Cuckoo Search Algorithm Method Research Articles

A global MPPT technique for PV systems under partial shading conditions

ABSTRACT Maximum Power Point Tracking (MPPT) algorithms are used to obtain maximum power from PV (Photovoltaic) systems in variable atmospheric conditions. Partial shading conditions (PSCs) have an important effect causing the maximum power points of PV systems to change. In this study, a new hybrid MPPT algorithm was proposed by combining Voltage Scanning (VSC) and Cuckoo Search Algorithm (CSA) methods to obtain maximum power from a PV system running under PSC. To test the proposed method, a 252.6 kW grid-connected PV system with 609 panels, including 7 serial panels and 87 parallel strings, was created in the MATLAB/Simulink environment. This system was tested under 10 different PSCs by using VSC, Particle Swarm Optimization (PSO), CSA, and the proposed MPPT methods. The average tracking efficiency and speed of the proposed method under these conditions were obtained as 99.74% and 0.3s, respectively. Under PSCs, the proposed method achieved the highest tracking efficiency of 99.92%, while the highest tracking speed that it achieved was 0.22s. On the other hand, the superiority of the proposed hybrid MPPT algorithm was clearly demonstrated by comparing it to the VSC, PSO, and CSA methods.

Optimal reservoir operation using parametric elitist cuckoo search algorithm

Optimal reservoir operation has become a challenging problem due to streamflow uncertainties. Frameworks for optimal reservoir operation play a vital role in the management of water resources. Effective and judicious utilisation of water from reservoirs helps in the management of periods of water deficit. The main objective in reservoir optimisation is to design operating rules that can be used to derive real-time decisions on reservoir release. In this study, a metaheuristic optimisation algorithm – the parametric elitist cuckoo search algorithm (Pecsa), based on the basic cuckoo search algorithm (CSA) – was applied to develop optimal operation decisions for the RSSR in the MRP, India. To evaluate the performance of Pecsa, the results obtained were compared with another heuristic method (the time-variant elitist mutation particle swarm optimisation algorithm) and with the standard CSA and particle swarm optimisation methods. Pecsa was found to improve system operation and outperformed the other methods in terms of achieving a faster convergence rate. Pecsa also showed only minor variations in the optimal pattern of release policies compared with those obtained using the standard CSA. The implications of the results and suggestions for further research are discussed.

Optimization of location and size of distributed generations for maximizing their capacity and minimizing power loss of distribution system based on cuckoo search algorithm

Maximizing capacity of distributed generations (DGs) embed into distribution network is a solution to attract investment for DGs installation on the distribution system. This paper introduces a approach of optimizing location and capacity of DGs for maximizing DGs capacity and minimizing the system’s power loss based on cuckoo search algorithm (CSA). The proposed problem and method are simulated on two test systems including the 33-node and 69-node networks. The numerical results have demonstrated that the proposed method not only reduces power losses but also maximizes the power of DGs embed into the distribution network. The results also introduce that the proposed CSA method is better performance that some previous methods in terms of power loss and DGs capacity. The results obtained in many independent runs for two test systems indicate that CSA in one of the reliable methods for the DGs placement problems.

Water Level Control Prototype Using Fuzzy Logic Control Optimized by the Cuckoo Search Algorithm Method

A boiler is a vessel used to produce water vapor which will be used as heating or propulsion. In the process industry, there are usually boilers that can produce steam to be used to transfer heat to a process. The steam produced by this boiler comes from water that is fed to the boiler and then boiled. After being raised at a certain temperature, the steam is ready to flow to the plant, such as a heater or turbine. In the process of converting water into steam, there are often problems so that the output is not what you want, such as the volume of water that is too large or below the setpoint. The volume of water that does not match the setpoint will cause many problems such as overpressure and overheat which causes the vapor pressure not to the desired specifications. In addition, failure in the control process can also result in system failure which can be fatal, such as damage to the vessel due to poorly controlled feed water which can even cause an explosion because the vessel is not strong enough to withstand the pressure generated [1]. The problems that arise at this time are the Fuzzy method in the Water Level Control System prototype to adjust the long time parameters and poor control accuracy, one of the efforts that is still very likely to be developed is; build an optimization method to solve the complexity of the optimization process, adjust the value of Fuzzy control parameters for a short time and have good control accuracy. The right method is the Cuckoo Search Algorithm (CSA).

Economic Load Dispatch With Multiple Fuel Options and Valve Point Effect Using Cuckoo Search Algorithm With Different Distributions

The cuckoo search algorithm (CSA), a new meta-heuristic algorithm based on natural phenomenon of the cuckoo species and Lévy flights random walk has been widely and successfully applied to several optimization problems so far. In the article, two modified versions of CSA, where new solutions are generated using two distributions including Gaussian and Cauchy distributions in addition to imposing bound by best solutions mechanisms are proposed for solving economic load dispatch (ELD) problems with multiple fuel options. The advantages of CSA with Gaussian distribution (CSA-Gauss) and CSA with Cauchy distribution (CSA-Cauchy) over CSA with Lévy distribution and other meta-heuristic are fewer parameters. The proposed CSA methods are tested on two systems with several load cases and obtained results are compared to other methods. The result comparisons have shown that the proposed methods are highly effective for solving ELD problem with multiple fuel options and/nor valve point effect.

Optimization of process parameters of GMP for 3D ultrathin glass based on cuckoo search algorithm

From the current development trend of mobile phone industry, 3D curved glass screen is undoubtedly the hot spot of the industry, and glass molding processing (GMP) is the main method of 3D curved glass manufacturing at present. 3D curved glass is a kind of curved glass which is formed by heating, softening, and annealing process in the mold. In GMP, process parameters not only determine the efficiency and quality of curved glass processing, but also affect the emission of CO2. Moreover, there is no definite mathematical model for the influence of these involved process parameters on the effect of hot bending. In this paper, the GMP experiment of the finished glass is carried out, and the relationship between the processing parameters and the processing quality of the finished glass is summarized. Furthermore, the experimental data is analyzed to investigate the influence of process parameters on performances of GMP. By cuckoo search algorithm method, the parameters of GMP are optimized.

Optimization of overcurrent relay coordination using cuckoo search algorithm in electronic induction systems

Overcurrent relay protection is one of the electrical power supply protection mechanisms that serve to prevent the system from overcurrent. The main issue that occurs when developing the coordination of overcurrent relays is the timing determination of the primary and back-up relay operation. The objective of this study is to develop an optimization method to result a faster operating time of relay coordination. The optimization method was developed and implemented in case of PT Semen Padang (Indarung VI) at 150 kV PLN’s source, which was operated with conventional calculation method. The Cuckoo Search Algorithm (CSA) method was proposed as optimization method for calculation. The optimization was used to set the relay time and the secondary current (Ipickup) according to the British standard to obtain the optimal Coordination Time Interval (CTI). The CSA method was calculated by MATLAB R2016 software, while the relay testing was done on the ETAP 12.6.0. Results of simulation indicate that the CSA method more capable and faster than the manual calculations in calculating relay setting coordination since almost all of the CTI values were reached in 2.0 to 4.0 seconds.

Sistem Kontrol Governor Menggunakan Pid Yang Dioptimasi Dengan Metode Cuckoo Search Algorithm (Csa)

Indonesia has enormous potential for the Hydroelectric Power Plant (PLTA). Considering that PLTA is an environmentallyfriendly power plant, the components of this plant are worthy of further development. One of them is the development of the hydropower governoor control system. The PID controller is a control system that is often used in the control of theGovernoor System in a hydropower plant because of its simple controller. At present hydro power plants still use theconventional method of PID control trial-error on average. For this method it is difficult to adjust parameters and it takes a long time and the accuracy of controls is not good. Therefore a smart method is needed to overcome this problem.In the past few years, researchers have used many intelligent methods (Artificial Intelligent) to determine the DC PIDparameters. One of them is Cucckoo Search Algorithm (CSA) which is inspired by the behavior of cuckoo birds in placingtheir eggs. By using the CSA method, it is expected to provide a better system response than using the old system PIDcontrol (trial-error). The final goal of the PID control system used in the hydro generator is to regulate the movement ofthe turbine guide vane, which will affect the electrical power produced by the generator. Here the Guide Vane blade settingscan be likened to adjusting the angle of a DC motor. In this study a miniature prototype system governor will be createdthat is controlled by using a PID controller optimized using the Cuckoo Search Algorithm method. In experiments withthe trial error method, the Kp = 3, Ki = 1 and Kd = 10 values with the magnitude of the control parameters obtainedthe average settling time for set point 4 to set point 8 of 24.2 second. Furthermore, the CSA method is used for tuning the PID. After being tuned to the CSA method the values of Kp, Ki and Kd are different for each set point. For set point 4,Kp = 0.99, Ki = 1.00 and Kd = 0.99, while the average settling time is also better than PID trial error, which is 18.8 second.

Implementation of Smooth and Non-Smooth Fuel Cost Function for Economic Load Dispatch using Cuckoo Search Method

Economic Load Dispatch (ELD) is an important topic in the operation of power plants which can help to build up effecting generating management plans. The ELD problem has no smooth cost function with equality and inequality constraints which make it difficult to be effectively solved. The paper presents an application of Cuckoo Search Algorithm (CSA) to solve Economic Load Dispatch (ELD) problems with smooth and non-smooth fuel cost objective functions. Main objective of ELD is to determine the most economic generating dispatch required to satisfy the predicted load demands including line losses over a certain period of time while relaxing various equality and inequality constraints. The unit Min/Max operational constraints, effects of valve-point loading ripples and line losses are considered for the practical applications. This paper describes the implementation of smooth and non smooth fuel cost function by CSA Method and its comparison with BAT method. We have used 6 and 12 bus system for calculating their total fuel cost.

Cuckoo Search Algorithm Using Different Distributions for Short - Term Hydrotherm al Scheduling with Reservoir Vo lume Constraint

This paper proposes a cuckoo search algorithm (CSA) using different distributions for solving the short-term hydrothermal scheduling (ST-HTS) problem with reservoir storage constraint on hydropower plants. The CSA method is a new meta-heuristic algorithm inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds of other species for solving optimization problems. The advantages of the CSA method are few control parameters and effective for optimization problems with complicated constraints. In the proposed CSA, three distributions have been used including Levy distribution, Gaussian distribution and Cauchy distribution. The proposed method has been tested on two test systems and the obtained results have been compared to that from other methods available in the literature. The result comparisons have indicated that the proposed method is a very favorable method for solving the short term hydrothermal scheduling problems with reservoir constraint.

Cuckoo search algorithm for combined heat and power economic dispatch

This paper proposes a cuckoo search algorithm (CSA) for solving for the combined heat and power economic dispatch (CHPED) problem considering valve point loading effects on fuel cost function of pure power generation units and electrical power losses in transmission systems. The main objective of the CHPED problem is to minimize the total fuel cost for producing electricity and heat supplying to a load demand. The proposed CSA method inspired from the reproduction behavior of cuckoo birds has attracted many researchers implementing to engineering optimization problems since it has showed several advantages of few control parameters, high solution quality and fast computational time. The effectiveness and robustness of the proposed CSA have been validated on five different systems including three systems with quadratic fuel cost function of pure power units neglecting transmission losses and two systems with nonconvex fuel cost function of pure power units. The result comparisons between the CSA method and other methods for the test systems have revealed that the CSA method can obtain higher quality solution with faster computational time than many other methods. Therefore, the proposed CSA method can be a very efficient method for solving the CHPED problem.

Cuckoo Search Algorithm for Minimization of Power Loss and Voltage Deviation

This paper proposes a cuckoo search algorithm (CSA) to solve the optimal reactive power dispatch (ORPD) problem in power system operation considering the power loss and voltage deviation. The proposed CSA is a nature inspired algorithm from the cuckoo species laying their eggs in the nest of other species. There is a probability of the host bird to discover the alien eggs in its nest and the host bird either destroys the strange egg or simply ignores the host and builds a new one. The advantages of the CSA method are few control parameters and high optimal solution quality. The proposed method has been tested on the IEEE 30 bus system and the result comparison has indicated that the proposed method can obtain better solution quality than many other methods. Therefore, the proposed CSA method can be an alternative method for solving the ORPD problem.

Distribution network reconfiguration for power loss minimization and voltage profile improvement using cuckoo search algorithm

Abstract This paper proposes a reconfiguration methodology based on a cuckoo search algorithm (CSA) for minimizing active power loss and the maximizing voltage magnitude. The CSA method is a new meta-heuristic algorithm inspired from the obligate brood parasitism of some cuckoo species which lay their eggs in the nests of other host birds of other species for solving optimization problems. Compared to other methods, CSA method has fewer control parameters and is more effective in optimization problems. The effectiveness of the proposed CSA has been tested on three different distribution network systems and the obtained test results have been compared to those from other methods in the literature. The simulation results show that the proposed CSA can be an efficient and promising method for distribution network reconfiguration problems.

Optimal chiller loading for energy conservation using a new differential cuckoo search approach

The electrical energy consumption in a multi-chiller system increases if the chillers are managed improperly, therefore significant energy savings can be achieved by optimizing the chiller operations of heating, ventilation and cooling systems. Recently, optimization methods for optimal chiller loading have been proposed. In general, the aim of the optimization problem is to minimize chillers energy consumption keeping the cooling demand satisfied. As an efficient optimization method, the CSA (cuckoo search algorithm) has been proposed for solving continuous parameters optimization problems. CSA is based on the obligate brood-parasitic behavior of some cuckoo species in combination with the Lévy flight behavior of some birds and fruit flies. Preliminary studies show that it is promising and could outperform existing algorithms. This paper proposes a new CSA approach using differential operator (DCSA) to solve the optimal chiller loading design problem. The results of optimal chiller loading are analyzed on three case studies taken from literature to confirm the validity of the proposed algorithm. Simulations using case studies are presented and compared with the best known solutions. The comparison results with the classical CSA and other optimization methods demonstrate that the proposed DCSA (differential CSA) proves to be an effective and efficient at locating promising solutions in terms of minimum energy consumption.

Cuckoo search algorithm for short-term hydrothermal scheduling

This paper proposes a cuckoo search algorithm (CSA) for solving short-term fixed-head hydrothermal scheduling (HTS) problem considering power losses in transmission systems and valve point loading effects in fuel cost function of thermal units. The CSA method is a new meta-heuristic algorithm inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds of other species for solving optimization problems. The advantages of the CSA method are few control parameters and effective for optimization problems with complicated constraints. The effectiveness of the proposed CSA has been tested on different hydrothermal systems and the obtained test results have been compared to those from other methods in the literature. The result comparison has shown that the CSA can obtain higher quality solutions than many other methods. Therefore, the proposed CSA can be an efficient method for solving short-term fixed head hydrothermal scheduling problems.