During the planning and operating stage of the power system, system operators have the main focus on the secure and optimal use of the system components. Op- timal power flow (OPF) tool provides the solution under both constraints. In OPF method, production cost function is minimized as objective function via optimal values of the control variables, subject to the system security and limits of the sys- tem components. OPF constraints are divided into two categories inequality and equality constraints.
TSSCOPF is, however, a nonlinear optimization problem with both algebraic and differential equations in the time domain. It considers optimal and stable operations simultaneously. As a special requirement of the system, the initial or feasible op- erating point should be able to withstand the disturbance and can move to a new stable equilibrium state after the clearance of the disturbance without disturbing the equality and the inequality constraints. Due to huge dimension of TSSCOPF problem (especially, for system dealing with detailed machine models), it is really a
tough exercise to deal with this type of problem. For a given power system configu- ration, although the number of possible contingencies are numerous, there are a few critical contingencies that may cause instability. After analyzing and filtration, the major contingency is selected and the TSSCOPF procedure is applied to find out the optimal operating point.
In the past, classical optimization techniques such as interior point method [119], and Linear Programming (LP) [120] were employed for Transient Stability Con- straints Optimal Power Flow (TSCOPF) solution. These techniques have many limitations and some drawbacks. They need an acceptable starting point that is close to the solution in order not to be stuck in local optimum and have poor con- vergence. In Ref. [120], a linear programming (LP) based computational procedure was developed to solve an algebraic NP problem. Therefore, many heuristic opti- mization techniques have recently become more and more attractive for researcher to obtain solution of TSCOPF problem. Some of them are Particle Swarm Op- timization (PSO) [121], Genetic Algorithm (GA) [122], and Differential Evolution (DE) [123]. However the most important task is to incorporate in OPF operation both transient stability and security constraints subject to the severe disturbance.
Therefore in this thesis an attempt has been made to develop a meta heuristic based solution of the TSSCOPF to enhance the transient staility and static security of power systems. An OPF problem has been formulated as a constrained optimiza- tion problem by incorporating different constraints i.e. transmission, generation and stability constrains. A variant of a new meta heuristic algorithm Grey Wolf Opti- mizer (GWO) namely, Intelligent Grey Wolf Optimizer (IGWO) has been proposed and is employed to reschedule the generator with minimum fuel cost, such that the transient severity is minimized. The proposed approach is then tested on IEEE 30- bus 6-generator and 39-bus 10-generator system. In order to prove the accuracy of the IGWO algorithm, the results are compared with other state-of-the-art algorithms namely GA [121], PSO [121], ABC [124], CABC [124], WOA [125] and CWOA [125]
algorithms. Efforts have been made to enhance the transient stability and security under the current operating condition subjected to optimal power generation.
Figure 1.1 represent the thesis structure. The thesis is divided into seven chapters, this chapter presents brief introduction of the terminologies used and research work carried out in this thesis with description of the research motivation.
Figure1.1:Thesisstructure
Chapter 2 gives the detailed literature survey of the existing methods of the power system contingency analysis, transient stability analysis & its control methods and stability enhancement methods along with the limitations of these existing methods.
Finally the research objectives framed are presented based on the literature survey.
Chapter 3 describes the development of an improved version of Grey Wolf Opti- mizer (GWO) named as Intelligent Grey Wolf Optimizer (IGWO). The details of the development along with the benchmarking of the proposed variant on different type of functions such as multi-modal, unimodal and fixed dimension are also presented.
Chapter 4 presents concept of contingency analysis of power system and the proposed ANN-based approach for ranking and screening. Simulation results and the performance evaluation of the proposed methodology for various test systems are presented.
Chapter 5 describes the proposed method for the real-time transient stability assessment. It also presents the proposed method for coherency identification, and coherency based preventive control technique. Applicability or proposed methods on standard IEEE test systems are also discussed.
Chapter 6 presents the design and implementation of Improved Grey Wolf Opti- mization (IGWO) for the TSSCOPF. The IGWO is implemented in order to resched- ule the generator with minimum fuel cost such that the stability is maximized. In order to identify the efficiency of the proposed IGWO algorithm, the results obtained are compared with the other state-of-the-art algorithms.
In chapter 7 finally conclusions of the research work are presented along with the description of the future scope of this research work.
Literature Survey
[This chapter begins with the detailed literature survey of the existing methods of the power system contingency analysis, transient stability analysis & its control meth- ods and stability enhancement methods along with the limitations of these existing methods. Finally the research objectives framed are presented based on the literature survey. ]
P
ower system failures triggered by instability cause considerable loss of power sup- ply over large areas. Major blackouts may affect millions of consumers for several hours [34]. The recovery of normal operational conditions is a complicated pro- cess which requires a lot of time and efforts from control room personnel. For this reason, special attention is paid in providing sufficient stability margin in power systems both at the stage of network planning and at operational level. However it is not possible to prevent power system from collapse for all possible contingencies under all operating conditions. Moreover, unforeseen disturbance may occur in the system leading to the system failure. With the help of Phasor Measurement Units (PMUs) and Wide Area Management System (WAMS), it is now possible to mea- sure and transmit phase and magnitude of the desired quantity to the control center from remote locations at very high speed and frequency. With this information it is possible to develop methods for analyzing the power system stability of the system in real-time and initiate the control action whenever the system is deemed to be unstable following a large disturbance.15
Static Security Assessment
Transient Stability Assessment
Power System Stability enhancment
methods Power System Stability
Contingency
Analysis/Assessment Online Contingency Classification
Online Monitoring
and Assessment Coherency Identification
TSSCOPF Problem
Formulation Meta-heuristic Algorithms
Figure 2.1: Study of power system stability
A lot of investigation work has been available in the field of power system stability and its enhancement, which has led to the development of various methodologies and approaches to deal with the problem. Figure 2.1 represent the main domains of the power system stability for the study and research purposes. For planning and operation of modern power system for specially stability point of view, there is a need to study the important issues like steady state security, transient stability and their enhancement methods. A brief literature survey related to the research work in these issues is presented in the following sections and based on the critical review of the literature, the research objectives are formulated.