An Improved Hybrid Micro-Grid Load Frequency Control Scheme for an Autonomous System

This paper presents an improved hybrid micro-grid load frequency control scheme for an autonomous system. The micro-grid system comprises of renewable and non-renewable energy-based Power Generating Units (PGU) which consist of Solar Photovoltaic, WT Generator, Solar Thermal Power Generator, Diesel Engine Generator, Fuel Cell (FC) with Aqua Electrolizer (AE). However, power produce from renewable sources in microgrid are intermittent in supply, hence make it difficult to maintain power balance between generated power and demand. Therefore, Battery energy storage system, ultra-capacitor and flywheel energy storage systems make up the energy storage units. These separate units are selected and combined to form two different scenarios in this study. This approach mitigates frequency fluctuations during disturbances (sudden load changes) by ensuring balance between the generated power and demand. For each scenario, Moth flame optimization algorithm optimized Proportional-Integral controllers were utilized to control the micro-grid (to minimize fluctuations from the output power of the non-dispatchable sources and from sudden load change). The results of the developed scheme were compared with that of Quasi-Oppositional Harmony Search Algorithm for overshoot and settling time of the frequency deviation. From the results obtained, the proposed scheme outperformed that of the quasi-oppositional harmony search algorithm optimized controller by an average percentage improvement of 35.95% and 28.76% in the case of overshoot and settling time when the system step input was suddenly increased. All modelling analysis were carried out in MATLAB R2019b environment. Keywords—Frequency Deviation, Micro-grid, Moth flame optimization algorithm, Quasi-Oppositional Harmony Search Algorithm.

Migration-Based Moth-Flame Optimization Algorithm

Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that demonstrates sufficient efficiency in tackling various optimization tasks. However, MFO cannot provide competitive results for complex optimization problems. The algorithm sinks into the local optimum due to the rapid dropping of population diversity and poor exploration. Hence, in this article, a migration-based moth–flame optimization (M-MFO) algorithm is proposed to address the mentioned issues. In M-MFO, the main focus is on improving the position of unlucky moths by migrating them stochastically in the early iterations using a random migration (RM) operator, maintaining the solution diversification by storing new qualified solutions separately in a guiding archive, and, finally, exploiting around the positions saved in the guiding archive using a guided migration (GM) operator. The dimensionally aware switch between these two operators guarantees the convergence of the population toward the promising zones. The proposed M-MFO was evaluated on the CEC 2018 benchmark suite on dimension 30 and compared against seven well-known variants of MFO, including LMFO, WCMFO, CMFO, CLSGMFO, LGCMFO, SMFO, and ODSFMFO. Then, the top four latest high-performing variants were considered for the main experiments with different dimensions, 30, 50, and 100. The experimental evaluations proved that the M-MFO provides sufficient exploration ability and population diversity maintenance by employing migration strategy and guiding archive. In addition, the statistical results analyzed by the Friedman test proved that the M-MFO demonstrates competitive performance compared to the contender algorithms used in the experiments.

An Improved Moth-Flame Optimization Algorithm with Adaptation Mechanism to Solve Numerical and Mechanical Engineering Problems

Moth-flame optimization (MFO) algorithm inspired by the transverse orientation of moths toward the light source is an effective approach to solve global optimization problems. However, the MFO algorithm suffers from issues such as premature convergence, low population diversity, local optima entrapment, and imbalance between exploration and exploitation. In this study, therefore, an improved moth-flame optimization (I-MFO) algorithm is proposed to cope with canonical MFO’s issues by locating trapped moths in local optimum via defining memory for each moth. The trapped moths tend to escape from the local optima by taking advantage of the adapted wandering around search (AWAS) strategy. The efficiency of the proposed I-MFO is evaluated by CEC 2018 benchmark functions and compared against other well-known metaheuristic algorithms. Moreover, the obtained results are statistically analyzed by the Friedman test on 30, 50, and 100 dimensions. Finally, the ability of the I-MFO algorithm to find the best optimal solutions for mechanical engineering problems is evaluated with three problems from the latest test-suite CEC 2020. The experimental and statistical results demonstrate that the proposed I-MFO is significantly superior to the contender algorithms and it successfully upgrades the shortcomings of the canonical MFO.

Neuroadaptive Robust Speed Control for PMSM Servo Drives with Rotor Failure

In this paper, a neuroadaptive robust trajectory tracking controller is utilized to reduce speed ripples of permanent magnet synchronous machine (PMSM) servo drive under the presence of a fracture or fissure in the rotor and external disturbances. The dynamics equations of PMSM servo drive with the presence of a fracture and unknown frictions are described in detail. Due to inherent nonlinearities in PMSM dynamic model, in addition to internal and external disturbances; a traditional PI controller with fixed parameters cannot correctly regulate the PMSM performance under these scenarios. Hence, a neuroadaptive robust controller (NRC) based on a category of on-line trained artificial neural network is used for this purpose to enhance the robustness and adaptive abilities of traditional PI controller. In this paper, the moth-flame optimization algorithm provides the optimal weight parameters of NRC and three PI controllers (off-line) for a PMSM servo drive. The performance of the NRC is evaluated in the presence of a fracture, unknown frictions, and load disturbances, likewise the result outcomes are contrasted with a traditional optimized PID controller and an optimal linear state feedback method.

A New Moth-Flame Optimization Algorithm for Discounted {0-1} Knapsack Problem

The discounted {0–1} knapsack problem may be a kind of backpack issue with gathering structure and rebate connections among things. A moth-flame optimization algorithm has shown good searchability combined with an effective solution presentation designed for the discounted {0-1} knapsack problem. A new encoding scheme used a shorter length binary vector to help reduce the search domain and speed up the computing time. A greedy repair procedure is used to help the algorithm have fast convergence and reduce the gap between the best-found solution and the optimal solution. The experience results of 30 discounted {0-1} knapsack problem instances are used to evaluate the proposed algorithm. The results demonstrate that the proposed algorithm outperforms the two binary PSO algorithms and the genetic algorithm in solving 30 DKP01 instances. The Wilcoxon rank-sum test is used to support the proposed declarations.