Get Thesis Now !

PROJECT TOPIC:  POWER OUTPUT MAXIMIZATION USING OPTIMAL FLOW TECHNIQUE
Department:  Electrical Electronics
AMOUNT:  20,000
FORMAT:   MS WORD
PAGES:  93
 
Get Complete Work

Abstract Preview

1.2 Statement of the problem

In any power generation and distribution system, a continuous balance must be maintained between electrical generation and varying load demand, while the system frequency, voltage levels, and security must all be kept constant and the cost of generation maintained at minimal level. Numerous classical techniques such as Lagrange, linear programming, non-linear programming and quadratic programming-based methods have been proposed for attaining these objectives.

Optimal Load Flow (OPF) problems have been solved through researches on optimization techniques based on linear or nonlinear programming algorithms which are generally limited to convex regular, multi-modal, discontinuous and not differentiable functions. These functions are being optimized based on stochastic sampling methods .

The growing interest for the application of Artificial Intelligence (AI) techniques to power engineering has introduced the potentials of using the state-of-the art methods in many problems in power systems. AI methods are very promising, still evolving and are being applied in various fields of human endeavor. The four methods that are currently perceived as affiliated in some measure with the AI field and have gained prominence as frameworks for solving different problems are Neural Networks (NN), Simulated Annealing (SA), Tabu Search (TS) and Micro-Genetic Algorithm (M-GA). M-GA, NN and TS are inspired by principles derived from biological processes while SA is derived from material sciences. These methods need not be viewed competitively, and they comprise the emergence of promise for conquering the combinatorial explosion in a variety of decision-making arenas. NN have claimed intriguing successes in pattern recognition applications, but have generally performed below expectation in optimization settings. SA, TS and M-GA have the attractive feature of assured convergence under appropriate assumptions

Low optimization found in most electricity industries in developing countries like Nigeria and the adverse effects on the overall performance of the organization ranging from production loss to high production cost.  Optimization is very important in order to ensure constant electricity supply andoptimal efficiency of electricity. Unfortunately, the Nigerian power industry for decades has been characterized by frequent power interruptions, low customer voltages, constant machine breakdowns, etc. This has negatively influenced production costs in industry. An efficient optimization model will minimize these problems. Hence, this necessitates the need to investigate the optimization of power supply in a gas plant. The optimization of the power plants and transmission lines in the electricity industry is crucial for meeting demand. Consequently, timely optimization plays a major role reducing breakdowns and avoiding expensive production shutdowns.

The study will critically evaluate the followings in Afam power plant

• Activities carried out to achieve asset availability, reliability and integrity at a minimal and acceptable cost.

• Strategies that enhance long term plant viability.

• Maintenance management programs and Business programs that ensure profitability by providing availability of the plant at optimal maintenance cost.

1.3     Aims and objectives

The main aim of this work is to design a model for the optimization of power inAfam Power plant, Using optimal flow technique and artificial neural network.

Its specific objectives are:

1. The study is aimed to minimizing objective function including investment cost of equipments and cost of energy destruction in Afam power plant.

2. To determine the performance evaluation of Afam power plant from the viewpoint of the second law of thermodynamics and thermoeconomics.

 3. To determine the various applications of thermoeconomic optimization techniques in Afam power plant.

4. To identify the location, magnitude and source of the real thermodynamic losses ('energy waste') in an energy system (energy destruction and energy losses) in Afam power plant.

5.To calculate the cost associated with the energy destruction and energy losses in Afam power plant.

6. To assess the production costs of each product (output) in an energy-conversion system that has more than one product.

7. To facilitate feasibility and optimization studies during the design phase for an energy system, as well as process improvement studies for Afam power plant.

8. To assist in decision-making procedures concerning plant operation and maintenance and allocation of research funds.

9. To compare technical alternatives.

Download