Search

• To propose sooty-tern optimization (STO) for designing PSS parameters of multi-machine power system. • The performance of STO is evaluated using eigenvalue analysis, simulation results and performance indices. • The results are matched with other powerful optimization techniques. • Robustness is verified on hidden operating cases also under critical disturbances. This paper reports a comparative study among four bio-inspired meta-heuristic techniques i.e. Sooty-Tern Optimization (STO), Grey Wolf Optimization (GWO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) to tune the robust Power System Stabilizer (PSS) parameters of the multi-machine power system. These approaches are successfully tested on two bench-mark systems: sixteen-machine, sixty-eight-bus New England Extended Power Grid (NEEPG) and three-machine, nine-bus Western System Coordinating Council (WSCC). The efficacy of planned PSS via STO and GWO is validated by extensive non-linear simulations, eigenvalue analysis, and performance indices for numerous operating conditions under decisive perturbations, and outcomes are matched with those of GA and PSO techniques. In addition, the robustness is also tested for these algorithms. The results indicate that the PSS design using STO and GWO improves the small-signal stability and damping performance for mitigating inter-area and local area modes of low-frequency oscillations compared to GA and PSO. [ABSTRACT FROM AUTHOR]

How to suppress low frequency oscillations effectively in inter-connected power systems has been a challenging problem for many years. The problem may be solved by accurate control of absolute rotor angles. The structure of a rotor angle controller is shown in this paper. The input of the controller is the absolute rotor angle measured by local phasor measurement unit (PMU). The controller ensures that this rotor angle remains unchanged in rotating coordinate system determined by pulse per second (PPS). This way, zero steady state error control of angle and frequency can be achieved at the same time. Since the rotor angle and speed remain constant under normal conditions, it is possible to get information of all kinds of oscillations through local measurement after disturbance. Analysis in this paper also shows that the stability margin and the characteristic of the damping torque provided by the angle controller can be improved significantly after a compensation block is added. When used together with power system stabilizer (PSS), the angle controller can suppress inter-area oscillations effectively without help of any remote measurement. [ABSTRACT FROM AUTHOR]

This paper presents the Parallel Vector Evaluated Improved Honey Bee Mating Optimization (VEIHBMO) as a novel multi objective technique to obtain a set of optimal Power System Stabilizers (PSSs) parameters. It includes a feedback signal of a remote machine and local and remote input signal ratios for each machine in a multi-machine power system under various operating conditions. This goal is formulated as a multi objective optimization process with two competing and non-commensurable fitness functions. The objectives considered in this paper are the time domain based integral square time of square error (ISTSE) and eigenvalues based comprising the damping factor. The effectiveness of the proposed method is demonstrated on 4-machine two-area and 16-machine five-area power system under different loading conditions. The system performance is assessed through the time multiplied absolute value of the error (ITAE), eigenvalues and figure of demerit (FD) analysis performance indices. The simulation results show that the performance of the proposed stabilizer is comparable to that which could be obtained by the conventional design, but without the need for the estimation and computation of the external system parameters. [ABSTRACT FROM AUTHOR]

• A novel energy storage system based oscillation damping scheme; • A novel PSS/POD node selection approach based on controllability Gramian; • A unit load energy function averaging approach based ESS sizing; • Enhanced system performance under high wind power penetration. This paper proposes a novel power oscillation damping approach for transient stability enhancement of a power system with high penetration of wind power, where the energy storage system (ESS) is utilized to assist the damping of power oscillation with power system stabilizer and power oscillation damper. First of all, the ESS-based power system stabilizer and power oscillation damper structures are proposed to enhance the flexibility of power input and output of the wind turbines and the synchronous generators. Subsequently, the effect of installing ESSs for the enhancement the SGs and WTs' damping torque is analyzed. In the meantime, we proposed an optimal allocation approach to decide the installation nodes of the ESSs by minimizing the Euclidean distance of the system's uncontrollable oscillation modes to its controllability Gramian, along with the optimal sizing approach of ESSs via a novel unit load energy function averaging approach. Simulations are conducted to validate the effectiveness of the proposed scheme, and the superior performance of the proposed approach is also proven via comparative studies. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a new framework for the valuation of small signal stability as an ancillary service through Power System Stabilizers (PSSs). PSSs can improve small signal stability and simultaneously reduce the generation costs in a competitive market. This saving could be treated as a source to compensate the service provided by PSSs. This paper proposes an allocation approach to compensate the PSSs, proportional to their effectiveness in overall system small signal behavior. NSGA-II approach is used to solve the resulting multi objective optimization problem. A test system is used to represent the effectiveness of the proposed method. [Copyright &y& Elsevier]

Abstract: A new robust power system stabilizer (PSS) design using Quantitative Feedback Theory (QFT) for damping electromechanical modes of oscillations and enhancing power system stability is proposed in this paper. The design procedure is carried out on a multi-input–multi-output (MIMO), non-minimum phase and unstable plant. A multi-machine electric power system with system parametric uncertainties is considered as a case study. To show the effectiveness of the QFT technique, the proposed method is compared with a conventional PSS (CPSS) whose parameters are tuned using the classical lead-lag compensation and genetic algorithms. Several nonlinear time-domain simulation tests indicate that the suggested control scheme is robust to the changes in the system parameters and also to successfully reject the disturbances. The results also show that the performance of the QFT method given in this paper is more desirable than CPSS and genetic algorithm (GA). [Copyright &y& Elsevier]

Abstract: This paper presents the simulation and the experimental validation of the designed robust power system stabilizer (RPSS) to stabilize a linearized uncertain power system using Glover–McFarlane’s H ∞ loop shaping design procedure. Guidance for setting the feedback configuration for loop shaping, weighing functions selection and synthesis are also presented. The efficiency of the designed controller is simulated using Matlab/Simulink and tested by implementing on real time environment using dSPACE work stations DS1005 and DS1104. The real time experimental results of RPSS are compared with that of the conventional power system stabilizer (CPSS) for a three phase fault. Also, the real time simulation results of RPSS are compared with the off-line simulation results of RPSS, thus validating the simulation results with the experimental results. Justification of robustness is also presented by considering three different operating points. The proposed method presented in this paper shows the effectiveness of the RPSS in damping the power system oscillations. [Copyright &y& Elsevier]

• The relationship between the generalized Nyquist curve and characteristic loci is used to obtain a new stability margin. • The analytical expression of stability margin reveals the relationship between stability margin and controllers. • Hydro unit tends to contribute negative damping to the ultra-low frequency oscillation. • Thermal unit tends to contribute positive damping to the ultra-low frequency oscillation. A characteristic locus method developed from the celebrated generalized Nyquist criterion is proposed for power systems small-disturbance stability analysis and control design in this paper. First, a loop transformation is introduced to re-formulate the familiar Heffron-Phillips model. The new model has a feedback control structure so that the generalized Nyquist criterion is applicable when concluding the stability of the closed-loop system. Subsequently, the relationship between generalized Nyquist curve and characteristic loci is utilized to acquire a new stability margin. The effectiveness of the stability margin is demonstrated using Cassini Oval Theorem. An analytical expression of the stability margin is derived as well. It is convenient to quantify the effects of control loops on stability margin by applying this expression. Moreover, the analytical relationship between stability margin and frequency responses of controllers provides information for the design of the controllers. The utility of the proposed method is confirmed through case studies. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient optimization algorithm named chaotic teaching–learning algorithm (CTLA), to solve multimachine power system stabilizers design problem. The original teaching learning algorithm as competitive to other optimization algorithms, used two phases to proceed to the global optimal solution: ‘teacher phase’ and ‘learner phase’. However, during the second phase an adequate interaction between the teacher and the learners in entire search space are not guaranteed and the algorithm may be trapped in local optima. Thus, in the proposed CTLA a new phase named “chaotic phase” is added in order to overcome this drawback. The performance of the CTLA is investigated by using a set of benchmark functions. To demonstrate the effectiveness of the proposed algorithm in power systems, the conventional lead-lag power system stabilizers (PSSs) are tuned for: three machines nine bus system (WSCC) and the ten machine thirty-nine bus New England power systems. The performance of the proposed CTLA-based PSS (CTLAPSSs) under different loading conditions and disturbances is investigated through eigen-value analysis, non-linear time domain-simulations and some performance indices. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive fuzzy power system stabilizer is developed based on robust synergetic control theory and terminal attractor techniques. The main contribution consists in making the dynamic system insensitive to parameters variation. This aim is achieved using a new synergetic controller design such that power system states start, evolve and remain on a designer chosen attractor toward the equilibrium point therefore avoiding transient mode. Rendering the design more robust, fuzzy logic systems are used to approximate the unknown power system dynamic functions without calling upon usual model linearization and simplifications. Based on an indirect adaptive scheme and Lyapunov theory, adaptation laws are developed to make the controller handle parameters variations due to the different operating conditions occurring on the power system and to guarantee stability. The performance of the proposed stabilizer is evaluated for a single machine infinite bus system and for a multi machine power system under different type of disturbances. Simulation results show the effectiveness and robustness of the proposed stabilizer in damping power system oscillations under various disturbances and better overall performance than classical PSS and some other types of power stabilizers. [ABSTRACT FROM AUTHOR]

In this paper, authors present a method to select the optimal combination between the type of the power system stabilizer (PSS) and the location of a Superconducting Fault Current Limiter (SFCL) in an Electrical Power Grid (EPG) to improve the transient stability in case of fault. The angular separation between generators rotors when different types of short-circuit appears in the EPG is used as the main criterion of the proposed method to select this optimal combination. To complete the study, the optimal resistive value of the SFCL is evaluated to improve the global stability of the EPG. Results show that an optimal combination between the type of PSS, the location of SFCL in the EPG and the resistive value of SFCL exists in order to obtain the smallest variation of the angular separation of generators rotors, i.e. the best stability of the power system in case of fault. [ABSTRACT FROM AUTHOR]

In this paper, a new saturated control design for uncertain power systems is proposed. The developed saturated control scheme is based on linear matrix inequality (LMI) optimization to achieve prescribed dynamic performance measures, e.g., settling time and damping ratio. In this design, the closed-loop poles are forced to lie within a desired region. The proposed design provides robustness against system uncertainties. The simulation results of both a single machine infinite bus and a multi-machine power systems are given to validate the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid approach for tuning and placement of power system stabilizers (PSS) in multi-machine power systems is provided with the aim of reducing low-frequency oscillations (LFO) and improving power system dynamic stability with wide range of changes in system parameters and operating point of the system. PSS parameters are adjusted using Particle Swarm Optimization algorithm (PSO), and using Takagi-Sugeno (TS) fuzzy, optimal location for the PSS is determined. The employed fuzzy system has two inputs, the real part and the damping coefficient of network eigenvalues. The results of test on a grid four machine-two area sample, show optimal performance of the proposed method in improving system stability and reducing low-frequency oscillations of local and inter-area modes. [ABSTRACT FROM AUTHOR]

In this paper, a type-2 fuzzy logic power system stabilizer with differential evolution algorithm is proposed. As an extension of type-1 fuzzy logic theory, type-2 fuzzy logic theory can effectively improve the control performance by uncertainty of membership function especially when we have to confront with less expert knowledge or unpredicted external disturbances. The corresponding parameters and rule base of type-2 fuzzy logic power system stabilizer are optimally tuned by using differential evolution algorithm for multi-machine power system. Through simulation under different operational conditions, the results demonstrate the effectiveness of the proposed approach for damping the power system electromechanical oscillations. [ABSTRACT FROM AUTHOR]

The ever-increasing size and complexity of the present-day power systems puts a higher demand on the power system stabilizer for low-frequency oscillation damping. Alternate design methods of the conventional power system stabilizer or control structures to replace it, proposed over many decades, have not been widely adopted for practical use due to the impediments like non-linear and complex structure, and lack of fast adjustments. This paper proposes an improved neuro-adaptive control scheme, based on online system identification and simultaneous control, to replace conventional power system stabilizer. A simple, linear neural identifier, with a few adjustable connection weights, is used which ensures minimal computational burden and fast learning capability. The parameters of the controller, designed using optimal control with one-sample-ahead output prediction, are related to the identifier parameters. An adaptive learning rate, derived using Lyapunov stability theorems, guarantees stability of convergence of the learning algorithm as well as an optimal speed of convergence. Improved oscillation-damping performance over a wide range of operating conditions, in comparison with a well-designed conventional power system stabilizer and some other alternative controllers reported in literature, has been validated through simulation studies carried out on two different power systems. It is demonstrated that a simple linear neural identifier, which approximates a local linear model of a system, by adjustment of its parameters online, is faithfully able to track the varying dynamics of the system. Hence, it is not always appropriate to use complex structures and non-linear activation functions in neural network-based adaptive control applications which may pose difficulties in the implementation of these controllers. Also, the proposed control scheme is model-free, easier to implement and needs local measurements only. • Design of a neural-network-based control that exhibits excellent oscillation damping. • Better damping characteristics as compared to some other reported PSS designs. • Design of a simple neural-identifier that adjusts its parameters online. • A linear identifier tracks the complex and varying dynamics of the system online. • Use of few weights reduces computational burden and favour its practical realization. • An adaptive learning rate is derived that guarantees stability of convergence. • The control scheme is model-free and needs local measurements only. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a novel Fuzzy Gravitational Search Algorithm (FGSA) for optimal design of multimachine power system stabilizers (PSSs). The FGSA technique is characterized as simple, robust and capable to solve difficult combinatorial optimization problems. For achieving optimal tuning of PSS parameters, the problem is formulated as an optimization problem with the time domain-based objective function over a wide range of operating conditions and is solved by the proposed FGSA technique. The performance of the proposed FGSA based-PSS design is validated for two multimachine systems: a 3-machine 9-bus system and a 10-machine 39-bus. The effectiveness and robustness of proposed method is demonstrated using many performance indices. The results prove that the proposed FGSA assures a well damping to the electromechanical modes of oscillations for a wide range of system operation conditions. The superiority of the proposed method is proved compared to different optimization methods. [Copyright &y& Elsevier]

Abstract: In this paper coordination scheme to improve the stability of a power system by optimal design of power system stabilizer and static synchronous series compensator (SSSC)-based controller is presented. Time delays owing to sensor and signal transmission delays are included in the design. The coordinated design problem is formulated as an optimization problem and hybrid bacteria foraging optimization algorithm and particle swarm optimization (hBFOA–PSO) is employed to search for the optimal controller parameters. The performance of the proposed controllers is evaluated for both single-machine infinite-bus power system and multi-machine power system. Results are presented over a wide range of loading conditions and system configurations to show the effectiveness and robustness of the proposed coordinated design approach. It is observed that the proposed controllers provide efficient damping to power system oscillations under a wide range of operating conditions and under various disturbances. Further, simulation results show that, in a multi-machine power system, the modal oscillations are effectively damped by the proposed approach. [Copyright &y& Elsevier]

Abstract: To ensure the small-signal stability of a power system, power system stabilizers (PSSs) are extensively applied for damping low frequency power oscillations through modulating the excitation supplied to synchronous machines, and increasing interest has been focused on developing different PSS schemes to tackle the threat of damping oscillations to power system stability. This paper examines four different PSS models and investigates their performances on damping power system dynamics using both small-signal eigenvalue analysis and large-signal dynamic simulations. The four kinds of PSSs examined include the Conventional PSS (CPSS), Single Neuron based PSS (SNPSS), Adaptive PSS (APSS) and Multi-band PSS (MBPSS). A steep descent parameter optimization algorithm is employed to seek the optimal PSS design parameters. To evaluate the effects of these PSSs on improving power system dynamic behaviors, case studies are carried out on an 8-unit 24-bus power system through both small-signal eigenvalue analysis and large-signal time-domain simulations. [Copyright &y& Elsevier]

Abstract: Genetic Algorithms (GAs) are the most popular used methods of the evolutionary algorithm family. GA effectiveness highly depends on the choice of the search space range for each parameter to be optimized. The search space being a set of potential solutions may contain the global optimum and/or other local optimums. Being often a problem-based experience, a bad choice of search spaces will result in poor solutions. In this paper, a novel optimization approach based on GAs is proposed. It consists in moving the search space range during the optimization process toward promising areas that may contain the global optimum. This dynamic search space allows the GA to diversify its population with new solutions that are not available with fixed search space. As a result, the GA optimization performance can be improved in terms of solution quality and convergence rate. The proposed approach is applied to optimal design of multimachine power system stabilizers. A 16-machine, 68-bus power system is considered. The obtained results are evaluated and compared with other results obtained by ordinary GAs. Eigenvalue analysis and nonlinear system simulations demonstrate the effectiveness of the proposed approach in damping the electromechanical oscillations and enhancing the system dynamic stability. [Copyright &y& Elsevier]

Abstract: Conventional power system stabilizer (CPSS) has been widely used as a supplementary controller to damp out the low frequency oscillations. The tuning of CPSS parameters for nonlinear power systems in a robust way in order that the overall system stability can be improved is a major drawback of CPSS. Several meta-heuristic optimization techniques have been tried out to overcome this shortcoming. This paper presents a new technique named cultural algorithms (CAs) to tune the PSS parameters. This technique is robust and computationally efficient as compared with other meta-heuristic algorithms. Simulations are carried out on two typical multi-machine electric power systems. The results clearly verify that the proposed method improves the dynamic stability of the system, especially when the operating point changes. [Copyright &y& Elsevier]

Abstract: This paper presents a novel and efficient method for the optimal tuning and placement of power system stabilizer (PSS). The proposed modified particle swarm optimization (MPSO) integrates the particle swarm optimization with passive congregation (PSOPC) and the chaotic sequence. Passive congregation helps each swarm member in receiving a multitude of information from other members and thus decreases the possibility of a failed attempt at detection or a meaningless search. In addition, the chaotic sequence concept is introduced to improve the global searching capability and prevent the premature convergence due to local minima. The proposed optimization procedure handles the problem-specific constraints, in this case it is related to low frequency oscillations in power systems and PSS, using a penalty function. Based on this, tuning and placement of PSS over a wide range of system configurations is formulated as a multi-objective function where the main objective is the aggregation of the three objectives related to the damping ratio and damping factor, and the number of PSS. The robustness of the proposed PSS tuning technique is verified on a multi-machine power system under different operating conditions. The performance of the proposed MPSO is also compared to the PSOPS and PSO, genetic algorithm through eigenvalue analysis, nonlinear time-domain simulation and statistical tests. Finally, the proposed method is applied to find out the best candidate machines to be equipped with PSSs. The obtained results showed that the new method can find the best placements and the optimum PSSs parameters simultaneously with an excellent global damping performance. [Copyright &y& Elsevier]

Abstract: Excitation supplementary control is used in power systems to enhance the damping of low frequency oscillations due to the mechanical modes of the generators. Traditionally, frequency deviation is fed back to the exciter through phase lag-lead compensators to form the supplementary control. This supplementary control is known as power system stabilizer (PSS). An optimal power system stabilizer (OPSS) based on linear quadratic regulator design and utilizing the conventional phase lag-lead structure of PSS is presented in this paper. Furthermore, a suboptimal power system stabilizer (SOPSS) is proposed by neglecting the coupling gains between machines and feeding back only the speed deviation and the PSS states. The OPSS and SOPSS are compared with the conventional phase lag-lead power system stabilizer and simulation results of several power system examples are presented. [Copyright &y& Elsevier]

Abstract: This paper presents the design of local decentralized power system stabilizer (PSS) controllers, using selected suitable remote signals as supplementary inputs, for a separate better damping of specific inter-area modes, for large-scale power systems. System identification technique is used for deriving lower order state-space models suitable for control design. The lower-order model is identified by probing the network in open loop with low-energy pulses or random signals. The identification technique is then applied to signal responses, generated by time-domain simulations of the large-scale model, to obtain reduced-order model. Lower-order equivalent models, thus obtained, are used to design each local PSS controller separately for each of the inter-area modes of interest. The PSS controller uses only those local and remote input signals in which the assigned single inter-area mode is most observable and is located at a generator which is most effective in controlling that mode. The PSS controller, designed for a particular single inter-area mode, also works mainly in a frequency band given by the natural frequency of the assigned mode. The locations of the local PSS controllers are obtained based on the amplitude gains of the frequency responses of the best-suited measurement to the inputs of all generators in the interconnected system. For the selection of suitable local and supplementary remote input signals, the features or measurements from the whole system are pre-selected first by engineering judgment and then using a clustering feature selection technique. Final selection of local and remote input signals is based on the degree of observability of the considered single mode in them. To provide robust behavior, H ∞ control theory together with an algebraic Riccati equation approach has been applied to design the controllers. The effectiveness of the resulting PSS controllers is demonstrated through digital simulation studies conducted on a sixteen-machine, three-area test power system. [Copyright &y& Elsevier]

Abstract: This paper deals with a design of two level power system stabilizer. Former one is conventional, while the latter level is designed using one of the following two methods: fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS). The main function of the conventional level is to stabilize unstable or poorly stable systems, while the second – which is designed using (FIS) or (ANFIS) – improves the total response in order to achieve required results. This technique is applied on a three machine nine bus power system. As expected, the Adaptive neuro-fuzzy inference system (ANFIS) damps out the low frequency oscillations in the better manner than fuzzy inference system (FIS). [Copyright &y& Elsevier]

Abstract: In this paper, three particle swarm optimization (PSO) based power system stabilizers (PSSs) are developed for three power systems. The system under study here is a power pool consisting of 3 power systems. System I represents the Egyptian power system, system II represents the Jordan and Syrian power systems, and system III for the Libyan power system, which are originally self standing and completely independent systems. As a matter of fact each of them should equipped with its own PSS. For this reason this work is started by designing an optimum power stabilizer for each of them standing alone. After which, the developed PSSs are firstly installed one at a time. Then the three PSSs are installed together in the interconnected power system and their effect on its dynamic performance is studied. As a test for stabilization efficiency, the detailed power system model is subjected to a forced outage of a 600-MW generator, which is the biggest unit in the pool, when it is fully loaded. This outage results in loosing of about 3% of the spinning capacity of system I and about 2% of the spinning capacity of the whole interconnected system. The obtained results show an improvement in the power pool performance accompanied with an improvement in the inter-area oscillation. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: A robust coordination scheme to improve the stability of a power system by optimal design of multiple and multi-type damping controllers is presented in this paper. The controllers considered are power system stabilizer (PSS) and static synchronous series compensator (SSSC)-based controller. Local measurements are provided as input signals to all the controllers. The coordinated design problem is formulated as an optimization problem and differential evolution (DE) algorithm is employed to search for the optimal controller parameters. The performance of the proposed controllers is evaluated for both single-machine infinite-bus power system and multi-machine power system. Nonlinear simulation results are presented over a wide range of loading conditions and system configurations to show the effectiveness and robustness of the proposed coordinated design approach. It is observed that the proposed controllers provide efficient damping to power system oscillations under a wide range of operating conditions and under various disturbances. Further, simulation results show that, in a multi-machine power system, the modal oscillations are effectively damped by the proposed approach. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: In this paper, a novel Combinatorial Discrete and Continuous Action Reinforcement Learning Automata (CDCARLA) based approach for optimal design of multimachine power system stabilizers (PSSs) is presented. The proposed CDCARLA based design approach is a combined procedure of two optimization stages in discrete and continuous spaces for fast convergence and high optimization efficiency. The potential of the proposed approach in seeking the optimal settings of the widely used conventional lead-lag PSSs’ parameters is investigated and assessed in multimachine power systems. The performance and robustness of the proposed CDCARLA based PSS is evaluated under different power system disturbances. The performance of the proposed stabilizer is also compared with other stabilizers reported in the literature including the multi-band PSSs for a two-area four-machine power system. Simulation results show the effectiveness and robustness of the proposed CDCARLA PSS in damping local and inter area oscillation modes under various disturbances, and confirm its superiority in comparison with other types of PSSs. [Copyright &y& Elsevier]

Abstract: The tuning of the PSS parameters for a multi-machine power system is usually formulated as an objective function with constraints consisting of the damping factor and damping ratio. A novel mixed-integer ant direction hybrid differential evolution algorithm, called MIADHDE, is proposed to solve this kind of problem. The MIADHDE is improved from ADHDE by the addition of accelerated phase and real variables. The performances of three different objective functions are compared to the MIADHDE in this paper. Both local and remote feedback signals of machine speed deviation measurements can be selected as input signals to the PSS controllers in the proposed objective function. The New England 10-unit 39-bus standard power system, under various system configurations and loading conditions, is employed to illustrate the performance of the proposed method with the three different objective functions. Eigenvalue analysis and nonlinear time domain simulation results demonstrate the effectiveness of the proposed algorithm and the objective function with a remote signal. [Copyright &y& Elsevier]

• New algorithm particle swarm optimization with oscillatory inertia factor with exponential decay. • Simultaneous adjustment of the AVR and PSS parameters through the PSO-OED optimization technique. • Better adjustment of the AVR and PSS providing a better dynamic performance and transient system response. • New objective function capable of analyzing the performance of several generators simultaneously. This paper presents a new algorithm for simultaneous tuning of the optimal parameters of Automatic Voltage Regulator (AVR) and the Power System Stabilizer (PSS) using a new technique of Particle Swarm Optimization with Oscillating Exponential Decay (PSO-OED). The proposed algorithm was applied both the Single-Machine Infinite-Bus (SMIB) Power System and the 9-Bus Multi-Machine Power System. The results of the simulation show that the proposed method is efficient and guarantees an improvement in the dynamic stability and convergence rate of the simulations. [ABSTRACT FROM AUTHOR]

This paper presents a systematic approach for designing a self-tuning power system stabilizer (PSS) based on artificial neural network (ANN). An ANN is used for self-tuning the parameters of PSS in real-time. The nodes in the input layer of the ANN receive generator terminal active power (P), reactive power (Q), and voltage (Vt), while the nodes in the output layer provide the optimum PSS parameters, e.g. stabilizing gain (KSTAB), time constants (T1 and T2). A new approach for the selection of number of neurons in the hidden layer has been proposed. Investigations reveal that the dynamic performance of the system with self-tuning PSS based on ANN (ST-ANNPSS) is quite robust over a wide range of loading conditions and equivalent reactance, Xe. [Copyright &y& Elsevier]

Power system stability enhancement via robust coordinated design of a power system stabilizer and a static VAR compensator-based stabilizer is thoroughly investigated in this paper. The coordinated design problem of robust excitation and SVC-based controllers over a wide range of loading conditions and system configurations are formulated as an optimization problem with an eigenvalue-based objective function. The real-coded genetic algorithm is employed to search for optimal controller parameters. This study also presents a singular value decomposition-based approach to assess and measure the controllability of the poorly damped electromechanical modes by different control inputs. The damping characteristics of the proposed schemes are also evaluated in terms of the damping torque coefficient over a wide range of loading conditions. The proposed stabilizers are tested on a weakly connected power system. The non-linear simulation results and eigenvalue analysis show the effectiveness and robustness of the proposed approach over a wide range of loading conditions. [Copyright &y& Elsevier]

An integrated tabu–fuzzy knowledge based controller applied to enhance the performance of power system stabilizer (PSS) is presented in this paper. The fuzzy knowledge based controller (FKBC) has been developed to perform the function of a PSS and to provide a supplementary signal to the excitation system of the synchronous generator. The method used for storing and representing the fuzzy rules is called the fuzzy associative memory (FAM) matrix. The well-defined FAM determines the performance of the FKBC and hence the tabu search algorithm is proposed and applied to determine the construction and optimization of the FAM. The controller has been tested in the case of single machine infinite bus and multi-machine systems for various types of disturbance. To show the effectiveness of the proposed controller, the comparison with the conventional PSS is presented. [Copyright &y& Elsevier]

This paper is devoted to the coordination of secondary voltage control and adaptive parameters resetting of the power system stabilizer, in order to increase stability margins in real time operation. Secondary voltage control and the power system stabilizer are two control loops, which affect the same system parameter on different bases — that parameter is the voltage set-point of the automatic voltage regulator. It was found that their effects were complementary. In that way, through the proper coordination of actions of these two control loops open up a wide range of possibilities for ensuring the stability of bulk power systems in real time. For the establishment of this coordination, an on-line sequential algorithm is proposed which is based on adaptive resetting of the voltage set-point of the automatic voltage regulator and the PSS parameters. The efficiency of the proposed algorithm is confirmed through simulations on a real-life multi-machine bulk power system. [Copyright &y& Elsevier]