Grid connected solar photovoltaic (SPV) systems are becoming more and more common due to steadily rising energy demand. The advantages of photovoltaic power generation, such as its eco-friendliness, low maintenance requirements, and lack of noise, are making it as a significant renewable energy source (RES). This framework presents the modeling and control design of PV grid tied system implemented with integrated single ended primary inductance (SEPIC) Luo converter. The main goal of this work includes investigating solar PV system behaviour and creating an effective grid connected solar power. Solar PV module tracks maximum power, with an aid of chaotic cascaded fuzzy a maximum power point tracking (MPPT) has developed. The DC voltage obtained is fed to 1Φ voltage source inverter (VSI) for conversion of AC voltage. In comparison to typical PWM control, the spectrum performance of the examined voltages is improved by adjusting the nominal duty cycle of main switch of SEPIC-Luo converter. So that PV output impedance is equivalent to DC-DC converter's input resistance. Finally, the obtained AC voltage is supplied to 1Φ grid for further applications. With less THD, an efficiency of 96% is achieved when the implementation of the suggested system is carried out using MATLAB/Simulink.

In this paper, the proposed Pulse Modulated Switching Cascaded H-bridge Multilevel Inverter (PM-SCHM) can be utilized for the reduction of Total Harmonic Distortion (THD). In high and medium power applications, multilevel inverters are commonly used. Because of the presence of harmonics in this scenario, THD has an impact on the overall efficiency of power electronics devices. The frequency analysis of a multilevel inverter is based on frequency spectra and numerical computation. A squared multilevel voltage waveform can be obtained by using an infinite range of switching harmonics. The proposed PM-SCHM scheme consists of a multi-level inverter arranged in an H-bridge sensor Cascaded manner. To compensate harmonic current magnitude in opposite direction Shunt Active Power Filter (APF) is used. A pulse modulation scheme is applied to the proposed PM-SCHM scheme for the arrangement of components using Linear Quadratic Regulator (LQR). The constructed LQR scheme utilizes a load for compensation with source voltage. This research focused on 5th order harmonics with a fundamental frequency of 50Hz. The comparative analysis of simulation results exhibited that the proposed Pulse Modulated Switching based Cascaded H bridge sensor Multilevel Inverter PM-SCHM scheme exhibits significant performance rather than the conventional technique.

Minimization of Nitrogen Oxides from Fossil Fuel Power Plants using Swarm Intelligence Technique - written by R. Anandhakumar published on 2020/02/06 download full article with reference data and citations

The unit commitment problem is the determination of deciding on and off status of on line participating generating units. This paper presents a solution to the unit commitment problem using modified water evaporation optimization algorithm. The unit commitment problem involves determining the start-up and shut-down schedules for generating units to meet the forecasted demand at the minimum cost. The commitment schedule must satisfy the other constraints such as the generating limits, spinning reserve, minimum up and down time, ramp level and individual units. The proposed algorithm gives the committed units and economic load dispatch for each specific hour of operation. Numerical simulations were carried out on ten-generator thermal unit power systems over a 24 hour period. The produced schedule was compared with several other methods. The result demonstrated the accuracy of the proposed method.

Unit commitment which is considered as a large scale, nonlinear, mixed-integer optimization problem plays a very important role in optimal operation of power systems. Solving the UC problem is a complex decisionmaking process since multiple constraints must be satisfied and a good UC solution method can substantially contribute to annual savings of production cost. In this paper combined economic environment unit commitment by using modified water evaporation optimization algorithm has been proposed. In order to show the feasibility of the proposed algorithm it has been tested with 10 unit system and the results are compared with existing method.

This paper presents Water Evaporation Optimization (WEO) algorithm for solving Economic Dispatch (ED) problem with multiple fuel options. The objective of the problem is to identify the most economical fuel for each generating unit in order to minimize the total fuel cost while satisfying system constraints. The valve point loading effects should also be considered to obtain a realistic and more accurate ED solution. The proposed WEO algorithm is based on the evaporation of a tiny amount of water molecules on the solid surfaces with different wettability which can be studied by molecular dynamics simulations. The proposed algorithm is implemented and tested on ten generating unit test system. The obtained results have shown that the proposed method is efficient for solving ED problem with multiple fuel options and favorable for implementation in large scale problems.

Purpose – The purpose of this paper is to solve the realistic problem of source maintenance scheduling (SMS) based on reliability criterion. A novel effective optimization technique is proposed to solve the problem at hand. Design/methodology/approach – The problem has been formulated as a combinatorial optimization task, with the goal of maximizing reliability by minimizing the sum of squares of the reserve loads while satisfying unit and system constraints. This paper employs a nature inspired algorithm known as Teaching Learning Based Optimization (TLBO) for solving the SMS problem based on reliability. Findings – The results reveal that optimal maintenance schedules of generating units has been obtained using TLBO algorithm with minimized values of sum of squares of reserve loads while satisfying system and operational constraints. It is also found that the inclusion of resource constraints (RC) in the model have significant effects on the objective function value which provides a deep insight of the proposed methodology. Originality/value – The contribution of this paper is that an efficient nature inspired algorithm has been applied to solve source maintenance scheduling problem in viewpoint of the planning for future system capacity expansion. The incorporation of exclusion and RC in the model makes the analysis about the impact of SMS on the system reliability more reasonable.

In order to enhance the performance and lifetime of any equipment, maintenance is essential. The major power system components including generators and transmission lines require periodical maintenance and in this regard, the present work details Integrated Maintenance Scheduling (IMS) for the secure operation. The IMS problem has been formulated as a complex optimization problem that affects unit commitment and economic dispatch schedules. Most of the methodologies adopt decomposition approaches for the solution of IMS. In this work, Teaching Learning Based Optimization (TLBO) has been used as a prime optimization tool as it has been proved to be an effective optimization algorithm when applied to various practical optimization problems and its implementation is simple involving less computational effort. The methodology has been tested on standard test systems and it works well while including generator contingency. Numerical results comparison indicates that this method is a promising alternative for solving IMS problem. (C) 2014 Elsevier B.V. All rights reserved.

Effective generator maintenance scheduling (GMS) is very important to a power utility for the economical and reliable operation of a power system. An optimal GMS increases the operation reliability, reduces power generating cost and extends the generator lifetime. The GMS problem has been formulated as a combinatorial optimisation task, with explicit and simultaneous treatment of multiple objectives: maximisation of reliability, minimisation of fuel costs and minimisation of constraint violations. Many mathematical methods and heuristic search techniques have been reported to find the optimal solution of GMS problem. However, these methods have many limitations and require valid approximations. This paper formulates a general GMS problem using a reliability criterion and a novel bio-inspired search technique, namely artificial bee colony (ABC), is applied to determine the optimal generator maintenance schedule. The performance and effectiveness of the proposed algorithm in solving the GMS problem is illustrated and compared with the recent reports on the standard 21-unit test system with two different peak load demands and crew requirements and a practical 49-unit Nigerian power system. The simulation results show that the proposed ABC is a very effective method for GMS problems.

PurposeThe purpose of this paper is to solve the maintenance management problems of generating units under the reliability criterion.Design/methodology/approachThe problem has been formulated as a combinatorial optimization task, with explicit and simultaneous treatment of multiple objectives: maximization of reliability, minimization of fuel costs and minimization of constraint violations. This paper formulates a general generator maintenance management (GMM) problem using a reliability criterion and a novel bio‐inspired search technique, namely, artificial bee colony (ABC) algorithm is applied to determine the optimal generator maintenance schedule.FindingsA novel meta‐heuristic search technique based algorithm has been developed to determine the optimal maintenance schedule of generating units to improve the system reliability.Originality/valueThe contribution of the paper is that an efficient bio‐inspired algorithm based solution technique has been developed to solve a very important problem for a power utility, i.e. the economical and reliable operation of a power system.

The goal of an optimal Generator Maintenance Scheduling (GMS) is solved in order to generate optimal preventive maintenance schedule of generating units for economical and reliable operation of a power system while satisfying system load demand and crew constraints. In this paper a Modified Artificial Bee Colony (MABC) algorithm is applied to solve the GMS optimization problem efficiently. The MABC algorithm is proposed in order to handle the system constraints effectively and obtain the better maintenance schedules. The efficacy of the proposed algorithm is illustrated with 13 generating units and 21 generating units with two different load demands. The simulation results are compared with Discrete Particle Swarm Optimization (DPSO), Modified Discrete Particle Swarm Optimization (MDPSO) and Multiple Swarms - Modified discrete Particle Swarm Optimization (MS- MDPSO) which is also population based heuristic search algorithms. From the numerical results, it is found that the MABC based approach is able to provide a better solution for GMS.

This paper presents an efficient analytical approach using Composite Cost Function (CCF) for solving the Economic Dispatch problem with Multiple Fuel Options (EDMFO). The solution methodology comprises two stages. Firstly, the CCF of the plant is developed and the most economical fuel of each set can be easily identified for any load demand. In the next stage, for the selected fuels, CCF is evaluated and the optimal scheduling is obtained. The Proposed Method (PM) has been tested on the standard ten-generation set system; each set consists of two or three fuel options. The total fuel cost obtained by the PM is compared with earlier reports in order to validate its effectiveness. The comparison clears that this approach is a promising alterna-tive for solving EDMFO problems in practical power system.

paper proposes an Artificial Bee Colony (ABC) algorithm to Generator Maintenance Scheduling (GMS) in competitive market. In the regulated market the problem of generating optimal maintenance schedules of generating units for the purpose of maximizing economic benefits and improving reliable operation of a power system, subject to satisfying system constraints. In case of deregulated market, the self-governing generation company GENCO prepares GMS aims to maximize their revenue with less consideration on reliability. The Independent System Operator (ISO) receives the maintenance schedules from GENCO and compares with ISO schedules for sanction. This paper proposes an ABC algorithm to solve the GMS in GENCO to maximize their revenue without considering expected renewal cost. Numerical examples on 4 and 32 unit power producers are utilized to demonstrate the effectiveness of the proposed ABC algorithm.

This paper proposes using the composite cost function to solve the dynamic economic dispatch problem in power system operation. The dynamic economic dispatch must not only satisfy the system load demand and the spinning reserve capacity, but some practical operation constraints of generators, such as ramp rate limits are also considered in practical generator operation. The feasibility of the proposed composite cost function solution method is demonstrated on sample power system and it is compared with other stochastic methods in terms of solution quality and computation efficiency. The numerical simulation results showed that the proposed method was indeed capable of obtaining the higher quality solutions efficiently in dynamic economic dispatch problems. Copyright (C) 2010 Praise Worthy Prize S.r.l - All rights reserved.

This paper presents a new approach via composite cost function to solve the unit commitment problem. The unit com-mitment problem involves determining the start-up and shut-down schedules for generating units to meet the fore-casted demand at the minimum cost. The commitment schedule must satisfy the other constraints such as the generating limits, spinning reserve, minimum up and down time, ramp level and individual units. The proposed algorithm gives the committed units and economic load dispatch for each specific hour of operation. Numerical simulations were carried out using three cases: four-generator, seven-generator, and ten-generator thermal unit power systems over a 24 h period. The produced schedule was compared with several other methods, such as Dynamic programming, Branch and bound, Ant colony system, and traditional Tabu search. The result demonstrated the accuracy of the proposed method.