Search

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.

High altitude hypoxia stress is the key cause of high-altitude pulmonary edema and spleen contraction. The molecular mechanism of immune response of various tissue systems to hypoxia stress remains lacking. In this study, we applied proteomics combined with metabolomics to explore the key molecular profilings involved in high altitude hypoxia response in the spleen of mice. The results showed that 166 proteins were significantly up-regulated, and only 39 proteins were down-regulated. Bioinformatics analysis showed that mineral absorption, neuroactive ligand–receptor interaction, arachidonic acid metabolism, IL-17 signaling pathway and NOD-like preceptor signaling pathway were significantly enriched in the list of 166 upregulated differentially expressed proteins (DEPs). Among these metabolic pathways, the former three pathways were co-identified in KEGG terms from LC-MS/MS based metabolic analysis. We further found that both arachidonate 15-lipoxygenase and hematopoietic prostaglandin D synthase were upregulated by around 30% and 80% for their protein levels and mRNA levels, respectively. Most downstream metabolites were upregulated accordingly, such as prostaglandin A2 and D2. This study provides important evidence that arachidonic acid metabolism potentially promotes spleen hypoxia response through a combined analysis of proteomics and metabolism, which could bring new insights for the spleen targeted rational design upon arachidonic acid metabolism of new therapies.