Robust Nonlinear Current Sensorless Control of the Boost Converter with Constant Power Load

The boost converter feeding a constant power load (CPL) is a non-minimum phase system that is prone to the destabilizing effects of the negative incremental resistance of the CPL and presents a major challenge in the design of stabilizing controllers. A PWM-based current-sensorless robust sliding mode controller is developed that requires only the measurement of the output voltage. An extended state observer is developed to estimate a lumped uncertainty signal that comprises the uncertain load power and the input voltage, the converter parasitics, the component uncertainties and the estimation of the derivative of the output voltage needed in the implementation of the controller. A linear sliding surface is used to derive the controller, which is simple in its design and yet exhibits excellent features in terms of robustness to external disturbances, parameter uncertainties, and parasitics despite the absence of the inductor’s current feedback. The robustness of the controller is validated by computer simulations.

Short-Term Power Load Forecasting with Hybrid TPA-BiLSTM Prediction Model Based on CSSA

Since the existing prediction methods have encountered difficulties in processing themultiple influencing factors in short-term power load forecasting,we propose a bidirectional long short-term memory(BiLSTM)neural network model based on the temporal pattern attention(TPA)mechanism.Firstly,based on the grey relational analysis,datasets similar to forecast day are obtained.Secondly,thebidirectional LSTM layermodels the data of thehistorical load,temperature,humidity,and date-type and extracts complex relationships between data from the hidden row vectors obtained by the BiLSTM network,so that the influencing factors(with different characteristics)can select relevant information from different time steps to reduce the prediction error of the model.Simultaneously,the complex and nonlinear dependencies between time steps and sequences are extracted by the TPA mechanism,so the attention weight vector is constructed for the hidden layer output of BiLSTM and the relevant variables at different time steps are weighted to influence the input.Finally,the chaotic sparrow search algorithm(CSSA)is used to optimize the hyperparameter selection of the model.The short-term power load forecasting on different data sets shows that the average absolute errors of short-termpower load forecasting based on our method are 0.876 and 4.238,respectively,which is lower than other forecastingmethods,demonstrating the accuracy and stability of our model.

Optimization of support vector machine power load forecasting model based on data mining and Lyapunov exponents

According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.

Capacity Maximization Based Power Loading Analysis for Digital Channelized Satcom Systems

For digital channelized frequency division multiple access based satellite communication(SATCOM) systems,it is a challenging but critical issue to improve the transponder power and spectrum efficiency simultaneously under limited and non-linear high-power amplifier conditions.In this paper,different from the traditional link supportability designs aiming at minimizing the total transponder output power,a maximal sum Shannon capacity optimization objective is firstly raised subject to link supportability constraints.Furthermore,an efficient multilevel optimization(MO) algorithm is proposed to solve the considered optimization problem in the case of single link for each terminal.Moreover,in the case of multiple links for one terminal,an improved MO algorithm involving Golden section and discrete gradient searching procedures is proposed to optimize power allocation over all links.Finally,several numerical results are provided to demonstrate the effectiveness of our proposals.Comparison results show that,by the MO algorithm,not only all links' supportability can be guaranteed but also a larger sum capacity can be achieved with lower complexity.

THE VARIABILITY CHARACTERISTICS AND PREDICTION OF GUANGDONG POWER LOAD DURING 2002 – 2004

The variability characteristics of Guangdong daily power load from 2002 to 2004 and its connection to meteorological variables are analyzed with wavelet analysis and correlation analysis. Prediction equations are established using optimization subset regression. The results show that a linear increasing trend is very significant and seasonal change is obvious. The power load exhibits significant quasi-weekly (5 – 7 days) oscillation, quasi-by-weekly (10 – 20 days) oscillation and intraseasonal (30 – 60 days) oscillation. These oscillations are caused by atmospheric low frequency oscillation and public holidays. The variation of Guangdong daily power load is obviously in decrease on Sundays, shaping like a funnel during Chinese New Year in particular. The minimum is found at the first and second day and the power load gradually increases to normal level after the third day during the long vacation of Labor Day and National Day. Guangdong power load is the most sensitive to temperature, which is the main affecting factor, as in other areas in China. The power load also has relationship with other meteorological elements to some extent during different seasons. The maximum of power load in summer, minimum during Chinese New Year and variation during Labor Day and National Day are well fitted and predicted using the equation established by optimization subset regression and accounting for the effect of workdays and holidays.

A Weighted Combination Forecasting Model for Power Load Based on Forecasting Model Selection and Fuzzy Scale Joint Evaluation

To solve the medium and long term power load forecasting problem,the combination forecasting method is further expanded and a weighted combination forecasting model for power load is put forward.This model is divided into two stages which are forecasting model selection and weighted combination forecasting.Based on Markov chain conversion and cloud model,the forecasting model selection is implanted and several outstanding models are selected for the combination forecasting.For the weighted combination forecasting,a fuzzy scale joint evaluation method is proposed to determine the weight of selected forecasting model.The percentage error and mean absolute percentage error of weighted combination forecasting result of the power consumption in a certain area of China are 0.7439%and 0.3198%,respectively,while the maximum values of these two indexes of single forecasting models are 5.2278%and 1.9497%.It shows that the forecasting indexes of proposed model are improved significantly compared with the single forecasting models.

Practicality of power load management system in Chongqing City verified

Began from early 1992, Chongqing Power Supply Bureau had spent 3 and half years to build up a power load management system consisting of I master station, 6 relay stations, 1280 terminals and the distributed monitoring device. This system distributes in the hilly and mountainous areas where geographically complicated and the load widely scatters, it can supervise about 72% load and curtail more than 15% load

Forecasting the Demand of Short-Term Electric Power Load with Large-Scale LP-SVR

This research studies short-term electricity load prediction with a large-scalelinear programming support vector regression (LP-SVR) model. The LP-SVR is compared with other three non-linear regression models: Collobert’s SVR, Feed-Forward Neural Networks (FFNN), and Bagged Regression Trees (BRT). The four models are trained to predict hourly day-ahead loads given temperature predictions, holiday information and historical loads. The models are trained on-hourly data from the New England Power Pool (NEPOOL) region from 2004 to 2007 and tested on out-of-sample data from 2008. Experimental results indicate that the proposed LP-SVR method gives the smallest error when compared against the other approaches. The LP-SVR shows a mean absolute percent error of 1.58% while the FFNN approach has a 1.61%. Similarly, the FFNN method shows a 330 MWh (Megawatts-hour) mean absolute error, whereas the LP-SVR approach gives a 238 MWh mean absolute error. This is a significant difference in terms of the extra power that would need to be produced if FFNN was used. The proposed LP-SVR model can be utilized for predicting power loads to a very low error, and it is comparable to FFNN and over-performs other state of the art methods such as: Bagged Regression Trees, and Large-Scale SVRs.

Impact of Electric Vehicle Charging on Power Load Based on TOU Price

Large-scale electric vehicle charging has a significant impact on power grid load, disorderly charging will increase power grid peak load. This article proposes an orderly charging mechanism based on TOU price. To build an orderly charging model by researching TOU price and user price reaction model. This article research the impact of electric vehicle charging on grid load by orderly charging model. With this model the grid’s peak and valley characteristics, the utilization of charging equipment, the economics of grid operation can all be improved.

Two Algorithms of Power Loading for MCM System in Nakagami Fading Channel

We consider two non-iterative algorithms of adaptive power loading for multicarrier modulation (MCM) system, The first one minimizes the average power of the system transmitter and ensures the preset average bit-error rate, while the second reduces the average transmitting power subject to the given values of demanded bit-error rate and of the outage probability. The algorithms may be used for power-efficient management of the up-link in cellular communication, where mobile terminals use rechargeable batteries, or of the downlink in satellite communication with solar power source of a transponder. We present performance analysis of the adaptive MCM systems supported by computer simulation for the case of the m-Nakagami fading and additive white Gaussian noise in the forward and backward channels. Evaluation of the power gain of the proposed strategies and its comparison with uniform power loading shows that the gain depends on the fading depth and average signal to noise ratio in the system sub-channels.

Study on green power supply modes for heavy load in Remote Areas

In this study,the present situation and characteristics of power supply in remote areas are summarized.By studying the cases of power supply projects in remote areas,the experience is analyzed and described,and the applicability of related technologies,such as grid-forming storage and power load management,is studied,including grid-connection technologies,such as grid-forming converters and power load management.On this basis,three power-supply modes were proposed.The application scenarios and advantages of the three modes were compared and analyzed.Based on the local development situation,the temporal sequences of the three schemes are described,and a case study was conducted.The study of the heavy-load power supply mode in remote areas contributes to solving the problem of heavy-load green power consumption in remote areas and promoting the further development of renewable energy.

Enhancing source domain availability through data and feature transfer learning for building power load forecasting

During the initial phases of operation following the construction or renovation of existing buildings,the availability of historical power usage data is limited,which leads to lower accuracy in load forecasting and hinders normal usage.Fortunately,by transferring load data from similar buildings,it is possible to enhance forecasting accuracy.However,indiscriminately expanding all source domain data to the target domain is highly likely to result in negative transfer learning.This study explores the feasibility of utilizing similar buildings(source domains)in transfer learning by implementing and comparing two distinct forms of multi-source transfer learning.Firstly,this study focuses on the Higashita area in Kitakyushu City,Japan,as the research object.Four buildings that exhibit the highest similarity to the target buildings within this area were selected for analysis.Next,the two-stage TrAdaBoost.R^(2) algorithm is used for multi-source transfer learning,and its transfer effect is analyzed.Finally,the application effects of instance-based(IBMTL)and feature-based(FBMTL)multi-source transfer learning are compared,which explained the effect of the source domain data on the forecasting accuracy in different transfer modes.The results show that combining the two-stage TrAdaBoost.R^(2) algorithm with multi-source data can reduce the CV-RMSE by 7.23%compared to a single-source domain,and the accuracy improvement is significant.At the same time,multi-source transfer learning,which is based on instance,can better supplement the integrity of the target domain data and has a higher forecasting accuracy.Overall,IBMTL tends to retain effective data associations and FBMTL shows higher forecasting stability.The findings of this study,which include the verification of real-life algorithm application and source domain availability,can serve as a theoretical reference for implementing transfer learning in load forecasting.

State-of-charge Balance Control and Safe Region Analysis for Distributed Energy Storage Systems with Constant Power Loads

This paper presents a fully distributed state-of-charge balance control (DSBC) strategy for a distributed energy storage system (DESS). In this framework, each energy storage unit (ESU) processes the state-of-charge (SoC) information from its neighbors locally and adjusts the virtual impedance of the droop controller in real-time to change the current sharing. It is shown that the SoC balance of all ESUs can be achieved. Due to virtual impedance, voltage deviation of the bus occurs inevitably and increases with load power. Meanwhile, widespread of the constant power load (CPL) in the power system may cause instability. To ensure reliable operation of DESS under the proposed DSBC, the concept of the safe region is put forward. Within the safe region, DESS is stable and voltage deviation is acceptable. The boundary conditions of the safe region are derived from the equivalent model of DESS, in which stability is analyzed in terms of modified Brayton-Moser's criterion. Both simulations and hardware experiments verify the accuracy of the safe region and effectiveness of the proposed DSBC strategy.

A Power Load Prediction by LSTM Model Based on the Double Attention Mechanism for Hospital Building

This work proposed a LSTM(long short-term memory)model based on the double attention mechanism for power load prediction,to further improve the energy-saving potential and accurately control the distribution of power load into each department of the hospital.Firstly,the key influencing factors of the power loads were screened based on the grey relational degree analysis.Secondly,in view of the characteristics of the power loads affected by various factors and time series changes,the feature attention mechanism and sequential attention mechanism were introduced on the basis of LSTM network.The former was used to analyze the relationship between the historical information and input variables autonomously to extract important features,and the latter was used to select the historical information at critical moments of LSTM network to improve the stability of long-term prediction effects.In the end,the experimental results from the power loads of Shanxi Eye Hospital show that the LSTM model based on the double attention mechanism has the higher forecasting accuracy and stability than the conventional LSTM,CNN-LSTM and attention-LSTM models.

Modeling of comprehensive power load of fishery energy internet considering fishery meteorology

Accurate calculation for comprehensive power load of fishery energy internet plays a significant role in reasonable using of energy and reducing environmental pollution.However,as fishery power load is of greatly unique meteorology sensitivity,it continues to be a difficult problem.Therefore,the research of fishery meteorology is an important part of the rational development of fishery resources,the protection of production safety,and the pursuit of high and stable yield.This paper makes a deep study on the power load of the fishery energy internet under the influence of fishery meteorology and takes onshore fish pond as the research object.First of all,the power load is divided into three parts:oxygen enrichment power load,feeding power load,and water replenishment and drainage power load.The impact mechanism of fishery meteorology(including temperature,surface wind speed,precipitation,relative humidity,etc.)on it is described,and then the overall power load is obtained through modeling and integration.Finally,taking the Yuguang Complementary Project in Zhouquan Town,Tongxiang,Zhejiang Province,China as an example,using the meteorological data of its typical spring day and using the MATLAB tool to solve,the hourly comparison of the three types of power loads,the comprehensive power load demand,the full-day electricity charge forecast and the total annual power consumption are calculated.The annual power consumption per hectare and per kilogram of output calculated by simulation are basically consistent with the order of magnitude of the survey data,which proves the validity of the model proposed.The model established in this paper is an original work,and the exploration of fishery energy internet can draw lessons from it.

Active stabilization methods of electric power systems with constant power loads:a review

Modern electric power systems have increased the usage of switching power converters.These tightly regulated switching power converters behave as constant power loads(CPLs).They exhibit a negative incremental impedance in small signal analysis.This negative impedance degrades the stability margin of the interaction between CPLs and their feeders,which is known as the negative impedance instability problem.The feeder can be an LC input filter or an upstream switching converter.Active damping methods are preferred for the stabilization of the system.This is due to their higher power efficiency over passive damping methods.Based on different sources of damping effect,this paper summarizes and classifies existing active damping methods into three categories.The paper further analyzes and compares the advantages and disadvantages of each active damping method.

Time series modeling and filtering method of electric power load stochastic noise

Stochastic noises have a great adverse effect on the prediction accuracy of electric power load.Modeling online and filtering real-time can effectively improve measurement accuracy.Firstly,pretreating and inspecting statistically the electric power load data is essential to characterize the stochastic noise of electric power load.Then,set order for the time series model by Akaike information criterion(AIC)rule and acquire model coefficients to establish ARMA(2,1)model.Next,test the applicability of the established model.Finally,Kalman filter is adopted to process the electric power load data.Simulation results of total variance demonstrate that stochastic noise is obviously decreased after Kalman filtering based on ARMA(2,1)model.Besides,variance is reduced by two orders,and every coefficient of stochastic noise is reduced by one order.The filter method based on time series model does reduce stochastic noise of electric power load,and increase measurement accuracy.

Load Forecasting of the Power System: An Investigation Based on the Method of Random Forest Regression

Accurate power load forecasting plays an important role in the power dispatching and security of grid.In this paper,a mathematical model for power load forecasting based on the random forest regression(RFR)was established.The input parameters of RFR model were determined by means of the grid search algorithm.The prediction results for this model were compared with those for several other common machine learning methods.It was found that the coefficient of determination(R^(2))of test set based on the RFR model was the highest,reaching 0.514 while the corresponding mean absolute error(MAE)and the mean squared error(MSE)were the lowest.Besides,the impacts of the air conditioning system used in summer on the power load were discussed.The calculation results showed that the introduction of indexes in the field of Heating,Ventilation and Air Conditioning(HVAC)could improve the prediction accuracy of test set.

Adaptive Subband Bit and Power Loading Algorithm and its Application to OFDM Based IEEE 802 .16e

For OFDM systems with hundreds or thousands subcarriers with the adaptive bit and power loading according to each subcarrier , the signaling overhead will be awfully large. However, the adaptive bit and power loading according to “subband” is an effective solution to this problem, with which the signaling overhead is expected to be dramatically decreased at the cost of some performance loss. In this paper, based on Ref . [5] but with some modification to the subband bit and power loading algorithm, we apply the algorithm to the IEEE 802.16e OFDM system. The results show that the modified subband bit and power loading algorithm can achieve better BER performance and the signaling overhead is reduced by 75% at the cost of performance loss less than 1 dB if the number of subcarriers per subband is 4 when the BER is around 10^-3.

Adaptive power loading with BER-constraint for OFDM systems

A novel adaptive power loading algorithm with the constraint of target overall bit error rate （BER） for orthogonal frequency division multiplexing （OFDM） systems is proposed in this article. The proposed algorithm aims to minimize the required transmit power with fixed data rate and uniform （nonadaptive） bit allocation, while guaranteeing the target overall BER. The power loading is based on the unequal-BER （UBER） strategy that allows unequal mean BERs on different subcarriers. The closed-form expressions for optimal BER and power distributions are derived in this article. Simulation results indicate the superiority of the proposed algorithm in terms of BER performance and algorithmic complexity.