Power transformers are important equipment in power systems and their reliability directly concerns the safety of power networks. Dissolved gas analysis (DGA) has shown great potential for detecting the incipient fault of oil-filled power transformers. In order to solve the misdiagnosis problems of traditional fault diagnosis approaches, a novel fault diagnosis method based on hypersphere multiclass support vector machine (HMSVM) and Dempster–Shafer (D–S) Evidence Theory (DET) is proposed. Firstly, proper gas dissolved in oil is selected as the fault characteristic of power transformers. Secondly, HMSVM is employed to diagnose transformer fault with selected characteristics. Then, particle swarm optimization (PSO) is utilized for parameter optimization. Finally, DET is introduced to fuse three different fault diagnosis methods together, including HMSVM, hybrid immune algorithm (HIA), and kernel extreme learning machine (KELM). To avoid the high conflict between different evidences, in this paper, a weight coefficient is introduced for the correction of fusion results. Results indicate that the fault diagnosis based on HMSVM has the highest probability to identify transformer faults among three artificial intelligent approaches. In addition, the improved D–S evidence theory (IDET) combines the advantages of each diagnosis method and promotes fault diagnosis accuracy.

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In the current research on data query for two-tiered WSN, the privacy-preservation range query is one of the hotspots. However, there are some problems in the existing researches in two-tiered wireless sensor networks such as high computational and communication costs for security comparison items and high energy consumption of sensing nodes. In this paper, a privacy-preservation range query protocol based on the integration reversal 0-1 encoding with Bloom filter is researched and designed. In the sensing data submission stage, the optimized reversal 0-1 encoding, HMAC algorithm, AES encryption algorithm and variable-length Bloom filter are used for generating the maximum–minimum comparison encoding and constructing a shorter verification index chain to reduce computational and communication costs of sensing nodes; in the private data range query stage, the base station uses the HMAC algorithm to convert the plaintext query range into the ciphertext query range and sends it to the storage node. In the storage node, the bitmap encoding information of the verification index chain is calculated with the comparison rule of the reversal 0-1 encoding and it is returned to the base station together with the verification index chain and the data ciphertext that compliance with the query rule; in the data integrity verification stage, the integrity of the query results using the verification index chain and bitmap encoding is verified at the base station. In the experimental section, the Cortex-M4 development board equipped with the Alios-Things operating system as sensing node and the Cortex-A9 development board equipped with the Linux operating system as storage node are implemented in this protocol, which is compared with the existing protocols in three aspects: the number of data collected in each cycle, the length of data and the number of data dimensions. The experimental results show that the energy consumption of this protocol is lower under the same experimental environment.