Mojtaba Alizadeh

Direct adaptive power system stabilizer design using fuzzy wavelet neural network with self-recurrent consequent part

The main disadvantage of FWNN is that the application domain is limited to static problems due to its feed-forward network structure. Therefore, we propose to use a self-recurrent wavelet neural network (SRWNN) in the consequent part of FWNN, solving the control problem for chaotic systems.Our proposed structure requires fewer wavelet nodes than the networks with feed-forward structure, due to the dynamic behavior of the recurrent network.Finding the optimal learning rates is a challenging task in the classic gradient-based learning algorithms. Hence, in our proposed framework, all of the learning rates are determined optimally based on Lyapunov stability theory.We develop a controller based on the proposed network structure and use it for damping the oscillations in the multi-machine power system. This paper aims to propose a stable fuzzy wavelet neural-based adaptive power system stabilizer (SFWNAPSS) for stabilizing the inter-area oscillations in multi-machine power systems. In the proposed approach, a self-recurrent Wavelet Neural Network (SRWNN) is applied with the aim of constructing a self-recurrent consequent part for each fuzzy rule of a Takagi-Sugeno-Kang (TSK) fuzzy model. All parameters of the consequent parts are updated online based on Direct Adaptive Control Theory (DACT) and employing a back-propagation-based approach. The stabilizer initialization is performed using an approach based on genetic algorithm (GA). A Lyapunov-based adaptive learning rates (LALRs) algorithm is also proposed in order to speed up the stabilization rate, as well as to guarantee the convergence of the proposed stabilizer. Therefore, due to having a stable powerful adaptation law, there is no requirement to use any identification process. Kundurs four-machine two-area benchmark power system and six-machine three-area power system are used with the aim of assessing the effectiveness of the proposed stabilizer. The results are promising and show that the inter-area oscillations are successfully damped by the SFWNAPSS. Furthermore, the superiority of the proposed stabilizer is demonstrated over the IEEE standard multi-band power system stabilizer (MB-PSS), and the conventional PSS.

Full-adaptive THEN-part equipped fuzzy wavelet neural controller design of FACTS devices to suppress inter-area oscillations

By incorporating Self-Recurrent Wavelet Neural Networks (SRWNNs) into Takagi-Sugeno-Kang (TSK) fuzzy model, this paper not only develops a novel Indirect Stable Adaptive Fuzzy Wavelet Neural Controller (ISAFWNC), but also uses it as a supplementary damping controller of Flexible AC Transmission System (FACTS) devices. In the proposed approach, the SRWNN is employed to construct a full-adaptive self-recurrent consequent part for each fuzzy rule of a TSK fuzzy model. A Stable Back-Propagation (SBP) algorithm with the aid of an Adaptive SRWNN-Identifier (ASRWNNI) is then employed to adjust fuzzy rules in real-time operation while the closed-loop stability is guaranteed by a Lyapunov-based approach. The proposed controller is thus able to handle the plant uncertainty by both the concepts of fuzzy logic and ASRWNNI while the local details of non stationary signals can be decomposed in terms of the dilation and translation parameters of the self-recurrent wavelet neural networks. A Genetic Algorithm (GA) based approach is proposed to choose the initial values of the dilation and the translation parameters of the wavelet and thus to increases the training speed and convergence rate of the proposed control scheme, since the BP convergence rate depends on the selection of the initial values of the network parameters. Simulations results of both two-machine two-area and benchmark four-machine two-area power systems, respectively equipped with a Static Synchronous Series Compensator (SSSC) and a Unified Power Flow Controller (UPFC) demonstrate the effectiveness of the proposed ISAFWNC design.

Wavelet neural adaptive proportional plus conventional integral-derivative controller design of SSSC for transient stability improvement

Although the PI or PID (PI/PID) controllers have many advantages, their control performance may be degraded when the controlled object is highly nonlinear and uncertain; the main problem is related to static nature of fixed-gain PI/PID controllers. This work aims to propose a wavelet neural adaptive proportional plus conventional integral-derivative (WNAP+ID) controller to solve the PI/PID controller problems. To create an adaptive nature for PI/PID controller and for online processing of the error signal, this work subtly employs a one to one offline trained self-recurrent wavelet neural network as a processing unit (SRWNN-PU) in series connection with the fixed-proportional gain of conventional PI/PID controller. Offline training of the SRWNN-PU can be performed with any virtual training samples, independent of plant data, and it is thus possible to use a generalized SRWNN-PU for any systems. Employing a SRWNN-identifier (SRWNNI), the SRWNN-PU parameters are then updated online to process the error signal and minimize a control cost function in real-time operation. Although the proposed WNAP+ID is not limited to power system applications, it is used as supplementary damping controller of static synchronous series compensator (SSSC) of two SSSC-aided power systems to enhance the transient stability. The nonlinear time-domain simulation and system performance characteristics in terms of ITAE revealed that the WNAP+ID has more control proficiency in comparison to PID controller. As additional simulations, the features of the proposed controller are compared to those of the literature while some of its promising features like its fast noise-rejection ability and its high online adapting ability are also highlighted.

Adaptive PID controller design for wing rock suppression using self-recurrent wavelet neural network identifier

AbstractThis paper presents a novel control scheme based on auto-tuning PID controller to suppress wing rock phenomena. Due to having a complex dynamic, wing rock motion identification is not a simple task, and this complexity can adversely affect the performance of PID controller. Employing a wavelet neural network based identifier, this paper develops an auto tuning adaptive PID controller to tackle the problem. Since having an acceptable control performance inevitably involves having a meticulously trained identifier, the training performance is of utmost importance. Aiming at boosting the training efficacy, a two-phase algorithm encompassing Bees algorithm and Back-Propagation (BP) is proposed by this paper to train the proposed identifier respectively in off-line and on-line modes. Due to its inherent capability in sifting the global minima, Bees algorithm is employed to find initial values of weights around which it is then possible to conduct a local search by means of BP based online training. Therefore, the identifier can precisely furnish the proposed PID controller with the system sensitivity in on-line mode. The adaption of PID controller can thus be performed in each time step. The performance of this method has been presented in simulation results and the comparison section confirms the effectiveness of proposed scheme.

On‐line self‐learning PID based PSS using self‐recurrent wavelet neural network identifier and chaotic optimization

Purpose – The power system is complex multi‐component dynamic system with many operational levels made up of a wide range of energy sources with many interaction points. Low frequency oscillations are observed when large power systems are interconnected by relatively weak tie lines. These oscillations may sustain and grow to cause system separation if no adequate damping is available. The present paper aims to propose an on‐line self‐learning PID (OLSL‐PID) controller in order to damp the low frequency power system oscillations in a single‐machine system.Design/methodology/approach – The proposed OLSL‐PID is used as a controller in order to damp the low frequency power system oscillations. It has a local nature because of its powerful adaption process based on back‐propagation (BP) algorithm that is implemented through an adaptive self‐recurrent wavelet neural network identifier (ASRWNNI). In fact PID controller parameters are updated in on‐line mode, using BP algorithm based on the information provided b...

On-line identification of synchronous generator using Self Recurrent Wavelet Neural Networks via Adaptive Learning Rates

In this paper, the Self-Recurrent Wavelet Neural Network (SRWNN) is used as a model predictor for identify a synchronous generator. Further, a hybrid algorithm combining Chaotic Global Search (CGS) algorithm with Back-Propagation (BP) algorithm, referred to as CGS-BP algorithm, is proposed to train the weights of SRWNN-Identifier (SRWNNI). And also, the gradient-descent method using Adaptive Learning Rates (ALRs) is applied to train all weights of the SRWNNI, in on-line mode. The ALRs are derived from discrete lyapunov stability theorem. Finally, the proposed SRWNNI are evaluated on a single machine infinite bus power system under different operating conditions and disturbances to demonstrate their effectiveness and robustness. Also, the SRWNNI is compared with Wavelet Neural Network Identifier (WNNI) and Multi-Layer Perceptron Identifier (MLPI).

A Modular Neural Block to Enhance Power System Stability

Conventional supplementary controllers (CSCs) can still be widely observed in power system utilities. This work aims to develop a modular neural block (MNB) to improve control performance and stability of CSCs-aided power systems. The proposed MNB is actually a one-to-one offline trained self-recurrent wavelet neural network (SRWNN) which can be modularity added to the PI/PD/PID/Lag-Lead controllers to enhance their performance by adding an adaptive property to them. Independent of the plant model, the MNB is initially trained offline using virtual training-samples. As a prefabricated one-to-one neural block, it can then be copied to required numbers and added to the lag-lead controller or any/all branches of the PI/PD/PID controller in series connection. The employed MNB(s) is then re-trained online to increase control performance by minimizing a predefined cost-function. The online training is performed by back-propagation (BP) algorithm while the closed-loop stability is guaranteed by an efficient Lyapunov-based approach. The proposed approach is thus a model-free scheme which is simple enough for implementation. Ability of the MNB to enhance the performance of the CSCs and dynamic stability of power systems is demonstrated by the simulation results of two small power plants and an IEEE 10-machine 39-bus system.