Optimal robust adaptive fuzzy synergetic power system stabilizer design

A new particle swarm optimized robust indirect adaptive power system stabilizer is developed based on recently developed synergetic control methodology. Fuzzy systems are used in an adaptive scheme to approximate the system using a nonlinear model while synergetic control guarantees robustness and the use of a chatter free continuous control law which makes the controller easy to implement. In addition the controller parameters are optimized using PSO approach. Simulation of severe operating conditions of a power system is conducted to validate the effectiveness of the proposed approach while stability is guaranteed via Lyapunov synthesis.     

Chaotic ant swarm optimization for fuzzy-based tuning of power system stabilizer

In this paper, chaotic ant swarm optimization (CASO) is utilized to tune the parameters of both single-input and dual-input power system stabilizers (PSSs). This algorithm explores the chaotic and self-organization behavior of ants in the foraging process. A novel concept, like craziness, is introduced in the CASO to achieve improved performance of the algorithm. While comparing CASO with either particle swarm optimization or genetic algorithm, it is revealed that CASO is more effective than the others in finding the optimal transient performance of a PSS and automatic voltage regulator equipped single-machine-infinite-bus system. Conventional PSS (CPSS) and the three dual-input IEEE PSSs (PSS2B, PSS3B, and PSS4B) are optimally tuned to obtain the optimal transient performances. It is revealed that the transient performance of dual-input PSS is better than single-input PSS. It is, further, explored that among dual-input PSSs, PSS3B offers superior transient performance. Takagi Sugeno fuzzy logic (SFL) based approach is adopted for on-line, off-nominal operating conditions. On real time measurements of system operating conditions, SFL adaptively and very fast yields on-line, off-nominal optimal stabilizer variables.     

A novel power swing blocking scheme using adaptive neuro-fuzzy inference system

A power swing may be caused by any sudden change in the configuration or the loading of an electrical network. During a power swing, the impedance locus moves along an impedance circle with possible encroachment into the distance relay zone, which may cause an unnecessary tripping. In order to prevent the distance relay from tripping under such condition, a novel power swing blocking (PSB) scheme is proposed in this paper. The proposed scheme uses an adaptive neuro-fuzzy inference systems (ANFIS) for preventing distance relay from tripping during power swings. The input signals to ANFIS, include the change of positive sequence impedance, positive and negative sequence currents, and power swing center voltage. Extensive tests show that the proposed PSB has two distinct features that are advantageous over existing schemes. The first is that the proposed scheme is able to detect various kinds of power swings thus block distance relays during power swings, even if the power swings are fast or the power swings occur during single pole open conditions. The second distinct feature is that the proposed scheme is able to clear the blocking if faults occur within the relay trip zone during power swings, even if the faults are high resistance faults, or the faults occur at the power swing center, or the faults occur when the power angle is close to 180°.     

Comparative seeker and bio-inspired fuzzy logic controllers for power system stabilizers

Seeker optimization algorithm (SOA) is a new heuristic population-based search algorithm. In this paper, SOA is utilized to tune the parameters of both single-input and dual-input power system stabilizers (PSSs). In SOA, the act of human searching capability and understandings are exploited for the purpose of optimization. In SOA-based optimization, the search direction is based on empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. Conventional PSS (CPSS) and the three dual-input IEEE PSSs (namely PSS2B, PSS3B and PSS4B) are optimally tuned to obtain the optimal transient performances. From simulation study it is revealed that the transient performance of the dual-input PSS is better than the single-input PSS. It is further explored that among the dual-input PSSs, PSS3B offers the best optimal transient performance. While comparing the SOA with recently reported optimization algorithms like bacteria foraging optimization (BFO) and genetic algorithm (GA), it is revealed that the SOA is more effective than either BFO or GA in finding the optimal transient performance. Sugeno fuzzy logic (SFL)-based approach is adopted for on-line, off-nominal operating conditions. On real time measurements of system operating conditions, SFL adaptively and very fast yields on-line, off-nominal optimal stabilizer parameters.     

基于最小二乘法的最优自适应电力系统稳定器

针对电力系统的强非线性特征及其运行过程中易受扰动的特点,采用基于系统辨识的自适应控制方法,通过选取合适的线性模型对典型的电力系统进行线性化处理,研究了对一般最小二乘法的改进,并结合最优自适应控制方法,设计出一种新型的最优自适应电力系统稳定器.在单机-无穷大系统中将其与传统的电力系统稳定器分别进行仿真.结果表明,在系统受到多种扰动时,这种最优自适应电力系统稳定器都能够有效地抑制低频振荡,使系统迅速恢复稳定运行,从而显示出了该最优自适应电力系统稳定器的设计具有理论研究和实际应用价值.     

Discrete-time adaptive sliding mode power system stabilizer with only input/output measurements

In this paper a discrete-time adaptive sliding mode control method is newly developed and applied to the power system stabilization problem. A controllable canonical form of state space realization is constructed using the parameters identified by the on-line recursive least squares method and the system state is estimated from the input/output measurements and the simple state transformations. The identified parameters and the estimated state are then used by the discrete-time sliding mode control, which is suitable for the digital equipment. The most important advantage of the proposed power system stabilizer (PSS) is that it is able to maintain its regulating performance with a slower sampling period than that of the conventional sliding mode PSS because it is developed in a pure discrete-time domain. Another advantage of the proposed PSS is that it needs neither a mathematical model of the power system nor the full-state measurements because they are identified through on-line identifications. Several computer simulations for the linear power system are performed to verify the performance of the proposed PSS. In the computer simulations for various circumstances which are probable in a power system are considered, such as transitions of the active and reactive powers, change of parameters of the synchronous machine, line-to-ground faults and measurement noise. As a result, a new power system stabilizer which can operate in a wide range of operating conditions and can overcome various disturbances and measurement noises is proposed.     

Damping control of inter-area oscillations in the European power system using aggregated models

In the first part of the paper, a new approach to the reduction of large dynamic network models is presented. This method is implemented in the program package PSD and is used here in an exemplary fashion to reduce the model of the European power system to a model usable for the solution of a number of problems. In the second part, the paper describes a new damping concept of inter-area oscillations in large power systems. Additionally installed second-level damping controllers are acting on the voltage regulator reference inputs of selected generators. They are using global information about the swing profiles of the inter-area oscillations to be damped. The damping controllers are designed as a H_∞-norm optimal decentral control system based on both a topological and a frequency range decomposition of the control task. The solution is demonstrated for the reduced model of the European system. Received: 30 April 2001/Accepted: 8 June 2001     

Damping of sub-synchronous resonance and low-frequency power oscillation in a series-compensated transmission line using gate-controlled series capacitor

In this paper, the impact of different gate-controlled series capacitor (GCSC) control methodologies on sub-synchronous resonance (SSR) problems of series capacitive compensated transmission lines is analyzed. The low-frequency power oscillation (LFPO) damping using GCSC also is studied. In these studies, the effect of the rating of the GCSC is also considered. Three different control methodologies are proposed: open loop, constant power, and Takagi-Sugeno (TS) fuzzy control. It is shown that the GCSC can damp both the SSR and LFPO using the constant power control (CPC) methodology. It is also shown that when the parameters of the CPC is optimized by the TS fuzzy controller, the third methodology can present a cost-effective solution for both the SSR and LFPO damping. In this work, the IEEE First Benchmark Model is employed, including a GCSC device, and the study is performed using MATLAB program.     

Evaluation of available transfer capability using fuzzy multi-objective contingency-constrained optimal power flow

In trying to determine the available transfer capability (ATC), this paper primarily sets out to develop a fuzzy logic approach to parallelizing contingency-constrained optimal power flow (CCOPF). This algorithm may be used by utilities to optimize economy interchange for severe contingencies analyzed without disclosing details of their operating costs to competitors. In fact, the ultimate objective of fuzzy multi-objective CCOPF (FMCCOPF) is to carry out the minimization of both the base case (pre-contingency) operating cost and the post-contingency correction times as conflicting but fuzzy goals. Besides, the Benders decomposition is applied to partition the fuzzy formulation with contingency constraints, which allows for post-contingency corrective rescheduling, motivated by the improvement of computational efficiency using parallel processing. The feasibility of the proposed method is comprehensively realized by a comparison with the conventional optimal power flow (OPF) and the CCOPF with respect to the same array of transactions, base case, and generator/line outages for the IEEE-30 bus system and the IEEE-118 bus system.     

A fuzzy adaptive chaotic ant swarm optimization for economic dispatch

This paper developed a fuzzy adaptive chaotic ant swarm optimization (FCASO) algorithm for solving the economic dispatch (ED) problems of thermal generators in power systems. The FCASO algorithm introduces a fuzzy system to dynamically tune the characteristic parameters ψ_d and r_i of chaotic swarm optimization (CASO). The proposed method was applied to two cases of power systems. The simulation results demonstrate the applicability and effectiveness of the proposed algorithm to the practical ED problem.     

Power system stabilizer design using Strength Pareto multi-objective optimization approach

Power system stabilizers (PSSs) are the most well-known and effective tools to damp power system oscillation caused by disturbances. To gain a good transient response, the design methodology of the PSS is quite important. The present paper, discusses a new method for PSS design using the multi-objective optimization approach named Strength Pareto approach. Maximizations of the damping factor and the damping ratio of power system modes are taken as the goals or two objective functions, when designing the PSS parameters. The program generates a set of optimal parameters called Pareto set corresponding to each Pareto front, which is a set of optimal results for the objective functions. This provides an excellent negotiation opportunity for the system manager, manufacturer of the PSS and customers to pick out the desired PSS from a set of optimally designed PSSs. The proposed approach is implemented and examined in the system comprising a single machine connected to an infinite bus via a transmission line. This is also done for two familiar multi-machine systems named two-area four-machine system of Kundur and ten-machine 39-bus New England system. Parameters of the Conventional Power System Stabilizer (CPSS) are optimally designed by the proposed approach. Finally, a comparison with famous GAs is given.     

Optimal reactive power dispatch using an adaptive genetic algorithm

This paper presents an adaptive genetic algorithm (AGA) for optimal reactive power dispatch and voltage control of power systems. In the adaptive genetic algorithm, the probabilities of crossover and mutation, p_c and p_m, are varied depending on the fitness values of the solutions and the normalized fitness distances between the solutions in the evolution process to prevent premature convergence and refine the convergence performance of genetic algorithms. The AGA applied for optimal power system reactive power dispatch is evaluated on an IEEE 30-bus power system in which the control of bus voltages, tap position of transformers and reactive power sources are involved to minimize the transmission loss of the power system.     

Damping enhancement of multimachine power system using adaptive generator control system with neural networks

The authors proposed a nonlinear adaptive generator control system with neutral networks for improving damping of power systems, and showed its effectiveness in a one-machine infinite bus test power system in a previous paper. The proposed neurocontrol system adaptively generates appropriate supplementary control signals to the conventional controllers such as the automatic voltage regulator and speed governor so as to enhance transient stability and damping of the power system. In this paper, the applicability of the proposed neurocontrol system to multimachine power systems is discussed. Digital time simulations are carried out for a 4-machine test power system, where one or several synchronous generators is equipped with the neurocontrol system. As a result, also in the multimachine power system, the proposed adaptive neurocontrol systems improve the system damping effectively and they work adaptively against the wide changes of the operating conditions and the network configuration.     

Detection and classification of single and combined power quality disturbances using fuzzy systems oriented by particle swarm optimization algorithm

In this paper, a new approach for the detection and classification of single and combined power quality (PQ) disturbances is proposed using fuzzy logic and a particle swarm optimization (PSO) algorithm. In the proposed method, suitable features of the waveform of the PQ disturbance are first extracted. These features are extracted from parameters derived from the Fourier and wavelet transforms of the signal. Then, the proposed fuzzy system classifies the type of PQ disturbances based on these features. The PSO algorithm is used to accurately determine the membership function parameters for the fuzzy systems. To test the proposed approach, the waveforms of the PQ disturbances were assumed to be in the sampled form. The impulse, interruption, swell, sag, notch, transient, harmonic, and flicker are considered as single disturbances for the voltage signal. In addition, eight possible combinations of single disturbances are considered as the PQ combined types. The capability of the proposed approach to identify these PQ disturbances is also investigated, when white Gaussian noise, with various signal to noise ratio (SNR) values, is added to the waveforms. The simulation results show that the average rate of correct identification is about 96% for different single and combined PQ disturbances under noisy conditions.     

Robust decentralized PID-based power system stabilizer design using an ILMI approach

Thanks to its essential functionality and structure simplicity, proportional-integral-derivative (PID) controllers are commonly used by industrial utilities. A robust PID-based power system stabilizer (PSS) is proposed to properly function over a wide range of operating conditions. Uncertainties in plant parameters, due to variation in generation and load patterns, are expressed in the form of a polytopic model. The PID control problem is firstly reduced to a generalized static output feedback (SOF) synthesis. The derivative action is designed and implemented as a high-pass filter based on a low-pass block to reduce its sensitivity to sensor noise. The proposed design algorithm adopts a quadratic Lyapunov approach to guarantee α-decay rate for the entire polytope. A constrained structure of Lyapunov function and SOF gain matrix is considered to enforce a decentralized scheme. Setting of controller parameters is carried out via an iterative linear matrix inequality (ILMI). Simulation results, based on a benchmark model of a two-area four-machine test system, are presented to compare the proposed design to a well-tuned conventional PSS and to the standard IEEE-PSS4B stabilizer.     

A new approach for power system online DSA using distributed processing and fuzzy logic

In this paper, a new approach for power system online dynamic security assessment, as well as a tool for calculating the proposed fuzzy dynamic security index is presented. This proposal is based on a three-stage fuzzy inference system, which composes the fuzzy dynamic security index making use of seven performance indexes herein defined. The calculation of the performance indexes is based on the results obtained through dynamic simulations of the system behaviour after each one of the credible contingencies in a given operation state. With the aim of reducing the calculation time a novel distributed processing of the dynamic simulations is also developed. High voltage systems are used to illustrate the ideas presented in the paper.     

Stabilizing control of power system using fuzzy control

This paper presents an application of fuzzy control to enhance power system stability. The proposed control consists of the controller for large disturbance (FU 1), the fuzzy controller for small disturbance (FU 2), and the fuzzy judgment mechanism (FU 3). FU 1 is determined based on the fuzzy controller [FU 1(F)] is determined according to the control rules and its input signals, i.e., speed deviation and acceleration at every sampling time of the machine. FU 2 consists of two controllers, namely, FU 2-ω and FU 2-P; FU 2-ω has the same mechanism as FU 1, while the output signal of FU 2-P is determined according to the rules together with the change of error of electrical power and terminal voltage. To obtain the optimal desired control signal during both the large and the small disturbances, the operations of FU 1 and FU 2 are judged by FU 3, where the magnitude of speed deviation is chosen as its input signal. The determined control signal is fed to AVR of the machine. The implementation of the proposed control is simple due to the small amount of calculations and required data. The effectiveness of the proposed control is demonstrated by the one-machine infinite-bus system model and very good system performance is obtained throughout all the simulations.     

A fuzzy logic supervisor for active and reactive power control of a fixed speed wind energy conversion system

In this paper, we present the design of a fuzzy logic supervisor for the control of active and reactive power which is generated by fixed speed wind energy conversion systems (WECS). First, the modelling of a three-phase induction generator driven by a horizontal axis wind turbine is described. An adjustable capacitor bank is plugged at the connection point with a Static Var Compensator (SVC), which is controlled to regulate the rms voltage. The obtained model is reduced by taking into consideration the dynamics of the system. A fuzzy logic-based supervisor is proposed in order to minimize variations of the generated active power and the stator voltage. The regulation of the rms voltage is performed while imposing a reactive power reference. The pitch angle of the turbine blades is set to obtain the maximum wind power. The obtained performances of the proposed supervisor are then presented.     

Design of a nonlinear power system stabilizer using synergetic control theory

Electromechanical oscillations of small magnitude and low frequency exist in the interconnected power system and often persist for long periods of time. Power system stabilizers (PSSs) are traditionally used to provide damping torque for the synchronous generators to suppress the oscillations by generating supplementary control signals for the generator excitation system. Numerous techniques have previously been proposed to design PSSs but many of them are synthesized based on a linearized model. This paper presents a nonlinear power system stabilizer based on synergetic control theory. Synergetic synthesis of the PSS is based fully on a simplified nonlinear model of the power system. The dynamic characteristics of the proposed PSS are studied in a typical single-machine infinite-bus power system and compared with the cases with a conventional PSS and without a PSS. Simulation results show the proposed PSS is robust for such nonlinear dynamic system and achieves better performance than the conventional PSS in damping oscillations.