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Abstract This paper proposes a novel evaluation method to address the challenge of evaluating insulation degradation in power transformer windings based on incomplete online Internet of Things (IoT) sensing data. The method leverages the Wasserstein Slim Generative Adversarial Imputation Network with Gradient Penalty algorithm to fill the irregularly missing power transformer IoT perception data, including voltage, current, temperature, and partial discharge. Subsequently, electrical, thermal, and mechanical performance degradation damage indicators for transformer winding insulation are constructed using the filled and complete IoT perception data. By applying the tensor fusion algorithm, the characteristics of these degradation damage indicators are fused, leading to the development of a comprehensive degradation evaluation index for the winding insulation. The evaluation of the winding insulation degradation state is achieved through the minimum quantization error method. The proposed method is validated using the real‐world transformer IoT perception data, and the experimental results demonstrate its ability to accurately assess the degree of winding insulation degradation, regardless of the presence of random or continuous irregularities in IoT sensing data.

To solve the problem of the lack of prediction methods that depend on IoT sensing data for the insulation degradation of power transformer windings, an online transformer winding insulation degradation prediction method based on multi-source IoT sensing data is innovatively proposed, which can realize the online prediction of winding insulation deterioration under the condition of considering the influence of various performance deterioration factors such as electricity, heat and machinery. First, the GAT-LSTM algorithm is used to predict the future trend of power transformer voltage, current, temperature, and partial discharge IoT sensing data, which have periodic and fluctuating features. Then, based on the prediction results of transformer IoT sensing data, the electrical, thermal, and mechanical performance degradation damage indicators of the transformer winding insulation are constructed. Finally, based on the tensor fusion algorithm, the three degradation damage indicators are fused into a comprehensive degradation trend prediction model for winding insulation, using the minimum quantization error method to achieve accurate prediction of the winding insulation degradation trend. This method was validated using actual IoT sensing data from transformers, and the experimental results indicate that the proposed method can accurately predict the evolution trend of transformer IoT sensing data and the degradation trend of winding insulation. Relying on the data of the previous three years and five years to carry out the deterioration trend prediction experiment of the winding insulation, the mean absolute error accuracy of the proposed method are 0.0120 and 0.0121, respectively. Compared with other algorithms, the accuracy advantage exceeds 0.005. The significance of this research is to predict the performance degradation trend of winding insulation by constructing a degradation model, and to take operation and maintenance measures before serious failures occur.

Abstract As renewable power generation increases in distribution networks, the real‐time power balance is becoming a tough challenge. Unlike simple peak‐load shedding or demand turn‐down scenarios, generation following (GF) requires persistent and precise control due to the temporal response performance of controlled resources. This motivates a comprehensive control design considering the temporal response limits and execution performance of air conditioner clusters (ACCs) when providing GF. Accordingly, this paper proposes a self‐constraint model predictive control (SMPC) that properly allocates the generation following task among different ACCs, consisting of three main parts: response rehearsal, distributed consistency‐based power allocation, and real‐time task execution. Specifically, the rehearsal knowledge of ACCs is evaluated by introducing model predictive control (MPC) to track power signals with different values and thus obtain prior factors, including the upward/downward limits and control cost function. On this basis, the coherence of the incremental response costs of different clusters is achieved to allocate the GF signals. Once the optimised following signals (OFS) are obtained, a real‐time MPC for generation following task execution is employed, where the OFS are used as reference and the upward/downward limits are used as constraints. Simulations are conducted to verify the feasibility and effectiveness of the proposed method.

Abstract Accurately evaluating the response capability of electric vehicles (EVs) is very important for an EV aggregator (EVA) to formulate reasonable bidding strategies in ancillary service markets. Most of the methods proposed in the existing literature only evaluate EVs’ temporal response capability while ignoring their spatial distribution. In addition, the evaluation results provided by the existing methods are typically deterministic, which fails to characterize the uncertainty of EV trip. The above two issues pose high risk of economic loss for the EVA. To this end, a probabilistic evaluation method of spatial‐temporal response capability for EVA is proposed in this paper. The gravity model is adopted to calculate a spatial transfer probability matrix describing EV owners’ trip characteristics between different areas at each time in a region which is divided into several different areas according to the evaluation requirements. Then, the trip chains of EVs are modelled based on the spatial transfer probability matrix, and the states of charge (SOCs) of EVs are tracked in the process. The spatial‐temporal response capability of EVA is evaluated based on charging–discharging states and states of charge of EVs, and the probabilistic evaluation results of response capability are obtained by multiple Monte Carlo simulations. The effectiveness of the proposed method is verified on a real dataset from San Francisco, CA, USA.

Abstract This study is for the case where the available data of power transformer oil–paper insulation is limited to a small amount furfural data, to solve the problems in oil–paper insulation degradation modelling, such as few samples available, unknown function form of the degradation process, differences of individual transformers among degradation processes, and commonality of degradation trends. A power transformer oil–paper insulation degradation modelling and prediction method based on functional principal component analysis (FPCA) is proposed. First, discrete furfural data of oil–paper insulation degradation are converted into continuous functional data, and the common degradation information of transformers is extracted based on functional time warping technology. Second, the principal components of insulation degradation are extracted based on FPCA method, and the difference of degradation information of individual transformers is obtained by analysing the differential of principal component scores. Subsequently, power transformer oil–paper insulation degradation model is constructed, and finally, the degradation model is updated based on Bayesian theory and the oil–paper insulation degradation is predicted. The example results show that compared with traditional transformer oil–paper insulation degradation modelling method, the proposed method has obvious superiority in model accuracy.

Accurately quantitative evaluation of the response capability of electric vehicle aggregator (EVA), i.e., the adjustable range of power, is the premise and basis of its participation in the demand response (DR). In response to the current EVA’s response capability evaluation, ignoring the autonomy of EV decision-making behavior may lead to overly idealized evaluation results, as well as the problem of evaluating only from a single time scale. To this end, this paper proposes a response capability evaluation model of EVA based on evolutionary game and response anticipation. Based on the physical constraints of EVs and their willingness to discharge, response capability evaluation models are constructed for individual EVs of different response types. Considering the autonomy of decision-making behavior of EVs, an evolutionary game model with multi-strategy sets of EVs is constructed. Based on the idea of response anticipation, a regulation strategy is proposed for EVA to achieve the dynamic update of EVA’s response capability. The proposed model is verified to be able to accurately evaluate the EVA’s response capability through simulation examples, and the EVA response capability evaluation is extended from a single time scale to multiple time scales. The evaluation results can be used as the basis for EVA to participate in the electricity market bidding.

This paper proposes a hybrid control strategy of air-conditioning loads (ACLs) for participating in peak load reduction. The hybrid control strategy combines the temperature setpoint adjustment (TSA) control and on/off control together to make full use of response potentials of ACLs. The primary free transport model of ACLs has been established in literature at or near a fixed temperature setpoint. In this paper, a wide-range transport (WRT) model suitable for larger value of TSA is proposed. The WRT model can be constructed easily through the parameter of devices and indoor and outdoor temperature. To modulate the aggregate response characteristics of ACLs more friendly to the power grid, the safe protocol (SP) is adopted and integrated into the WRT model, which achieves a good unification of oscillation suppression and efficient modeling. Moreover, the hybrid control strategy is implemented based on the WRT model, and the model predictive control (MPC) controller is designed considering the tracking error and control switch cost. At last, the superiority of the hybrid control strategy is verified and the performance of ACLs for peak load reduction under this controller is simulated. The simulation results show that the hybrid control strategy could exploit the load reduction potential of ACLs fully than the TSA mode and track the reference signal more accurately.

Aggregate thermostatically controlled loads (ATCLs) are a suitable candidate for power imbalance on demand side to smooth the power fluctuation of renewable energy. A new control scheme based on an improved bilinear aggregate model of ATCLs is investigated to suppress power imbalance. Firstly, the original bilinear aggregate model of ATCLs is extended by the second-order equivalent thermal parameter model to optimize accumulative error over a long time scale. Then, to ensure the control performance of tracking error, an improved model predictive control algorithm is proposed by integrating the Lyapunov function with the error transformation, and theoretical stability of the proposed control algorithm is proven. Finally, the simulation results demonstrate that the accuracy of the improved bilinear aggregate model is enhanced; the proposed control algorithm has faster convergence speed and better tracking accuracy in contrast with the Lyapunov function-based model predictive control without the prescribed performance.

As a new and great source of potential energy storage technology, the spiral spring energy storage (SSES) technology uses a permanent magnet synchronous machine (PMSM) to tighten or release the spiral spring for energy conversion. In allusion to the feature of simultaneous variations in torque and inertia for spiral springs in operation, it is difficult for the SSES system to show good control performance in regulating the power from/to the grid under a conventional vector control method. A power coordinated control scheme of the SSES system with the grid is proposed to integrate the grid-side converter (GSC) with the machine-side converter (MSC) based on backstepping control under the current vector orientation. First, the mathematical models of GSC and PMSM under the current vector orientation coordinate frame are established. Second, the coordinated control scheme is designed by the backstepping control principle, and its stability is proved in theory. Then, the optimal control parameters in the control scheme are determined by investigating the desired control performance. Ultimately, the simulation and experimental results show that the proposed control scheme with the chosen control parameters coordinates the GSC and the MSC well. The power signal is tracked accurately and rapidly, and the operation performance of the SSES system and its energy exchange with the grid are both improved effectively.

Electric boilers (EBs) provide an alternative method to deal with the accommodation of curtailed wind power. To pursue the minimum coal consumption in the system, a dispatching model integrating combined-heat-and-power (CHP) plants and EBs in different locations is developed, and the penalty of wind power curtailment and cost of EB employment are also incorporated in the model. The transmission loss and transportation lag of heat-supply network as well as the elasticity of heat load are considered in this paper. A kind of constrained programming with stochastic and fuzzy parameters is applied to deal with the uncertainties. A case in East Inner Mongolia in China demonstrates that the EBs are able to absorb curtailed wind power and supply the heat. The results indicate that the utility of EBs in the primary or secondary heat-supply network to accommodate curtailed wind power is mainly related to the efficiency of heat transmission and the elasticity of heat load.

To store energy from the grid into spiral torsion spring (STS) smoothly and efficiently via PMSM, a multi-objective control problem of flexible load's vibration, PMSM's torque ripple, and electrical loss is raised, where the current studies on vibration and torque ripple are mostly addressed separately, not to mention electrical loss. This research attempts to propose a multi-objective integrative control scenario that can simultaneously solve these problems satisfactorily in a unitary nonlinear control framework. Firstly, a dynamic mathematical model of PMSM is built under stator current vector orientation, and then the model of PMSM is combined with the vibration model of STS to establish the overall system model of STS driven by PMSM with considering motor's electrical loss. Then, a backstepping control principle-based multi-objective integrative control approach is proposed to realize the suppression of flexible load's vibration and the reduction of PMSM's torque ripple and electrical loss concurrently. Meanwhile, this research also designs a wide range speed identification method based on the least square algorithm with a forgetting factor. Simulation and experimental results have verified that the proposed integrative control method enables the state variables to track their respective references quickly and accurately, both torque ripple and load vibration are effectively suppressed, and the operating efficiency of the whole system is improved.

Due to the development of intelligent electric devices and advanced metering infrastructures, demand response will be widely utilized in the trading of the electricity market and maintain energy balance of the power system. In this paper, a two-stage nested bilevel model for the optimal bidding strategy of a load agent (LA) with incentive-based demand response in day-ahead and balancing markets is proposed. In the upper-level model, the optimal trading strategy of the LA is formulated to maximize the operating profit of the LA in the day-ahead energy and balancing markets. On the other hand, the lower-level proposes the clearing market model of the independent system operator, which aim to maximize social welfare. The LA acts as a price-maker in the first-stage of the bilevel model, which is bilevel nonlinear programming (BNLP) problem. Karush-Kuhn-Tucker conditions and dual theory are used to transform the BNLP into single-level programming. The LA acts as a price-taker accepts the day-ahead energy clearing-price in the second-stage of the bilevel model, which is a bilevel mixed-integer linear stochastic programming problem. Finally, implementing the two-stage nested bilevel model on modifying the 8-bus power system demonstrates the applicability of the proposed model and analysis the sensitivity of the LA' profit to unit price and the committed reserve capacity demand of the day-ahead reserve market.

Home energy management systems (HEMSs) enable residential customers to efficiently participate in demand response programs in order to obtain optimal benefits. Traditional HEMSs only manage household electric appliances to reduce the electricity consumption cost while the optimal scheduling of natural gas appliances has been overlooked. Due to the increasing popularity of natural gas appliances in modern smart homes, the electricity consumption of residential customers connected to the natural gas network is significantly affected by the use of natural gas appliances. To consider the interaction between electric and natural gas appliances in households, a day-ahead optimal joint scheduling model of electric and natural gas appliances for HEMS is proposed. Firstly, all household appliances are classified into several categories and the mathematical model of each appliance is presented. Then, a day-ahead optimal joint scheduling model of both electric and natural gas appliances for HEMS is formulated, in which the objective function is to minimize the household's energy cost and the dissatisfaction level caused by the shifting, reduction and replacement of loads in response to the time varying prices. Case studies using realistic data indicate that the proposed model can save the total energy costs up to 30% for customers whilst ensuring their satisfaction levels.

Wind power curtailment is of great importance with the increase of large-scale wind power connected to the grid. A new concept of redundant wind power accommodated by dispatching electric water heaters (EWHs) is developed in the paper. Precise predictions of wind power and EWHs load power are the basis for this work. A hybrid multi-kernel prediction approach integrating an adaptive fruit fly optimization algorithm (AFOA) and multi-kernel relevance vector machine (MKRVM) is proposed to deal with the sample distribution of multisource heterogeneous features uncovered by an energy entropy method, where AFOA is used to determine the kernel parameters in MKRVM adaptively and avoid the arbitrariness. For the large computation of the prediction approach, parallel computation based on the Hadoop cluster is used to accelerate the calculation. Then, an economic dispatching model for accommodating wind power is built taking into account the penalty of curtailed wind power and the operating cost of EWHs. The proposed scheme is implemented in an intelligent residential district. The results show that the optimization performance of the hybrid prediction approach is superior to those of four usual optimization algorithms in this case. Regular or orderly scheduling of EWHs enables accommodation of superfluous wind power and reduces dispatch cost.

The operational performance of the spiral spring energy storage system is affected by the vibration of the spiral spring and the electrical loss of the permanent magnet synchronous motor. It is important to eliminate vibration and reduce electrical loss. A unified control scenario for speed regulation and vibration suppression based on the minimum electrical loss is proposed. First, the spiral spring is equivalent to an Euler–Bernoulli beam and its dynamic model suitable for control is established via the Lagrange equation. Then, the unified control scenario is proposed through nonlinear backstepping control. The speed controller and current controller including modal vibration suppression and minimum electrical loss operation of the system are established, and the stability of the controller is theoretically proved. Moreover, for unknown vibration mode of the spiral spring, a vibration mode–based estimation method with the least-squares algorithm is designed. Aiming at the uncertainty of the permanent magnet synchronous motor’s iron loss resistance, an estimation algorithm based on an adaptive neural fuzzy inference system is designed. The experimental results verify the correctness and effectiveness of the proposed control scheme. In comparison with traditional backstepping control, the proposed control method can effectively suppress the vibration of the spiral spring and realize the stable and highly efficient energy storage operation of the system.

For an innovative spiral spring energy storage system, the permanent magnet synchronous generator (PMSG) is utilized as the energy conversion device due to its simple structure, low weight and high torque. During power generation, the output torque and moment of inertia of the spiral spring are changing continuously and simultaneously and the parameters of the PMSG show uncertainties. Furthermore, the DC link voltage of the converter should be stable and the power injected into the grid needs to be controlled. First, the change features of the external power source and the uncertainties of the generator's internal parameters are expressed as the comprehensive disturbances, which are introduced into the dynamic model of the PMSG and also modify the dynamic model. Then, the high gain observers are utilized to estimate the comprehensive disturbances, and an improved robust backstepping control scheme integrating L2 gain and high gain observers is proposed. Secondly, the gridside inverter controller for the DC voltage loop and reactive power loop is designed based on the backstepping theory. Finally, hardware implementation is fulfilled to verify the presented algorithm. The results show that high gain observers are able to accurately estimate the external and internal interferences; the proposed control scheme can effectively suppress the external and internal interferences and guarantees output current, operating speed of the PMSG and output reactive power to correctly track respective references, and effectively stabilize the DC link voltage.

With the development of smart grid and active distribution network, the flexible load recourse would play a key role in the electricity market. In this paper, we proposed a framework that the distributed storage energy systems, electric vehicles, and temperature control loads are aggregated in the flexible load aggregator, trading in day-ahead energy and reserve markets. The framework is modeled as a bilevel optimization model. In the propose model, the operation problem of the FLA is modeled in upper-level problem, which is to maximize the profit of the aggregator. The biding and offering strategic of Gencos and flexible load aggregator in the independent system operator are presented in lower-level problem, which aim at improving the social benefits. Karush–Kuhn–Tucker and dual theory are used to transform the nonlinear bilevel problem to a mixed-integer linear programming of single-level model. Finally, the numerical studies based on modifying PJM-5bus power system, showing the effectiveness of the proposed framework and bilevel model.

A fast excitation predictive control method for multi-machine power system is presented. The multi-step prediction technique is realized via system dynamic model. Some inequality constraints on states, inputs and outputs are considered in rolling optimization. The Gramian balanced reduction technique and the improved optimization algorithm are used in order to save the time of open-loop optimization in model predictive control. A 50-machine power system is used to verify the effectiveness of this approach. Compared with simulated results under different controllers, this method can greatly reduce the calculating-time. The voltages of generator terminals are maintained within the set points. The stability of power system is improved.

Combined with two approaches of sliding mode control and backstepping control, an adaptive sliding mode–based backstepping control scenario on the basis of nonlinear disturbance observer is proposed to complete maximum torque per ampere control for permanent magnet–assisted synchronous reluctance motor. The constraint relation of permanent magnet–assisted synchronous reluctance motor under maximum torque per ampere control is built, and the design of the controller is elaborated in detail. The uncertainties of modeling errors considering unmatched items are estimated through a presented nonlinear disturbance observer. The adaptive law reflecting the modeling error of the system is constructed. Globally asymptotic stability and convergence of the tracking error for the system are validated through Lyapunov stability criterion. Simulation and experimental results illustrate that external disturbances and uncertainties are observed correctly by nonlinear disturbance observer; the close-loop system controlled by the proposed controller can track the references rapidly and precisely, and the designed controller has a good robust ability.

An active distribution network (ADN) differs from a traditional distribution network in many aspects, one of which is the integration of a large amount of distributed generation (DG), especially intermittent photovoltaics (PVs). The integration of intermittent PVs has both pros and cons for the distribution system. As the platform on which new techniques work and the main body of a greener future energy system, the development of an ADN has to be sustainable, need-oriented, and environmentally friendly, and the traditional technical⁻economic evaluation method cannot meet the requirements and provide advice in the decision-making process. Based on the concept of sustainable development, we used an ADN with the integration of a large number of distributed PVs (DGPVs) as an example and established a multi-dimensional index system to evaluate the sustainable development level (SDI) of the ADN. The analysis was based on a platform we built with consideration of the investment feasibility of the DGPVs’ investors, state and industrial policies, and their interactions with the distribution system. We first compared the development of DGPVs and the SDI of the ADN as the carrier of DGPVs under different state policies, and second, we compared the SDIs of three city ADNs with different solar resources and demand levels, but under the same state policy. The analysis results showed that different integration levels of DGPVs can be set for a city/area ADN with different solar resources and demand to achieve a comparable SDI, and a comprehensive incentive mechanism could be adopted for the development of DGPVs. In this way, the benefits of different parties can be considered at the same time and finally, the coordination of the sustainable development of multi-parties can be achieved.

Short-term solar irradiance forecasting (STSIF) is of great significance for the optimal operation and power predication of grid-connected photovoltaic (PV) plants. However, STSIF is very complex to handle due to the random and nonlinear characteristics of solar irradiance under changeable weather conditions. Artificial Neural Network (ANN) is suitable for STSIF modeling and many research works on this topic are presented, but the conciseness and robustness of the existing models still need to be improved. After discussing the relation between weather variations and irradiance, the characteristics of the statistical feature parameters of irradiance under different weather conditions are figured out. A novel ANN model using statistical feature parameters (ANN-SFP) for STSIF is proposed in this paper. The input vector is reconstructed with several statistical feature parameters of irradiance and ambient temperature. Thus sufficient information can be effectively extracted from relatively few inputs and the model complexity is reduced. The model structure is determined by cross-validation (CV), and the Levenberg-Marquardt algorithm (LMA) is used for the network training. Simulations are carried out to validate and compare the proposed model with the conventional ANN model using historical data series (ANN-HDS), and the results indicated that the forecast accuracy is obviously improved under variable weather conditions.

Most distributed photovoltaic systems (DPVSs) are normally located behind the meter and are thus invisible to utilities and retailers. The accurate information of the DPVS capacity is very helpful in many aspects. Unfortunately, the capacity information obtained by the existing methods is usually inaccurate due to various reasons, e.g., the existence of unauthorized installations. A two-stage DPVS capacity estimation approach based on support vector machine with customer net load curve features is proposed in this paper. First, several features describing the discrepancy of net load curves between customers with DPVSs and those without are extracted based on the weather status driven characteristic of DPVS output power. A one-class support vector classification (SVC) based DPVS detection (DPVSD) model with the input features extracted above is then established to determine whether a customer has a DPVS or not. Second, a bootstrap-support vector regression (SVR) based DPVS capacity estimation (DPVSCE) model with the input features describing the difference of daily total PV power generation between DPVSs with different capacities is proposed to further estimate the specific capacity of the detected DPVS. A case study using a realistic dataset consisting of 183 residential customers in Austin (TX, U.S.A.) verifies the effectiveness of the proposed approach.

Accurate solar PV power forecasting can provide expected future PV output power so as to help the system operator to dispatch traditional power plants to maintain the balance between supply and demand sides. However, under non-stationary weather conditions, such as cloudy or partly cloudy days, the variability of solar irradiance makes the accurate PV power forecasting a very hard task. Ensemble forecasting based on multiple models established by different theory has been proved as an effective means on improving forecasting accuracy. Classification modeling according to different patterns could reduce the complexity and difficulty of intro-class data fitting so as to improve the forecasting accuracy as well. When combining the two above points and focusing on the different fusion pattern specifically in terms of hourly time dimension, a time-section fusion pattern classification based day-ahead solar irradiance ensemble forecasting model using mutual iterative optimization is proposed, which contains multiple forecasting models based on wavelet decomposition (WD), fusion pattern classification model, and fusion models corresponding to each fusion pattern. First, the solar irradiance is forecasted using WD based models at different WD level. Second, the fusion pattern classification recognition model is trained and then applied to recognize the different fusion pattern at each hourly time section. At last, the final forecasting result is obtained using the optimal fusion model corresponding to the data fusion pattern. In addition, a mutual iterative optimization framework for the pattern classification and data fusion models is also proposed to improve the model’s performance. Simulations show that the mutual iterative optimization framework can effectively enhance the performance and coordination of pattern classification and data fusion models. The accuracy of the proposed solar irradiance day-ahead ensemble forecasting model is verified when compared with a standard Artificial Neural Network (ANN) forecasting model, five WD based models and a single ensemble forecasting model without time-section fusion classification.

For a caged spiral power spring, its moment of inertia varies with the time in energy storage and is closely related to the states of spring strip. Due to continuous changing of the shape in energy storage, calculation of variable moment of inertia for spiral power spring is a highly challenging but important issue. A general analytical model and calculation method of variable moment of inertia for spiral power spring are proposed in this article. First, the contraction process of spiral power spring in energy storage is clearly revealed and analytical model of spiral power spring on the basis of contraction process is presented. In terms of the contraction characteristic, a general calculation method based on divisions of stage and state for variable moment of inertia is put forward. Finally, a spiral power spring is designed, and a prototype of the spring is developed. The proposed model and general calculation method are applied into the prototype to calculate its variable moment of inertia, and they are evaluated by a developed experimental scheme.

Accurate solar photovoltaic (PV) power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST) solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN) and support vector machines (SVM) are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples.

For the technology of mechanical elastic energy storage utilizing spiral torsion springs as the energy storage media presented previously, a global multivariable control algorithm based on nonlinear internal model principle under multiclass external disturbances is proposed. The nonlinear external disturbances with nonharmonic periodic characteristics are generated by multiclass nonlinear external systems. New equations of nonlinear internal model are designed to estimate the multiclass external disturbances. On the basis of constructing the control law of nominal system, a state feedback controller is designed to guarantee the closed-loop system globally uniformly bounded, and a Lyapunov function is constructed to theoretically prove the global uniform boundedness of the multivariable closed-loop system signals. The simulation results verify the correctness and effectiveness of the presented algorithm.

We study the global disturbance rejection problem for a class of general multivariable nonlinear systems with multiclass nonharmonic disturbances. The paper first introduces the importance and state of the art for disturbance rejection problem and describes the control problem in the form of mathematical expressions. It stresses the multiclass disturbances produced by the exosystem satisfying certain characteristic conditions. Then, the nonlinear internal models are designed in accordance with different characteristics of multiclass external disturbances. On the basis of introduction of the control law for disturbance-free system, a multivariable state feedback controller is devised in terms of the designed internal model equations and corresponding assumptions. A Lyapunov function is constructed to theoretically prove the global uniform boundness of all signals for the multivariable closed-loop system. Finally, the presented method is applied to implement the speed control and reject the multiclass nonharmonic disturbances for a two-input motor drive system. The simulation results testify correctness and effectiveness of the presented algorithm.

The structural scheme of mechanical elastic energy storage (MEES) system served by permanent magnet synchronous motor (PMSM) and bidirectional converters is designed. The aim of the research is to model and control the complex electromechanical system. The mechanical device of the complex system is considered as a node in generalized coordinate system, the terse nonlinear dynamic model of electromechanical coupling for the electromechanical system is constructed through Lagrange-Maxwell energy method, and the detailed deduction of the mathematical model is presented in the paper. The theory of direct feedback linearization (DFL) is applied to decouple the nonlinear dynamic model and convert the developed model from nonlinear to linear. The optimal control theory is utilized to accomplish speed tracking control for the linearized system. The simulation results in three different cases show that the proposed nonlinear dynamic model of MEES system is correct; the designed algorithm has a better control performance in contrast with the conventional PI control.

The module temperature is the most important parameter influencing the output power of solar photovoltaic (PV) systems, aside from solar irradiance. In this paper, we focus on the interdisciplinary research that combines the correlation analysis, mutual information (MI) and heat transfer theory, which aims to figure out the correlative relations between different meteorological impact factors (MIFs) and PV module temperature from both quality and quantitative aspects. The identification and confirmation of primary MIFs of PV module temperature are investigated as the first step of this research from the perspective of physical meaning and mathematical analysis about electrical performance and thermal characteristic of PV modules based on PV effect and heat transfer theory. Furthermore, the quantitative description of the MIFs influence on PV module temperature is mathematically formulated as several indexes using correlation-based feature selection (CFS) and MI theory to explore the specific impact degrees under four different typical weather statuses named general weather classes (GWCs). Case studies for the proposed methods were conducted using actual measurement data of a 500 kW grid-connected solar PV plant in China. The results not only verified the knowledge about the main MIFs of PV module temperatures, more importantly, but also provide the specific ratio of quantitative impact degrees of these three MIFs respectively through CFS and MI based measures under four different GWCs.