Differential Evaluation Research Articles

Heuristic Model to Compute Indices for Classification of Incidence and Non-Incidence of Thunderstorms Over Ranchi With Atmospheric Parameter

Prediction of incidences of thunderstorms using different techniques is quite well established. To add to this knowledge, in this paper, a heuristic equation is proposed which relates the correlation coefficient of atmospheric parameters with the number of hourly incidences of thunderstorms. There are four ways to compute the indices values from the proposed heuristic equation. These indices values are used in the classification of the hourly incidences of thunderstorms. The proposed equation and indices work well, as tested on pre-monsoon hourly atmospheric data from 2018 and validated with hourly data from April 2019 and 2020. From the four indices, the first index value is computed with normalized average values of parameters of only hourly incidence of thunderstorms data of 2016-2017 in the month-wise method. The other three indices use optimization techniques namely, the Teaching Learning Based Optimization (TLBO) technique, Differential Evaluation (DE), and Simulated Annealing (SA). TLBO shows better classification of hourly incidences of thunderstorms for 2018 atmospheric data. TLBO is also precisely validating the hourly incidences of thunderstorms for April 2019 and April 2020 hourly atmospheric data. It performed better with 2020 data by 88%. The variations of atmospheric parameters before, after or, during the incidences of thunderstorms are also depicted.

Performance analysis of hydrogen aqua equaliser fuel-cell on AGC of Wind-hydro-thermal power systems with sunflower algorithm optimised fuzzy-PDFPI controller

ABSTRACT This article investigates the effect of hydrogen aqua equaliser (HAE) with hydrogen repository tank and fuel cell (FC) as an energy storage element for frequency control in an interconnected power system. The system under study consists of a combined structure of hydro – thermal along with wind, diesel and gas units as well as energy storage elements. An advanced fuzzy-based PDF plus PI controller is proposed and a Sunflower optimisation (SFO) technique is used to optimise the controller parameter. The superiority of the prospective SFO algorithm is demonstrated over other techniques like genetic algorithm (GA), differential evaluation (DE) & particle swarm optimisation (PSO). The projected FPDFPI controller is studied over PID and PDFPI controller to show its efficacy. The effect of the HAE-FC units in an interconnected power system is studied. Sensitivity analysis and OP-5700 OPAL-RT Hil module-based Hardware-in-the-Loop (HiL) real-time simulation has also been carried out.

A Hybrid Evolutionary-Based MPPT for Photovoltaic Systems Under Partial Shading Conditions

Under partial shading conditions (PSCs), photovoltaic (PV) system characteristics vary and may have multiple power peaks. Conventional maximum power point tracking (MPPT) methods are unable to track the global peak. In addition, it takes a considerable time to reach the maximum power point (MPP). To address these issues, this paper proposes an improved hybrid MPPT method using the conventional evolutional algorithms, i.e., Particle Swarm Optimization (PSO) and Differential Evaluation (DE). The main feature of the proposed hybrid MPPT method is the advantage of one method compensates for shortcomings of the other method. Furthermore, the algorithm is simple and rapid. It can be easily implemented on a low-cost microcontroller. To evaluate the performance of the proposed method, MATLAB simulations are carried out under different PSCc. Experimental verifications are conducted using a boost converter setup, an ET-M53695 panel and a TMS320F28335 DSP. Finally, the simulation and hardware results are compared to those from the PSO and DE methods. The superiority of the hybrid method over PSO and DE methods is highlighted through the results.

DE Optimized LFC of Three Area Interconnected Power System with DFIG Wind System under Deregulated Environment

Load frequency control (LFC) of an interconnected three-area power system with HVDC link under deregulated environment in presence of wind system is investigated. Integration of renewable sources in to exiting plants will affect the system frequency and hence design of a suitable controller is needed to maintain frequency within limits. Study of impact of wind penetration in to deregulated environment is a key factor. The secondary PID controller improves the overall system performance during sudden load disturbances and random variations of wind input. The optimal values of PID controllers in all three areas and pitch control in wind system are tuned by using Differential Evaluation (DE) algorithm.

Simultaneous Optimization of Network Reconfiguration and DG Installation Using Heuristic Algorithms

Network reconfiguration and distributed generation (DG) installation are important approaches for loss mitigation and system efficiency improvement. To date, these approaches are generally implemented separately. In this work, simultaneous network reconfigurations and DG installation is solved in order to minimize real power loss and improve system efficiency using popular heuristic algorithms such as Artificial Bee Colony (ABC), Differential Evaluation (DE), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). While decision making is carried out for open switches and DG sizing by given algorithms, optimal locations of DGs are decided using loss sensitivity factor in order to reduce searching space. Different cases of network reconfiguration and DG installation are implemented to compare the performances of given heuristic algorithms. Branch current, bus voltages and DG capacity are considered as constraints and 69 bus system is used for simulation. This study demonstrates that simultaneous network reconfiguration and DG installation presents better solutions than any other cases and ABC has proven minimum losses and maximum voltage improvement.DOI: 10.5755/j01.eie.25.1.22729

Analysis of channel allocation and management using tlbo, ga & de algorithms in cellular networks

Accuracy of spectrum sensing in cellular mobile networks can be increased by reducing the sensing error probability, call blocking probability & by increasing network throughput. In our Project work, an advanced algorithms like Teaching Learning Based Optimization (TLBO), Genetic (GA) and Differential Evaluation (DE) Algorithms are used to analyze the sensing error probability, call blocking probability and network throughput. We will propose a channel allocation and management scheme with the above mentioned algorithms for supporting mobile users. With the help of these algorithms, sensing error and call blocking probabilities are analyzed to find out an optimal value for reducing the errors to some extent and improving network throughput. Channel Allocation in cellular networks is a key aspect with channel impairments and non-ideal antenna patterns. Finally, performance analysis is done through comparison of simulation results.

Power System Stability Analysis of SMIB with FOPID using Cuckoo Search Optimized Differential Evaluation

Due to complexity in the power system there is always a loss of the stability due to the fault. Whenever a fault is intercepted in system, the whole system goes to severe transients. These transients cause oscillation in phase angle which leads poor power quality. The nature of oscillation is increasing instead being sustained, which leads system failure in form of generator damage. To reduce and eliminate the unstable oscillations one needs to use a stabilizer which can generate a perfect compensatory signal in order to minimize the harmonics generated due to instability.This paper presents a Power System stabilizer to reduce oscillations due to small signal disturbance. Additionally, a hybrid approach is proposed using FOPID stabilizer with the PSS connected SMIB. Cuckoo Search (CS) Optimized Differential Evaluation (DE) approach is used for the parameter tuning of the stabilizer. The efficiency of proposed approach is observed by rotor angle and power angle deviations in the SMIB system.

A hybrid genetic algorithm for analog active filter component selection

Analog filters are circuits that process electrical signals on a frequency-dependent basis. Analog filter circuits can be implemented using resistors, capacitors and inductors and they can also be implemented with only resistors and capacitors with the use of active circuit equipments. The selection of optimum component values is difficult because of the number of possible filter combinations. This paper presents an effective algorithm that hybridizes the Genetic Algorithm (GA) to form a Hybrid Genetic Algorithm (HGA) and proposes the use of Tabu Search (TS) for the analog active filter component selection. The GA has a powerful global searching ability and so is used to perform exploitation and because TS also has good local searching ability, it too is applied to perform exploitation. Thus, the proposed HGA will have very good searching ability. In this study, a band-pass Sallen-Key filter circuit was used, and the filter component values were found using HGA with LabVIEW. The results are compared to other reported state-of-the-art algorithms, such as the artificial bee colony (ABC) and differential evaluation (DE) to demonstrate the effectiveness and efficiency of the developed method. It can be said that the developed LabVIEW based HGA gives satisfactory results with regard to amplitude response, the minimum fitness values and the computational time.

Maximizing the Firm Energy Yield Preserving Total Energy Generation Via an Optimal Reservoir Operation

As specified from the marketing standpoint, firm hydro-electrical energy must be available on an assured basis. To achieve the firm energy yield at maximum level, simultaneous optimization of operational variables is utilized. The variables include reservoir releases over historical stream-inflow records as well as the plant factor. Here, the plant factor denotes the percentage of day for operating a peak-time power plant at full capacity. To this end, a differential evaluation (DE) algorithm equipped with an indirect constraint handling is employed, in which an adaptive penalty system imposes the desirable reliability and preserves the total energy generation. To implement the model for a real example, a reservoir dam named Karun4, in southwest of Iran, is applied. The experimental results show the firm energy produced by the current and developed method are 49.2 and 127.5 (109 watt hours), respectively. Also, the results reveal the maximum capability of generating the firm energy yield with target reliability whereas the total energy generation is preserving.

Quadrature Mirror Filter Bank Design for Mitral Valve Doppler Signal Using Artificial Bee Colony Algorithm

In this paper, a new design method was developed for quadrature mirror filter (QMF) bank. Previous QMF bank design methods focused on frequency response properties such as pass band and stop band parameters with symmetrical filters. With the proposed method, QMF bank was designed using on time domain signals as mitral valve signal with unsymmetrical filter. The design problem was formulated as an objective function of difference between QMF bank’s input and output signals. QMF bank signal disparity was compared with correlation and peak reconstruction error (PRE). QMF bank prototype filter parameters were optimized with Artificial Bee Colony (ABC) and Differential Evaluation (DE) Algorithms. Design outputs were compared with previous works. DOI: http://dx.doi.org/10.5755/j01.eie.23.1.17584

Multi-Objective Optimal Power Flow using BAT Search Algorithm with Unified Power Flow Controller for Minimization of Real Power Losses

In this paper a multi objective optimal power flow (OPF) is obtained by using BAT search algorithm (BAT) with Unified power flow controller (UPFC). UPFC is a voltage source converter type Flexible Alternating Current Transmission System (FACTS) device. It is able to control the voltage magnitudes, voltage angles and line impedances individually or simultaneously. UPFC along with BAT algorithm is used to minimize the total real power generation cost, real power losses in OPF control. The BAT algorithm based OPF has been examined and tested on a 5 bus test system and modified IEEE 30 bus system without and with UPFC. The results obtained with BAT algorithm are compared with Differential Evaluation (DE).

Allocation of SVCs & IPFCs in an Electrical Power System using DE

The proposed method is used to place SVCs & IPFCs optimally in an electrical power system to reduce active power losses and as well as to improve voltage profile in different load and contingency conditions using Differential Evaluation (DE) Technique. The simulations are performed on an IEEE 14-bus system and results are presented. Keywords: SVC, IPFC, Different load, Contingency, Power loss, Voltage profile, DE, IEEE 14 bus.

Structure stability analysis of Ni<SUB align="right">n(n = 2 - 22) using nature inspired algorithms: a performance study

Using nature inspired algorithms (NIAs) with plane-wave self-consistent field (PWSCF), density functional theory (DFT) and pseudo-potentials, the minimisation of potential energy and relative stability for Nin clusters (n = 2 – 22) were studied. The genetic algorithm (GA), differential evaluation (DE), particle swarm optimisation (PSO) and artificial bee colony (ABC) are used for global structural optimisations with space-fixed Cartesian coordinates. Clusters obtained from NIA search were further optimised by using first principals total energy calculations. We found that GA gives minimum energy for Nin (n = 6, 11, 16, 21), DE gives minimum energy for Nin (n = 2, 4, 7, 9, 15, 17), PSO gives minimum energy for Nin (n = 3, 10, 12, 14, 18, 20) and ABC gives minimum energy for Nin (n = 5, 8, 13, 19, 22).

Big bang-big crunch based optimized controller for automatic generation control and automatic voltage regulator system

In modern power system one of the major problems is to control generator output frequency with excitation voltage at a specified level. Automatic generation control (AGC) maintains system frequency, where as automatic voltage regulator (AVR) controls the terminal voltage of synchronous generator when subjected to load perturbation. This paper presents a Big Bang- Big Crunch based optimum gains of controllers to maintain both system frequency and voltage magnitude. The proposed method, have shown better performance over particle swarm optimization (PSO) and differential evaluation (DE) optimization method for AGC-AVR system.

Modeling the recrystallization process using inverse cellular automata and genetic algorithms: Studies using differential evolution

An inverse modeling approach was taken up in this work to model the process of recrystallization using cellular automata (CA). Using this method after formulating a CA model of recrystallization, differential evaluation (DE), a real-coded variant of genetic algorithms, was used to search for the value of nucleation rate, providing an acceptable matching between the theoretical and experimentally observed values of fraction-recrystallized (X). Initially, the inverse modeling was attempted with a simple CA strategy, in which each of the CA cells had an equal probability of becoming nucleated. DE searched for the value of the nucleation rate yielding the best results for single-crystal iron at 550 °C. A good match could not be simultaneously achieved this way for the early stages of recrystallization as well as for the later stages. To overcome this difficulty, the CA grid was divided into two zones, having lower and higher probabilities of nucleation. This resulted in good correspondence between the predicted and experimental values of X for the entire duration of recrystallization. The introduction of a distribution in the probability of nucleation made the model even closer to the actual process, in which the probability of nucleation is often nonuniform due to nonuniformity in dislocation density as well as the presence of grain/interface boundaries.