Hyper-spherical Search Research Articles

Hyper Spherical Search (HSS) Algorithm Based Optimization and Real-Time Stability Study of Tidal Energy Conversion System

Hyper Spherical Search (HSS) Algorithm Based Optimization and Real-Time Stability Study of Tidal Energy Conversion System

Hyper-Spherical Search Algorithm for Maximum Power Point Tracking of Solar Photovoltaic Systems under Partial Shading Conditions

Maximum Power Point Tracking (MPPT) for photovoltaic systems has widely been studied. However, studying the impact of partial shading (due to buildings, trees, clouds, adjacent arrays, and so on) on the MPPT and achieving the global maximum is still a challenging topic. This is because not only there is a global maximum power point (GMPP) but also there are several local maximums due to the non-linear nature of the power-voltage curve after partial shading. Therefore, the conventional MPPT methods fail to track the GMPP and are commonly trapped at one of the local maximum power points. This study utilizes the Hyper-Spherical Search (HSS) algorithm in MATLAB to achieve the GMPP while improving the efficiency, convergence speed, dynamic response, and reducing the losses. To track the GMPP using the HSS algorithm, the output power for the array (PPV) is defined as the objective function, and the duty cycle of the DC-DC converter is selected as the particles’ position (control variable). The performance of the proposed algorithm has been studied in three different partial shading patterns, and the simulation results confirm the capability of the algorithm in the rapid tracking of GMPP points. In addition, if the GMPP position changes over time, it will track the new GMPP with minimal oscillations. The proposed method along with PSO, P&O, ABC, and Dragon algorithms has been applied for various scenarios, and the obtained results using HSS have been compared with the four mentioned algorithms, which confirmed the effectiveness of the proposed method. Briefly, the advantages of the HSS algorithm in finding GMPP can be stated as simple implementation with few parameters, strong exploration, and exploitation during the tracking process, fast-tracking and low fluctuations during tracking, low oscillation at steady-state, high dynamic efficiency, and non-convergence to LMPP points.

Hyper-Spherical Search Optimized Fuzzy Logic Control Considering Operating Conditions for Hybrid Tram

Rule-based control strategy has significant advantages of strong robustness and high flexibility, and has gradually become a classical implementation method to optimize the energy management performance of modern energy-storage trams. However, it also faces the problems of over-reliance on expert experience and poor adaptability to operating conditions. Therefore, an improved dynamic power distribution method for tram lithium battery/supercapacitor energy storage system is proposed, which introduces road slope and running speed into the decision variable layer of traditional fuzzy logic control input. Firstly, the membership functions and domain are formulated according to operating conditions, the response time of high power density of supercapacitor is adjusted to improve the dynamic performance of the tram. Secondly, the hyper-spherical search algorithm is used to optimize the weight of fuzzy control rules, the peak current of the lithium battery is minimized and the life span of the energy storage system is increased. Finally, the fuzzy control strategies combined with different decision variables are compared and analyzed by using the data of modern tram western suburban line in Beijing. The results show that the proposed strategy achieves multiple superior to traditional fuzzy control strategy in power distribution, charge state offset ranges of lithium battery and supercapacitor, and energy storage system overall efficiency. Compared with the traditional fixed weight scheme, the peak current of the lithium battery is reduced by 31.02% and the continuous tram mileage is increased by 22.45% in the rule weight dynamic optimization scheme.

Optimization for Micro-energy Grid Dispatch Based on Non-supplementary Fired Compressed Air Energy Storage Aided Energy Hub and Hybrid Hyper-spherical Search

Micro-energy grids have shown superiorities over traditional electricity and heating management systems. This paper presents a hybrid optimization strategy for micro-energy grid dispatch with three salient features. First, to enhance the ability to support new storage equipment, an energy hub model is proposed using the non-supplementary fired compressed air energy storage (NSF-CAES). This provides flexible dispatch for cooling, heating and electricity. Second, considering the unique characteristics of the NSF-CAES, a sliding time window (STW) method is designed for simple but effective energy dispatch. Third, for the optimization of energy dispatch, we blend the differential evolution (DE) with the hyper-spherical search (HSS) to formulate a hybrid DE-HSS algorithm, which enhances the global search ability and accuracy. Comparative case studies are performed using real data of scenarios to demonstrate the superiorities of the proposed scheme.

Intelligent traffic analysis: A heuristic high-dimensional image search algorithm based on spatiotemporal probability for constrained environments

However, most heuristic algorithms are solely based on image features, due to the lack of diverse attributes of samples. Rather than rely on the existing datasets, this paper fully processes the original data. Firstly, the original videos were collected from several intelligent traffic surveillance cameras (monitoring nodes). Then, the three categories of objects in all videos were classified by a video image classifier. For each object in the critical class of cars, four attributes were identified, including ID, region of interest (ROI), sampling time and sampling position (position of sampling node). Next, the moving attitude (i.e. moving direction and speed) of each object was estimated through moving target tracking, and added to the preprocessed data. On this basis, a basic hyper-spherical hash search algorithm was created for the initial identification of similar objects. Furthermore, 93% of the objects were accurately identified through the recognition of license plate. Thereafter, a spatiotemporal correlation Bayesian network (STCBN) was established to predict the probability of each object to appear at a certain node in a period of time. Based on the probability, the heuristic factor was designed and used to enhance the basic hyper-spherical hash search algorithm. The enhancement improves the search performance, while reducing the environmental requirements of a single node. Therefore, the enhanced algorithm can be deployed effectively in edge computing systems. Our method was proved effective through experiment on a number of actual monitoring nodes.

Optimization of Hybrid Renewablesystem using Evolutinary Algorithm-PID Controllers

Paper proposes with the optimal design methodology to optimize the configuration of hybrid energy system.Due to cost effectiveness and realibility we use hybrid systems. Here it represents Wind-Turbine/PV as a Hybrid RES because of its several advantages. To enable quality power delivery and reliable power transmission, Battery Energy storage plays an imp role also called Energy Management System (EMS) plays decisive role. Load side variation often causes power outage and even faults in overall grid network. To alleviate such issues, in this research paper a load sensitive adaptive-Energy Management model is developed that considers both Output side variations as well as Input generation patterns. Here used an algorithms such as Adaptive Genetic Algorithm (AGA),FlowerPollunationAlgorithm,Hyper Spherical Search and Particle Swarm Optimization for Optimzing an Hybrid Renewable energy system.

Small-Signal Stability Analysis of Hybrid Power System With Quasi-Oppositional Sine Cosine Algorithm Optimized Fractional Order PID Controller

This article deals with the frequency instability problem of a hybrid energy power system (HEPS) coordinated with reheat thermal power plant. A stochastic optimization method called a sine-cosine algorithm (SCA) is, initially, applied for optimum tuning of fractional-order proportional-integral-derivative (FOPI-D) controller gains to balance the power generation and load profile. To accelerate the convergence mobility and escape the solutions from the local optimal level, quasi-oppositional based learning (Q-OBL) is integrated with SCA, which results in QOSCA. In this work, the PID-controller’s derivative term is placed in the feedback path to avoid the set-point kick problem. A comparative assessment of the energy-storing devices is shown for analyzing the performances of the same in HEPS. The qualitative and quantitative evaluation of the results shows the best performance with the proposed QOSCA: FOPI-D controller compared to SCA-, grey wolf optimizer (GWO), and hyper-spherical search (HSS) optimized FOPI-D controller. It is also seen from the results that the proposed QOSCA: FOPI-D controller has satisfactory disturbance rejection ability and shows robust performance against parametric uncertainties and random load perturbation. The efficacy of the designed controller is confirmed by considering generation rate constraint, governor dead-band, and boiler dynamics effects.

Optimal Network Reconfiguration in Active Distribution Networks with Soft Open Points and Distributed Generation

In this study, we allocated soft open points (SOPs) and distributed generation (DG) units simultaneously with and without network reconfiguration (NR), and investigate the contribution of SOP losses to the total active losses, as well as the effect of increasing the number of SOPs connected to distribution systems under different loading conditions. A recent meta-heuristic optimization algorithm called the discrete-continuous hyper-spherical search algorithm is used to solve the mixed-integer nonlinear problem of SOPs and DGs allocation, along with new NR methodology to obtain radial configurations in an efficient manner without the possibility of getting trapped in local minima. Further, multi-scenario studies are conducted on an IEEE 33-node balanced benchmark distribution system and an 83-node balanced distribution system from a power company in Taiwan. The contributions of SOP losses to the total active losses, as well as the effect of increasing the number of SOPs connected to the system, are investigated to determine the real benefits gained from their allocation. It was clear from the results obtained that simultaneous NR, SOP, and DG allocation into a distribution system creates a hybrid configuration that merges the benefits offered by radial distribution systems and mitigates drawbacks related to losses, power quality, and voltage violations, while offering a far more efficient and optimal network operation. Also, it was found that the contribution of the internal loss of SOPs to the total loss for different numbers of installed SOPs is not dependent on the number of SOPs and that loss minimization is not always guaranteed by installing more SOPs or DGs along with NR. One of the findings of the paper demonstrates that NR with optimizing tie-lines could reduce active losses considerably. The results obtained also validate, with proper justifications, that SOPs installed for the management of constraints in LV feeders could further reduce losses and efficiently address issues related to voltage violations and network losses.

Improved hyper-spherical search algorithm for voltage total harmonic distortion minimization in 27-level inverter

Multi-level inverters (MLIs) have become popular in different applications such as industrial power control systems and distributed generations. There are different forms of MLIs. The cascaded MLIs (CMLIs) have some special advantages among them such as more different output voltage levels using the same number of components and higher power quality. In this paper, a 27-level inverter switching algorithm considering total harmonic distortion (THD) minimization is investigated. Switching angles of the inverter switches are achieved by minimizing a THD-based objective function . In order to minimize the THD-based objective function, the hyper-spherical search (HSS) algorithm, as a novel optimization algorithm, is improved and the results of improved HSS (IHSS) are compared with HSS algorithm and other five evolutionary algorithms to show the advantages of IHSS algorithm.

Multi-machine optimal power system stabilizers design based on system stability and nonlinearity indices using Hyper-Spherical Search method

Design of power system stabilizer (PSS) withstanding critical situations is a challenging task. In this paper, an effective method for the design of a coordinated multiple PSS in power systems are offered and weaknesses of the existing methods are disclosed. The setup includes the nonlinear model of the system, a specific multi-objective function and a newly developed meta-heuristic Hyper-Spherical Search (HSS) optimization algorithm. The objective function includes both eigenvalue stability index (SI) and nonlinearity index (NI). The employed new NI is the second order modal interaction of the lightly damped electromechanical modes. The performance of the proposed Stability-Nonlinearity index (SN) based PSS design by HSS (SNPSS-HSS) is compared with SNPSS-GA, SI based PSS (SPSS) and the conventional design method (CPSS) under various operating conditions and disturbances. The investigations are conducted over both a single lead PSS and PSS2B. The performance is detailed using time domain simulations, SI and NI analysis. A four-machine two-area system and the IEEE 39-bus system are utilized for test purpose. The results of extensive simulations indicate the superiority of SNPSS-HSS design with respect to the others. The obtained relative stability is higher and the nonlinearity index is lower than the product of the other designs. Besides, as it is expected, using a high degree of freedom PSS2B improves the performance of the aforementioned algorithms.

Application of hyper-spherical search algorithm for optimal coordination of overcurrent relays considering different relay characteristics

Minimization of the discrimination time between the backup and main overcurrent relays is one of the most important issues in the relays coordination of a power system. Finding the Time Setting Multipliers (TSMs) by the evolutionary algorithm has been investigated in the previous papers. In this paper, in addition to TSM, different characteristics of the overcurrent relays are considered to improve coordination. On the other hand, the coordination problem can be considered as an optimization problem, which can be solved by artificial intelligent methods. Recently, a novel optimization algorithm, Hyper-Sphere Search (HSS) algorithm, has been introduced. The HSS algorithm is adapted to the problem of this paper, in order to select the characteristics of the overcurrent relays and their TSMs. The result of HSS is compared with the genetic algorithm which is used in the previous studies. The simulation results on the sample network show the effectiveness of HSS and relays characteristics in terms of better coordination.

Optimization of Cost in Reverse Logistics using HSS Algorithm

In today's world, organizations are increasingly managing the return of goods from the consumer to the supplier. This is encouraged for value recovery, environmental protection and sustainable development. Producing the quantities, satisfying the expected demands and transporting the product to the end consumer at lowest cost are the ultimate goal. Reuse, remanufacturing, collection, processing and material recycling require an effective reverse logistics network for product recovery. This forms a closed loop logistics system. Data acquisition of consumer from where the used products are returned, specifying location for remanufacturing, redistributing and sending back to the consumer are important factors which must be coordinated. This research work proposes a model with various reverse logistic (RL) facilities for solving the cost minimization problems in the RL network. The proposed model is providing the ultimate possibilities of minimum and maximum of RL expenditures for various numbers of return products using Hyper-Spherical Search algorithm (HSSA) in MATLAB.

Application of Hyper-Spherical Search algorithm for optimal energy resources dispatch in residential microgrids

This paper presents a residential hybrid thermal/electrical grid-connected home energy system (HES), including a fuel-cell with combined heat and power and a battery-based energy storage system. The minimum operation cost of this integrated energy system is achieved by proper scheduling of different energy resources, found by applying a new powerful optimization algorithm, the Hyper-Spherical Search (HSS) algorithm, to the system's scheduling problem. This is the first time that HSS is used to face the energy resource dispatch problem. The HSS has been only tested in mathematical problems in the previous study. The optimization procedure generates an efficient look-up table in which the powers generated by different energy resources are determined for all time intervals. The effect of different electricity tariffs for purchasing electricity from the main grid on the operation costs of the system is investigated. Moreover, a battery is properly dispatched in the energy system to decrease the operation costs. A real load demand is used in the simulation. The results of HSS are compared with the harmony search algorithm and the colonial competitive algorithm to show the power and effectiveness of HSS to find the optimal dispatch strategy of energy resources for the first time. This is the first time that HSS is compared with CCA. The results of this paper are expected to contribute to home energy systems and real projects.

Hyper-Spherical Search (HSS) algorithm: a novel meta-heuristic algorithm to optimize nonlinear functions

This paper proposes a novel optimization algorithm called Hyper-Spherical Search (HSS) algorithm. Like other evolutionary algorithms, the proposed algorithm starts with an initial population. Population individuals are of two types: particles and hyper-sphere centers that all together form particle sets. Searching the hyper-sphere inner space made by the hyper-sphere center and its particle is the basis of the proposed evolutionary algorithm. The HSS algorithm hopefully converges to a state at which there exists only one hyper-sphere center, and its particles are at the same position and have the same cost function value as the hyper-sphere center. Applying the proposed algorithm to some benchmark cost functions shows its ability in dealing with different types of optimization problems. The proposed method is compared with the genetic algorithm (GA), particle swarm optimization (PSO) and harmony search algorithm (HSA). The results show that the HSS algorithm has faster convergence and results in better solutions than GA, PSO and HSA.