Optimal reactive power dispatch using a novel optimization algorithm

The problem of optimal reactive power dispatch (ORPD) is one of the most popular and widely discussed problem in power system engineering all over the world. Optimal reactive power dispatch is one of the sub-problems of the optimal power flow which is complex and nonlinear problem, which can be formulated as both single- and multi-objective. In this paper, the problem has been formulated as a single-objective problem to minimize the active power losses in the transmission lines. A recently proposed powerful and reliable meta-heuristic algorithm known as the JAYA algorithm has been applied to solve the ORPD problem. The algorithm has been applied on the standard IEEE 14, 30, 57 and 118 bus systems. The simulation results using the proposed algorithm when compared with the results from other algorithms and few others reported in the literature prove that the JAYA algorithm is the most superior among all.

A novel approach of a dynamic multi objective optimization of a power distribution system

A new dynamic multi objective optimization approach is covered in this paper. The technique for optimizing the power distribution system is dynamic reconfiguration. The goal is to propose an optimal dynamic reconfiguration which minimizes the active power losses and the voltage deviation of the nodes of the power distribution system according to the energy available at the source, while constantly guaranteeing the supply of the electrical energy to priority consumers. The reliability indices considered in this paper are the system average interruption frequency index (SAIFI) and the system average interruption duration index (SAIDI) and are used to check the reliability of the optimal configurations obtained. This study subdivides a day into periods. The variations in the available power of the source and the power requested by the load, cause a new optimal configuration of the network at each period. In this work, the load adapts to the source and the optimal network topology evolves according to the maximum available power of the source. A mathematical formulation of the dynamic optimization problem by period or piece is proposed. The dynamic approach consists in acquiring the power of the load and of the source by period or piece and to compare them. When the available energy is sufficient, an optimal configuration that minimizes the power losses and voltage deviation while ensuring the supply of electrical energy to all consumers in the network is proposed. On the other hand, when the available energy is insufficient, an optimal topology of the power system minimizing the power losses and voltage deviation while guaranteeing the supply of electrical energy to priority consumers of the network is proposed. The optimal solutions per period are obtained using the MIP and MINLP methods. The approach is implemented on standard IEEE 15, 33 and 69 node power distribution system. The results obtained are satisfactory and prove the effectiveness of this new vision for the conduct of the power distribution system.

Double-objective optimization-based firefly algorithm of a stand-alone photovoltaic/water pumping system for water supply in rural and remote areas: a case study

A techno-economic study of a stand-alone PV water pumping system for water supply is done in this paper. An optimal design of the system is realized thanks to a double-objective optimization based on a proposed operational strategy of the system and on firefly algorithm (FA). The two objective functions simulated simultaneously using FA are: the function defining the cost of water (COW) and the function which defines the loss of water supply probability (LWSP). The system is designed to supply water to around 328 households in Cameroon, each composed by an average of six (06) persons. For different LWSP, the double-objective simulation determines the optimal configurations of the system with their related cost. The optimal and reliable (LWSP of 0%) system configuration obtained is composed by a number of 7 PV modules, a reservoir volume of 98.4 m3 with 2 days autonomy, a total dynamic head of 40 m, and a pump power of around 1 kW. The related COW corresponding to this configuration is 0.1185 $/m3, and the total investment cost is 27,506 $. The sensitivity analysis of the system revealed that the PV size, the volume of reservoir storage, and the efficiency of the pump influence the system both technically and economically, whereas the total dynamic head only influences the system economically. The validation of the optimized system has been done by comparison with manufacturer datasheets. It is demonstrated in this paper that the techno-economic analysis of energy systems based on multi-objective optimization using firefly algorithm is a powerful tool for decision-making.

Social distancing using IoT approach

In the coronavirus outbreak pandemic by COVID-19, the World Health Organization (WHO) has been issuing several guidelines through all government agencies. In line with those guidelines, social distancing in the population has been a major prevention practice, compelled by all government agencies worldwide. Despite strong recommendations to maintain at least one-and-a-half-meter distance between the persons, the guideline is not scrupulously followed. To overcome this situation, an IoT-based technical solution is proposed through this paper. PIR sensor is used for the detection of a target in the vicinity (1.5 m). Upon violation of social distancing norms, the system will trigger an audio alarm after the detection of the target object. The research paper model is prepared by considering the needs of the people. Many researchers are focusing on tracking affected persons, but few are focusing on the social distancing preventive. The suggested portable device will always notify the person who is violating the norm of 1.5 m. The proposed device will minimize the possibility of transmission and reduce the infection rate of COVID-19. The device uses a PIR sensor depending upon the applicability area of the human being.

Correction to: Improvement of Grey System Model using Particle Swarm Optimization

Following publication of the original article [1], the authors reported an error in the title and body text.

Butterfly optimizer-assisted optimal integration of REDG units in hybrid AC/DC distribution micro-grids based on minimum operational area

This paper presents the impact of optimal location and sizing of renewable and non-renewable-based distributed generators in the AC/DC micro-grid system using the latest optimizer called butterfly optimization algorithm with an aim to minimize power loss. Generally, hybrid AC/DC micro-grids systems are modeled by separating AC and DC feeders with the help of high-power converters (HPC).AC grids sustained by substation and DC grids are maintained by their individual DG units. While planning of DGs in the hybrid AC/DC systems, the power loss incurred by HPCs is not considered avoiding complexity by many authors. In this paper, the sizing of DGs is determined by the operational area required by the type of DG technology as one variable and all possible candidate buses in the respective zones of AC/DC micro-grid system are another variable with due consideration of HPC losses in AC/DC micro-grid system. A hybrid AC/DC MG system is developed by classifying the existing benchmark 33-bus and 69-bus radial distribution systems into various AC/DC zones. To evaluate the proposed approach, it is implemented on aforementioned micro-grid systems and the obtained results are verified with other existing approaches in the literature. The results proved that the proposed approach is better than the other approaches in technical aspects.

Improvement of gray system model using particle swarm optimization

An improvement of the traditional gray system model, GM(1,1), to enhance forecast accuracy, has been realized using the particle swarm optimization (PSO) algorithm. Unlike the GM(1,1) which uses a fixed adjacent neighbor weight for all data sets, the proposed PSO-improved model, PSO-GM(1,1), determines an optimal adjacent neighbor weight, based on the presented data set. This optimal adjacent neighbor weight so determined is the principal factor that enhances forecast accuracy. The performance of the proposed model was evaluated using generated monotonic increasing and decreasing data sets as well as measured energy consumption data for a laptop computer, desktop computer, printer, and photocopier. The performance of PSO-GM(1,1) was compared with that of GM(1,1), and two other models in literature that sought to improve the performance of GM(1,1). The PSO-GM(1,1) outperformed the traditional model and the two other models. For the monotonic increasing data, the mean absolute percentage error (MAPE) for the proposed model was 0.007% as against a MAPE value of 20.383% for the GM(1,1). For the monotonic decreasing data, the PSO-GM(1,1) again outperformed GM(1,1), yielding a MAPE of 0.057% compared to a value of 13.407% for the traditional model. For the measured laptop computer energy data, the obtained MAPE for the PSO-GM(1,1) was 0.675% while the values for the two models were 4.052% and 2.991%. For the measured desktop computer energy data, the obtained MAPE for the PSO-GM(1,1) was 0.0018% while the values for the two models were 0.0018% and 1.163%. For the data associated with the printer, the MAPEs were 8.414% for the PSO-GM(1,1), 20.957% for the first model and 9.080% for the second model. For the measured photocopier energy data, the obtained MAPE for the PSO-GM(1,1) was 0.901% while the values for the two models were 3.799% and 0.943%. Thus, the proposed PSO-GM(1,1) greatly improves forecast accuracy and is recommended for adoption, for forecasting.

Comparative performance analysis of hybrid differential evolution and pattern search technique for frequency control of the electric power system

In the current situation, operation and control of power system is a greater challenge. The most significant situation in power system control is load frequency control. In the present work, a hybrid differential evolution and pattern search (hDE-PS) method has been suggested for frequency regulation of electrical power systems. Fractional-order proportional integral derivative (FOPID) controller is implemented for design and analysis purpose. The suggested control method has been applied for two electrical power systems model, i.e., 2-area diverse source power system with/without HVDC linkage and 2-area thermal system. The performances of the suggested controller have been evaluated with PID and optimal controller. The simulation results indicate that system performances are enhanced with the suggested approach for identical structure. Robustness of the suggested approach has been analyzed by variation in random load and the system parameters. The suggested method (hDE-PS tuned FOPID) is further investigated with a 2-area thermal system. The performance of the recommended approach is analyzed by equating the results with other newly available approaches, like Genetic Algorithm (GA), Bacteria Foraging Optimization Algorithm (BFOA), Particle Swarm Optimization (PSO), hybrid BFOA and PSO (hBFOA-PSO), multi-objective Non-dominated Sorting Genetic Algorithm (NSGA)-II and Firefly Algorithm for the similar structure.

An advanced hybrid meta-heuristic algorithm for solving small- and large-scale engineering design optimization problems

An advanced hybrid algorithm (haDEPSO) is proposed in this paper for small- and large-scale engineering design optimization problems. Suggested advanced, differential evolution (aDE) and particle swarm optimization (aPSO) integrated with proposed haDEPSO. In aDE a novel, mutation, crossover and selection strategy is introduced, to avoid premature convergence. And aPSO consists of novel gradually varying parameters, to escape stagnation. So, convergence characteristic of aDE and aPSO provides different approximation to the solution space. Thus, haDEPSO achieve better solutions due to integrating merits of aDE and aPSO. Also in haDEPSO individual population is merged with other in a pre-defined manner, to balance between global and local search capability. The performance of proposed haDEPSO and its component aDE and aPSO are validated on 23 unconstrained benchmark functions, then solved five small (structural engineering) and one large (economic load dispatch)-scale engineering design optimization problems. Outcome analyses confirm superiority of proposed algorithms over many state-of-the-art algorithms.

Current state of communication systems based on electrical power transmission lines

Power line communication technology is a retrofit alternative technology for last mile information technology. Despite several challenges, such as inadequate standards and electromagnetic compatibility, it is maturing. In this review, we have analysed these obstacles and its current application status.