Evolutionary Programming Technique Research Articles

Optimal SVC allocation via symbiotic organisms search for voltage security improvement

It is desirable that a power system operation is in a normal operating condition. However, the increase of load demand in a power system has forced the system to operate near to its stability limit whereby an increase in load poses a threat to the power system security. In solving this issue, optimal reactive power support via SVC allocation in a power system has been proposed. In this paper, Symbiotic Organisms Search (SOS) algorithm is implemented to solve for optimal allocation of SVC in the power system. IEEE 26 Bus Reliability Test System is used as the test system. Comparative studies are also conducted concerning Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) techniques based on several case studies. Based on the result, SOS has proven its superiority by producing higher quality solutions compared to PSO and EP. The results of this study can benefit the power system operators in planning for optimal power system operations.

Particle Swarm Optimization for Solving Economic Load Dispatch Problem

This work deals with the optimization of power flow through interconnected system. The work deals with efficient and reliable evolutionary based approach to solve the economic load dispatch (ELD) with line flows and voltage constraints. The work employs PSO algorithm for ELD. PSO is a robust, stochastic computational technique based on movement and intelligence of swarm. The work introduces a conceptual overview and detailed explanation of PSO algorithm as well as shows how it can be used for solving ELD problems. Inherent shortcoming of the traditional methods of finding the ELD is discussed along with other evolutionary methods of finding ELD. A comparative study of different evolutionary programming technique is done and it is shown that particle swarm optimization offer a better result with greater repeatability and lesser time. The feasibility of the proposed method is demonstrated by using a six generator interconnected system having different cost functions and voltage constraints. The results are compared with those obtained by G.A.

Optimal Tuning of SVC-PI Controller using Whale Optimization Algorithm for Angle Stability Improvement

This paper proposed a new swarm-based optimization technique for tuning conventional proportional-integral (PI) controller parameters of a static var compensator (SVC) which controls a synchronous generator in a single machine infinite bus (SMIB) system. As one of the Flexible Alternating Current Transmission Systems (FACTS) devices, SVC is designed and implemented to improve the damping of a synchronous generator. In this study, two parameters of PI controller namely proportional gain, K<sub>P</sub> and integral gain, K<sub>I</sub> are tuned with a new optimization method called Whale Optimization Algorithm (WOA). This technique mimics the social behavior of humpback whales which is characterized by their bubble-net hunting strategy in order to enhance the quality of the solution. Validation with respect to damping ratio and eigenvalues determination confirmed that the proposed technique is more efficient than Evolutionary Programming (EP) and Artificial Immune System (AIS) in improving the angle stability of the system. Comparison between WOA, EP and AIS optimization techniques showed that the proposed computation approach gives better solution and faster computation time.

Maximum Loadability Enhancement with a Hybrid Optimization Method

Nowadays, a power system is operating in a stressed condition due to the increase in demand in addition to constraint in building new power plants. The economics and environmental constraints to build new power plants and transmission lines have led the system to operate very close to its stability limits. Hence, more researches are required to study the important requirements to maintain stable voltage condition and hence develop new techniques in order to address the voltage stability problem. As an action, most Reactive Power Planning (RPP) objective is to minimize the cost of new reactive resources while satisfying the voltage stability constraints and labeled as Secured Reactive Power Planning (SCRPP). The new alternative optimization technique called Adaptive Tumbling Bacterial Foraging (ATBFO) was introduced to solve the RPP problems in the IEEE 57 bus system. The comparison common optimization Meta-Heuristic Evolutionary Programming and original Bacterial Foraging techniques were chosen to verify the performance using the proposed ATBFO method. As a result, the ATBFO method is confirmed as the best suitable solution in solving the identified RPP objective functions.

Sintering aid (ZnO) effect on proton transport in BaCe0.35Zr0.5Y0.15O3‐δ and electrode phenomena studied by distribution function of relaxation times

AbstractIn this work, we have employed an evolutionary programming technique to analyse the impedance spectroscopy (IS) of a ceramic proton conductor BaCe0.35Zr0.5Y0.15O3‐δ (BCZY) to study its proton transport properties as well as the electrode reactions kinetics at the Pt|BCZY interface. The distribution functions of relaxation times (DFRT) obtained from the impedance analysis were used to evaluate the physical parameters such as relaxation time constant, resistance, reactance/capacitance, conductivities, activation energies, grain‐boundary space charge potential Φ(0, T), and electrode polarization resistance. The effect of ZnO sintering aid (2 wt%) on charge‐transfer processes at bulk, grain boundaries and Pt|BCZY interface were thoroughly studied. Φ(0, T) is increased and grain‐boundary conductivity (σgb) is decreased by an order of magnitude due to the presence of Zn at the grain boundaries, whereas bulk properties appear to be unchanged. Variation in σgb with temperature is explained by correlating the grain‐boundary capacitance (Cgb), relaxation time (τgb), space charge effect, and the defect associations (eg, Y‐OH). The anodic functions, for example, hydrogen oxidation kinetics at the electrode (Pt) surface and Pt‐BCZY interface, are explained through the symmetrical cell study and the behavior of relaxation time constants (τ) with temperature.

A hybrid optimization technique for solving economic dispatch problem

Economic dispatch (ED) is one the main power system problems solved during operational planning phase. It is solved by power system engineers with the objective to find the best setting of dispatchable generating units output so that the total production cost is cheap and at the same time abiding the system constraints. For the past ten years, researchers and engineers have been focusing on hybrid optimization techniques to solve ED problems. The strength of each hybridized technique is combined to form an improved optimization technique. Most of the hybrid techniques are combination of two or more meta-heuristic techniques. This paper proposed a new hybrid technique termed as immune evolutionary programming (IEP) to solve economic dispatch problem. The technique is a combination of two population based optimization techniques which are artificial immune system (AIS) and evolutionary programming (EP). In order to study the effectiveness of the proposed technique, the results produced have been compared with the results produced using AIS and EP techniques. It is found that IEP succeeded to give the lowest total production cost compared to the other two techniques.

Sustainable Environmental Economic Dispatch Optimization with Hybrid Metaheuristic Modification

Today’s Economic Dispatch (ED) solutions are featured with environmental obligations. Hence, the significant objective functions contribute to cost minimization, lower emission and less total system losses. As an alternative, New Meta Heuristic Evolutionary Programming (NMEP) technique was proposed to optimize the individual ED problem categorized as Single Objective Environmental Economic Dispatch (SOEELD), developed from an integration of original Meta Heuristic Evolutionary Programming (Meta-EP) with Artificial Immune System (AIS) with new arrangement in the mutation and cloning processes. The comparative analysis was conducted between the original Meta-EP and classical method of Hadi Saadat to verify the performance of NMEP method. Each particular objective function identified the best possible outcomes through the NMEP method. The simulations were conducted using MATLAB programming which tested both standard IEEE 26 and 57 bus systems.

Load Management for Voltage Control Study Using Parallel Immunized-computational Intelligence Technique

The increase of power demand is a crucial issue in the power system community in many parts of the world. Malaysia has also witnessed the familiar scenario due to the current development throughout the country has invited the urgency of increase in the power supply. Since Malaysia practices vertical system; where the electricity is supplied by only one utility, load management is an important issue so that the delivery of electricity is implemented without discrimination. Parallel Computational Intelligence will be developed which can alleviate and avoid all the unsolved issues, highlighting the weakness of current schemes. Parallel Computational Intelligence is developed to manage the optimal load in making sure the system maintains the stability condition, within the voltage limits. This paper presents evolutionary programming (EP) technique for optimizing the voltage profile. In this study, 3 algorithms which are Gaussian, Cauchy and Parallel EP were developed to solve optimal load management problem on IEEE 26-bus Reliability Test System (RTS). Results obtained from the study revealed that the application of Parallel EP has significantly reduced the time for the optimization process to complete.

A New Under-Frequency Load Shedding Scheme Based on Adaptive Neuro-Fuzzy Inference System and Evolutionary Programming Shedding Priority

Frequency stability of islanded distribution system is a topic of interest, due to the significant penetration of distributed generation (DG). This paper proposes a new under frequency load shedding technique, which address the over-shedding issue caused by fixed priority. The proposed scheme consists of two units; first, the power imbalance estimation unit (PIEU), which use adaptive neuro-fuzzy inference system (ANFIS) to estimates the power imbalance. Second, the load shed unit (LSU), which is based on binary evolutionary programming (BEP) technique to shed the optimal loads. To validate the performance of the proposed UFLS scheme, different simulation studies have been conducted using PSCAD software. Moreover, the response of the proposed scheme is compared with another UFLS scheme having fixed priority loads. From the simulation results, the proposed scheme can shed the optimal loads, which is not achieved in the existing load shedding schemes.

Multi-Objective Evolutionary Programming for Static VAR Compensator (SVC) in Power System Considering Contingencies (N-m)

<span>Static VAR Compensators (SVCs) is a Flexible Alternating Current Transmission System (FACTS) device that can control the power flow in transmission lines by injecting capacitive or inductive current components at the midpoint of interconnection line or in load areas. This device is capable of minimizing the overall system losses and concurrently improves the voltage stability. A line index, namely <em>SVSI</em> becomes indicator for the placement of SVC and the parameters of SVCs are tuned by using the multi-objective evolutionary programming technique, effectively able to control the power. The algorithm was tested on IEEE-30 Bus Reliability Test System (RTS). Comparative studies were conducted based on the performance of SVC in terms of their location and sizing for installations in power system.</span>

Evolutionary Computational Algorithm by Blending of PPCA and EP-Enhanced Supervised Classifier for Microarray Gene Expression Data

In DNA microarray technology, gene classification is considered to be difficult because the attributes of the data, are characterized by high dimensionality and small sample size. Classification of tissue samples in such high dimensional problems is a complicated task. Furthermore, there is a high redundancy in microarray data and several genes comprise inappropriate information for accurate classification of diseases or phenotypes. Consequently, an efficient classification technique is necessary to retrieve the gene information from the microarray experimental data. In this paper, a classification technique is proposed that classifies the microarray gene expression data well. In the proposed technique, the dimensionality of the gene expression dataset is reduced by Probabilistic PCA. Then, an Artificial Neural Network (ANN) is selected as the supervised classifier and it is enhanced using Evolutionary programming (EP) technique. The enhancement of the classifier is accomplished by optimizing the dimension of the ANN. The enhanced classifier is trained using the Back Propagation (BP) algorithm and so the BP error gets minimized. The well-trained ANN has the capacity of classifying the gene expression data to the associated classes. The proposed technique is evaluated by classification performance over the cancer classes, Acute myeloid leukemia (AML) and Acute Lymphoblastic Leukemia (ALL). The classification performance of the enhanced ANN classifier is compared over the existing ANN classifier and SVM classifier.

Influence of TCSC Devices on Congestion Management in a Deregulated Power System Using Evolutionary Programming Technique

Congestion management is one of the technical challenges in power system deregulation. In deregulated electricity market it may always not be possible to dispatch all of the contracted power transactions due to congestion of the transmission corridors. Transmission congestion occurs when there is insufficient transmission capacity to simultaneously accommodate all constraints for transmission of a line. Flexible Alternative Current Transmission System (FACTS) devices can be an alternative to reduce the flows in the heavily loaded lines, resulting in an increased loadability, low system loss, improved stability of the network, reduced cost of production and fulfilled contractual requirement by controlling the power flow in the network. A method to determine the optimal location of FACTS has been suggested based on reduction of total system VAR power losses. The simulation was done on IEEE 14 bus system and results were obtained.

Frequency Domain Inverse Profiling of Buried Dielectric Elliptical-Cylindrical Objects Using Evolutionary Programming

An efficient method based on the evolutionary programming (EP) technique is proposed for inverse profiling of 2-D buried dielectric objects with elliptical cross sections. In particular, EP with Cauchy mutation operator (EP-CMO), as its first reported implementation to inverse problems, is utilized as a stochastic optimization tool for quantitatively reconstructing buried objects. Moreover, the method of moments technique in conjunction with conjugate gradient-fast Fourier transform method is used, as a fast and simple frequency domain forward solver, in each iteration of the proposed method. Numerical results for different case studies are presented and analyzed. To assess the proposed EP-CMO method, the results are also compared statistically with that of three other well-known optimization techniques, namely, EP with Gaussian mutation, particle swarm optimization, and genetic algorithms. The results reveal that EP-CMO is a significantly more robust and efficient optimization tool in reconstruction of this class of buried objects.

Log-normal based mutation evolutionary programming technique for solving economic dispatch problem considering loss minimization

Electricity delivery to the consumer should be implemented in such a way that, cost is minimal, loss is minimal and voltage is within the acceptable limit. In general, the voltage level should be within 95% to 105% of the nominal limit in accordance to most international standard within the power engineering community. This phenomenon is addressed as secure voltage level. The dispatch of electricity is controlled by a dispatch body of the utility in a country. Economic dispatch requires a reliable optimization technique so loss is minimal. This paper presents Log-Normal Evolutionary Programming (LNEP) technique for solving Economic Dispatch (ED) problem considering loss minimization. Validations on the IEEE 6-bus and IEEE 26-bus test systems demonstrated that LNEP is feasible and convincing is addressing the issues. It was revealed that the proposed LNEP gives better solution to solve ED problem than the Classical EP and traditional load flow. Keywords: economic dispatch; evolutionary programming, optimization

NMEP based Gaussian Mutation Process on Optimizing Fitness Function for MOEED

The increment of Economic Dispatch (ED) problem is very distressing today. In view of countless of the researchers doing the research to minimize the ED problem day after day, the multi objective New Meta Heuristic Evolutionary Programming (NMEP) techniques are proposed to optimize the multi objective function in ED problem called as Multi Objective Environmental Economic Dispatch (MOEED). The techniques mimic the original Meta Heuristic Evolutionary Programming (Meta-EP) and merge with Artificial Immune System (AIS) with some improvement in Gaussian mutation process and cloning process. The NMEP produced two objective function result simultaneously by exercising the weighted sum method. In order to justify the result, the comparison between the NMEP and Meta-EP techniques is conducted with difference case number of alpha. Therefore, the outcome of the simulation shows the NMEP approach is better than Meta-EP in the both case numbers of alpha. The simulation is operated using MATLAB simulation based on standard IEEE 26 bus system in the laboratory.

Chaos Embedded Symbiotic Organisms Search Technique for Optimal FACTS Device Allocation for Voltage Profile and Security Improvement

<p>Due to the ever-increasing energy demand, power system operators have attempted to cope with these demands while keeping the power system remain operable. Economic constraints have forced the power system operator to abandon their effort in expanding the power system. The increased load demand can cause the power system to suffer from voltage instability and voltage collapse, especially during contingency condition. Hence, a strategy is required to maintain the steady state operation of a power system. Various research has been conducted to tackle this problem. Therefore, this paper presents the implementation of Chaos Embedded Symbiotic Organisms Search technique to solve optimal FACTS device allocation problem in power transmission system. Various practical constraints are also considered in the optimisation process to emulate the real-life constraints in power system. The optimisation process is conducted on a 26-bus IEEE RTS has validated that the results obtained has not violated the power system stability. The results provided by the proposed optimisation technique has successfully improved the voltage profile and voltage security in the system. Comparative studies are also conducted involving Particle Swarm Optimization and Evolutionary Programming technique resulting good results agreement and superiority of the proposed technique. Results obtained from this study would be beneficial to the power system operators regarding optimisation in power system operation for the implementation in real power transmission network.</p>

State-of-charge observers for lead-acid storage units used in autonomous solar applications

The article provides practical means to monitor batteries that work in the so-called float cycling regime. The main interest of the case study is destined for autonomous solar applications. The problem of the state-of-charge estimation of lead-acid storage unit is studied. Two approaches are considered and compared: the sliding mode technique and the fuzzy logic modelling. Based on the grounds of observers design theory, it was shown that the sliding mode technique exhibits robustness properties in the face of parameters imprecision and unknown non-linear behaviour. The fuzzy logic modelling approach allows designing the monitoring scheme using the reduced number of available information. An evolutionary programming technique is applied to adjust the parameters of the fuzzy logic resolution scheme. The results are compared with reference state-of-charge measurements taken from an industrial battery charge regulator. The developed models are potentially extensible to other types of storage units.

Evolutionary Programming-based Simulation of Bilateral Real Power Contracts by Optimal Placement of Flexible AC Transmission System Devices Using Contingency Analysis

Under a restructuring atmosphere, energy customers and producers generally create various bilateral real power markets. The transmission company usually carries out these bilateral power markets contained by the restrictions allowed through the power network design and working situation. The market operator can misuse the “power markets” and earn additional revenue. Flexible Alternate Current Transmission System devices are considered to be one such technology that helps in improving the bilateral real power markets. In this work, a new methodology for correct placement of Flexible Alternate Current Transmission System devices in the restructured power markets is proposed. A generalized evolutionary programming technique is projected to find out the optimal bilateral real power markets with and without Flexible Alternate Current Transmission System devices under a restructuring atmosphere. Flexible Alternate Current Transmission System devices, such as the Thyristor-Controlled Series Compensator, Static VAR Compensator, and Unified Power Flow Controller, are employed. Utilization of these devices helps in minimizing the cost and also helps in reducing the total overloads, excess power flow, and severity of overloading. A sample 4-bus system and 24-bus extra high-voltage Indian system are presented as examples to exemplify the use of this formulation to reduce the price of the bilateral power market.

Impedance spectroscopy analysis inspired by evolutionary programming as a diagnostic tool for SOEC and SOFC

Impedance spectroscopy (IS) is an effective tool for the analysis of solid oxide fuel cell (SOFC) and solid oxide electrolysis cell (SOEC) performance. The challenge using this characterization tool lies within the analysis method. Impedance spectroscopy genetic programming (ISGP) is a novel analysis technique for impedance spectroscopy data. The ISGP utilizes evolutionary programming techniques for finding the most suitable distribution function of relaxation times (DFRT). This approach leads toward a better analysis of impedance spectroscopy results as compared to other analysis tools such as equivalent circuits or deconvolution techniques.In this work, SOFC and SOEC were examined during operation by IS measurements and the results were analyzed using ISGP. The aim of this work is to show examples of DFRT models which reflect the physical processes occurring during the operation. It is demonstrated that despite the low impedance (in the mΩ range) and the narrow available bandwidth, ISGP can provide consistent DFRT models.

The prediction of profile deviations from multi process machining of complex geometrical features using combined evolutionary and neural network algorithms with embedded simulation

The capability to generate complex geometrical features at tight tolerances and fine surface roughness is a key element in the implementation of Creep Feed grinding process in specialist applications such as the aerospace manufacturing environment. Based on the analysis of 3D cutting forces this paper proposes a novel method of predicting the profile deviations of tight geometrical features generated using Creep Feed grinding. In this application, there are several grinding passes made at varying depths providing an incremental geometrical change with the last cut generating the final complex feature. With repeatable results from co-ordinate measurements both the radial and tangential forces can be gauged versus the accuracy of the ground features. The tangential force was found more sensitive to the deviation of actual cut depth from the theoretical one. However, to make a more robust prediction on the profile deviation its values were considered as a function of both force components (proportional to force: power was also included). For multi process, one machining platforms hole making was also investigated in terms of monitoring the force to ensure the mean cylinder was kept within required tolerances and with minimal subsequent machining (due to these imposed accuracies this is also considered a complex feature). Genetic programming (GP), an evolutionary programming technique, has been used to compute the prediction rules of part profile deviations based on the extracted radial and tangential force correlated with the said chosen “gauging” methodology (for grinding process). GP was also used to correlate the force and flank wear (VB) for hole deviations. It was found that using this technique, complex rules can be achieved and used online to dynamically control the geometrical accuracy of ground and drilled hole features. The GP complex rules are based on the correlation between the measured forces and recorded deviation of the theoretical profile (both grinding and hole making). The mathematical rules are generated from Darwinian evolutionary strategy which provides the mapping between different output classes. GP works from crossover recombination of different rules and the best individual is evaluated in terms of the given ‘best fitness value so far’ which closes on an optimal solution. The best obtained GP terminal sets were realised in rule-based embedded coded systems which were finally implemented into a real-time Simulink simulation. This realisation gives a view of how such a control regime can be utilised within an industrial capacity. Neural networks were used for GP decision verification ensuring less sensitivity to possible outliers giving more robustness to the integrated system.