Fuzzy Probabilistic Neural Network Research Articles

Enhancing the Dynamic Stability of Integrated Offshore Wind Farms and Photovoltaic Farms Using STATCOM with Intelligent Damping Controllers

To build a large-scale renewable energy integrated system in the power system, power fluctuation mitigation and damping measures must be implemented during grid connection. PID damping controllers and traditional intelligent controllers with pole configuration are usually used for improving damping. Integration of large wind power plants and photovoltaic power plants into the power system faces transient power oscillation and fault ride-through (FRT) capability under fault conditions. Therefore, this paper proposes a static synchronous compensator (STATCOM) damper based on a recurrent Petri fuzzy probabilistic neural network (RPFPNN) to improve the transient stability of the power system when large offshore wind farms and photovoltaic power plants are integrated into the power system, suppress power fluctuation, and increase FRT capability. To verify the effectiveness of the proposed control scheme, a three-phase short circuit fault at the connected busbar is modeled in the time domain as part of a nonlinear model. From the comparison of simulation results, the proposed control scheme can effectively slow down the transient fluctuation of power supply to the grid-connected point when the grid is faulty, reach steady-state stability within 1–1.5 s, and reduce overshoot by more than 50%. It can also provide system voltage support at an 80% voltage drop and assist in stabilizing the system voltage to increase FRT capability. It also improves stability more than PID controllers when disturbances are present. Therefore, it maximizes the stability and safety of the power grid system.

Fixed-Time Sliding Mode Synchronization of Uncertain Fractional-Order Hyperchaotic Systems by Using a Novel Non-Singleton-Interval Type-2 Probabilistic Fuzzy Neural Network

In this study, we proposed a sliding mode control method based on fixed-time sliding mode surface for the synchronization of uncertain fractional-order hyperchaotic systems. In addition, we proposed a novel self-evolving non-singleton-interval type-2 probabilistic fuzzy neural network (SENSIT2PFNN) to estimate the uncertain dynamics of the system. Moreover, an adaptive compensator was designed to eliminate the influences of random uncertainty and fuzzy uncertainty, thereby yielding an asymptotically stable controlled system. Furthermore, an adaptive law was introduced to optimize the consequence parameters of SENSIT2PFNN. The membership layer and rule base of SENSIT2PFNN were optimized using the self-evolving algorithm and whale optimization algorithm, respectively. The simulation results verified the effectiveness of the proposed methods for the synchronization of uncertain fractional-order hyperchaotic systems.

Enhanced Dynamic Performance in Hybrid Power System Using a Designed ALTS-PFPNN Controller

The large-scale, nonlinear and uncertain factors of hybrid power systems (HPS) have always been difficult problems in dynamic stability control. This research mainly focuses on the dynamic and transient stability performance of large HPS under various operating conditions. In addition to the traditional synchronous power generator, wind-driven generator and ocean wave generator, the hybrid system also adds battery energy storage system and unified power flow controller (UPFC), making the system more diversified and more consistent with the current actual operation mode of the complex power grid. The purpose of this study is to propose an adaptive least squares Petri fuzzy probabilistic neural network (ALTS-PFPNN) for UPFC installed in the power grid to enhance the behavior of HPS operation. The proposed scheme improves the active power adjustment and dynamic performance of the integrated wave power generation and offshore wind system under a large range of operating conditions. Through various case studies, the practicability and robustness of ALTS-PFPNN method are verifying it by comparison and analysis with the damping controller based on the designed proportional integral differential (PID) and the control scheme without UPFC. Time-domain simulations were performed using Matlab-Simulink to validate the optimal damping behavior and efficiency of the suggested scheme under various disturbance conditions.

A Novel Data-Driven Estimation Method for State-of-Charge Estimation of Li-Ion Batteries

With the increasing proportion of Li-ion batteries in energy structures, studies on the estimation of the state of charge (SOC) of Li-ion batteries, which can effectively ensure the safety and stability of Li-ion batteries, have gained much attention. In this paper, a new data-driven method named the probabilistic threshold compensation fuzzy neural network (PTCFNN) is proposed to estimate the SOC of Li-ion batteries. Compared with other traditional methods that need to build complex battery models, the PTCFNN only needs data learning to obtain nonlinear mapping relationships inside Li-ion batteries. In order to avoid the local optimal value problem of traditional BP neural networks and the fixed reasoning mechanism of traditional fuzzy neural networks, the PTCFNN combines the advantages of a probabilistic fuzzy neural network and a compensation fuzzy neural network so as to improve the learning convergence speed and optimize the fuzzy reasoning mechanism. Finally, in order to verify the estimation performance of the PTCFNN, a 18650-20R Li-ion battery was used to carry out the estimation test. The results show that the mean absolute error and mean square error are very small under the conditions of a low-current test and dynamic-current test, and the overall estimation error is less than 1%, which further indicates that this method has good estimation ability.

Adaptive Fuzzy Approximation Control of PV Grid-Connected Inverters

Three-phase inverters are widely used in grid-connected renewable energy systems. This paper presents a new control methodology for grid-connected inverters using an adaptive fuzzy control (AFC) technique. The implementation of the proposed controller does not need prior knowledge of the system mathematical model. The capabilities of the fuzzy system in approximating the nonlinear functions of the grid-connected inverter system are exploited to design the controller. The proposed controller is capable to achieve the control objectives in the presence of both parametric and modelling uncertainties. The control objectives are to regulate the grid power factor and the dc output voltage of the photovoltaic systems. The closed-loop system stability and the updating laws of the controller parameters are determined via Lyapunov analysis. The proposed controller is simulated under different system disturbances, parameters, and modelling uncertainties to validate the effectiveness of the designed controller. For evaluation, the proposed controller is compared with conventional proportional-integral (PI) controller and Takagi–Sugeno–Kang-type probabilistic fuzzy neural network controller (TSKPFNN). The results demonstrated that the proposed AFC showed better performance in terms of response and reduced fluctuations compared to conventional PI controllers and TSKPFNN controllers.

Interior Permanent Magnet Synchronous Motor Drive System with Machine Learning-Based Maximum Torque per Ampere and Flux-Weakening Control

An interior permanent magnet synchronous motor (IPMSM) drive system with machine learning-based maximum torque per ampere (MTPA) as well as flux-weakening (FW) control was developed and is presented in this study. Since the control performance of IPMSM varies significantly due to the temperature variation and magnetic saturation, a machine learning-based MTPA control using a Petri probabilistic fuzzy neural network with an asymmetric membership function (PPFNN-AMF) was developed. First, the d-axis current command, which can achieve the MTPA control of the IPMSM, is derived. Then, the difference value of the dq-axis inductance of the IPMSM is obtained by the PPFNN-AMF and substituted into the d-axis current command of the MTPA to alleviate the saturation effect in the constant torque region. Moreover, a voltage control loop, which can limit the inverter output voltage to the maximum output voltage of the inverter at high-speed, is designed for the FW control in the constant power region. In addition, an adaptive complementary sliding mode (ACSM) speed controller is developed to improve the transient response of the speed control. Finally, some experimental results are given to demonstrate the validity of the proposed high-performance control strategies.

Online Autotuning Technique for IPMSM Servo Drive by Intelligent Identification of Moment of Inertia

In this article, a real-time moment of inertia identification technique using Petri probabilistic fuzzy neural network with an asymmetric membership function (PPFNN-AMF) for an interior permanent magnet synchronous motor (IPMSM) servo drive is proposed. The estimated moment of inertia will be used in the online design of an integral–proportional (IP) speed controller to achieve the gains autotuning of the IPMSM servo drive. In the proposed method, the dynamic analysis of a field-oriented control IPMSM servo drive system with an IP speed controller is constructed first. Then, a heuristic approach using the PPFNN-AMF is proposed for the real-time identification of the moment of inertia of the IPMSM servo drive system. Moreover, the network structure and the convergence analysis of the PPFNN-AMF are devised and derivated. Furthermore, an IPMSM servo drive based on a high-performance digital signal processor is developed. Finally, from the experimental results, the gains of the IP speed controller can be tuned online effectively at different operating conditions with robust control characteristics.

Study on the Prediction of Driving Braking Behaviour Based on FPNN

The driving braking recognized and predicted was complicated. The driving braking samples of various braking actions were gained, by means of 3 setting of driving braking including braking of automobile in front, roadblocks cutting off in other lane were planed and simulated in the real road environment. Experimental data of driving actions including automobile velocity, braking velocity and sustained action time were acquired by the driving action collecting system. Taking advantage of fuzzy aggregation analysis, the driving braking action sample was unified for the fuzzy probabilistic neural network (FPNN). Under different action groups of braking action information selected from braking action samples, the FPNN network was constructed and trained. The analytical results show that the hit rate is 95.3 % when the training braking action data number is 46. Meanwhile, the results show that the relative fuzzy unified and FPNN is useful with sufficient driving braking action data. The driving FPNN are a valid way for prediction of driving action time.

DG System Using PFNN Controllers for Improving Islanding Detection and Power Control

In this study, an intelligent controlled distributed generator (DG) system is proposed for tracking control and islanding detection. First, a DC/AC inverter with DC power supply is adopted to emulate a DG system and control the active and reactive power outputs. Moreover, in order to comply with the standard for interconnection with the power grid, a novel active islanding detection method is proposed for the inverter-based DG system. In the proposed active islanding detection method, a perturbation signal is designed to inject into the d-axis current of the DG system which causes the frequency at the terminal of the RLC load to deviate when the power grid breaks down. The feasibility of the proposed active islanding detection method is verified according to the UL 1741 test configuration. Furthermore, in order to improve the tracking control of the active and reactive powers of the inverter-based DG system, and to effectively reduce the detection time of islanding phenomenon, two probabilistic fuzzy neural network (PFNN) controllers are adopted to take the place of the conventional proportional-integral (PI) controllers. In addition, the network structure and the online learning algorithm of the adopted PFNN are presented in details. Finally, some experimental results of the proposed active islanding detection method using PFNN controllers are proposed to validate the effectiveness and feasibility of the tracking control and islanding detection.

Fuzzy probabilistic neural network in document classification tasks

Fuzzy probabilistic neural network in document classification tasks

DC-Link Voltage Regulation Using RPFNN-AMF for Three-Phase Active Power Filter

Due to the instantaneous power following into or out of the DC-link capacitor in a three-phase shunt active power filter (APF), the DC-link voltage regulation control plays an important role in the shunt APF especially under nonlinear load change. In this paper, for the purpose of improving the DC-link voltage regulation control in the shunt APF under nonlinear load variation and reducing the total harmonic distortion of the current effectively, a novel recurrent probabilistic fuzzy neural network with an asymmetric membership function (RPFNN-AMF) controller is developed to substitute for the conventional proportional-integral controller. Moreover, the network structure, the online learning algorithm, and the convergence analysis of the proposed RPFNN-AMF are detailedly introduced. Finally, the effectiveness and feasibility of the shunt APF using the proposed RPFNN-AMF controller for the DC-link voltage regulation control and the compensation of harmonic current are verified by some experimental results.

A Three-Phase Four-Leg Inverter-Based Active Power Filter for Unbalanced Current Compensation Using a Petri Probabilistic Fuzzy Neural Network

A three-phase four-leg inverter-based shunt active power filter (APF) is proposed to compensate three-phase unbalanced currents under unbalanced load conditions in grid-connected operation in this study. Since a DC-link capacitor is required on the DC side of the APF to release or absorb the instantaneous apparent power, the regulation control of the DC-link voltage of the APF is important especially under load variation. In order to improve the regulation control of the DC-link voltage of the shunt APF under variation of three-phase unbalanced load and to compensate the three-phase unbalanced currents effectively, a novel Petri probabilistic fuzzy neural network (PPFNN) controller is proposed to replace the traditional proportional-integral (PI) controller in this study. Furthermore, the network structure and online learning algorithms of the proposed PPFNN are represented in detail. Finally, the effectiveness of the three-phase four-leg inverter-based shunt APF with the proposed PPFNN controller for the regulation of the DC-link voltage and compensation of the three-phase unbalanced current has been demonstrated by some experimental results.

Intelligent tracking control of a PMLSM using self‐evolving probabilistic fuzzy neural network

This study presents a self-evolving probabilistic fuzzy (PF) neural network with asymmetric membership function (SPFNN-AMF) controller for the position servo control of a permanent magnet linear synchronous motor (PMLSM) servo drive system. In the beginning, the dynamic model for the PMLSM is analysed on the basis of field-oriented control. Subsequently, an SPFNN-AMF control system, which integrates the advantages of self-evolving NN, PF logic system, and AMF, is proposed to handle vagueness, randomness, and time-varying uncertainties of the PMLSM servo drive system during the control process. For the SPFNN-AMF, the proposed learning algorithm consists of the structure learning and parameter learning in which the former is used to grow and prune the fuzzy rules automatically, whereas the latter is utilised to train the network parameters dynamically. Finally, detailed experimental results of two position commands tracking at different operation conditions demonstrate the validity and robustness of the proposed SPFNN-AMF for controlling the PMLSM servo drive system.

Intelligent PV Power Smoothing Control Using Probabilistic Fuzzy Neural Network with Asymmetric Membership Function

An intelligent PV power smoothing control using probabilistic fuzzy neural network with asymmetric membership function (PFNN-AMF) is proposed in this study. First, a photovoltaic (PV) power plant with a battery energy storage system (BESS) is introduced. The BESS consisted of a bidirectional DC/AC 3-phase inverter and LiFePO4 batteries. Then, the difference of the actual PV power and smoothed power is supplied by the BESS. Moreover, the network structure of the PFNN-AMF and its online learning algorithms are described in detail. Furthermore, the three-phase output currents of the PV power plant are converted to the dq-axis current components. The resulted q-axis current is the input of the PFNN-AMF power smoothing control, and the output is a smoothing PV power curve to achieve the effect of PV power smoothing. Comparing to the other smoothing methods, a minimum energy capacity of the BESS with a small fluctuation of the grid power can be achieved by the PV power smoothing control using PFNN-AMF. In addition, a personal computer- (PC-) based PV power plant emulator and BESS are built for the experimentation. From the experimental results of various irradiance variation conditions, the effectiveness of the proposed intelligent PV power smoothing control can be verified.

Multiple-fault Diagnosis of Car Engines Using Fuzzy Sparse Bayesian Extreme Learning Machine

For any faults of car engines, the diagnosis can be performed based on variety of symptoms. Traditionally, the description of the faulty symptom is just existence or not. However, this descriptioncannot lead to a high accuracy because the symptom sometimes appears in different degrees. Therefore, a knowledge representation method which could precisely reflect the nature of the symptom is necessary. In this paper, the fuzzy logic isfirstly appliedto quantify the degreesand uncertaintiesof symptoms.A probabilistic classification system is then constructed by using the fuzzified symptoms and a new technique, namely, Fuzzy Sparse Bayesian Extreme Learning Machine (FSBELM).Moreover, both Fuzzy Probabilistic Neural Network (FPNN) and Fuzzy Probabilistic Support Vector Machine (FPSVM) are usedto respectively construct similarclassification systems forcomparison with FSBELM. Experimental results show that FSBELM produces better performance than FPNN and FPSVM in terms of diagnostic accuracy and computational time.

A State Recognition Approach for Complex Equipment Based on a Fuzzy Probabilistic Neural Network

Due to the traditional state recognition approaches for complex electromechanical equipment having had the disadvantages of excessive reliance on complete expert knowledge and insufficient training sets, real-time state identification system was always difficult to be established. The running efficiency cannot be guaranteed and the fault rate cannot be reduced fundamentally especially in some extreme working conditions. To solve these problems, an online state recognition method for complex equipment based on a fuzzy probabilistic neural network (FPNN) was proposed in this paper. The fuzzy rule base for complex equipment was established and a multi-level state space model was constructed. Moreover, a probabilistic neural network (PNN) was applied in state recognition, and the fuzzy functions and quantification matrix were presented. The flowchart of proposed approach was designed. Finally, a simulation example of shearer state recognition and the industrial application with an accuracy of 90.91% were provided and the proposed approach was feasible and efficient.

Reactive Power Control of Three-Phase Grid-Connected PV System During Grid Faults Using Takagi–Sugeno–Kang Probabilistic Fuzzy Neural Network Control

An intelligent controller based on the Takagi–Sugeno–Kang-type probabilistic fuzzy neural network with an asymmetric membership function (TSKPFNN-AMF) is developed in this paper for the reactive and active power control of a three-phase grid-connected photovoltaic (PV) system during grid faults. The inverter of the three-phase grid-connected PV system should provide a proper ratio of reactive power to meet the low-voltage ride through (LVRT) regulations and control the output current without exceeding the maximum current limit simultaneously during grid faults. Therefore, the proposed intelligent controller regulates the value of reactive power to a new reference value, which complies with the regulations of LVRT under grid faults. Moreover, a dual-mode operation control method of the converter and inverter of the three-phase grid-connected PV system is designed to eliminate the fluctuation of dc-link bus voltage under grid faults. Furthermore, the network structure, the online learning algorithm, and the convergence analysis of the TSKPFNN-AMF are described in detail. Finally, some experimental results are illustrated to show the effectiveness of the proposed control for the three-phase grid-connected PV system.

MULTILAYER ADAPTIVE FUZZY PROBABILISTIC NEURAL NETWORK IN CLASSIFICATION PROBLEMS OF TEXT DOCUMENTS

The problem of text documents classification based on fuzzy probabilistic neural network in real time mode is considered. A differentnumber of classes, which may include such documents, can be allocated in an array of text documents. It is assumed that the data classes canhave an n-dimensional space of different shape and mutually overlap. The architecture of the multlayer adaptive fuzzy probabilistic neuralnetwork, which allow to solve the problem of classification in sequential mode as new data become available, is.proposed. An algorithm fortraining the multilayer adaptive fuzzy probabilistic neural network is proposed, and the problem of classification is solved on the basis of theproposed architecture in terms of intersecting classes, which allows to determine the belonging a single instance of a text document to differentclasses with varying degrees of probability. Classifying neural network architecture characterized by simple numerical implementation and highspeed training, and is designed to handle large data sets, characterized by the feature vectors of high dimension. The proposed neural networkand its learning method designed to work in conditions of overlapping classes, differing both the form and size.

Takagi–Sugeno–Kang type probabilistic fuzzy neural network control for grid‐connected LiFePO 4 battery storage system

A Takagi-Sugeno-Kang type probabilistic fuzzy neural network (TSKPFNN) control is proposed to control a grid-connected LiFePO 4 battery storage system in this study. First, the modelling of the battery and bidirectional AC-DC converter are described in detail. Then, the active and reactive power controls using phase-lock loop are briefly introduced. Moreover, to improve the control performance of the grid-connected LiFePO 4 battery storage system, the TSKPFNN control, which combines the advantages of Takagi-Sugeno-Kang type fuzzy logic system and three-dimensional membership function, is developed. The network structure, online learning algorithm using delta adaptation law and convergence analysis of the TSKPFNN are described in detail. Furthermore, a 32-bit fixed-point digital signal processor, TMS320F28035, is adopted for the implementation of the proposed intelligent controlled battery storage system. Finally, some experimental results are illustrated to show the validity of the proposed TSKPFNN control for the grid-connected LiFePO 4 battery storage system.

DSP-Based Probabilistic Fuzzy Neural Network Control for Li-Ion Battery Charger

A DSP-based probabilistic fuzzy neural network (PFNN) controller to control a two-stage ac-dc charger is pro- posed in this study. The charger is composed of an ac-dc boost converter with power factor correction and a phase-shift full- bridge dc-dc converter. Moreover, the designed charger adopts a constant-current and constant-voltage (CC-CV) charging strategy to charge lithium-ion battery packs. To improve the transient of voltage regulation during load variation, a PFNN controller is pro- posed to replace the traditional proportional-integral controller. Furthermore, the discontinuous charging voltage and current during the transition between the CC and CV charging modes can also be reduced significantly using the proposed PFNN controller. The network structure and the online learning algorithms of the PFNN controller are introduced in detail. In addition, the control performances of the proposed PFNN control system for CC-CV charging are evaluated by experimental results.