ngling Li

A maximum power point tracking method for PV system with improved gravitational search algorithm

Display Omitted The output characteristic curve of the solar panel converts from a single peak curve into a multi-peaks curve under the partial shading conditions.This maximum power point tracking method is modified on the basis of the GSA.IGSA-MPPT is not only reduced the tracking time, but also improved the tracking accuracy compared to PSO-MPPT and GSA-MPPT.The average tracking time of IGSA-MPPT was reduced by 0.023s and 0.0116s.The average increase rates of maximum power increased by 1.7071% and 0.7001% compared with PSO-MPPT and GSA-MPPT. Photovoltaic (PV) system has gradually become research focus in the field of renewable energy power generation, and the output efficiency of PV system is the major concern of researchers. There are obvious non-linear characteristics in the output of PV system, and it will be greatly affected by external environment. For achieving the maximum output power, PV system must operate under the guidance of maximum power point tracking (MPPT) methods The tracking time and accuracy of these methods need to be improved. Therefore, this study contributes to increase output efficiency of PV system by improving the tracking time and accuracy of existing MPPT methods Specifically, a MPPT method with improved gravitational search algorithm (IGSA-MPPT) was proposed. The dynamic weight was added in the change factor of the gravity constant and the related factors of memory and population information exchange were added into the updating formula of particle velocity. IGSA-MPPT not only reduced the tracking time, but also improved the tracking accuracy and mitigated the fluctuations of the reference voltage. Finally, simulation results are compared with the of MPPT methods with particle swarm Optimization (PSO-MPPT) and gravitational search algorithm (GSA-MPPT). The average tracking time of IGSA-MPPT was reduced by 0.023s and 0.0116s, and the average increase rates of maximum power were increased by 1.7071% and 0.7001% compared with PSO-MPPT and GSA-MPPT. In the simulations of PV system under the varying irradiance and temperature, the tracking speed and tracking accuracy of IGSA-MPPT were higher than those of PSO-MPPT, GSA-MPPT, GWO-MPPT, ICO-MPPT, and FCGSA-MPPT. In summary, IGSA-MPPT has better performance in tracking time and accuracy than other comparison algorithms. It can improve output efficiency of PV system in practical application.

Biogeography-based optimization based on population competition strategy for solving the substation location problem

Abstract Location planning of electrical substations is a power planning problem, in which expert knowledge is used to determine ideal substation locations. Using intelligent planning to help electric power experts to reasonably plan the location of a substation can not only ensure the reliability of power supply, but also save on cost to a great extent. In order to improve the economics of electric power planning, this study proposes a combined biogeography-based optimization with population competition algorithm (BBOPC) method. Competition strategy can enhance the search ability of the algorithm due to the dispersed populations involved. A comparative evaluation against the biogeography-based optimization (BBO), competitive strategy based on BBO (CBBO), modified mutation operator based on BBO (MBBO), and BBOPC methods is carried out in selecting the optimal position for a substation. The final results show that, (1) for a simple substation location problem, the minimum total investment cost achieved by BBOPC was less than that of CBBO, currently the most cost-effective method whose total investment cost is relatively better. (2) For a complex substation location problem, the minimum total investment cost achieved by BBOPC was less than that of MBBO. (3) BBOPC demonstrates better convergence characteristics and robustness compared to the other approaches. The proposed BBOPC method can help power experts develop reasonable power planning, which can help the power system effectively achieve operational reliability and economy.

An Improved Wavelet Packet-Chaos Model for Life Prediction of Space Relays Based on Volterra Series

In this paper, an improved algorithm of wavelet packet-chaos model for life prediction of space relays based on volterra series is proposed. In the proposed method, the high and low frequency time sequence components of performance parameters are obtained by employing the improved wavelet packet transform to decompose the performance parameters of the relay into multiple scales. Then the optimization algorithm of parameters in volterra series is improved, and is used to construct a chaotic forecasting model for the high and low frequency time sequence components gained by the wavelet packet transform. At last, the chaotic forecasting results of the high and low frequency components are combined by taking the wavelet packet reconstruction approach, so as to predict the lifetime of the studied space relay. The algorithm can predict the life curve of the relay accurately and reflect the characteristics of the relay performance with sufficient accuracy. The proposed method is validated via a case study of a space relay.

Enhancing the Lithium-ion battery life predictability using a hybrid method

Abstract This study contributes to proposing the improved bird swarm algorithm optimization least squares support vector machine (IBSA-LSSVM) model to predict the remaining life of lithium-ion batteries. By improving the prediction accuracy of the model, the safety and reliability of the new energy storage system are improved. In order to avoid the bird swarm algorithm (BSA) getting into the local optimal solution, the levy flight strategy is introduced into the improved bird swarm algorithm (IBSA), which improves the convergence performance of the algorithm. Hence, this study is to verify the effectiveness of the proposed hybrid IBSA-LSSVM model. The following work has been done: (1) test functions are used to test particle swarm optimization (PSO), differential evolution algorithm (DE), BSA and IBSA; (2) the back propagation neural network (BP) model, support vector machine (SVM) model, quantum particle swarm optimization support vector machine (QPSO-SVM) model, BSA-LSSVM model and IBSA-LSSVM model are tested with the B5, B6 and B18 batteries. The following findings are obtained: (1) the five test functions are used to test the PSO, DE, BSA and IBSA algorithms in 20 dimensions, 50 dimensions and 80 dimensions. The results show that the convergence accuracy and convergence stability of IBSA algorithm is higher than those of the other three algorithms; (2) the residual life of B5, B6 and B18 batteries are predicted by the BSA-LSSVM, SVM, QPSO-SVM, BP and IBSA-LSSVM models. The test results show that the root mean square error of the IBSA-LSSVM model for B5 battery is 0.01, the root mean square error for B6 battery is 0.06, and the root mean square error for B18 battery is 0.02. The results show that the prediction accuracy of proposed model is higher than that of the other models.

A novel method to solve sustainable economic power loading dispatch problem

The purpose of this paper is to deal with the economic requirements of power system loading dispatch and reduce the fuel cost of generation units. In order to optimize the scheduling of power load, an improved chicken swarm optimization (ICSO) is proposed to be adopted, for solving economic load dispatch (ELD) problem.,The ICSO increased the self-foraging factor to the chicks whose activities were the highest. And the evolutionary operations of chicks capturing the rooster food were increased. Therefore, these helped the ICSO to jump out of the local extreme traps and obtain the global optimal solution. In this study, the generation capacity of the generation unit is regarded as a variable, and the fuel cost is regarded as the objective function. The particle swarm optimization (PSO), chicken swarm optimization (CSO), and ICSO were used to optimize the fuel cost of three different test systems.,The result showed that the convergence speed, global search ability, and total fuel cost of the ICSO were better than those of PSO and CSO under different test systems. The non-linearity of the input and output of the generating unit satisfied the equality constraints; the average ratio of the optimal solution obtained by PSO, CSO, and ICSO was 1:0.999994:0.999988. The result also presented the equality and inequality constraints; the average ratio of the optimal solution was 1:0.997200:0.996033. The third test system took the non-linearity of the input and output of the generating unit that satisfied both equality and inequality constraints; the average ratio was 1:0.995968:0.993564.,This study realizes the whole fuel cost minimization in which various types of intelligent algorithms have been applied to the field of load economic scheduling. With the continuous evolution of intelligent algorithms, they save a lot of fuel cost for the ELD problem.,The ICSO is applied to solve the ELD problem. The quality of the optimal solution and the convergence speed of ICSO are better than that of CSO and PSO. Compared with PSO and CSO, ICSO can dispatch the generator more reasonably, thus saving the fuel cost. This will help the power sector to achieve greater economic benefits. Hence, the ICSO has good performance and significant effectiveness in solving the ELD problem.

Web-Based Design and Implementation of Smart Home Appliances Control System

Minimising energy wastage is vital for economic development. This paper deployed a web-based design and implementation of smart electric home appliances control, which enable owners to remotely use web access to efficiently control their home appliances energy usage. Node MCU, breadboard, light emitting diodes (LED), relays, resistors, electric wires, Wi-Fi device (smart phone, tablets or computer), jumper wires, transistor, direct current (DC) motor, universal serial board (USB) cable, printed circuit board (PCB), C programming language and Arduino IDE (Integrated Development Environment) were used, for circuit design and implementation. C programming codes were written and compiled in Arduino IDE device, then uploaded into Node MCU through USB cable to control energy usage of electrical appliance (DC motor) connected to the circuit. The control was achieved by turning ON the DC motor when needed and OFF, when not in use, remotely via web. Whenever appliance was OFF, LED showed red color and green LED lights up when appliance was ON. Developed control system performed excellently and efficiently by significantly minimizing energy wastage, when appliances are not in use. Future research should consider smart home control systems able to record quantities of energy consumed at any period of the day.

The State of the Power Sector in Rwanda: A Progressive Sector With Ambitious Targets

The Government of Rwanda through its power sector has very ambitious targets to achieve 512 MW installed power generation capacity, from its current 216 MW power generation and have universal access (100%) by 2023/24. It is also determined to achieve 52% on-grid connections and 48% off-grid connections by 2023/24. Literature review, analyses, and site visits to various branch offices of the Rwanda Energy Group (REG) were used to evaluate and determine the success of the power sector in achieving its goals, targets and aspirations. Also, hydropower has a high generation percentage (46.8%), because it has longer plant life, higher capacity factor and availability, numerous rivers coupled with Rwanda embarking upon upgrade and vigorous expansion programmes. Furthermore, the potential to generate electricity economically with local resources including, hydropower, peat, lake gas methane and geothermal energy has however, been estimated to total around 1613 MW. The country is therefore, utilising less than 10% of its local electricity potential, excluding a substantial solar resource, while incurring a large foreign outflow. The Rwanda’s electricity tariff was estimated to be about 22.2% more expensive, compared to the highest electricity tariff of other East African Community (EAC) countries. The reports of Electricity Access Roll-out Programme (EARP) also show that the number of new customer connections increased from 364,000 households in June 2012 to more than 700,000 households (31% of the total households in Rwanda) in 2017.

Reliability measure model for electromechanical products under multiple types of uncertainties

Abstract Reliability measure is a fundamental problem in reliability engineering due to the electromechanical products should be with high performance speed and position control with the flexibility to adapt to the rapidly changing needs. The coexistence of random uncertainty, fuzzy uncertainty and incomplete trust uncertainty is common. However, a reliability measure model for this scenario has not been fully studied. Hence, the fuzzy reliability measure (FRM) model is proposed, which enables the reliability of any combination of random and fuzzy variables to be solved. In order to further consider the incomplete trust uncertainty in the reliability measure process, the improved fuzzy reliability measure (IFRM) model is presented based on the FRM model. The hyper entropy of the cloud model is used to consider the incomplete trust uncertainty, providing improved processing. Besides, the concept of credibility is introduced to indicate the degree of trust in the reliability measure results. The reliability-credibility curve is viewed as the reliability measure result of the IFRM model. The reliability and credibility of the electromechanical products under multiple types of uncertainties can be obtained. Besides, the reliability-credibility curve showed the probability of reliability being greater than a certain value and read one certain credibility and its corresponding reliability interval. The calculated results were compared with those obtained from the other methods, which demonstrated the accuracy and wider applicability of the IFRM model. The IFRM model establishes the relationship between reliability and credibility and used as a theoretical basis for either reliability studies or the development and improvement of the reliability of electromechanical products.