This paper presents a Fast genetic algorithm for solving Hydrothermal coordination (HTC) problem.
Genetic Algorithms (GAs) perform powerful global searches, but their long computation times, put a
limitation when solving large scale optimization problems. The present paper describes a Fast GA (FGA)
to overcome this limitation, by starting with random solutions within the search space and narrowing
down the search space by considering the minimum and maximum errors of the population members.
Since the search space is restricted to a small region within the available search space the algorithm
works very fast. This algorithm reduces the computational burden and number of generations to
converge. The proposed algorithm has been demonstrated for HTC of various combinations of Hydro
thermal systems. In all the cases Fast GA shows reliable convergence. The final results obtained using
Fast GA are compared with simple (conventional) GA and found to be encouraging.
Genetic Algorithm for Solving the Economic Load DispatchSatyendra Singh
In this paper, comparative study of two approaches, Genetic Algorithm
(GA) and Lambda Iteration method (LIM) have been used to provide
the solution of the economic load dispatch (ELD) problem. The ELD
problem is defined as to minimize the total operating cost of a power
system while meeting the total load plus transmission losses within
generation limits. GA and LIM have been used individually for solving
two cases, first is three generator test system and second is ten
generator test system. The results are compared which reveals that GA
can provide more accurate results with fast convergence characteristics
and is superior to LIM.
This paper discusses the possible applications of particle swarm optimization (PSO) in the Power system. One of the problems in Power System is Economic Load dispatch (ED). The discussion is carried out in view of the saving money, computational speed – up and expandability that can be achieved by using PSO method. The general approach of the method of this paper is that of Dynamic Programming Method coupled with PSO method. The feasibility of the proposed method is demonstrated, and it is compared with the lambda iterative method in terms of the solution quality and computation efficiency. The experimental results show that the proposed PSO method was indeed capable of obtaining higher quality solutions efficiently in ED problems.
Genetic Algorithm for Solving the Economic Load DispatchSatyendra Singh
In this paper, comparative study of two approaches, Genetic Algorithm
(GA) and Lambda Iteration method (LIM) have been used to provide
the solution of the economic load dispatch (ELD) problem. The ELD
problem is defined as to minimize the total operating cost of a power
system while meeting the total load plus transmission losses within
generation limits. GA and LIM have been used individually for solving
two cases, first is three generator test system and second is ten
generator test system. The results are compared which reveals that GA
can provide more accurate results with fast convergence characteristics
and is superior to LIM.
This paper discusses the possible applications of particle swarm optimization (PSO) in the Power system. One of the problems in Power System is Economic Load dispatch (ED). The discussion is carried out in view of the saving money, computational speed – up and expandability that can be achieved by using PSO method. The general approach of the method of this paper is that of Dynamic Programming Method coupled with PSO method. The feasibility of the proposed method is demonstrated, and it is compared with the lambda iterative method in terms of the solution quality and computation efficiency. The experimental results show that the proposed PSO method was indeed capable of obtaining higher quality solutions efficiently in ED problems.
Many traditional optimization methods have been successfully used from years to deal with ELD problem. However these techniques have limitations in many aspects as they provide inaccurate results. The objective is to minimize total fuel cost of power generation so as to meet the power demands to satisfy all constraints. In present paper, the parameters of the fuzzy logic are tuned using genetic algorithms. By using GA with fuzzy logic leads to an intelligent dimension for ELD solution space to obtain an optimum solution for ELD
ECONOMIC LOAD DISPATCH USING PARTICLE SWARM OPTIMIZATIONMln Phaneendra
In this ppt particle swarm optimization (PSO) is applied to allot the active power among the generating stations satisfying the system constraints and minimizing the cost of power generated.The viability of the method is analyzed for its accuracy and rate of convergence. The economic load dispatch problem is solved for three and six unit system using PSO and conventional method for both cases of neglecting and including transmission losses. The results of PSO method were compared with conventional method and were found to be superior.
Profit based unit commitment for GENCOs using Parallel PSO in a distributed c...IDES Editor
In the deregulated electricity market, each
generating company has to maximize its own profit by
committing suitable generation schedule termed as profit
based unit commitment (PBUC). This article proposes a
Parallel Particle Swarm Optimization (PPSO) solution to the
PBUC problem. This method has better convergence
characteristics in obtaining optimum solution. The proposed
approach uses a cluster of computers performing parallel
operations in a distributed environment for obtaining the
PBUC solution. The time complexity and the solution quality
with respect to the number of processors in the cluster are
thoroughly tested. The method has been applied to 10 unit
system and the results show that the proposed PPSO in a
distributed cluster constantly outperforms the other methods
which are available in the literature.
Economic Load Dispatch (ELD) is a process of scheduling the required load demand among available generation units such that the fuel cost of operation is minimized. The ELD problem is formulated as a non-linear constrained optimization problem with both equality and inequality constraints. In this paper, two test systems of the ELD problems are solved by adopting the Cuckoo Search (CS) Algorithm. A comparison of obtained simulation results by using the CS is carried out against six other swarm intelligence algorithms: Particle Swarm Optimization, Shuffled Frog Leaping Algorithm, Bacterial Foraging Optimization, Artificial Bee Colony, Harmony Search and Firefly Algorithm. The effectiveness of each swarm intelligence algorithm is demonstrated on a test system comprising three-generators and other containing six-generators. Results denote superiority of the Cuckoo Search Algorithm and confirm its potential to solve the ELD problem.
HYDROTHERMAL COORDINATION FOR SHORT RANGE FIXED HEAD STATIONS USING FAST GENE...ecij
This paper presents a Fast genetic algorithm for solving Hydrothermal coordination (HTC) problem. Genetic Algorithms (GAs) perform powerful global searches, but their long computation times, put a limitation when solving large scale optimization problems. The present paper describes a Fast GA (FGA) to overcome this limitation, by starting with random solutions within the search space and narrowing down the search space by considering the minimum and maximum errors of the population members. Since the search space is restricted to a small region within the available search space the algorithm
works very fast. This algorithm reduces the computational burden and number of generations to converge. The proposed algorithm has been demonstrated for HTC of various combinations of Hydro thermal systems. In all the cases Fast GA shows reliable convergence. The final results obtained using
Fast GA are compared with simple (conventional) GA and found to be encouraging.
Short term Multi Chain Hydrothermal Scheduling Using Modified Gravitational S...IJARTES
This paper proposes the modified Gravitational
search algorithm (GSA) to solve short term multi chain
hydrothermal scheduling problem while satisfying all
operational and physical constraints. The effect of the valve
point loading has been considered. Gravitational search
algorithm is based on the Newton’s law of gravitation. All
objects attract each other and global movement is towards
the heavier masses .However GSA has certain randomness
in search direction resulting in the weak local search ability.
In modified GSA, a time varying maximum velocity equation
is used which controls the exploration and improves the
convergence rate which strengthens its local search ability
and the quality of the hydrothermal solution.
Many traditional optimization methods have been successfully used from years to deal with ELD problem. However these techniques have limitations in many aspects as they provide inaccurate results. The objective is to minimize total fuel cost of power generation so as to meet the power demands to satisfy all constraints. In present paper, the parameters of the fuzzy logic are tuned using genetic algorithms. By using GA with fuzzy logic leads to an intelligent dimension for ELD solution space to obtain an optimum solution for ELD
ECONOMIC LOAD DISPATCH USING PARTICLE SWARM OPTIMIZATIONMln Phaneendra
In this ppt particle swarm optimization (PSO) is applied to allot the active power among the generating stations satisfying the system constraints and minimizing the cost of power generated.The viability of the method is analyzed for its accuracy and rate of convergence. The economic load dispatch problem is solved for three and six unit system using PSO and conventional method for both cases of neglecting and including transmission losses. The results of PSO method were compared with conventional method and were found to be superior.
Profit based unit commitment for GENCOs using Parallel PSO in a distributed c...IDES Editor
In the deregulated electricity market, each
generating company has to maximize its own profit by
committing suitable generation schedule termed as profit
based unit commitment (PBUC). This article proposes a
Parallel Particle Swarm Optimization (PPSO) solution to the
PBUC problem. This method has better convergence
characteristics in obtaining optimum solution. The proposed
approach uses a cluster of computers performing parallel
operations in a distributed environment for obtaining the
PBUC solution. The time complexity and the solution quality
with respect to the number of processors in the cluster are
thoroughly tested. The method has been applied to 10 unit
system and the results show that the proposed PPSO in a
distributed cluster constantly outperforms the other methods
which are available in the literature.
Economic Load Dispatch (ELD) is a process of scheduling the required load demand among available generation units such that the fuel cost of operation is minimized. The ELD problem is formulated as a non-linear constrained optimization problem with both equality and inequality constraints. In this paper, two test systems of the ELD problems are solved by adopting the Cuckoo Search (CS) Algorithm. A comparison of obtained simulation results by using the CS is carried out against six other swarm intelligence algorithms: Particle Swarm Optimization, Shuffled Frog Leaping Algorithm, Bacterial Foraging Optimization, Artificial Bee Colony, Harmony Search and Firefly Algorithm. The effectiveness of each swarm intelligence algorithm is demonstrated on a test system comprising three-generators and other containing six-generators. Results denote superiority of the Cuckoo Search Algorithm and confirm its potential to solve the ELD problem.
HYDROTHERMAL COORDINATION FOR SHORT RANGE FIXED HEAD STATIONS USING FAST GENE...ecij
This paper presents a Fast genetic algorithm for solving Hydrothermal coordination (HTC) problem. Genetic Algorithms (GAs) perform powerful global searches, but their long computation times, put a limitation when solving large scale optimization problems. The present paper describes a Fast GA (FGA) to overcome this limitation, by starting with random solutions within the search space and narrowing down the search space by considering the minimum and maximum errors of the population members. Since the search space is restricted to a small region within the available search space the algorithm
works very fast. This algorithm reduces the computational burden and number of generations to converge. The proposed algorithm has been demonstrated for HTC of various combinations of Hydro thermal systems. In all the cases Fast GA shows reliable convergence. The final results obtained using
Fast GA are compared with simple (conventional) GA and found to be encouraging.
Short term Multi Chain Hydrothermal Scheduling Using Modified Gravitational S...IJARTES
This paper proposes the modified Gravitational
search algorithm (GSA) to solve short term multi chain
hydrothermal scheduling problem while satisfying all
operational and physical constraints. The effect of the valve
point loading has been considered. Gravitational search
algorithm is based on the Newton’s law of gravitation. All
objects attract each other and global movement is towards
the heavier masses .However GSA has certain randomness
in search direction resulting in the weak local search ability.
In modified GSA, a time varying maximum velocity equation
is used which controls the exploration and improves the
convergence rate which strengthens its local search ability
and the quality of the hydrothermal solution.
PuShort Term Hydrothermal Scheduling using Evolutionary Programmingblished pa...Satyendra Singh
In this paper, Evolutionary Programming method
is used for short term hydrothermal scheduling which minimize
the total fuel cost while satisfying the constraints. This paper
developed and studies the performance of evolutionary programs
in solving hydrothermal scheduling problem. The effectiveness of
the developed program is tested for the system having one hydro
and one thermal unit for 24 hour load demand. Numerical results
show that highly near-optimal solutions can be obtained by
Evolutionary Programming.
This paper analyses the optimal power system planning with DGs used as real and reactive power compensator. Recently planning of DG placement reactive power compensation are the major problems in distribution system. As the requirement in the power is more the DG placement becomes important. When planned to make the DG placement, cost analysis becomes as a major concern. And if the DGs operate as reactive power compensator it is most helpful in power quality maintenance. So, this paper deals with the optimal power system planning with renewable DGs which can be used as a reactive power compensators. The problem is formulated and solved using popular meta-heuristic techniques called cuckoo search algorithm (CSA) and particle swarm optimization (PSO). the comparative results are presented.
Dynamic Economic Dispatch Assessment Using Particle Swarm Optimization TechniquejournalBEEI
This paper presents the application of particle swarm optimization (PSO) technique for solving the dynamic economic dispatch (DED) problem. The DED is one of the main functions in power system planning in order to obtain optimum power system operation and control. It determines the optimal operation of generating units at every predicted load demands over a certain period of time. The optimum operation of generating units is obtained by referring to the minimum total generation cost while the system is operating within its limits. The DED based PSO technique is tested on a 9-bus system containing of three generator bus, six load bus and twelve transmission lines.
Operation cost reduction in unit commitment problem using improved quantum bi...IJECEIAES
Unit Commitment (UC) is a nonlinear mixed integer-programming problem. UC used to minimize the operational cost of the generation units in a power system by scheduling some of generators in ON state and the other generators in OFF state according to the total power outputs of generation units, load demand and the constraints of power system. This paper proposes an Improved Quantum Binary Particle Swarm Optimization (IQBPSO) algorithm. The tests have been made on a 10-units simulation system and the results show the improvement in an operation cost reduction after using the proposed algorithm compared with the ordinary Quantum Binary Particle Swarm Optimization (QBPSO) algorithm
Reliability Constrained Unit Commitment Considering the Effect of DG and DR P...IJECEIAES
Due to increase in energy prices at peak periods and increase in fuel cost, involving Distributed Generation (DG) and consumption management by Demand Response (DR) will be unavoidable options for optimal system operations. Also, with high penetration of DGs and DR programs into power system operation, the reliability criterion is taken into account as one of the most important concerns of system operators in management of power system. In this paper, a Reliability Constrained Unit Commitment (RCUC) at presence of time-based DR program and DGs integrated with conventional units is proposed and executed to reach a reliable and economic operation. Designated cost function has been minimized considering reliability constraint in prevailing UC formulation. The UC scheduling is accomplished in short-term so that the reliability is maintained in acceptable level. Because of complex nature of RCUC problem and full AC load flow constraints, the hybrid algorithm included Simulated Annealing (SA) and Binary Particle Swarm Optimization (BPSO) has been proposed to optimize the problem. Numerical results demonstrate the effectiveness of the proposed method and considerable efficacy of the time-based DR program in reducing operational costs by implementing it on IEEE-RTS79.
Energy Consumption Saving in Embedded Microprocessors Using Hardware Accelera...TELKOMNIKA JOURNAL
This paper deals with the reduction of power consumption in embedded microprocessors.
Computing power and energy efficiency are becoming the main challenges for embedded system
applications. This is, in particular, the caseof wearable systems. When the power supply is provided by
batteries, an important requirement for these systems is the long service life. This work investigates a
method for the reduction of microprocessor energy consumption, based on the use of hardware
accelerators. Their use allows to reduce the execution time and to decrease the clock frequency, so
reducing the power consumption. In order to provide experimental results, authors analyze a case of study
in the field of wearable devices for the processing of ECG signals. The experimental results show that the
use of hardware accelerator significantly reduces the power consumption.
Source resizing and improved power distribution for high available island mic...Mohamed Ghaieth Abidi
A microgrid is a small-scale smart network that contains distributed resources and loads and serves several technical, economic, and environmental aims. In this kind of power system, the energy generated by different sources is often collected in an adequate bus system (ac or dc busses) and transported to loads across a distribution system. In case of islanding operation of a microgrid, each production insufficiency or distribution system fault can cause a partial or total interruption of power supply required by loads of the microgrid. This unavailability can last longer because of the randomness and intermittent behavior of renewable sources and the importance of the maintenance actions of the distribution system. To guarantee high availability of power supply, microgrid must be able to produce and to transfer the power energy requested by loads. The sizing of distributed sources and the design of the distribution system present an important challenge to achieve this goal. This paper offers a new global methodology carried out in two steps: in the first, it aims to optimize the source sizing by adding some microsources for ensuring the balance between sources production and loads requirement, then, in a second step, it aims to enhance the distribution system design by the creation of new paths of power transmission for transferring the produced energy from sources to loads. The proposed optimization methodology aims to achieve the requested availability rate requested by each load (considering their priorities) by taking into account some technical and economic factors. In this methodology based on genetic algorithms, objective functions are defined, and several electrical and economic constraints are formulated. The graph theory is used to represent the architecture of the microgrid distribution system, and Matpower tool of MATLAB is used to model it. An application and implementation of the proposed optimization methodology in a Tunisian petroleum platform indicate that the proposed solution is efficient in optimizing of power supply availability in its microgrid.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Similar to HYDROTHERMAL COORDINATION FOR SHORT RANGE FIXED HEAD STATIONS USING FAST GENETIC ALGORITHM (20)
Electrical & Computer Engineering: An International Journal (ECIJ)ecij
Electrical & Computer Engineering: An International Journal (ECIJ) is a peer-reviewed, open access journal that addresses the impacts and challenges of Electrical and Computer Engineering. The journal documents practical and theoretical results which make a fundamental contribution for the development Electrical and Computer Engineering.
Electrical & Computer Engineering: An International Journal (ECIJ)ecij
Electrical & Computer Engineering: An International Journal (ECIJ) is a peer-reviewed,
open access journal that address the impacts and challenges of Electrical and Computer
Engineering. The journal documents practical and theoretical results which make a fundamental
contribution for the development Electrical and Computer Engineering.
Electrical & Computer Engineering: An International Journal (ECIJ)ecij
Electrical & Computer Engineering: An International Journal (ECIJ) is a peer-reviewed,
open access journal that address the impacts and challenges of Electrical and Computer
Engineering. The journal documents practical and theoretical results which make a fundamental
contribution for the development Electrical and Computer Engineering.
Electrical & Computer Engineering: An International Journal (ECIJ)ecij
Electrical & Computer Engineering: An International Journal (ECIJ) is a peer-reviewed,
open access journal that address the impacts and challenges of Electrical and Computer
Engineering. The journal documents practical and theoretical results which make a fundamental
contribution for the development Electrical and Computer Engineering
This work investigates and evaluates the electric energy interruptions to the residential sector resulting from severe power outages. The study results show that this sector will suffer tangible and intangible losses should these outages occur during specific times, seasons, and for prolonged durations. To reduce these power outages and hence mitigate their adverse consequences, the study proposes practical measures that
can be adopted without compromising the consumers’ needs, satisfaction, and convenience.
GRID SIDE CONVERTER CONTROL IN DFIG BASED WIND SYSTEM USING ENHANCED HYSTERES...ecij
The standard grid codes suggested, that the wind generators should stay in connected and reliable active and reactive power should be provided during uncertainties. This paper presents an independent control of Grid Side Converter (GSC) for a doubly fed induction generator (DFIG). A novel GSC controller has
been designed by incorporating a new Enhanced hysteresis comparator (EHC) that utilizes the hysteresis band to produce the suitable switching signal to the GSC to get enhanced controllability during grid unbalance. The EHC produces higher duty-ratio linearity and larger fundamental GSC currents with
lesser harmonics. Thus achieve fast transient response for GSC. All these features are confirmed through
time domain simulation on a 15 KW DFIG Wind Energy Conversion System (WECS).
Electrical & Computer Engineering: An International Journal (ECIJ)ecij
Electrical & Computer Engineering: An International Journal (ECIJ) is a peer-reviewed,
open access journal that address the impacts and challenges of Electrical and Computer
Engineering. The journal documents practical and theoretical results which make a fundamental
contribution for the development Electrical and Computer Engineering.
PREPARATION OF POROUS AND RECYCLABLE PVA-TIO2HYBRID HYDROGELecij
Nano TiO2, one of the most effective photocatalysts, has extensive usein fields such as air purification,
sweage treatment, water spitting, reduction of CO2, and solar cells. Nowadays, the most promising method to
recycle nano TiO2during the photocatalysis is to immobilize TiO2onto matrix, such as polyvinyl alcohol
(PVA). However, due to the slow water permeability of PVA after cross-linking, the pollutants could not
contact with nano TiO2photocatalyst in time. To overcome this problem, we dispersed calcium carbonate
particles into a PVA-TiO2 mixture and then filmed the glass. PVA-TiO2-CaCO3 films were obtained by
drying. Through thermal treatment, we obtained the cross-linked PVA-TiO2-CaCO3 films. Finally, the
calcium carbonate in the film was dissolved by hydrochloric acid, and the porous PVA-TiO2 composite
photocatalyst was obtained. The results show the addition of CaCO3 has no obvious effect on PVA
cross-linking and that a large number of cavities have been generated on the surface and inside of porous
PVA-TiO2 hybrid hydrogel film. The size of the holes is about 5-15μm, which is consistent with that of
CaCO3.The photocatalytic rate constant of porous PVA-TiO2 hybrid hydrogel film is 2.49 times higher than
that of nonporous PVA-TiO2 hybrid hydrogel film.
4th International Conference on Electrical Engineering (ELEC 2020)ecij
4th International Conference on Electrical Engineering (ELEC 2020)aims to bring together researchers and practitioners from academia and industry to focus on recent systems and techniques in the broad field of Electrical Engineering. Original research papers, state-of-the-art reviews are invited for publication in all areas of Electrical Engineering.
Electrical & Computer Engineering: An International Journal (ECIJ)ecij
Electrical & Computer Engineering: An International Journal (ECIJ) is a peer-reviewed, open access journal that addresses the impacts and challenges of Electrical and Computer Engineering. The journal documents practical and theoretical results which make a fundamental contribution for the development Electrical and Computer Engineering.
4th International Conference on Bioscience & Engineering (BIEN 2020) ecij
4th International Conference on Bioscience & Engineering (BIEN 2020) will provide an excellent International forum for sharing knowledge and results in theory, methodology and applications impacts and challenges of Bioscience and Engineering. The goal of this Conference is to bring together researchers and practitioners from academia and industry to focus on Bioscience and Engineering advancements and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of Bioscience and Engineering.
Electrical & Computer Engineering: An International Journal (ECIJ)ecij
Scope & Topics
Electrical & Computer Engineering: An International Journal (ECIJ) is a peer-reviewed, open access journal that addresses the impacts and challenges of Electrical and Computer Engineering. The journal documents practical and theoretical results which make a fundamental contribution for the development Electrical and Computer Engineering.
Electrical & Computer Engineering: An International Journal (ECIJ)
ISSN: 2201-5957
https://wireilla.com/engg/ecij/index.html
Paper Submission
Authors are invited to submit papers for this journal through E-mail: ecijjournal@wireilla.com .
Important Dates
•Submission Deadline: March 28, 2020
Contact US
Here's where you can reach us: ecijjournal@wireilla.com
GRID SIDE CONVERTER CONTROL IN DFIG BASED WIND SYSTEM USING ENHANCED HYSTERES...ecij
The standard grid codes suggested, that the wind generators should stay in connected and reliable active and reactive power should be provided during uncertainties. This paper presents an independent control of Grid Side Converter (GSC) for a doubly fed induction generator (DFIG). A novel GSC controller has been designed by incorporating a new Enhanced hysteresis comparator (EHC) that utilizes the hysteresis band to produce the suitable switching signal to the GSC to get enhanced controllability during grid unbalance. The EHC produces higher duty-ratio linearity and larger fundamental GSC currents with lesser harmonics. Thus achieve fast transient response for GSC. All these features are confirmed through time domain simulation on a 15 KW DFIG Wind Energy Conversion System (WECS).
UNION OF GRAVITATIONAL AND ELECTROMAGNETIC FIELDS ON THE BASIS OF NONTRADITIO...ecij
The traditional principle of solving the problem of combining the gravitational and electromagnetic fields is associated with the movement of the transformation of parameters from the electromagnetic to the gravitational field on the basis of Maxwell and Lorentz equations. The proposed non-traditional principle
is associated with the movement of the transformation of parameters from the gravitational to the electromagnetic field, which simplifies the process. Nave principle solving this task by using special physical quantities found by M. Planck in 1900: - Planck’s length, time and mass), the uniqueness of which is that they are obtained on the basis of 3 fundamental physical constants: the velocity c of light in vacuum, the Planck’s constant h and the gravitational constant G, which reduces them to the fundamentals of the Universe. Strict physical regularities were obtained for the based on intercommunication of 3-th
fundamental physical constants c, h and G, that allow to single out wave characteristic νG from G which is identified with the frequency of gravitational field. On this base other wave and substance parameters were strictly defined and their numerical values obtained. It was proved that gravitational field with the given wave parameters can be unified only with electromagnetic field having the same wave parameters that’s why it is possible only on Plank’s level of world creation. The solution of given problems is substantiated by well-known physical laws and conformities and not contradiction to modern knowledge about of material world and the Universe on the whole. It is actual for development of physics and other branches of science and technique.
USING MACHINE LEARNING TO BUILD A SEMI-INTELLIGENT BOT ecij
Nowadays, real-time systems and intelligent systems offer more and more control interface based on voice recognition or human language recognition. Robots and drones will soon be mainly controlled by voice. Other robots will integrate bots to interact with their users, this can be useful both in industry and entertainment. At first, researchers were digging on the side of "ontology reasoning". Given all the technical constraints brought by the treatment of ontologies, an interesting solution has emerged in last years: the construction of a model based on machine learning to connect a human language to a knowledge
base (based for example on RDF). We present in this paper our contribution to build a bot that could be used on real-time systems and drones/robots, using recent machine learning technologies.
MODELING AND SIMULATION OF SOLAR PHOTOVOLTAIC APPLICATION BASED MULTILEVEL IN...ecij
As the solar market is blooming and forecasted to continue this trend in the coming years. The efficiency and reliability of PV based system has always been a contention among researchers. Therefore, multilevel inverters are gaining more assiduity as it has multitude of benefits. It offers high power capability along with low output harmonics. The main disadvantage of MLI is its complexity and requirement of large
number of power devices and passive components. This paper presents a topology that achieves 37.5% reduction in number of passive components and power devices for five-level inverter. This topology is basically based on H-bridge with bi-directional auxiliary switch. This paper includes a stand-alone PV system in which designing and simulation of Boost converter connected with multilevel inverter for ac load is presented. Perturb and observe MPPT algorithm has been implemented to extract maximum power. The premier objective is to obtain Voltage with less harmonic distortion economically. Multicarrier Sinusoidal
PWM techniques have been implemented and analysed for modulation scheme. The Proposed system is simulated n MATLAB/Simulink platform.
Investigation of Interleaved Boost Converter with Voltage multiplier for PV w...ecij
This paper depicts the significance of Interleaved Boost Converter (IBC) with diode-capacitor multiplierwith PV as the input source. Maximum Power Point Tracking (MPPT) was used to obtain maximum power from the PV system. In this, interleaving topology is used to reduce the input current ripple, output voltage ripple, power loss and to suppress the ripple in battery current in the case of Plugin Hybrid Electric Vehicle (PHEV). Moreover, voltage multiplier cells are added in the IBC configuration to reduce the narrow turn-off periods. Two MPPT techniques are compared in this paper: i) Perturb and Observe (P&O) algorithm ii) Fuzzy Logic . The two algorithms are simulated using MATLAB and the comparison of performance parameters like the ripple is done and the results are verified.
A COMPARISON BETWEEN SWARM INTELLIGENCE ALGORITHMS FOR ROUTING PROBLEMSecij
Travelling salesman problem (TSP) is a most popular combinatorial routing problem, belongs to the class of NP-hard problems. Many approacheshave been proposed for TSP.Among them, swarm intelligence (SI) algorithms can effectively achieve optimal tours with the minimum lengths and attempt to avoid trapping in local minima points. The transcendence of each SI is depended on the nature of the problem. In our studies, there has been yet no any article, which had compared the performance of SI algorithms for TSP perfectly. In this paper,four common SI algorithms are used to solve TSP, in order to compare the performance of SI algorithms for the TSP problem. These algorithms include genetic algorithm, particle swarm optimization, ant colony optimization, and artificial bee colony. For each SI, the various parameters and operators were tested, and the best values were selected for it. Experiments oversome benchmarks fromTSPLIBshow that
artificial bee colony algorithm is the best one among the fourSI-basedmethods to solverouting problems like TSP.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
From Siloed Products to Connected Ecosystem: Building a Sustainable and Scala...
HYDROTHERMAL COORDINATION FOR SHORT RANGE FIXED HEAD STATIONS USING FAST GENETIC ALGORITHM
1. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 2, Number 1, March 2013
HYDROTHERMAL COORDINATION FOR SHORT
RANGE FIXED HEAD STATIONS USING FAST
GENETIC ALGORITHM
1
M. Murali, 2B. Ramesh kumar, 3M. Sailaja Kumari and 4M. Sydulu
1,3,4
Department of Electrical Engineering, N.I.T Warangal, A.P, India
1
murali233.nitw@gmail.com
2
Andhra Pradesh Generating Corporation, India
2
boddukuriramesh@gmail.com
ABSTRACT
This paper presents a Fast genetic algorithm for solving Hydrothermal coordination (HTC) problem.
Genetic Algorithms (GAs) perform powerful global searches, but their long computation times, put a
limitation when solving large scale optimization problems. The present paper describes a Fast GA (FGA)
to overcome this limitation, by starting with random solutions within the search space and narrowing
down the search space by considering the minimum and maximum errors of the population members.
Since the search space is restricted to a small region within the available search space the algorithm
works very fast. This algorithm reduces the computational burden and number of generations to
converge. The proposed algorithm has been demonstrated for HTC of various combinations of Hydro
thermal systems. In all the cases Fast GA shows reliable convergence. The final results obtained using
Fast GA are compared with simple (conventional) GA and found to be encouraging.
KEYWORDS
Hydrothermal scheduling, Genetic algorithms, Optimal scheduling, Incremental fuel cost of generators
Nomenclature:
Kmax number of intervals
nt number of thermal plants
tk duration of interval k
Fik(Psik) fuel cost function of ith thermal plant in kth interval
Psik Thermal generation of ith plant in kth interval
PHik Hydro generation of ith plant in kth interval
PDk power demand in interval k
PLk system loss in interval k
Qik(PHik) water discharge rate of ith plant in interval k
Z objective function
ℒ augmented objective function
Psimin, Psimax minimum and maximum limits on ith thermal unit
PHimin, PHimax minimum and maximum limits on ith hydro unit
ai,bi,ci cost coefficients of ith thermal plant
xi,yi,zi water discharge coefficients of ith hydro plant
avl
Vi available water for ith hydro plant over the scheduling period
NET Net execution time
1
2. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 2, Number 1, March 2013
1. INTRODUCTION
Hydrothermal coordination (HTC) means determination of thermal power and hydro power
such that total system generation cost is minimum while satisfying the system constraints.
However the operating cost of hydro plant is negligible, so our objective is to minimize the total
fuel cost of thermal units, subjected to the water availability constraint and energy balance
condition for a given period of time. It is basically non-linear programming problem involving
non-linear objective function and a mixture of linear and non-linear constraints.
The HTC problem is divided into three types based on the duration of interval; these are long,
mid and short-range problems. Mid and long-range HTC is for one or more years on a weekly
or monthly basis [1]. The final output is the amount of water to be discharged at each
hydroelectric plant throughout the coming week. Short-range planning on the other hand is
concerned with distributing the generation among the available units over a day or week,
usually on an hourly basis, satisfying the operating constraints. In short-range problems a fixed
water head is assumed and the net head variation can be ignored for relatively large reservoirs,
in which case power generation is solely dependent on the water discharge [1].
Earlier, a wide variety of optimization techniques have been applied to solving HTC [2]
problems such as dynamic programming (DP), gradient search methods etc. But, because of the
drawbacks, such as drastic growth of computation, dimensionality requirement, and insecure
convergence properties these local optimization techniques are not suitable for HTC problem.
In recent years, due to their flexibility, versatility and robustness in seeking global optimal
solution, heuristic optimization techniques are gaining lot of interest.
Simple genetic algorithm is capable of locating the near optimal solutions, but it takes large
number of iterations to converge and taking large CPU time. In addition to simple GA, there
exists a number of advanced GAs, designed to have advanced features.
A number of heuristic techniques like GAs, simulated annealing, evolutionary strategy, particle
swarm optimization have been attempted for solution of HTC. All these techniques used large
number of variables which not only depend on number of generators, but also on number of
intervals in the planning horizon. [1] presented optimal gamma based GA for solving HTC
problem, which reduces the number of variables to be considered to a minimum. GA has also
been applied for solution of HTC. [2] proposed an efficient method for optimal scheduling of
fixed head hydro thermal plants. A fast computational genetic algorithm for solving the
economic load dispatch problem of thermal units has been presented in [3]. [6] presents short
term hydro thermal coordination by Lagrangian relaxation method using dual optimization
technique.[10] presented a fast evolutionary technique for short-term hydrothermal scheduling.
[11] presents HTC using GA. A unit based encoding scheme is used, which restricts the
applicability of GA to large-scale systems. In unit based encoding the chromosome length
increase with increase of number of units in the system.
In the present paper a fast computation GA [3] is used considering the optimal gamma based
approach for short term hydrothermal coordination. The paper presents a methodology to
overcome the long computation times involved with simple Genetic Algorithms.
2
3. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 2, Number 1, March 2013
2. PROBLEM FORMULATION
The main objective of HTC [1] problem is to determine the optimal schedule of both thermal
and hydro plants of the power systems while minimizing the total operating cost of the system,
i.e. fuel cost required for the systems thermal generation. It is required to meet the forecasted
load demand over the scheduled time, while satisfying various system and unit constraints. The
HTC problem can be defined as
Minimize
z = ∑୩୫ୟ୶ ∑୬୲ t ୩ . F୧୩ ሺPୱ୧୩ ሻ
୩ୀଵ ୧ୀଵ (1)
Subject to power balance constraint (2) and the water availability constraint (3)
∑௧ ܲௌ + ∑ ܲு − ܲ − ܲ = 0;
ୀଵ ୀଵ K=1, 2 ….kmax (2)
∑௫ ݐ . ܳ ሺܲு ሻ = ܸ௩ ; ݅ = 1,2, … … ݊ℎ
ୀଵ (3)
With
୫୧୬ ୫ୟ୶
Pୗ ≤ Pୗ ≤ Pୗ ; ݅ = 1,2 … ݊ݐ
୫୧୬ ୫ୟ୶
Pୌ ≤ Pୌ ≤ Pୌ ; ݅ = 1,2 … . ݊ℎ (4)
Where
ଶ
F୧୩ ሺPୱ୧୩ ሻ = a୧ Pୱ୧୩ + b୧ Pୱ୧୩ + c୧ $/h (5)
ଶ
Q ୧୩ ሺPୌ୧୩ ሻ = x୧ Pୌ୧୩ + y୧ Pୌ୧୩ + z୧ m3/h (6)
2.1 Classical ૃ – γ iteration method[4]
The Lagrange function for the HTC can be written as
ℒ = ∑୩୫ୟ୶[ ∑୬୲ t ୩ Fjk൫Pୱ୨ ൯ + λ୩ ൫Pୈ୩ + P୩ − ∑୬୦ Pୌ୨ − ∑୬୲ Pୱ୨ ൯] + γ ∑୬୦ [∑୩୫ୟ୶ t ୩ Q୨୩ ൫Pୌ୨୩ ൯ −V୨ୟ୴୪]
୩ୀଵ ୨ୀଵ ୨ୀଵ ୨ୀଵ ୨ୀଵ ୩ୀଵ (7)
The co-ordination equations from the above function can be obtained for interval k
ୢ ப
t ୩ ୢ ౡ + λ୩ ப ైౡ = λ୩ (8)
౩ౡ ౩ౡ
ୢ୯ሺౄౡ ሻ பైౡ
γt ୩ + λ୩ = λ୩ (9)
ୢౄౡ பౄౡ
the above co-ordination equations along with constraints (2)-(4) can be iteratively solved to
obtain optimal HTC.
3. PROPOSED METHODOLOGY
The GA is essentially a search process based on the mechanics of natural selection and natural
genetics to obtain a global optimal solution of a combinatorial optimization problem. The
execution of GA involves initialization of population of chromosomes. Based on the fitness
3
4. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 2, Number 1, March 2013
values, the process of generation of new chromosomes and selection of those with better fitness
values are continued until the desired conditions are satisfied.
In this method the values of the hydel plants are considered as GA decision variables. By
using the values of (γሻ gamma the optimal thermal and hydro powers are found using (8) and
(9). In fast GA approach the lower and upper limits of (gamma) are reduced based on the
constraint (3). Thus the search space is effectively reduced to be close to optimal value, due to
which the number of generations to get optimal solution are drastically reduced and reduced
CPU execution time.
3.1 Encoding and decoding
The implementation of GA starts with parameter encoding. Here binary representation is used
because of the ease of binary number manipulation. In this method values of all hydel plants
are considered as GA variables and each of them represented by L-binary bits. The resolution of
the solution depends upon length of chromosome. The higher the number of bits used, the finer
will be resolution. However, higher the number of bits used for encoding, slower will be
convergence. The total chromosome is obtained by concatenating the bits representing each
decision variable. The value of γ୧ for each sub-string of the chromosome is evaluated first by
decoding its binary values into its decimal equivalent Di and then the following expression is
computed
ᵞౣ౮ ିᵞౣ
ᵞ୧ = ᵞ୫୧୬ +
୧
ଶై ିଵ
∗ D୧ (10)
However the lower and upper bounds are chosen using (4).
3.2 Fitness function
Water availability constraint is considered as fitness function for the hydrothermal coordination
problem. The optimal value of γ gives optimal thermal and hydro powers.
The fitness function of each chromosome ‘i’ is given by
ଵ
FIT୧ = (11)
ଵା∑ [౬ౢ ି∑ౡౣ౮ ୲ౡ ୕ౠౡ ൫ౄ ౠౡ ൯]
ౠసభ ౠ ౡసభ
4
5. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 2, Number 1, March 2013
Figure 1.Flow chart of HTC using simple GA
3.3. HTC using SGA and FGA
For solution of HTC using SGA, values of each hydro plant is encoded in the chromosome.
The flowchart for implementing HTC using simple GA is shown in fig.1. For HTC using fast
GA, values of each hydro plant is encoded in chromosome. The flowchart for implementing
HTC using fast GA is shown in fig.2. In fast GA, after the errors of all chromosomes are
evaluated, search space is restricted by identifying minimum positive error, and setting the
gamma_act of this chromosome to be gamma_max. Then, identifying the chromosome with
minimum negative error, and set the gamma_act of this chromosome to be gamma_min. This
will largely reduce the search space from wide gamma_max, gamma_min to small region and
hence help in faster convergence.
5
6. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 2, Number 1, M
V March 2013
Figure 2. Flow chart of HTS using fast GA
4. RESULTS AND DISCUSSION
The developed algorithm is tested for four cases. In every case studied the chromosome length,
population size, probability of crossover, mutation and elitism considered with simple GA and
fast GA are same. Tournament selection, uniform crossover and bit wise mutation are used in all
the cases. The GA parameters considered for all the cases are Population size: 40, chromosome
length: 12 bits (one γ), Crossover probability: 0.85, Mutation probability: 0.005, Elitism
), 0.005,
probability: 0.19. The system data for all the cases is taken from [2].
Case- 1: It consists of one thermal and one hydro plant with 12 hour load demand of two
intervals.
Interval Demand( MW) Ps (MW) PH( MW) ૃ ሺ$ ⁄ )۶܅ۻ)
2400-1200 1200 560.2651 676.328 133.525
1200-2400 1500 691.922 868.407 138.291
Table 1. Case 1: Optimal solution using simple GA
6
7. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 2, Number 1, March 2013
Interval Demand( MW) Ps (MW) PH( MW) ૃ ($/)۶܅ۻ
2400-1200 1200 551.925 685.683 133.11
1200-2400 1500 683.79 861.03 37.87
Table 2. Case 1: Optimal solution using fast GA
Variable Simple GA Fast GA Ref.[1] Ref.[2]
Generations 102 7 51 -
NET(s) 1.387 0.23 19 -
Fuel cost($/day) 169471.321 169354.8 169637.5 169637.6
γ ($/ M cubic ft) 4
1.997071 4
1.997071 9
2.02837 2.02
Table 3. Comparison of results for case 1
Tables 1 and 2 present the results of one thermal and one hydro unit with 12 hour load demand
of each interval. Table 3 presents comparison of results for case 1 using simple GA and Fast
GA. It is obvious from the results that, for same GA parameters, Fast GA gives the optimal
solution in 7 generations as compared to 102 generations taken by simple GA. Further, a
reduction in Fuel cost can also be observed.
From table 3 it can be concluded that using fast GA optimal solution can be obtained with
reduced number of generations and with minimum CPU time.
1200
GA
1000
FASTGA
800
error
600
400
200
0
11
16
21
26
31
36
41
46
51
56
61
66
71
76
81
86
91
96
101
1
6
number of generations
Figure 3. Variation of error vs number of generations for case-1
Fig. 3 shows the variation of error of the best fit chromosome with number of generations. This
clearly demonstrates the superiority of the developed Fast GA over Simple GA.
Case-2: It consists of one thermal and one hydro generating station with hourly load demand for
each interval.
Tables 4 and 5 present the optimal solution of one thermal and one hydro unit with hourly load
demand of each interval (one day) using simple GA and Fast GA respectively. The results
obtained are in agreement with the results shown in [1] and [2]. For this case Fast GA provides
better optimal solution in just four iterations. Table 6 presents the comparison of results for
case 2. Fast GA gives a fuel cost of 95809.366 $/day compared to 95847.86 $/hr. A better
optimal solution is obtained in just four generations and shows reduced CPU time.
7
9. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 2, Number 1, March 2013
23 540 311.5288 243.7549 10.8464
24 503 263.4447 239.5553 10.6550
Table 5. Case 2: Optimal solution using Fast GA
Variable Simple GA Fast GA Ref.[1] Ref.[2]
Generations 116 4 15 -
NET(s) 6.09 0.306282 52 -
Fuel cost ($/day) 95847.86 95809.366 96024.368 96024.37
γ value ($/ M cubic ft) 29.1432 29.1356 28.2144 28.17
Table 6. Comparison of results for case 2
600
500
GA
400
error
300 FAST GA
200
100
0
103
109
115
1
7
13
19
25
31
37
43
49
55
61
67
73
79
85
91
97
number of generations
Figure 4.Variation of error vs number of generations for case-2
Fig. 4 shows the variation of error of the best fit chromosome with number of generations. This
clearly demonstrates the superiority of the developed Fast GA over Simple GA.
Case-3: It consists of one thermal and two hydro generating stations with hourly load demand
for each interval.
Interval Demand(MW) PS (MW) PH1 (MW) PH2 (MW) λ ($/MWh)
1 30 2.6831 18.5743 9.1293 3.0537
2 33 3.2817 19.8234 10.3414 3.0656
3 35 3.6813 20.6571 11.1504 3.0736
4 38 4.2815 21.9094 12.3656 3.0856
5 40 4.6821 22.7453 13.1767 3.0936
6 45 5.6855 24.8389 15.2082 3.1137
7 50 6.6915 26.9379 17.245 3.1338
8 59 8.5089 30.7299 20.9245 3.1702
9 61 8.9139 31.575 21.7445 3.1783
10 58 8.3065 30.3077 20.5148 3.1661
11 56 7.9021 29.4639 19.6961 3.158
12 57 8.1043 29.8857 20.1053 3.1621
13 60 8.7113 31.1523 21.3344 3.1742
14 61 8.9139 31.575 21.7445 3.1783
15 65 9.7252 33.2678 23.3872 3.1945
16 68 10.3349 34.5398 24.6215 3.2067
17 71 10.9341 35 25.8346 3.2187
9
10. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 2, Number 1, March 2013
18 62 9.1166 31.9978 22.1549 3.1823
19 55 7.7001 29.0423 19.287 3.154
20 50 6.6915 26.9379 17.245 3.1338
21 43 5.2839 24.0008 14.395 3.1057
22 33 3.2817 19.8234 10.3414 3.0656
23 31 2.8825 18.9904 9.5331 3.0577
24 30 2.6074 18.4164 8.9761 3.0521
Table 7. Case 3: Optimal solution using simple GA
Tables 7 and 8 present the optimal solution of one thermal and two hydro units with hourly load
demand of each interval (one day) using simple GA and Fast GA respectively. The results
obtained are in agreement with the results shown in[1]& [2]. For this case FGA provides better
optimal solution in twenty iterations.
Table 9 presents the comparison of case 3 with hourly load demand of each interval. Figure 5
shows the variation of error with number of generations for case 3. From table 9 and fig.5, it is
well established that Fast GA outperforms simple GA, and results are superior incomparison
with [1] and [2]. From the results it can be concluded that FGA gives better optimal solution
with minimum number of generations and reduced CPU time.
Interval Demand(MW) PS (MW) PH1 (MW) PH2 (MW) λ ($/MWh)
1 30 2.2866 18.5912 9.513 3.0457
2 33 2.8838 19.8406 10.7268 3.0577
3 35 3.2824 20.6746 11.537 3.0656
4 38 3.8812 21.9271 12.7539 3.0776
5 40 4.2808 22.7632 13.5661 3.0856
6 45 5.2818 24.8573 15.6005 3.1056
7 50 6.2853 26.9567 17.6402 3.1257
8 59 8.0983 30.7496 21.325 3.162
9 61 8.5024 31.5949 22.1462 3.17
10 58 7.8965 30.3273 20.9147 3.1579
11 56 7.493 29.4833 20.0948 3.1499
12 57 7.6947 29.9052 20.5046 3.1539
13 60 8.3003 31.1722 21.7355 3.166
14 61 8.5024 31.5949 22.1462 3.17
15 65 9.3117 33.2882 23.7912 3.1862
16 68 9.9199 34.5605 25.0272 3.1984
17 71 10.5174 35 26.2415 3.2103
18 62 8.7046 32.0179 22.5571 3.1741
19 55 7.2915 29.0617 19.6851 3.1458
20 50 6.2853 26.9567 17.6402 3.1257
21 43 4.8811 24.019 14.7861 3.0976
22 33 2.8838 19.8406 10.7268 3.0577
23 31 2.4856 19.0075 9.9174 3.0497
24 30 2.2103 18.4317 9.358 3.0442
Table 8. Case 3: Optimal solution using Fast GA
10
11. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 2, Number 1, March 2013
Variable Simple GA Fast GA Ref. [1] Ref. [2]
Generations 100 20 39 -
NET(s) 39.403 3.02 90 -
Fuel cost($/day) 848.867 843.5 848.488 848.346
γ1 ($/ M cubic ft) 95.84 95.6 90.668 90.66
γ2 ($/ M cubic ft) 49.39 49.202 48.533 48.53
Table 9. Comparison of results for Case 3
80 GA
70
FAST GA
60
50
error
40
30
20
10
0
1
7
13
19
25
31
37
43
49
55
61
67
73
79
85
91
97
number of generations
Figure 5. Variation of error with number of generations for case-3
Case- 4: It consists of two thermal and two hydro generating stations with hourly load demand
for each interval. For this problem the chromosome length is taken as 24 bits (for each gamma
12 bits) and remaining GA parameters are same. Tables 10 and 11 present the optimal solution
of two thermal and two hydro units with hourly load demand of each interval (one day) using
GA and Fast GA respectively. The results obtained are in agreement with the results shown in
[1] and [2]. For this case Fast GA provides better optimal solution in fifteen iterations.
Interval Demand (MW) PS1 (MW) PS2 (MW) PH1 (MW) PH2 (MW) λ ($/MWh)
1 400 81.8954 130.923 170.301 22.5657 3.6095
2 300 66.2693 82.0915 151.282 3.6782 3.5313
3 250 58.5157 57.8615 141.845 0 3.4926
4 250 58.5157 57.8615 141.845 0 3.4926
5 250 58.5157 57.8615 141.845 0 3.4926
6 300 66.2693 82.0915 151.282 3.6782 3.5313
7 450 89.7496 155.467 179.861 32.0592 3.6487
8 900 161.728 380.401 267.468 119.061 4.0086
9 1230 216.083 550.260 333.624 184.760 4.2804
10 1250 219.422 560.696 337.689 188.797 4.2971
11 1350 236.201 613.129 358.111 209.077 4.381
12 1400 244.641 639.506 368.384 219.279 4.4232
11
13. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 2, Number 1, March 2013
Variable Simple GA Fast GA Ref. [1] Ref.[2]
Generations 100 15 20 -
NET(s) 2.48 0.142 92 -
Fuel cost($/day) 53020.0 53015.5 53055.712 53051.47
γ1 ($/ M cubic ft) 9.509 9.5091 9.55142 9.466
γ2 ($/ M cubic ft) 5.74 5.75 5.82 5.70
Table 12. Comparison of results for Case 4
120
100 GA
80 FAST GA
error
60
40
20
0
1
6
11
16
21
26
31
36
41
46
51
56
61
66
71
76
81
86
91
96
number of generations
Figure 6. Variation of error Vs number of generations for case-4
A comparison of SGA and FGA for this case of two thermal and two hydro units with hourly
load demand of each interval is presented in Table 12. From the fig.6 it is obvious that FGA
gives better optimal solution in just 15 generations. Figures 7 to 10 display the optimal power
dispatched by hydro and thermal generators for cases1-4 using fast GA.
Figure 7. Power dispatch over the interval for case-1 using fast GA
Figure 8. Power dispatch over the interval for case-2 using fast GA
13
14. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 2, Number 1, March 2013
Figure 9. Power dispatch over the interval for case-3 using Fast GA
Figure 10. Power dispatch over the interval for case-4 using Fast GA
REFERENCES
[1] J.Sasikala, and M.Ramaswamy “optimal gamma based fixed head hydrothermal scheduling using
genetic algorithm”, Expert Systems with Applications,Volume 37, Issue 4, April 2010, Pages 3352-
3357.
[2] J Abdul Halim, Abdul Rashid, and Khalid Mohammed “An efficient method for optimal scheduling
of fixed head hydro and thermal plants”. IEEE Transactions on Power systems Vol.6, No.2, May
1991.
[3] M.Sailaja kumari and M.Sydulu, “A Fast computational genetic algorithm for economic load
dispatch”. International journal of recent trends in engineering Vol. 1. No.1, May 2009.
[4] A. j. Wood and B. F. Woolenberg, power generation, operation and control, New York: Willey,
1984.
[5] J. Yang and N.Chen, “Short term hydro thermal coordination using muti pass dynamic
programming”. IEEE Trans on Power syst., Vol.4, pp 1055-1056, Aug, 1989.
[6] N. Jiménez, A. J. Conejo. “Short-Term Hydro-Thermal Coordination by Lagrangian Relaxation:
Solution of the Dual Problem”. IEEE Transactions on Power Systems. Vol. 14, No. 1, pp. 89-95.
February 1999.
[7] Slobodan Ruzic, Aca VuEkoviC, Nikola RajakoviC, “A Flexible approach to short term
hydrothermal coordination.Part-2 dual problem solution procedure”, IEEE Trans on. Power syst.,
Vol.11, No.3, August 1996,PP 1572-1578.
[8] Chang. H.-C. & Chen. P. H. “Hydro thermal generation scheduling package: a genetic based
approach”. IEE proceedings –C Generation Transmission and Distribution, 145(4), 451-457.1998.
[9] Goldberg, David E, “Genetic algorithms in search optimization and machine learning” , Berlin:
springer, 2000.
[10] Sinha, Nidul, Chkrabarti .R & Chatttopadhayay. “Fast evolutionary programming techniques for
short –term hydrothermal scheduling”, Electric power system research, 66(2), 97-103.
[11] Christoforos E. Zoumas, Anastasios G. Bakirtzis, John B. Theocharis, Vasilios Petridis “A Genetic
Algorithm Solution Approach to the Hydrothermal Coordination Problem” IEEE Trans. Power
Syst.,vol. 19, no.2,pp.1356-1364,May.2004.
14
15. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 2, Number 1, March 2013
Authors
Matcha Murali was born in India, on February 20, 1985. He obtained his B.Tech
Degree in Electrical and Electronics Engineering from Jawaharlal Nehru
Technological University, Hyderabad, Andhra Pradesh, India in 2006 and M.Tech
degree in Power Systems from National Institute of Technology, Tiruchirapalli,
Tamilnadu, India in 2008. Currently he is pursuing Ph.D at National Institute of
Technology, Warangal, Andhra Pradesh, India. His present areas of interest are
Transmission Pricing, Artificial Intelligence & Meta heuristic technique applications
in Power Systems, Operation and control of Power Systems and Power System
Deregulation.
Boddukuri Ramesh was born in India, on March 1, 1987. He obtained his B. Tech
degree in Electrical and Electronics Engineering from Kakatiya University, Andhra
Pradesh, India in 2008 and M.Tech degree in Power Systems Engineering in
Electrical Engineering department at National Institute of Technology, Warangal,
Andhra Pradesh, India in 2011. Currently he is working as an Assistant Engineer at
APGENCO, Andhra Pradesh, India. His areas of interest include Power System
Deregulation, Artificial Intelligence Techniques, Operation and control of Power
Systems, Transmission pricing.
Matam Sailaja Kumari was born in India, on August 30, 1972. She obtained her B.E
and M.E degrees from University College of engineering, Osmania University,
Hyderabad, Andhra Pradesh, India in 1993, 1995 and Ph.D in 2008 from National
Institute of Technology, Warangal. She joined department of Electrical Engineering,
National Institute of Technology, Warangal, India in 1997 as Lecturer. Currently she
is Associate Professor in the department of Electrical Engineering, NIT Warangal.
Her research interests are in the area of Power system Deregulation, Transmission
pricing, Application of neural networks and genetic algorithms.
Maheswarapu Sydulu obtained his B.Tech (Electrical Engineering, 1978), M.Tech
(Power Systems, 1980), Ph.D (Electrical Engineering – Power Systems, 1993), all
degrees from Regional Engineering College, Warangal, Andhra Pradesh, India. His
areas of interest include Real Time power system operation and control, ANN, fuzzy
logic and Genetic Algorithm applications in Power Systems, Distribution system
studies, Economic operation, Reactive power planning and management. Presently he
is Professor of Electrical Engineering, at National Institute of Technology, Warangal
(formerly RECW).
15