Application of data mining method in power load bad data intelligent identification and correction

There is a number of bad data in the load database produced, thus the data must be cleaned before it is used to forecasting electric load or performing power system analysis. The WKFCM measures distance by kernel functions instead of the complicated Euclidean distance and this kernel based distance is used as dissimilarity function of target clustering formula which can reduce the calculation complexity. After the clustering, a super circle covering neural network based identification model for load data is proposed, and the bad data is modified. It is proved that the proposed data processing model has good effect.     

Cellular automata spatial temporal data model and forecasting approach

The standard cellular automata(CA) model is expanded to meet requests of space time dynamic simulation and forecast under the platform of geographic information system(GIS). Taking power load forecasting of the electric power industry as the specific application, the relations between dynamic model of the land use and power load space are established. The data and attribute data interactive discrete in spatial temporal data management have been solved. The CA theory is practically used to simulate the process of urban land use dynamic development, to forecast future land use types of each small area, to establish spatial load forecasting model. It breaks through the localization of all kinds of forecasting methods of traditional space time separation power prediction. The effectiveness of the prediction method is verified by example.     

Load Forecasting Based on BP ANN for Economical Operation of Electric Boiler with Heat Reservoir Under Time-sharing price

Along with the generalization of DSM and time-sharing price system, the use of electric boiler with heat reservoir becomes more and more extensive. Load forecasting of heat supply system is an important base in the study of economical operation of electric boiler with heat reservoir under time-sharing price. The BP ANN modeling of the load forecasting for a 1200 kW electric boiler is discussed. The result of hourly heat load forecasting accords well with the real heat load.     

Application of Combination Grey Model in Power Load Forecasting

Based on the gray forecast theory, this paper studies the principle and deficiency in power load forecasting by the basic grey model and other improved models, and introduces a new method -the combination grey model to forecast the long-medium power load. Based on an example, the basic grey model, other improved models and combination grey model are used to forecast power load and results of all models are analyzed and compared. The calculation results show that forecasting power load by grey theory is credible and simple. For this type of complex problems such as forecasting the long-medium power load, the combination grey model is specially useful because of it's high precision and facility. The method can be used as one of the tools of forecasting the long-medium power load.     

Hybrid Power Supply Based on One Cycle Control

A hybrid power supply is composed by both a linear and a switching source connected in parallel and it can improve the dynamic response. But the hybrid power supply cant not effectively reject the perturbations in the power supply. A hybrid power supply based on one cycle control theory is proposed in order to improve the capability and the control precision. A switching source based on one cycle control is adopted in the hybrid power supply. The analysis results indicate that the hybrid power supply can effectively reject the perturbations in the power supply. And the analysis results also show that not only the control precision is improved, but also the control circuit is simple. The simulation resuits given verify the correctness and effectiveness of the proposed method.     

Processing Fault Data Method in Load Forecasting Based on Wavelet Analysis

The reliability and reality of load historical data is the foundation of load forecasting.But,the impact load in running power system,and the disturb data in collecting load data through the SCADA may cause much fault data in load historical data. Focusing on solving this problem, a method through adjusting amplitade of its wavele modulus maxima and processing the wavelet decomposed detail signal by soft threshold based on wavelet analysis and singularity theory, then fault date can be eliminated,so that,the real historical imformation and regulation data can be gained by load forecasting.     

Data Analysis Method for Neural Network Based on Short-term Load Forecasting

In neural network based short-term load forecasting, complexity and redundancy of input data have a negative effect on network training efficiency and forecasting precision. Focusing on solving this problem, a multiple method of data processing is developed. Firstly a method called input variable contribution analysis is applied, which divides input variables into primary variables and minor variables according to their contribution to network output. Minor variables are tossed out. Then principal component analysis is applied to primary variables to eliminate linear correlation among them, thus reduce the variable dimension. Based on this method, the main components are gotten, and then simplified network structure is designed. The result shows that after data processing, the training time is reduced noticeably and forecasting precision is enhanced.     

The Application of Fuzzy Theory to Power Load Forecasting

A new method was presented for power load forecasting.Based on the fuzzyclustering technique,the historical samples of power load and its relative environmental factors wereclassified into several typical categories,the fuzzy numbers and sets were then used to describe thepatterns of load variation and the features of the environmental factors for every class. Finally,byunderstanding the state of future environmental factors, the future power load can be predictedthrough determining the category of load variation pottern. The validity of the proposed method wasverified with a practical medium-term load forecasting.     

A Fuzzy Regression Method for Long-Term and Medium-Term Load Forecasting

In view of the observation data fuzziness and load pattern fuzziness,a new fuzzy regression prediction method was presented for long-term and medium-term load forecasting. With the established fuzzy regression model, the future load value can be forecasted based on the fuzzy historical observation data. The validity of the proposed method was verified with the numerical example of a practical system.     

冬季稳定人工环境实验室内温度的新方法

人工环境实验室对温度的控制精度低于工艺性环境要求,但冬季若采用分体式空调制热模式控制,实验室温度的波动范围过大,影响采集实验数据的准确性。提出冬季采用空调制冷模式联合电取暖器稳定人工环境实验室内温度的新方法,在背景实验中将新方法和传统空调制热温度控制方法进行对比,结果表明：采用新方法能大幅度提高人工环境实验房间温度的稳定性。为对新方法中空调的设定制冷温度与电取暖器功率匹配问题做进一步研究而进行探究试验,实验结果表明：空调制冷量Q₁、测试房间热负荷Q₂、电取暖器功率Q₃和测试房间其他设备功率Q₄存在匹配关系,当空调设定温度相同时,|Q₁+Q₂-Q₃-Q₄|值越小,人工环境实验室内温度波动频率越稳定,振幅越小。     

Clustering Analysis for Time Feature of User Power Consumption Based on Structural Self-adaptation ANN

In view of the important effect of clustering analysis in data mining, the clustering rules and its curve are studied to solve the problem of determining clustering number. A kind of self-adaptation clustering ANN is presented based on SOFM ANN, which can automatically determine the clustering number. Based on practical sales data, the time feature analysis of power user consumption are carried out by using the self-adaptation clustering ANN, whose conclusion has the important referenced values for adjusting power price correspondingly and arranging power producing reasonably.     

Partition Algorithm of Power Network Based on the Optimum Clustering Principle

The paper construct the specimen of multi-dimensional space based on the definition of clustering, calculate the generator control sensitive to load and the Eulerian distance, partition the power system by the shortest distance method of clustering analysis. After the range of area num-ber is determined, the optimal area number and its validity is decided by Shannon function of entropy and Sugeno-Yasukaw rule. A case of IEEE 39-bus system is presented to verify the method.     

大型商场建筑夏季冷负荷动态预测模型

夏季建筑冷负荷的正确预测是实现大型复杂中央空调优化运行、节能降耗的关键。笔者探讨了商场建筑冷负荷的主要影响因素,确定了建筑动态冷负荷预测模型的输入,提出了夏季基于新风机组供电频率的商场顾客率间接测量方法,解决了商场内顾客量难以检测的难题。还提出了AFC-HCMAC神经网络预测模型算法,实现了大型商场建筑冷负荷的动态预测。仿真结果表明:顾客率在商场冷负荷预测中占有重要地位,在冷负荷预测模型中增加商场顾客率可显著提高预测精度;AFC-HCMAC神经网络预测算法与传统的HCMAC神经网络算法比较,可有效降低神经网络节点数,提高预测精度。     

Cost forecast model for power engineering based on based on back propagation neural networks

A novel power engineering cost forecast model was proposed by combining feature extraction and small sample learning. The initial data was preprocessed with principal component analysis to remove the correlation among the original indexes and get the potential independent indexes. The new indexes acted as the input set to build a new forecast model based on least squares support vector machines. The results of this model were compared with the forecast results getting from artificial neural network. By comparing the forecast results with different principal components number, the optimal number was determined to achieve the desired forecast effect. The prediction results indicate that the method can extract the feature of initial data effectively and is good at small sample learning . The expected forecasting results can be reached.     

A New Method of Diesel Engine Fuel System Fault Diagnosis

A method of diesel engine fuel system fault diagnosis based on wavelet transform and fuzzy C-means clustering is presented. Five characteristic parameters of reflecting fault state are distilled with wavelet transform of pressure wave of high-pressure oil pipe of diesel. The theory and generic approach of fuzzy C-means clustering algorithm (FCM) is given, and the validity of evaluating fuzzy clustering making use of partition coefficient, partition entropy and parting coefficient is pointed out. Identification of fault mode can be completed utilizing standard fault character modes established by FCM algorithm, and calculating and comparing the similarity degree between this standard mode and sample. The arithmetic is applied to all kinds of typical faults diagnose in the diesel engine fuel system. Measuring results indicate that the precision of fault diagnosis is increased with the analysis of wavelet and FCM.     

Recognizing the reliability non-coherence components ofdistribution substation typical configuration

Reliability non-coherence components (RNC) may exist in a power system due to the effects of distribution substation connection modes, components reliability performance, load level, and other factors. Recognizing RNCs and removing them from a power system can improve system reliability. The reliability non-coherence phenomenon is analyzed and the reliability non-coherence probability, frequency and energy indices, which can describe the degree of the system reliability non-coherence feature, are proposed. Based on the reliability evaluation theory of substation configuration, recognizing technique of RNCs of distribution substation is proposed. The theoretical analysis and example show that the breaker connects high voltage buses may be the RNC when the rated capacity of one transformer can meet the load requirement, and the reliability parameters of breakers have some effects on the reliability non-coherence features of the typical substation configuration.     

A New Method of Extracting Fault Components Under Power System Swing

To solve the problem that the existing method of extracting fault components is easily affected by power system swing,a new methed has been proposed in this paper,which can be used in high speed protection of transmission lines. This proped method ean greatly decrease unbal-ance outputs under power swing condilions without frequency tracking circuits,therefore it can make the protection using fault compnents operate correctly.     

A coordinated state transition control strategy for microgrid with multi-energy generation systems

A coordinated control strategy is proposed based on state transition for a microgrid containing continuous or intermittent DG, energy storage device and sensitive loads. According to the available operation states, a scheme of operation state transition is established through setting the current operation state and trigger event as inputs of MGCC and regarding the control modes of components as outputs. By considering the no deviation control for frequency and the capacity limitation of energy storage device under all operation states, control modes of components and trigger events of state transition under all defined states are designed respectively for a reliable power supply of sensitive load. The PSCAD/EMTDC simulation software is used for analysis of the three typical transition processes of operation states. The results show that the proposed coordinated control strategy is effective for a safe and reliable power supply to the sensitive loads and ensures a smoothing transition between operation states of microgrid as well as the no deviation control of system frequency.     

Power Plant Bid Decision-making Stragegy Based on Accounting Theory

The main theortical basis that management accountant can be regarded as electricity-generating commerce is brought forward.The main contents of management accountant,such as cost analysis,quantity benefit analysis,estimate analysis,the decision analysis,the budget,cost control,responsibility center,the possibility of the indetermination analysis theories on generating electricity,feasibility and advantages,are analyzed and demonstrated in detail.Based on these works and combining knowledge of computer,related knowledge about power and the latest rules of electric power market,the functional design for the power plant biding for on-net assistant system is completed.The power plant bid decision-making stragegy includes following functions: receiving and declareing the data of the electric power market,protecting cost analysis,protecting price decision,predicting load,safe area analysis,superior distribution,settle accounts,sensitivity analysis,controlling plan management and risk analysis etc.     

Combination Forecasting Models Using Regression Analysis Method

Using regression analysis method,the methods for solving the weights of combination forecasting model(CFM) are proposed. At first, the linear regress CFM are presented based on the least absolute criteria and least square criteria. Then the weights can be evaluated using the least square princinple. Because the objective function of CFM based on least absolute criteria is non differential, the traditional programming methods can not solve it. So the least square method with the modified weights is proposed to solve this problem. At the same time, methods for solving CFM is given with the aim of minimizing sum of percentage error absolutes. From many cases, the results show that the forecasting precision of CFM is very high and the effect of regression is remarkable.