of objective functions as well as the considered constraint set. The results from the proposed ICSA method are compared to other algorithms for comparison. The result comparison analysis indicates that the proposed ICSA approach is more robust than CCSA and other existing optimization approaches in finding solutions with significant quality and shortening simulation time. Consequently, it should lead to a conclusion that the proposed ICSA approach deserves to be applied for finding solutions of OLD problem in power system optimization field.

Social small group optimization algorithm for large-scale economic dispatch problem with valve-point effects and multi-fuel sources

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Mechanical Fault Diagnosis of an On-Load Tap Changer by Applying Cuckoo Search Algorithm-Based Fuzzy Weighted Least Squares Support Vector Machine

“…For example, CSA yields better global search effect while setting a smaller number of parameters and has a rapid convergence rate, with superior generalisation ability and stronger robustness. erefore, CSA has been widely used in many studies [15][16][17][18].…”

Investigation on the effect of wavelet mutation and opposition learning on slime mould algorithm

“…Recently, hybrid approaches have gained attention, combining two or more global and local optimization strategies to maximize the benefits of each method and achieve higher performance. Examples of such hybrid approaches [13,14] include modified social spider optimization (MSSO) [15], novel improved social spider optimization (NISSO) [16], fuzzy logic-based hybrid model of PSO and GSA (FPSOGSA) [17], quadratic programming (SQP) and cross entropy-based (CE) named CESQP [18], greedy heuristic search with opposition learning (OGHS) [19], improvement in social spider optimization (SSO) [20] to address the demerits of feature selection (FS) and overcome the failure of optimal local values (OSSO), robust cuckoo search [21], and new adaptive length adjusted crow search method [22]. These optimization techniques find the feasible results for specific search problem.…”