Confidence intervals for neural network based short-term loadforecasting

Using traditional statistical models, like ARMA and multilinear regression, confidence intervals can be computed for the short-term electric load forecasting, assuming that the forecast errors are independent and Gaussian distributed. In this paper, the 1 to 24 steps ahead load forecasts are obtained through multilayer perceptrons trained by the backpropagation algorithm. Three techniques for the computation of confidence intervals for this neural network based short-term load forecasting are presented: (1) error output; (2) resampling; and (3) multilinear regression adapted to neural networks. A comparison of the three techniques is performed through simulations of online forecasting     

An adaptive approach in distance protection using an artificial neural network

All relay settings are a compromise. Adaptive relaying accepts that relays that protect a power system may need to change their characteristics to suit the prevailing power system conditions. This philosophy has a wide range of applications covering many protective schemes. Here we consider a two-terminal transmission line, confirm that fault resistance and the location of faults can produce erroneous relay function and finally suggest ways to ensure the generation of the correct signal for relay operation. Retaining the microprocessor based framework, we show how artificial neural networks (ANNs) can be used effectively to achieve adaptive relaying for the above-mentioned problem. Adaptive relaying covers a large number of applications and the characteristics of relays vary widely, so the philosophy of adaptive relaying must vary accordingly. A modified multilayered perceptron model employs an additional node in the input layer. This additional input facilitates changes in the relay characteristic. The desired change in the quadrilateral relay characteristic is achieved by making appropriate changes in the thresholds and weights of the hidden layer neurons. A multiparameter adaptive scheme assumes that the additional input of the phase angle is available. Simulation results using ANNs for the different applications of adaptive relaying mentioned above are presented and discussed.     

An adaptive neural network approach for the estimation of power system frequency

A new approach to the estimation of power system frequency using an adaptive neural network is presented in this paper. This approach uses a linear adaptive neuron or an adaptive linear combiner called “Adaline” to identify the parameters of a discrete signal model of the power system voltage. Here, the learning parameters are adjusted to force the error between the actual and the computed signal samples to satisfy a stable difference error equation, rather than to minimize an error function. The proposed algorithm shows a high degree of robustness and estimation accuracy over a wide range of frequency changes. The technique is shown to be capable of tracking power system conditions and is immune to the effects of harmonics and random noise.     

An artificial neural network approach to transformer faultdiagnosis

This paper presents an artificial neural network (ANN) approach to the diagnosis and detection of faults in oil-filled power transformers based on dissolved gas-in-oil analysis. A two-step ANN method is used to detect faults with or without cellulose involved. Good diagnosis accuracy is obtained with the proposed approach     

用于APF的神经网络自适应谐波电流检测方法

介绍了一种应用于有源电力滤波器APF(Active Power Filter)的神经网络自适应谐波电流检测方法。该方法应用自适应噪声抵消技术ANCT(Adaptive Noise Canceling Technology),将基波电流作为噪声信号,从负载电流中滤除,得到谐波电流。采用两层人工神经网络实现噪声抵消。阐述了该神经网络的构造和权值自适应调整算法,应用Matlab对该方法进行了仿真研究。仿真结果表明该方法能够实时准确地检测谐波．而且计算量小．具有较强的自适应能力。     

基于多层前向神经网络的基波检测方法

基波检测是系统控制和电能质量调节装置的一项很重要的任务,检测方法的精确性和实时性是系统可靠控制和有效调节的基础和关键.提出了一种基于多层前向人工神经网络实现基波检测的方法,检测网络采用误差反向传播的神经网络,结合电力系统中畸变波形的特点生成训练样本,并对神经网络的结构和参数进行了优化研究.该方法可以快速而准确地检测出畸变电网中的基波信号,仿真与实验均表明其具有优良的检测效果和跟踪速度,而且具有一定的泛化和外推能力,对于未经训练的含有更高次谐波的样本同样具有良好的检测能力.     

滤波器-神经网络的谐波检测方法

电网谐波的高精度检测是电能计量和电能质量评估的基础.针对神经网络的谐波检测算法中,计算精度受基波频率精度影响较大的问题,提出用数字滤波结合牛顿反插值算法得到高精度的基波频率,然后用线性神经网络算法检测电力系统各次谐波的频率、幅值和相位.计算结果表明,该算法在频率波动和白噪声干扰的情况下,依然能得到高精度的谐波参数信息,其精度远高于FFT算法与加汉宁窗的FFT算法,在电力系统谐波测量中有一定的应用价值.     

A neural network approach to transform image coding

A neural network approach is presented for transform image coding. It is shown that the three steps in the conventional transform image coding, i.e. the unitary transform of spatial domain image data, the quantization of the transform domain data and the binary coding of the quantized data, can be unified into a one-step optimization problem. Then, the optimization problem is solved by an appropriately constructed Hopfield neural network whose input is the spatial domain image data and whose output is binary codes. A practical circuit implementation is given to perform the transform image coding. the circuit has rM² neurons, where r is the bit-rate, in bit/pixel, of the coding and M² is the size of the images. Each neuron consists of only a non-linear voltage amplifier, a linear voltage-controlled current source, a d.c. current source, a linear passive resistor, a linear passive capacitor, and a weighted voltage summer which can be made of a single op amp with some linear passive resistors. Moreover, each neuron is locally connected with no more than b - 1 other neurons by wires, where b is the maximum bit allocated to a transform domain coefficient. Therefore, our proposed approach is particularly suitable for low-bit-rate image coding and VLSI implementation. Furthermore, the analogue and parallel nature of our approach matches perfectly the high-speed requirement of real-time image coding.     

Cellular neural network for fast adaptive equalization

The use of an analogue neural network in the adaptive equalization of time-varying communication channels is proposed. the network is used to compute the coefficients of a linear transversal filter. the settling time decreases as the filter order increases and as the signal-to-noise ratio decreases. Owing to the real-time processing capabilities, the network can be useful when it is of interest to track fast variations, as in radio links. the special properties of the tap input correlation matrix result in a cellular network architecture which greatly simplifies the VLSI implementation. Simulation results are presented which point out very satisfactory performance.     

Artificial neural network approach to distance protection oftransmission lines

A distance relay for the protection of transmission lines is usually designed on the basis of fixed settings. The reach of such relays is therefore affected by the changing network conditions. The implementation of a pattern recognizer for power system diagnosis can provide great advances in the protection field. This paper demonstrates the use of an artificial neural network as a pattern classifier for a distance relay operation. The scheme utilizes the magnitudes of three phase voltage and current phasors as inputs. An improved performance with the use of an artificial neural network approach is experienced once the relay can operate correctly, keeping the reach when faced with different fault conditions as well as network configuration changes     

An adaptable automated procedure for short-term electricity loadforecasting

The Irish Electricity Supply Board requires forecasts of system demand or electrical load for: (a) one day ahead; and (b) 7-10 days ahead. Here, the authors concentrate on and give results only for one day ahead forecasts although the method is also applicable for 7-10 days ahead. A forecasting model has been developed which identifies a `normal' or weather-insensitive load component and a weather-sensitive load component. Linear regression analysis of past load and weather data is used to identify the normal load model. The weather-sensitive component of the load is estimated using the parameters of regression analysis. Certain design features of the short-term load forecasting system are important for its successful operation over time. These include adaptability to changing operational conditions, computational economy and robustness. An automated load forecasting system is presented here that includes these design features. A fully automated algorithm for updating the model is described in detail as are the techniques employed in both the identification and treatment of influential points in the data base and the selection of predictors for the weather-load model. Monthly error statistics of forecast load for only one day ahead are presented for recorded weather conditions     

An integrated approach to modeling and adaptive control

In the book (Adaptive Identification, Prediction and Control—Multi Level Recursive Approach), the concept of dynamical linearization of nonlinear systems has been presented. This dynamical linearization is formal only, not a real linearization. From the linearization procedure, we can find a new approach of system identification, which is on-line real-time modeling and real-time feedback control correction. The modeling and real-time feedback control have been integrated in the identification approach, with the parameter adaptation model being abandoned. The structure adaptation of control systems has been achieved, which avoids the complex modeling steps. The objective of this paper is to introduce the approach of integrated modeling and control. __________ Translated from Acta Automatica Sinica, 2004, 30(3): 380–389 (in Chinese)     

An adaptive modular artificial neural network hourly loadforecaster and its implementation at electric utilities

This paper describes a modular artificial neural network (ANN) based hourly load forecaster which has already been implemented at 20 electric utilities across the US and is being used on-line by several of them. The behavior or the load and its correlation with parameters affecting it (e.g. weather variables) are decomposed into three distinct trends of weekly, daily, and hourly. Each trend is modeled by a separate module containing several multi-layer feed-forward ANNs trained by the back-propagation learning rule. The forecasts produced by each module are then combined by adaptive filters to arrive at the final forecast. During the forecasting phase, the parameters of the ANNs within each module are adaptively changed in response to the system's latest forecast accuracy. The performance of the forecaster has been tested on data from these 20 utilities with excellent results. The on-line performance of the system has also been quite satisfactory and superior to other forecasting packages used by the utilities. Moreover, the forecaster is robust, easy to use, and produces accurate results in the case of rapid weather changes     

An efficient neural network approach for nanoscale FinFET modelling and circuit simulation

The present paper demonstrates the suitability of artificial neural network (ANN) for modelling of a FinFET in nano‐circuit simulation. The FinFET used in this work is designed using careful engineering of source–drain extension, which simultaneously improves maximum frequency of oscillation ƒ_max because of lower gate to drain capacitance, and intrinsic gain A_V0 = g_m/g_ds, due to lower output conductance g_ds. The framework for the ANN‐based FinFET model is a common source equivalent circuit, where the dependence of intrinsic capacitances, resistances and dc drain current I_d on drain–source V_ds and gate–source V_gs is derived by a simple two‐layered neural network architecture. All extrinsic components of the FinFET model are treated as bias independent. The model was implemented in a circuit simulator and verified by its ability to generate accurate response to excitations not used during training. The model was used to design a low‐noise amplifier. At low power (J_ds∼10 µA/µm) improvement was observed in both third‐order‐intercept IIP₃ (∼10 dBm) and intrinsic gain A_V0 (∼20 dB), compared to a comparable bulk MOSFET with similar effective channel length. This is attributed to higher ratio of first‐order to third‐order derivative of I_d with respect to gate voltage and lower g_ds in FinFET compared to bulk MOSFET. Copyright © 2009 John Wiley & Sons, Ltd.     

一种基于自适应神经网络的传感器故障检测方法

传感器的故障检测技术是自动控制系统能否正确并运行的先提条件,针对系统存在未知输入的特点,提出了一种新的检测方法,预测出正常情况下传感器的状况,并进行了大量仿真实验研究。与传感器实际输出比较,实验结果证明该故障检测方法的有效性和实用性。     

A variable structure adaptive neural network static VAR controller

A variable structure adaptive neural network power system static VAR stabilizer is developed. The static VAR compensator (SVC) controlled by the above proposed controller is used for voltage regulation and enhancing power system stability. The artificial neural network (ANN) is trained off-line using the variable structure control system Benchmark data at different operating conditions and external disturbances. Moreover, the trained ANN parameters (weights and biases) are tuned and updated on-line using the synchronous machine speed deviation state as the ANN output error to increasingly improve the power system performance. A sample digital simulation result of the power system speed deviation state responses when reference voltage, speed deviation state and input power disturbances take place are obtained. The digital simulation results prove the effectiveness and robustness of the present adaptive neural network in terms of a high performance power system.     

A novel approach to fuzzy wavelet neural network modeling and optimization

In this paper, an efficient approach of combining Takagi–Sugeno–Kang fuzzy system with wavelet based neural network is presented. The model replaces the constant or a linear function of inputs in conclusion part of traditional TSK fuzzy model with wavelet neural network (WNN), thus each rule uses fuzzy set to separate the input space into subspaces spanned by different wavelet functions. For finding the optimal values for parameters of our proposed fuzzy wavelet neural network (proposed-FWNN), a hybrid learning algorithm integrating an improved particle swarm optimization (PSO) and gradient descent algorithm is employed. The two-layer inline-PSO process is proposed in this paper, whose adjustment scheme is more fitting the consequent pattern learning based gradient descent optimization and will locate a good region in the search space. Simulation examples are given to test the efficiency of proposed-FWNN model for identification of the dynamic plants. It is seen that our modeling and optimization approach results in a better performance.     

Speed deviation driven adaptive neural network based power system stabilizer

The paper presents an online adaptive artificial neural network (ANN) based power system stabilizer (PSS). The proposed controller is first trained offline using a pole placement based state feedback gain technique at different operating points. The trained ANN parameters (weights and biases) are updated and tuned online using the speed deviation as the reinforcement signal. The proposed PSS is tested at different operating conditions and a variety of regulator gains. The digital results validate the effectiveness and reliability of the new PSS in terms of fast system response under different loading conditions compared with the conventional PI controller and the modern control theory approach of pole placement.     

基于动态面控制的间接自适应神经网络块控制

针对一类可转化为"标准块控制形"的多输入多输出的非线性系统,基于动态面控制技术,提出一种间接自适应神经网络控制器的设计方案.该方法通过引入1阶滤波器,消除了后推设计中由于反复对虚拟控制的求导而导致的复杂性问题,同时完全避免了反馈线性化方法中可能出现的控制器奇异性问题,且无需控制增益矩阵正定、可逆的条件.利用李亚普诺夫方法,证明了闭环系统是半全局一致终结有界,通过适当选取设计常数,跟踪误差可收敛到原点的一个小邻域内.仿真结果表明所提控制方法的有效性.